CINXE.COM

<!DOCTYPE html> <html lang="en" xmlns:fb="http://www.facebook.com/2008/fbml" class="wf-loading"> <head prefix="og: https://ogp.me/ns# fb: https://ogp.me/ns/fb# academia: https://ogp.me/ns/fb/academia#"> <meta charset="utf-8"> <meta name=viewport content="width=device-width, initial-scale=1"> <meta rel="search" type="application/opensearchdescription+xml" href="/open_search.xml" title="Academia.edu"> <title>Khaled Shaalan | The British University in Dubai - Academia.edu</title>  <link href="//a.academia-assets.com/images/favicons/favicon-production.ico" rel="shortcut icon" type="image/vnd.microsoft.icon"> <link rel="apple-touch-icon" sizes="57x57" href="//a.academia-assets.com/images/favicons/apple-touch-icon-57x57.png"> <link rel="apple-touch-icon" sizes="60x60" href="//a.academia-assets.com/images/favicons/apple-touch-icon-60x60.png"> <link rel="apple-touch-icon" sizes="72x72" href="//a.academia-assets.com/images/favicons/apple-touch-icon-72x72.png"> <link rel="apple-touch-icon" sizes="76x76" href="//a.academia-assets.com/images/favicons/apple-touch-icon-76x76.png"> <link rel="apple-touch-icon" sizes="114x114" href="//a.academia-assets.com/images/favicons/apple-touch-icon-114x114.png"> <link rel="apple-touch-icon" sizes="120x120" href="//a.academia-assets.com/images/favicons/apple-touch-icon-120x120.png"> <link rel="apple-touch-icon" sizes="144x144" href="//a.academia-assets.com/images/favicons/apple-touch-icon-144x144.png"> <link rel="apple-touch-icon" sizes="152x152" href="//a.academia-assets.com/images/favicons/apple-touch-icon-152x152.png"> <link rel="apple-touch-icon" sizes="180x180" href="//a.academia-assets.com/images/favicons/apple-touch-icon-180x180.png"> <link rel="icon" type="image/png" href="//a.academia-assets.com/images/favicons/favicon-32x32.png" sizes="32x32"> <link rel="icon" type="image/png" href="//a.academia-assets.com/images/favicons/favicon-194x194.png" sizes="194x194"> <link rel="icon" type="image/png" href="//a.academia-assets.com/images/favicons/favicon-96x96.png" sizes="96x96"> <link rel="icon" type="image/png" href="//a.academia-assets.com/images/favicons/android-chrome-192x192.png" sizes="192x192"> <link rel="icon" type="image/png" href="//a.academia-assets.com/images/favicons/favicon-16x16.png" sizes="16x16"> <link rel="manifest" href="//a.academia-assets.com/images/favicons/manifest.json"> <meta name="msapplication-TileColor" content="#2b5797"> <meta name="msapplication-TileImage" content="//a.academia-assets.com/images/favicons/mstile-144x144.png"> <meta name="theme-color" content="#ffffff"> <script> window.performance && window.performance.measure && window.performance.measure("Time To First Byte", "requestStart", "responseStart"); </script> <script> (function() { if (!window.URLSearchParams || !window.history || !window.history.replaceState) { return; } var searchParams = new URLSearchParams(window.location.search); var paramsToDelete = [ 'fs', 'sm', 'swp', 'iid', 'nbs', 'rcc', // related content category 'rcpos', // related content carousel position 'rcpg', // related carousel page 'rchid', // related content hit id 'f_ri', // research interest id, for SEO tracking 'f_fri', // featured research interest, for SEO tracking (param key without value) 'f_rid', // from research interest directory for SEO tracking 'f_loswp', // from research interest pills on LOSWP sidebar for SEO tracking 'rhid', // referrring hit id ]; if (paramsToDelete.every((key) => searchParams.get(key) === null)) { return; } paramsToDelete.forEach((key) => { searchParams.delete(key); }); var cleanUrl = new URL(window.location.href); cleanUrl.search = searchParams.toString(); history.replaceState({}, document.title, cleanUrl); })(); </script> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "profiles/works", 'action': "summary", 'controller_action': 'profiles/works#summary', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script type="text/javascript"> window.sendUserTiming = function(timingName) { if (!(window.performance && window.performance.measure)) return; var entries = window.performance.getEntriesByName(timingName, "measure"); if (entries.length !== 1) return; var timingValue = Math.round(entries[0].duration); gtag('event', 'timing_complete', { name: timingName, value: timingValue, event_category: 'User-centric', }); }; window.sendUserTiming("Time To First Byte"); </script> <meta name="csrf-param" content="authenticity_token" /> <meta name="csrf-token" content="aylJK6CsjtSmGOBa0-3kdeiAeCoEQiCsSs17PjTFmQ9OVan_8ZY8oWCxFj5BBMQ8OaC0PfwYHpkx4A2NK6x8Cg" /> <link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/wow-3d36c19b4875b226bfed0fcba1dcea3f2fe61148383d97c0465c016b8c969290.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/social/home-79e78ce59bef0a338eb6540ec3d93b4a7952115b56c57f1760943128f4544d42.css" /><script type="application/ld+json">{"@context":"https://schema.org","@type":"ProfilePage","mainEntity":{"@context":"https://schema.org","@type":"Person","name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan","image":"https://0.academia-photos.com/30994194/10626268/35155960/s200_prof_khaled.shaalan.jpg","sameAs":["http://scholar.google.com/citations?user=keLKdlgAAAAJ\u0026hl=en","https://www.linkedin.com/in/khaledshaalan","https://www.khaledshaalan.org/","https://orcid.org/0000-0003-0823-8390"]},"dateCreated":"2015-05-11T05:38:58-07:00","dateModified":"2025-01-01T07:57:23-08:00","name":"Khaled Shaalan","description":"Khaled is an accomplished full professor of Computer Science \u0026 AI with over thirty years of experience ‎in academia. ‎He has a strong background in teaching, mentoring, academic training, community ‎development, and academic ‎administration. Khaled has received numerous honors and awards for ‎academic excellence and outstanding ‎achievements. As the Co-Chair of the Faculty of Engineering and ‎information technology Council, he successfully built the IT section from ‎scratch and oversaw the ‎development of the academic programs' curricula. He has mentored academic staff, ‎established a team of ‎researchers, and increased program enrolment. Khaled has collaborated with international ‎institutions, ‎organizations, and corporations and has published extensively in high-quality international journals, ‎book ‎chapters, and conference proceedings. His expertise is widely recognized, and he is frequently invited to ‎‎evaluate research proposals, academic programs, PhD theses, and academic promotions. Khaled has also ‎taught ‎Computer Science courses at the undergraduate and graduate levels and served as chair of ‎numerous international ‎conferences and on the editorial boards of various international journals.‎","image":"https://0.academia-photos.com/30994194/10626268/35155960/s200_prof_khaled.shaalan.jpg","thumbnailUrl":"https://0.academia-photos.com/30994194/10626268/35155960/s65_prof_khaled.shaalan.jpg","primaryImageOfPage":{"@type":"ImageObject","url":"https://0.academia-photos.com/30994194/10626268/35155960/s200_prof_khaled.shaalan.jpg","width":200},"sameAs":["http://scholar.google.com/citations?user=keLKdlgAAAAJ\u0026hl=en","https://www.linkedin.com/in/khaledshaalan","https://www.khaledshaalan.org/","https://orcid.org/0000-0003-0823-8390"],"relatedLink":"https://www.academia.edu/127870015/Transformer_Models_in_Natural_Language_Processing_A_Comprehensive_Review_and_Prospects_for_Future_Development"}</script><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/heading-95367dc03b794f6737f30123738a886cf53b7a65cdef98a922a98591d60063e3.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/button-8c9ae4b5c8a2531640c354d92a1f3579c8ff103277ef74913e34c8a76d4e6c00.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/body-170d1319f0e354621e81ca17054bb147da2856ec0702fe440a99af314a6338c5.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/text_button-d1941ab08e91e29ee143084c4749da4aaffa350a2ac6eec2306b1d7a352d911a.css" /><style type="text/css">@media(max-width: 567px){:root{--token-mode: Parity;--dropshadow: 0 2px 4px 0 #22223340;--primary-brand: #0645b1;--error-dark: #b60000;--success-dark: #05b01c;--inactive-fill: #ebebee;--hover: #0c3b8d;--pressed: #082f75;--button-primary-fill-inactive: #ebebee;--button-primary-fill: #0645b1;--button-primary-text: #ffffff;--button-primary-fill-hover: #0c3b8d;--button-primary-fill-press: #082f75;--button-primary-icon: #ffffff;--button-primary-fill-inverse: #ffffff;--button-primary-text-inverse: #082f75;--button-primary-icon-inverse: #0645b1;--button-primary-fill-inverse-hover: #cddaef;--button-primary-stroke-inverse-pressed: #0645b1;--button-secondary-stroke-inactive: #b1b1ba;--button-secondary-fill: #eef2f9;--button-secondary-text: #082f75;--button-secondary-fill-press: #cddaef;--button-secondary-fill-inactive: #ebebee;--button-secondary-stroke: #cddaef;--button-secondary-stroke-hover: #386ac1;--button-secondary-stroke-press: #0645b1;--button-secondary-text-inactive: #b1b1ba;--button-secondary-icon: #082f75;--button-secondary-fill-hover: #e6ecf7;--button-secondary-stroke-inverse: #ffffff;--button-secondary-fill-inverse: rgba(255, 255, 255, 0);--button-secondary-icon-inverse: #ffffff;--button-secondary-icon-hover: #082f75;--button-secondary-icon-press: #082f75;--button-secondary-text-inverse: #ffffff;--button-secondary-text-hover: #082f75;--button-secondary-text-press: #082f75;--button-secondary-fill-inverse-hover: #043059;--button-xs-stroke: #141413;--button-xs-stroke-hover: #0c3b8d;--button-xs-stroke-press: #082f75;--button-xs-stroke-inactive: #ebebee;--button-xs-text: #141413;--button-xs-text-hover: #0c3b8d;--button-xs-text-press: #082f75;--button-xs-text-inactive: #91919e;--button-xs-icon: #141413;--button-xs-icon-hover: #0c3b8d;--button-xs-icon-press: #082f75;--button-xs-icon-inactive: #91919e;--button-xs-fill: #ffffff;--button-xs-fill-hover: #f4f7fc;--button-xs-fill-press: #eef2f9;--buttons-button-text-inactive: #91919e;--buttons-button-focus: #0645b1;--buttons-button-icon-inactive: #91919e;--buttons-small-buttons-corner-radius: 8px;--buttons-small-buttons-l-r-padding: 12px;--buttons-small-buttons-height: 44px;--buttons-small-buttons-gap: 8px;--buttons-small-buttons-icon-only-width: 44px;--buttons-small-buttons-icon-size: 20px;--buttons-small-buttons-stroke-default: 1px;--buttons-small-buttons-stroke-thick: 2px;--buttons-large-buttons-l-r-padding: 20px;--buttons-large-buttons-height: 54px;--buttons-large-buttons-icon-only-width: 54px;--buttons-large-buttons-icon-size: 20px;--buttons-large-buttons-gap: 8px;--buttons-large-buttons-corner-radius: 8px;--buttons-large-buttons-stroke-default: 1px;--buttons-large-buttons-stroke-thick: 2px;--buttons-extra-small-buttons-l-r-padding: 8px;--buttons-extra-small-buttons-height: 32px;--buttons-extra-small-buttons-icon-size: 16px;--buttons-extra-small-buttons-gap: 4px;--buttons-extra-small-buttons-corner-radius: 8px;--buttons-stroke-default: 1px;--buttons-stroke-thick: 2px;--background-beige: #f9f7f4;--error-light: #fff2f2;--text-placeholder: #6d6d7d;--stroke-dark: #141413;--stroke-light: #dddde2;--stroke-medium: #535366;--accent-green: #ccffd4;--accent-turquoise: #ccf7ff;--accent-yellow: #f7ffcc;--accent-peach: #ffd4cc;--accent-violet: #f7ccff;--accent-purple: #f4f7fc;--text-primary: #141413;--secondary-brand: #141413;--text-hover: #0c3b8d;--text-white: #ffffff;--text-link: #0645b1;--text-press: #082f75;--success-light: #f0f8f1;--background-light-blue: #eef2f9;--background-white: #ffffff;--premium-dark: #877440;--premium-light: #f9f6ed;--stroke-white: #ffffff;--inactive-content: #b1b1ba;--annotate-light: #a35dff;--annotate-dark: #824acc;--grid: #eef2f9;--inactive-stroke: #ebebee;--shadow: rgba(34, 34, 51, 0.25);--text-inactive: #6d6d7d;--text-error: #b60000;--stroke-error: #b60000;--background-error: #fff2f2;--background-black: #141413;--icon-default: #141413;--icon-blue: #0645b1;--background-grey: #dddde2;--icon-grey: #b1b1ba;--text-focus: #082f75;--brand-colors-neutral-black: #141413;--brand-colors-neutral-900: #535366;--brand-colors-neutral-800: #6d6d7d;--brand-colors-neutral-700: #91919e;--brand-colors-neutral-600: #b1b1ba;--brand-colors-neutral-500: #c8c8cf;--brand-colors-neutral-400: #dddde2;--brand-colors-neutral-300: #ebebee;--brand-colors-neutral-200: #f8f8fb;--brand-colors-neutral-100: #fafafa;--brand-colors-neutral-white: #ffffff;--brand-colors-blue-900: #043059;--brand-colors-blue-800: #082f75;--brand-colors-blue-700: #0c3b8d;--brand-colors-blue-600: #0645b1;--brand-colors-blue-500: #386ac1;--brand-colors-blue-400: #cddaef;--brand-colors-blue-300: #e6ecf7;--brand-colors-blue-200: #eef2f9;--brand-colors-blue-100: #f4f7fc;--brand-colors-gold-500: #877440;--brand-colors-gold-400: #e9e3d4;--brand-colors-gold-300: #f2efe8;--brand-colors-gold-200: #f9f6ed;--brand-colors-gold-100: #f9f7f4;--brand-colors-error-900: #920000;--brand-colors-error-500: #b60000;--brand-colors-success-900: #035c0f;--brand-colors-green: #ccffd4;--brand-colors-turquoise: #ccf7ff;--brand-colors-yellow: #f7ffcc;--brand-colors-peach: #ffd4cc;--brand-colors-violet: #f7ccff;--brand-colors-error-100: #fff2f2;--brand-colors-success-500: #05b01c;--brand-colors-success-100: #f0f8f1;--text-secondary: #535366;--icon-white: #ffffff;--background-beige-darker: #f2efe8;--icon-dark-grey: #535366;--type-font-family-sans-serif: Roboto;--type-font-family-serif: Georgia;--type-font-family-mono: IBM Plex Mono;--type-weights-300: 300;--type-weights-400: 400;--type-weights-500: 500;--type-weights-700: 700;--type-sizes-12: 12px;--type-sizes-14: 14px;--type-sizes-16: 16px;--type-sizes-18: 18px;--type-sizes-20: 20px;--type-sizes-22: 22px;--type-sizes-24: 24px;--type-sizes-28: 28px;--type-sizes-30: 30px;--type-sizes-32: 32px;--type-sizes-40: 40px;--type-sizes-42: 42px;--type-sizes-48-2: 48px;--type-line-heights-16: 16px;--type-line-heights-20: 20px;--type-line-heights-23: 23px;--type-line-heights-24: 24px;--type-line-heights-25: 25px;--type-line-heights-26: 26px;--type-line-heights-29: 29px;--type-line-heights-30: 30px;--type-line-heights-32: 32px;--type-line-heights-34: 34px;--type-line-heights-35: 35px;--type-line-heights-36: 36px;--type-line-heights-38: 38px;--type-line-heights-40: 40px;--type-line-heights-46: 46px;--type-line-heights-48: 48px;--type-line-heights-52: 52px;--type-line-heights-58: 58px;--type-line-heights-68: 68px;--type-line-heights-74: 74px;--type-line-heights-82: 82px;--type-paragraph-spacings-0: 0px;--type-paragraph-spacings-4: 4px;--type-paragraph-spacings-8: 8px;--type-paragraph-spacings-16: 16px;--type-sans-serif-xl-font-weight: 400;--type-sans-serif-xl-size: 32px;--type-sans-serif-xl-line-height: 46px;--type-sans-serif-xl-paragraph-spacing: 16px;--type-sans-serif-lg-font-weight: 400;--type-sans-serif-lg-size: 30px;--type-sans-serif-lg-line-height: 36px;--type-sans-serif-lg-paragraph-spacing: 16px;--type-sans-serif-md-font-weight: 400;--type-sans-serif-md-line-height: 30px;--type-sans-serif-md-paragraph-spacing: 16px;--type-sans-serif-md-size: 24px;--type-sans-serif-xs-font-weight: 700;--type-sans-serif-xs-line-height: 24px;--type-sans-serif-xs-paragraph-spacing: 0px;--type-sans-serif-xs-size: 18px;--type-sans-serif-sm-font-weight: 400;--type-sans-serif-sm-line-height: 32px;--type-sans-serif-sm-paragraph-spacing: 16px;--type-sans-serif-sm-size: 20px;--type-body-xl-font-weight: 400;--type-body-xl-size: 24px;--type-body-xl-line-height: 36px;--type-body-xl-paragraph-spacing: 0px;--type-body-sm-font-weight: 400;--type-body-sm-size: 14px;--type-body-sm-line-height: 20px;--type-body-sm-paragraph-spacing: 8px;--type-body-xs-font-weight: 400;--type-body-xs-size: 12px;--type-body-xs-line-height: 16px;--type-body-xs-paragraph-spacing: 0px;--type-body-md-font-weight: 400;--type-body-md-size: 16px;--type-body-md-line-height: 20px;--type-body-md-paragraph-spacing: 4px;--type-body-lg-font-weight: 400;--type-body-lg-size: 20px;--type-body-lg-line-height: 26px;--type-body-lg-paragraph-spacing: 16px;--type-body-lg-medium-font-weight: 500;--type-body-lg-medium-size: 20px;--type-body-lg-medium-line-height: 32px;--type-body-lg-medium-paragraph-spacing: 16px;--type-body-md-medium-font-weight: 500;--type-body-md-medium-size: 16px;--type-body-md-medium-line-height: 20px;--type-body-md-medium-paragraph-spacing: 4px;--type-body-sm-bold-font-weight: 700;--type-body-sm-bold-size: 14px;--type-body-sm-bold-line-height: 20px;--type-body-sm-bold-paragraph-spacing: 8px;--type-body-sm-medium-font-weight: 500;--type-body-sm-medium-size: 14px;--type-body-sm-medium-line-height: 20px;--type-body-sm-medium-paragraph-spacing: 8px;--type-serif-md-font-weight: 400;--type-serif-md-size: 32px;--type-serif-md-paragraph-spacing: 0px;--type-serif-md-line-height: 40px;--type-serif-sm-font-weight: 400;--type-serif-sm-size: 24px;--type-serif-sm-paragraph-spacing: 0px;--type-serif-sm-line-height: 26px;--type-serif-lg-font-weight: 400;--type-serif-lg-size: 48px;--type-serif-lg-paragraph-spacing: 0px;--type-serif-lg-line-height: 52px;--type-serif-xs-font-weight: 400;--type-serif-xs-size: 18px;--type-serif-xs-line-height: 24px;--type-serif-xs-paragraph-spacing: 0px;--type-serif-xl-font-weight: 400;--type-serif-xl-size: 48px;--type-serif-xl-paragraph-spacing: 0px;--type-serif-xl-line-height: 58px;--type-mono-md-font-weight: 400;--type-mono-md-size: 22px;--type-mono-md-line-height: 24px;--type-mono-md-paragraph-spacing: 0px;--type-mono-lg-font-weight: 400;--type-mono-lg-size: 40px;--type-mono-lg-line-height: 40px;--type-mono-lg-paragraph-spacing: 0px;--type-mono-sm-font-weight: 400;--type-mono-sm-size: 14px;--type-mono-sm-line-height: 24px;--type-mono-sm-paragraph-spacing: 0px;--spacing-xs-4: 4px;--spacing-xs-8: 8px;--spacing-xs-16: 16px;--spacing-sm-24: 24px;--spacing-sm-32: 32px;--spacing-md-40: 40px;--spacing-md-48: 48px;--spacing-lg-64: 64px;--spacing-lg-80: 80px;--spacing-xlg-104: 104px;--spacing-xlg-152: 152px;--spacing-xs-12: 12px;--spacing-page-section: 80px;--spacing-card-list-spacing: 48px;--spacing-text-section-spacing: 64px;--spacing-md-xs-headings: 40px;--corner-radius-radius-lg: 16px;--corner-radius-radius-sm: 4px;--corner-radius-radius-md: 8px;--corner-radius-radius-round: 104px}}@media(min-width: 568px)and (max-width: 1279px){:root{--token-mode: Parity;--dropshadow: 0 2px 4px 0 #22223340;--primary-brand: #0645b1;--error-dark: #b60000;--success-dark: #05b01c;--inactive-fill: #ebebee;--hover: #0c3b8d;--pressed: #082f75;--button-primary-fill-inactive: #ebebee;--button-primary-fill: #0645b1;--button-primary-text: #ffffff;--button-primary-fill-hover: #0c3b8d;--button-primary-fill-press: #082f75;--button-primary-icon: #ffffff;--button-primary-fill-inverse: #ffffff;--button-primary-text-inverse: #082f75;--button-primary-icon-inverse: #0645b1;--button-primary-fill-inverse-hover: #cddaef;--button-primary-stroke-inverse-pressed: #0645b1;--button-secondary-stroke-inactive: #b1b1ba;--button-secondary-fill: #eef2f9;--button-secondary-text: #082f75;--button-secondary-fill-press: #cddaef;--button-secondary-fill-inactive: #ebebee;--button-secondary-stroke: #cddaef;--button-secondary-stroke-hover: #386ac1;--button-secondary-stroke-press: #0645b1;--button-secondary-text-inactive: #b1b1ba;--button-secondary-icon: #082f75;--button-secondary-fill-hover: #e6ecf7;--button-secondary-stroke-inverse: #ffffff;--button-secondary-fill-inverse: rgba(255, 255, 255, 0);--button-secondary-icon-inverse: #ffffff;--button-secondary-icon-hover: #082f75;--button-secondary-icon-press: #082f75;--button-secondary-text-inverse: #ffffff;--button-secondary-text-hover: #082f75;--button-secondary-text-press: #082f75;--button-secondary-fill-inverse-hover: #043059;--button-xs-stroke: #141413;--button-xs-stroke-hover: #0c3b8d;--button-xs-stroke-press: #082f75;--button-xs-stroke-inactive: #ebebee;--button-xs-text: #141413;--button-xs-text-hover: #0c3b8d;--button-xs-text-press: #082f75;--button-xs-text-inactive: #91919e;--button-xs-icon: #141413;--button-xs-icon-hover: #0c3b8d;--button-xs-icon-press: #082f75;--button-xs-icon-inactive: #91919e;--button-xs-fill: #ffffff;--button-xs-fill-hover: #f4f7fc;--button-xs-fill-press: #eef2f9;--buttons-button-text-inactive: #91919e;--buttons-button-focus: #0645b1;--buttons-button-icon-inactive: #91919e;--buttons-small-buttons-corner-radius: 8px;--buttons-small-buttons-l-r-padding: 12px;--buttons-small-buttons-height: 44px;--buttons-small-buttons-gap: 8px;--buttons-small-buttons-icon-only-width: 44px;--buttons-small-buttons-icon-size: 20px;--buttons-small-buttons-stroke-default: 1px;--buttons-small-buttons-stroke-thick: 2px;--buttons-large-buttons-l-r-padding: 20px;--buttons-large-buttons-height: 54px;--buttons-large-buttons-icon-only-width: 54px;--buttons-large-buttons-icon-size: 20px;--buttons-large-buttons-gap: 8px;--buttons-large-buttons-corner-radius: 8px;--buttons-large-buttons-stroke-default: 1px;--buttons-large-buttons-stroke-thick: 2px;--buttons-extra-small-buttons-l-r-padding: 8px;--buttons-extra-small-buttons-height: 32px;--buttons-extra-small-buttons-icon-size: 16px;--buttons-extra-small-buttons-gap: 4px;--buttons-extra-small-buttons-corner-radius: 8px;--buttons-stroke-default: 1px;--buttons-stroke-thick: 2px;--background-beige: #f9f7f4;--error-light: #fff2f2;--text-placeholder: #6d6d7d;--stroke-dark: #141413;--stroke-light: #dddde2;--stroke-medium: #535366;--accent-green: #ccffd4;--accent-turquoise: #ccf7ff;--accent-yellow: #f7ffcc;--accent-peach: #ffd4cc;--accent-violet: #f7ccff;--accent-purple: #f4f7fc;--text-primary: #141413;--secondary-brand: #141413;--text-hover: #0c3b8d;--text-white: #ffffff;--text-link: #0645b1;--text-press: #082f75;--success-light: #f0f8f1;--background-light-blue: #eef2f9;--background-white: #ffffff;--premium-dark: #877440;--premium-light: #f9f6ed;--stroke-white: #ffffff;--inactive-content: #b1b1ba;--annotate-light: #a35dff;--annotate-dark: #824acc;--grid: #eef2f9;--inactive-stroke: #ebebee;--shadow: rgba(34, 34, 51, 0.25);--text-inactive: #6d6d7d;--text-error: #b60000;--stroke-error: #b60000;--background-error: #fff2f2;--background-black: #141413;--icon-default: #141413;--icon-blue: #0645b1;--background-grey: #dddde2;--icon-grey: #b1b1ba;--text-focus: #082f75;--brand-colors-neutral-black: #141413;--brand-colors-neutral-900: #535366;--brand-colors-neutral-800: #6d6d7d;--brand-colors-neutral-700: #91919e;--brand-colors-neutral-600: #b1b1ba;--brand-colors-neutral-500: #c8c8cf;--brand-colors-neutral-400: #dddde2;--brand-colors-neutral-300: #ebebee;--brand-colors-neutral-200: #f8f8fb;--brand-colors-neutral-100: #fafafa;--brand-colors-neutral-white: #ffffff;--brand-colors-blue-900: #043059;--brand-colors-blue-800: #082f75;--brand-colors-blue-700: #0c3b8d;--brand-colors-blue-600: #0645b1;--brand-colors-blue-500: #386ac1;--brand-colors-blue-400: #cddaef;--brand-colors-blue-300: #e6ecf7;--brand-colors-blue-200: #eef2f9;--brand-colors-blue-100: #f4f7fc;--brand-colors-gold-500: #877440;--brand-colors-gold-400: #e9e3d4;--brand-colors-gold-300: #f2efe8;--brand-colors-gold-200: #f9f6ed;--brand-colors-gold-100: #f9f7f4;--brand-colors-error-900: #920000;--brand-colors-error-500: #b60000;--brand-colors-success-900: #035c0f;--brand-colors-green: #ccffd4;--brand-colors-turquoise: #ccf7ff;--brand-colors-yellow: #f7ffcc;--brand-colors-peach: #ffd4cc;--brand-colors-violet: #f7ccff;--brand-colors-error-100: #fff2f2;--brand-colors-success-500: #05b01c;--brand-colors-success-100: #f0f8f1;--text-secondary: #535366;--icon-white: #ffffff;--background-beige-darker: #f2efe8;--icon-dark-grey: #535366;--type-font-family-sans-serif: Roboto;--type-font-family-serif: Georgia;--type-font-family-mono: IBM Plex Mono;--type-weights-300: 300;--type-weights-400: 400;--type-weights-500: 500;--type-weights-700: 700;--type-sizes-12: 12px;--type-sizes-14: 14px;--type-sizes-16: 16px;--type-sizes-18: 18px;--type-sizes-20: 20px;--type-sizes-22: 22px;--type-sizes-24: 24px;--type-sizes-28: 28px;--type-sizes-30: 30px;--type-sizes-32: 32px;--type-sizes-40: 40px;--type-sizes-42: 42px;--type-sizes-48-2: 48px;--type-line-heights-16: 16px;--type-line-heights-20: 20px;--type-line-heights-23: 23px;--type-line-heights-24: 24px;--type-line-heights-25: 25px;--type-line-heights-26: 26px;--type-line-heights-29: 29px;--type-line-heights-30: 30px;--type-line-heights-32: 32px;--type-line-heights-34: 34px;--type-line-heights-35: 35px;--type-line-heights-36: 36px;--type-line-heights-38: 38px;--type-line-heights-40: 40px;--type-line-heights-46: 46px;--type-line-heights-48: 48px;--type-line-heights-52: 52px;--type-line-heights-58: 58px;--type-line-heights-68: 68px;--type-line-heights-74: 74px;--type-line-heights-82: 82px;--type-paragraph-spacings-0: 0px;--type-paragraph-spacings-4: 4px;--type-paragraph-spacings-8: 8px;--type-paragraph-spacings-16: 16px;--type-sans-serif-xl-font-weight: 400;--type-sans-serif-xl-size: 42px;--type-sans-serif-xl-line-height: 46px;--type-sans-serif-xl-paragraph-spacing: 16px;--type-sans-serif-lg-font-weight: 400;--type-sans-serif-lg-size: 32px;--type-sans-serif-lg-line-height: 36px;--type-sans-serif-lg-paragraph-spacing: 16px;--type-sans-serif-md-font-weight: 400;--type-sans-serif-md-line-height: 34px;--type-sans-serif-md-paragraph-spacing: 16px;--type-sans-serif-md-size: 28px;--type-sans-serif-xs-font-weight: 700;--type-sans-serif-xs-line-height: 25px;--type-sans-serif-xs-paragraph-spacing: 0px;--type-sans-serif-xs-size: 20px;--type-sans-serif-sm-font-weight: 400;--type-sans-serif-sm-line-height: 30px;--type-sans-serif-sm-paragraph-spacing: 16px;--type-sans-serif-sm-size: 24px;--type-body-xl-font-weight: 400;--type-body-xl-size: 24px;--type-body-xl-line-height: 36px;--type-body-xl-paragraph-spacing: 0px;--type-body-sm-font-weight: 400;--type-body-sm-size: 14px;--type-body-sm-line-height: 20px;--type-body-sm-paragraph-spacing: 8px;--type-body-xs-font-weight: 400;--type-body-xs-size: 12px;--type-body-xs-line-height: 16px;--type-body-xs-paragraph-spacing: 0px;--type-body-md-font-weight: 400;--type-body-md-size: 16px;--type-body-md-line-height: 20px;--type-body-md-paragraph-spacing: 4px;--type-body-lg-font-weight: 400;--type-body-lg-size: 20px;--type-body-lg-line-height: 26px;--type-body-lg-paragraph-spacing: 16px;--type-body-lg-medium-font-weight: 500;--type-body-lg-medium-size: 20px;--type-body-lg-medium-line-height: 32px;--type-body-lg-medium-paragraph-spacing: 16px;--type-body-md-medium-font-weight: 500;--type-body-md-medium-size: 16px;--type-body-md-medium-line-height: 20px;--type-body-md-medium-paragraph-spacing: 4px;--type-body-sm-bold-font-weight: 700;--type-body-sm-bold-size: 14px;--type-body-sm-bold-line-height: 20px;--type-body-sm-bold-paragraph-spacing: 8px;--type-body-sm-medium-font-weight: 500;--type-body-sm-medium-size: 14px;--type-body-sm-medium-line-height: 20px;--type-body-sm-medium-paragraph-spacing: 8px;--type-serif-md-font-weight: 400;--type-serif-md-size: 40px;--type-serif-md-paragraph-spacing: 0px;--type-serif-md-line-height: 48px;--type-serif-sm-font-weight: 400;--type-serif-sm-size: 28px;--type-serif-sm-paragraph-spacing: 0px;--type-serif-sm-line-height: 32px;--type-serif-lg-font-weight: 400;--type-serif-lg-size: 58px;--type-serif-lg-paragraph-spacing: 0px;--type-serif-lg-line-height: 68px;--type-serif-xs-font-weight: 400;--type-serif-xs-size: 18px;--type-serif-xs-line-height: 24px;--type-serif-xs-paragraph-spacing: 0px;--type-serif-xl-font-weight: 400;--type-serif-xl-size: 74px;--type-serif-xl-paragraph-spacing: 0px;--type-serif-xl-line-height: 82px;--type-mono-md-font-weight: 400;--type-mono-md-size: 22px;--type-mono-md-line-height: 24px;--type-mono-md-paragraph-spacing: 0px;--type-mono-lg-font-weight: 400;--type-mono-lg-size: 40px;--type-mono-lg-line-height: 40px;--type-mono-lg-paragraph-spacing: 0px;--type-mono-sm-font-weight: 400;--type-mono-sm-size: 14px;--type-mono-sm-line-height: 24px;--type-mono-sm-paragraph-spacing: 0px;--spacing-xs-4: 4px;--spacing-xs-8: 8px;--spacing-xs-16: 16px;--spacing-sm-24: 24px;--spacing-sm-32: 32px;--spacing-md-40: 40px;--spacing-md-48: 48px;--spacing-lg-64: 64px;--spacing-lg-80: 80px;--spacing-xlg-104: 104px;--spacing-xlg-152: 152px;--spacing-xs-12: 12px;--spacing-page-section: 104px;--spacing-card-list-spacing: 48px;--spacing-text-section-spacing: 80px;--spacing-md-xs-headings: 40px;--corner-radius-radius-lg: 16px;--corner-radius-radius-sm: 4px;--corner-radius-radius-md: 8px;--corner-radius-radius-round: 104px}}@media(min-width: 1280px){:root{--token-mode: Parity;--dropshadow: 0 2px 4px 0 #22223340;--primary-brand: #0645b1;--error-dark: #b60000;--success-dark: #05b01c;--inactive-fill: #ebebee;--hover: #0c3b8d;--pressed: #082f75;--button-primary-fill-inactive: #ebebee;--button-primary-fill: #0645b1;--button-primary-text: #ffffff;--button-primary-fill-hover: #0c3b8d;--button-primary-fill-press: #082f75;--button-primary-icon: #ffffff;--button-primary-fill-inverse: #ffffff;--button-primary-text-inverse: #082f75;--button-primary-icon-inverse: #0645b1;--button-primary-fill-inverse-hover: #cddaef;--button-primary-stroke-inverse-pressed: #0645b1;--button-secondary-stroke-inactive: #b1b1ba;--button-secondary-fill: #eef2f9;--button-secondary-text: #082f75;--button-secondary-fill-press: #cddaef;--button-secondary-fill-inactive: #ebebee;--button-secondary-stroke: #cddaef;--button-secondary-stroke-hover: #386ac1;--button-secondary-stroke-press: #0645b1;--button-secondary-text-inactive: #b1b1ba;--button-secondary-icon: #082f75;--button-secondary-fill-hover: #e6ecf7;--button-secondary-stroke-inverse: #ffffff;--button-secondary-fill-inverse: rgba(255, 255, 255, 0);--button-secondary-icon-inverse: #ffffff;--button-secondary-icon-hover: #082f75;--button-secondary-icon-press: #082f75;--button-secondary-text-inverse: #ffffff;--button-secondary-text-hover: #082f75;--button-secondary-text-press: #082f75;--button-secondary-fill-inverse-hover: #043059;--button-xs-stroke: #141413;--button-xs-stroke-hover: #0c3b8d;--button-xs-stroke-press: #082f75;--button-xs-stroke-inactive: #ebebee;--button-xs-text: #141413;--button-xs-text-hover: #0c3b8d;--button-xs-text-press: #082f75;--button-xs-text-inactive: #91919e;--button-xs-icon: #141413;--button-xs-icon-hover: #0c3b8d;--button-xs-icon-press: #082f75;--button-xs-icon-inactive: #91919e;--button-xs-fill: #ffffff;--button-xs-fill-hover: #f4f7fc;--button-xs-fill-press: #eef2f9;--buttons-button-text-inactive: #91919e;--buttons-button-focus: #0645b1;--buttons-button-icon-inactive: #91919e;--buttons-small-buttons-corner-radius: 8px;--buttons-small-buttons-l-r-padding: 12px;--buttons-small-buttons-height: 44px;--buttons-small-buttons-gap: 8px;--buttons-small-buttons-icon-only-width: 44px;--buttons-small-buttons-icon-size: 20px;--buttons-small-buttons-stroke-default: 1px;--buttons-small-buttons-stroke-thick: 2px;--buttons-large-buttons-l-r-padding: 20px;--buttons-large-buttons-height: 54px;--buttons-large-buttons-icon-only-width: 54px;--buttons-large-buttons-icon-size: 20px;--buttons-large-buttons-gap: 8px;--buttons-large-buttons-corner-radius: 8px;--buttons-large-buttons-stroke-default: 1px;--buttons-large-buttons-stroke-thick: 2px;--buttons-extra-small-buttons-l-r-padding: 8px;--buttons-extra-small-buttons-height: 32px;--buttons-extra-small-buttons-icon-size: 16px;--buttons-extra-small-buttons-gap: 4px;--buttons-extra-small-buttons-corner-radius: 8px;--buttons-stroke-default: 1px;--buttons-stroke-thick: 2px;--background-beige: #f9f7f4;--error-light: #fff2f2;--text-placeholder: #6d6d7d;--stroke-dark: #141413;--stroke-light: #dddde2;--stroke-medium: #535366;--accent-green: #ccffd4;--accent-turquoise: #ccf7ff;--accent-yellow: #f7ffcc;--accent-peach: #ffd4cc;--accent-violet: #f7ccff;--accent-purple: #f4f7fc;--text-primary: #141413;--secondary-brand: #141413;--text-hover: #0c3b8d;--text-white: #ffffff;--text-link: #0645b1;--text-press: #082f75;--success-light: #f0f8f1;--background-light-blue: #eef2f9;--background-white: #ffffff;--premium-dark: #877440;--premium-light: #f9f6ed;--stroke-white: #ffffff;--inactive-content: #b1b1ba;--annotate-light: #a35dff;--annotate-dark: #824acc;--grid: #eef2f9;--inactive-stroke: #ebebee;--shadow: rgba(34, 34, 51, 0.25);--text-inactive: #6d6d7d;--text-error: #b60000;--stroke-error: #b60000;--background-error: #fff2f2;--background-black: #141413;--icon-default: #141413;--icon-blue: #0645b1;--background-grey: #dddde2;--icon-grey: #b1b1ba;--text-focus: #082f75;--brand-colors-neutral-black: #141413;--brand-colors-neutral-900: #535366;--brand-colors-neutral-800: #6d6d7d;--brand-colors-neutral-700: #91919e;--brand-colors-neutral-600: #b1b1ba;--brand-colors-neutral-500: #c8c8cf;--brand-colors-neutral-400: #dddde2;--brand-colors-neutral-300: #ebebee;--brand-colors-neutral-200: #f8f8fb;--brand-colors-neutral-100: #fafafa;--brand-colors-neutral-white: #ffffff;--brand-colors-blue-900: #043059;--brand-colors-blue-800: #082f75;--brand-colors-blue-700: #0c3b8d;--brand-colors-blue-600: #0645b1;--brand-colors-blue-500: #386ac1;--brand-colors-blue-400: #cddaef;--brand-colors-blue-300: #e6ecf7;--brand-colors-blue-200: #eef2f9;--brand-colors-blue-100: #f4f7fc;--brand-colors-gold-500: #877440;--brand-colors-gold-400: #e9e3d4;--brand-colors-gold-300: #f2efe8;--brand-colors-gold-200: #f9f6ed;--brand-colors-gold-100: #f9f7f4;--brand-colors-error-900: #920000;--brand-colors-error-500: #b60000;--brand-colors-success-900: #035c0f;--brand-colors-green: #ccffd4;--brand-colors-turquoise: #ccf7ff;--brand-colors-yellow: #f7ffcc;--brand-colors-peach: #ffd4cc;--brand-colors-violet: #f7ccff;--brand-colors-error-100: #fff2f2;--brand-colors-success-500: #05b01c;--brand-colors-success-100: #f0f8f1;--text-secondary: #535366;--icon-white: #ffffff;--background-beige-darker: #f2efe8;--icon-dark-grey: #535366;--type-font-family-sans-serif: Roboto;--type-font-family-serif: Georgia;--type-font-family-mono: IBM Plex Mono;--type-weights-300: 300;--type-weights-400: 400;--type-weights-500: 500;--type-weights-700: 700;--type-sizes-12: 12px;--type-sizes-14: 14px;--type-sizes-16: 16px;--type-sizes-18: 18px;--type-sizes-20: 20px;--type-sizes-22: 22px;--type-sizes-24: 24px;--type-sizes-28: 28px;--type-sizes-30: 30px;--type-sizes-32: 32px;--type-sizes-40: 40px;--type-sizes-42: 42px;--type-sizes-48-2: 48px;--type-line-heights-16: 16px;--type-line-heights-20: 20px;--type-line-heights-23: 23px;--type-line-heights-24: 24px;--type-line-heights-25: 25px;--type-line-heights-26: 26px;--type-line-heights-29: 29px;--type-line-heights-30: 30px;--type-line-heights-32: 32px;--type-line-heights-34: 34px;--type-line-heights-35: 35px;--type-line-heights-36: 36px;--type-line-heights-38: 38px;--type-line-heights-40: 40px;--type-line-heights-46: 46px;--type-line-heights-48: 48px;--type-line-heights-52: 52px;--type-line-heights-58: 58px;--type-line-heights-68: 68px;--type-line-heights-74: 74px;--type-line-heights-82: 82px;--type-paragraph-spacings-0: 0px;--type-paragraph-spacings-4: 4px;--type-paragraph-spacings-8: 8px;--type-paragraph-spacings-16: 16px;--type-sans-serif-xl-font-weight: 400;--type-sans-serif-xl-size: 42px;--type-sans-serif-xl-line-height: 46px;--type-sans-serif-xl-paragraph-spacing: 16px;--type-sans-serif-lg-font-weight: 400;--type-sans-serif-lg-size: 32px;--type-sans-serif-lg-line-height: 38px;--type-sans-serif-lg-paragraph-spacing: 16px;--type-sans-serif-md-font-weight: 400;--type-sans-serif-md-line-height: 34px;--type-sans-serif-md-paragraph-spacing: 16px;--type-sans-serif-md-size: 28px;--type-sans-serif-xs-font-weight: 700;--type-sans-serif-xs-line-height: 25px;--type-sans-serif-xs-paragraph-spacing: 0px;--type-sans-serif-xs-size: 20px;--type-sans-serif-sm-font-weight: 400;--type-sans-serif-sm-line-height: 30px;--type-sans-serif-sm-paragraph-spacing: 16px;--type-sans-serif-sm-size: 24px;--type-body-xl-font-weight: 400;--type-body-xl-size: 24px;--type-body-xl-line-height: 36px;--type-body-xl-paragraph-spacing: 0px;--type-body-sm-font-weight: 400;--type-body-sm-size: 14px;--type-body-sm-line-height: 20px;--type-body-sm-paragraph-spacing: 8px;--type-body-xs-font-weight: 400;--type-body-xs-size: 12px;--type-body-xs-line-height: 16px;--type-body-xs-paragraph-spacing: 0px;--type-body-md-font-weight: 400;--type-body-md-size: 16px;--type-body-md-line-height: 20px;--type-body-md-paragraph-spacing: 4px;--type-body-lg-font-weight: 400;--type-body-lg-size: 20px;--type-body-lg-line-height: 26px;--type-body-lg-paragraph-spacing: 16px;--type-body-lg-medium-font-weight: 500;--type-body-lg-medium-size: 20px;--type-body-lg-medium-line-height: 32px;--type-body-lg-medium-paragraph-spacing: 16px;--type-body-md-medium-font-weight: 500;--type-body-md-medium-size: 16px;--type-body-md-medium-line-height: 20px;--type-body-md-medium-paragraph-spacing: 4px;--type-body-sm-bold-font-weight: 700;--type-body-sm-bold-size: 14px;--type-body-sm-bold-line-height: 20px;--type-body-sm-bold-paragraph-spacing: 8px;--type-body-sm-medium-font-weight: 500;--type-body-sm-medium-size: 14px;--type-body-sm-medium-line-height: 20px;--type-body-sm-medium-paragraph-spacing: 8px;--type-serif-md-font-weight: 400;--type-serif-md-size: 40px;--type-serif-md-paragraph-spacing: 0px;--type-serif-md-line-height: 48px;--type-serif-sm-font-weight: 400;--type-serif-sm-size: 28px;--type-serif-sm-paragraph-spacing: 0px;--type-serif-sm-line-height: 32px;--type-serif-lg-font-weight: 400;--type-serif-lg-size: 58px;--type-serif-lg-paragraph-spacing: 0px;--type-serif-lg-line-height: 68px;--type-serif-xs-font-weight: 400;--type-serif-xs-size: 18px;--type-serif-xs-line-height: 24px;--type-serif-xs-paragraph-spacing: 0px;--type-serif-xl-font-weight: 400;--type-serif-xl-size: 74px;--type-serif-xl-paragraph-spacing: 0px;--type-serif-xl-line-height: 82px;--type-mono-md-font-weight: 400;--type-mono-md-size: 22px;--type-mono-md-line-height: 24px;--type-mono-md-paragraph-spacing: 0px;--type-mono-lg-font-weight: 400;--type-mono-lg-size: 40px;--type-mono-lg-line-height: 40px;--type-mono-lg-paragraph-spacing: 0px;--type-mono-sm-font-weight: 400;--type-mono-sm-size: 14px;--type-mono-sm-line-height: 24px;--type-mono-sm-paragraph-spacing: 0px;--spacing-xs-4: 4px;--spacing-xs-8: 8px;--spacing-xs-16: 16px;--spacing-sm-24: 24px;--spacing-sm-32: 32px;--spacing-md-40: 40px;--spacing-md-48: 48px;--spacing-lg-64: 64px;--spacing-lg-80: 80px;--spacing-xlg-104: 104px;--spacing-xlg-152: 152px;--spacing-xs-12: 12px;--spacing-page-section: 152px;--spacing-card-list-spacing: 48px;--spacing-text-section-spacing: 80px;--spacing-md-xs-headings: 40px;--corner-radius-radius-lg: 16px;--corner-radius-radius-sm: 4px;--corner-radius-radius-md: 8px;--corner-radius-radius-round: 104px}}</style><link crossorigin="" href="https://fonts.gstatic.com/" rel="preconnect" /><link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&family=Gupter:wght@400;500;700&family=IBM+Plex+Mono:wght@300;400&family=Material+Symbols+Outlined:opsz,wght,FILL,GRAD@20,400,0,0&display=swap" rel="stylesheet" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/common-57f9da13cef3fd4e2a8b655342c6488eded3e557e823fe67571f2ac77acd7b6f.css" /> <meta name="author" content="khaled shaalan" /> <meta name="description" content="Khaled is an accomplished full professor of Computer Science & AI with over thirty years of experience ‎in academia. ‎He has a strong background in teaching,…" /> <meta name="google-site-verification" content="bKJMBZA7E43xhDOopFZkssMMkBRjvYERV-NaN4R6mrs" /> <script> var $controller_name = 'works'; var $action_name = "summary"; var $rails_env = 'production'; var $app_rev = 'b22bb4b4c659370e45f7093516d2fe164f182615'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.Aedu = { hit_data: null }; window.Aedu.SiteStats = {"premium_universities_count":13939,"monthly_visitors":"135 million","monthly_visitor_count":135372775,"monthly_visitor_count_in_millions":135,"user_count":285064771,"paper_count":55203019,"paper_count_in_millions":55,"page_count":432000000,"page_count_in_millions":432,"pdf_count":16500000,"pdf_count_in_millions":16}; window.Aedu.serverRenderTime = new Date(1742120110000); window.Aedu.timeDifference = new Date().getTime() - 1742120110000; window.Aedu.isUsingCssV1 = false; window.Aedu.enableLocalization = true; window.Aedu.activateFullstory = false; window.Aedu.serviceAvailability = { status: {"attention_db":"on","bibliography_db":"on","contacts_db":"on","email_db":"on","indexability_db":"on","mentions_db":"on","news_db":"on","notifications_db":"on","offsite_mentions_db":"on","redshift":"on","redshift_exports_db":"on","related_works_db":"on","ring_db":"on","user_tests_db":"on"}, serviceEnabled: function(service) { return this.status[service] === "on"; }, readEnabled: function(service) { return this.serviceEnabled(service) || this.status[service] === "read_only"; }, }; window.Aedu.viewApmTrace = function() { // Check if x-apm-trace-id meta tag is set, and open the trace in APM // in a new window if it is. var apmTraceId = document.head.querySelector('meta[name="x-apm-trace-id"]'); if (apmTraceId) { var traceId = apmTraceId.content; // Use trace ID to construct URL, an example URL looks like: // https://app.datadoghq.com/apm/traces?query=trace_id%31298410148923562634 var apmUrl = 'https://app.datadoghq.com/apm/traces?query=trace_id%3A' + traceId; window.open(apmUrl, '_blank'); } }; </script>  <link href="https://fonts.googleapis.com/css?family=Roboto:100,100i,300,300i,400,400i,500,500i,700,700i,900,900i" rel="stylesheet"> <link rel="preload" href="//maxcdn.bootstrapcdn.com/font-awesome/4.3.0/css/font-awesome.min.css" as="style" onload="this.rel='stylesheet'"> <link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/libraries-a9675dcb01ec4ef6aa807ba772c7a5a00c1820d3ff661c1038a20f80d06bb4e4.css" /> <link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/academia-9982828ed1de4777566441c35ccf7157c55ca779141fce69380d727ebdbbb926.css" /> <link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system_legacy-056a9113b9a0f5343d013b29ee1929d5a18be35fdcdceb616600b4db8bd20054.css" /> <script src="//a.academia-assets.com/assets/webpack_bundles/runtime-bundle-005434038af4252ca37c527588411a3d6a0eabb5f727fac83f8bbe7fd88d93bb.js"></script> <script src="//a.academia-assets.com/assets/webpack_bundles/webpack_libraries_and_infrequently_changed.wjs-bundle-948be8fa2cdf77e0edc9d37d89e2ceac6298a9cec36b295126fe6066d89fe4f6.js"></script> <script src="//a.academia-assets.com/assets/webpack_bundles/core_webpack.wjs-bundle-3f0732fc165186a3e70d3de1b749e1a47b279db87e2188a19da2a0c42b75cab9.js"></script> <script src="//a.academia-assets.com/assets/webpack_bundles/sentry.wjs-bundle-5fe03fddca915c8ba0f7edbe64c194308e8ce5abaed7bffe1255ff37549c4808.js"></script> <script> jade = window.jade || {}; jade.helpers = window.$h; jade._ = window._; </script>  <script id="tag-manager-head-root">(function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer_old','GTM-5G9JF7Z');</script>  <script> window.gptadslots = []; window.googletag = window.googletag || {}; window.googletag.cmd = window.googletag.cmd || []; </script> <script type="text/javascript"> // TODO(jacob): This should be defined, may be rare load order problem. // Checking if null is just a quick fix, will default to en if unset. // Better fix is to run this immedietely after I18n is set. if (window.I18n != null) { I18n.defaultLocale = "en"; I18n.locale = "en"; I18n.fallbacks = true; } </script> <link rel="canonical" href="https://buid.academia.edu/KShaalan" /> </head>   <body class='c-profiles/works a-summary logged_out'>  <div id="fb-root"></div><script>window.fbAsyncInit = function() { FB.init({ appId: "2369844204", version: "v8.0", status: true, cookie: true, xfbml: true }); // Additional initialization code. if (window.InitFacebook) { // facebook.ts already loaded, set it up. window.InitFacebook(); } else { // Set a flag for facebook.ts to find when it loads. window.academiaAuthReadyFacebook = true; } };</script><script>window.fbAsyncLoad = function() { // Protection against double calling of this function if (window.FB) { return; } (function(d, s, id){ var js, fjs = d.getElementsByTagName(s)[0]; if (d.getElementById(id)) {return;} js = d.createElement(s); js.id = id; js.src = "//connect.facebook.net/en_US/sdk.js"; fjs.parentNode.insertBefore(js, fjs); }(document, 'script', 'facebook-jssdk')); } if (!window.defer_facebook) { // Autoload if not deferred window.fbAsyncLoad(); } else { // Defer loading by 5 seconds setTimeout(function() { window.fbAsyncLoad(); }, 5000); }</script> <div id="google-root"></div><script>window.loadGoogle = function() { if (window.InitGoogle) { // google.ts already loaded, set it up. window.InitGoogle("331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b"); } else { // Set a flag for google.ts to use when it loads. window.GoogleClientID = "331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b"; } };</script><script>window.googleAsyncLoad = function() { // Protection against double calling of this function (function(d) { var js; var id = 'google-jssdk'; var ref = d.getElementsByTagName('script')[0]; if (d.getElementById(id)) { return; } js = d.createElement('script'); js.id = id; js.async = true; js.onload = loadGoogle; js.src = "https://accounts.google.com/gsi/client" ref.parentNode.insertBefore(js, ref); }(document)); } if (!window.defer_google) { // Autoload if not deferred window.googleAsyncLoad(); } else { // Defer loading by 5 seconds setTimeout(function() { window.googleAsyncLoad(); }, 5000); }</script> <div id="tag-manager-body-root">  <noscript><iframe src="https://www.googletagmanager.com/ns.html?id=GTM-5G9JF7Z" height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>   <script> window.addEventListener('load', function() { if (document.querySelector('input[name="commit"]')) { document.querySelector('input[name="commit"]').addEventListener('click', function() { gtag('event', 'click', { event_category: 'button', event_label: 'Log In' }) }) } }); </script> </div> <script>var _comscore = _comscore || []; _comscore.push({ c1: "2", c2: "26766707" }); (function() { var s = document.createElement("script"), el = document.getElementsByTagName("script")[0]; s.async = true; s.src = (document.location.protocol == "https:" ? "https://sb" : "http://b") + ".scorecardresearch.com/beacon.js"; el.parentNode.insertBefore(s, el); })();</script><img src="https://sb.scorecardresearch.com/p?c1=2&c2=26766707&cv=2.0&cj=1" style="position: absolute; visibility: hidden" /> <div id='react-modal'></div> <div class='DesignSystem'> <a class='u-showOnFocus' href='#site'> Skip to main content </a> </div> <div id="upgrade_ie_banner" style="display: none;"><p>Academia.edu no longer supports Internet Explorer.</p><p>To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to <a href="https://www.academia.edu/upgrade-browser">upgrade your browser</a>.</p></div><script>// Show this banner for all versions of IE if (!!window.MSInputMethodContext || /(MSIE)/.test(navigator.userAgent)) { document.getElementById('upgrade_ie_banner').style.display = 'block'; }</script> <div class="DesignSystem bootstrap ShrinkableNav"><div class="navbar navbar-default main-header"><div class="container-wrapper" id="main-header-container"><div class="container"><div class="navbar-header"><div class="nav-left-wrapper u-mt0x"><div class="nav-logo"><a data-main-header-link-target="logo_home" href="https://www.academia.edu/"><img class="visible-xs-inline-block" style="height: 24px;" alt="Academia.edu" src="//a.academia-assets.com/images/academia-logo-redesign-2015-A.svg" width="24" height="24" /><img width="145.2" height="18" class="hidden-xs" style="height: 24px;" alt="Academia.edu" src="//a.academia-assets.com/images/academia-logo-redesign-2015.svg" /></a></div><div class="nav-search"><div class="SiteSearch-wrapper select2-no-default-pills"><form class="js-SiteSearch-form DesignSystem" action="https://www.academia.edu/search" accept-charset="UTF-8" method="get"><i class="SiteSearch-icon fa fa-search u-fw700 u-positionAbsolute u-tcGrayDark"></i><input class="js-SiteSearch-form-input SiteSearch-form-input form-control" data-main-header-click-target="search_input" name="q" placeholder="Search" type="text" value="" /></form></div></div></div><div class="nav-right-wrapper pull-right"><ul class="NavLinks js-main-nav list-unstyled"><li class="NavLinks-link"><a class="js-header-login-url Button Button--inverseGray Button--sm u-mb4x" id="nav_log_in" rel="nofollow" href="https://www.academia.edu/login">Log In</a></li><li class="NavLinks-link u-p0x"><a class="Button Button--inverseGray Button--sm u-mb4x" rel="nofollow" href="https://www.academia.edu/signup">Sign Up</a></li></ul><button class="hidden-lg hidden-md hidden-sm u-ml4x navbar-toggle collapsed" data-target=".js-mobile-header-links" data-toggle="collapse" type="button"><span class="icon-bar"></span><span class="icon-bar"></span><span class="icon-bar"></span></button></div></div><div class="collapse navbar-collapse js-mobile-header-links"><ul class="nav navbar-nav"><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/login">Log In</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/signup">Sign Up</a></li><li class="u-borderColorGrayLight u-borderBottom1 js-mobile-nav-expand-trigger"><a href="#">more&nbsp<span class="caret"></span></a></li><li><ul class="js-mobile-nav-expand-section nav navbar-nav u-m0x collapse"><li class="u-borderColorGrayLight u-borderBottom1"><a rel="false" href="https://www.academia.edu/about">About</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/press">Press</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="false" href="https://www.academia.edu/documents">Papers</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/terms">Terms</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/privacy">Privacy</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/copyright">Copyright</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/hiring"><i class="fa fa-briefcase"></i> We're Hiring!</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://support.academia.edu/hc/en-us"><i class="fa fa-question-circle"></i> Help Center</a></li><li class="js-mobile-nav-collapse-trigger u-borderColorGrayLight u-borderBottom1 dropup" style="display:none"><a href="#">less&nbsp<span class="caret"></span></a></li></ul></li></ul></div></div></div><script>(function(){ var $moreLink = $(".js-mobile-nav-expand-trigger"); var $lessLink = $(".js-mobile-nav-collapse-trigger"); var $section = $('.js-mobile-nav-expand-section'); $moreLink.click(function(ev){ ev.preventDefault(); $moreLink.hide(); $lessLink.show(); $section.collapse('show'); }); $lessLink.click(function(ev){ ev.preventDefault(); $moreLink.show(); $lessLink.hide(); $section.collapse('hide'); }); })() if ($a.is_logged_in() || false) { new Aedu.NavigationController({ el: '.js-main-nav', showHighlightedNotification: false }); } else { $(".js-header-login-url").attr("href", $a.loginUrlWithRedirect()); } Aedu.autocompleteSearch = new AutocompleteSearch({el: '.js-SiteSearch-form'});</script></div></div> <div id='site' class='fixed'> <div id="content" class="clearfix"> <script>document.addEventListener('DOMContentLoaded', function(){ var $dismissible = $(".dismissible_banner"); $dismissible.click(function(ev) { $dismissible.hide(); }); });</script> <script src="//a.academia-assets.com/assets/webpack_bundles/profile.wjs-bundle-e1d575982904827f1397a4a8ee10c2382667df3df47448516361b995ce9464bd.js" defer="defer"></script><script>$viewedUser = Aedu.User.set_viewed( {"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan","photo":"https://0.academia-photos.com/30994194/10626268/35155960/s65_prof_khaled.shaalan.jpg","has_photo":true,"department":{"id":672026,"name":"Computer Science","url":"https://buid.academia.edu/Departments/Computer_Science/Documents","university":{"id":4360,"name":"The British University in Dubai","url":"https://buid.academia.edu/"}},"position":"Faculty Member","position_id":1,"is_analytics_public":true,"interests":[{"id":1210,"name":"Informatics","url":"https://www.academia.edu/Documents/in/Informatics"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"},{"id":97745,"name":"Arabic NLP","url":"https://www.academia.edu/Documents/in/Arabic_NLP"},{"id":4696,"name":"Machine Translation","url":"https://www.academia.edu/Documents/in/Machine_Translation"},{"id":11128,"name":"Information Extraction","url":"https://www.academia.edu/Documents/in/Information_Extraction"},{"id":464,"name":"Information Retrieval","url":"https://www.academia.edu/Documents/in/Information_Retrieval"},{"id":106753,"name":"Question Answering System","url":"https://www.academia.edu/Documents/in/Question_Answering_System"},{"id":31963,"name":"Morphology (Languages And Linguistics)","url":"https://www.academia.edu/Documents/in/Morphology_Languages_And_Linguistics_"},{"id":2349,"name":"Semantics","url":"https://www.academia.edu/Documents/in/Semantics"},{"id":1609,"name":"E-learning","url":"https://www.academia.edu/Documents/in/E-learning"},{"id":29393,"name":"Healthcare","url":"https://www.academia.edu/Documents/in/Healthcare"},{"id":14493,"name":"Parsing","url":"https://www.academia.edu/Documents/in/Parsing"},{"id":29205,"name":"Named Entity Recognition","url":"https://www.academia.edu/Documents/in/Named_Entity_Recognition"},{"id":1535576,"name":"Spell Checking","url":"https://www.academia.edu/Documents/in/Spell_Checking"},{"id":557551,"name":"Grammar Checking","url":"https://www.academia.edu/Documents/in/Grammar_Checking"},{"id":23232,"name":"Intelligent Tutoring Systems","url":"https://www.academia.edu/Documents/in/Intelligent_Tutoring_Systems"},{"id":10641,"name":"Game Based Learning","url":"https://www.academia.edu/Documents/in/Game_Based_Learning"},{"id":1763,"name":"Mobile Learning","url":"https://www.academia.edu/Documents/in/Mobile_Learning"},{"id":1283625,"name":"Automatic Diacritization","url":"https://www.academia.edu/Documents/in/Automatic_Diacritization"},{"id":1241,"name":"Knowledge Management","url":"https://www.academia.edu/Documents/in/Knowledge_Management"},{"id":1827088,"name":"Intelligent Language Tutoring System","url":"https://www.academia.edu/Documents/in/Intelligent_Language_Tutoring_System"},{"id":3324,"name":"Arabic","url":"https://www.academia.edu/Documents/in/Arabic"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"}]} ); if ($a.is_logged_in() && $viewedUser.is_current_user()) { $('body').addClass('profile-viewed-by-owner'); } $socialProfiles = [{"id":13111336,"link":"http://scholar.google.com/citations?user=keLKdlgAAAAJ\u0026hl=en","name":"Google Scholar","link_domain":"scholar.google.com","icon":"//www.google.com/s2/u/0/favicons?domain=scholar.google.com"},{"id":48551322,"link":"https://www.linkedin.com/in/khaledshaalan","name":"Linkedin","link_domain":"www.linkedin.com","icon":"//www.google.com/s2/u/0/favicons?domain=www.linkedin.com"},{"id":75781133,"link":"https://www.khaledshaalan.org/","name":"Homepage","link_domain":"www.khaledshaalan.org","icon":"//www.google.com/s2/u/0/favicons?domain=www.khaledshaalan.org"},{"id":75781135,"link":"https://orcid.org/0000-0003-0823-8390","name":"ORCID","link_domain":"orcid.org","icon":"//www.google.com/s2/u/0/favicons?domain=orcid.org"}]</script><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://buid.academia.edu/KShaalan","location":"/KShaalan","scheme":"https","host":"buid.academia.edu","port":null,"pathname":"/KShaalan","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="ProfileCheckPaperUpdate" data-props="{}" data-trace="false" data-dom-id="ProfileCheckPaperUpdate-react-component-d18e2898-d95f-44f1-96d3-d16040f6f420"></div> <div id="ProfileCheckPaperUpdate-react-component-d18e2898-d95f-44f1-96d3-d16040f6f420"></div> <div class="DesignSystem"><div class="onsite-ping" id="onsite-ping"></div></div><div class="profile-user-info DesignSystem"><div class="social-profile-container"><div class="left-panel-container"><div class="user-info-component-wrapper"><div class="user-summary-cta-container"><div class="user-summary-container"><div class="social-profile-avatar-container"><img class="profile-avatar u-positionAbsolute" alt="Khaled Shaalan" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/30994194/10626268/35155960/s200_prof_khaled.shaalan.jpg" /></div><div class="title-container"><h1 class="ds2-5-heading-sans-serif-sm">Khaled Shaalan</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://buid.academia.edu/">The British University in Dubai</a>, <a class="u-tcGrayDarker" href="https://buid.academia.edu/Departments/Computer_Science/Documents">Computer Science</a>, <span class="u-tcGrayDarker">Faculty Member</span></div></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" data-broccoli-component="user-info.follow-button" data-click-track="profile-user-info-follow-button" data-follow-user-fname="Khaled" data-follow-user-id="30994194" data-follow-user-source="profile_button" data-has-google="false"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">add</span>Follow</button><button class="ds2-5-button hidden profile-cta-button grow js-profile-unfollow-button" data-broccoli-component="user-info.unfollow-button" data-click-track="profile-user-info-unfollow-button" data-unfollow-user-id="30994194"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">done</span>Following</button></div></div><div class="user-stats-container"><a><div class="stat-container js-profile-followers"><p class="label">Followers</p><p class="data">1,079</p></div></a><a><div class="stat-container js-profile-followees" data-broccoli-component="user-info.followees-count" data-click-track="profile-expand-user-info-following"><p class="label">Following</p><p class="data">58</p></div></a><a><div class="stat-container js-profile-coauthors" data-broccoli-component="user-info.coauthors-count" data-click-track="profile-expand-user-info-coauthors"><p class="label">Co-authors</p><p class="data">59</p></div></a><div class="js-mentions-count-container" style="display: none;"><a href="/KShaalan/mentions"><div class="stat-container"><p class="label">Mentions</p><p class="data"></p></div></a></div><a href="https://buid.academia.edu/KShaalan/Analytics"><div class="stat-container"><p class="label"><span class="js-profile-total-view-text">Public Views</span></p><p class="data"><span class="js-profile-view-count"></span></p></div></a></div><div class="user-bio-container"><div class="profile-bio fake-truncate js-profile-about" style="margin: 0px;">Khaled is an accomplished full professor of Computer Science & AI with over thirty years of experience ‎in academia. ‎He has a strong background in teaching, mentoring, academic training, community ‎development, and academic ‎administration. Khaled has received numerous honors and awards for ‎academic excellence and outstanding ‎achievements. As the Co-Chair of the Faculty of Engineering and ‎information technology Council, he successfully built the IT section from ‎scratch and oversaw the ‎development of the academic programs' curricula. He has mentored academic staff, ‎established a team of ‎researchers, and increased program enrolment. Khaled has collaborated with international ‎institutions, ‎organizations, and corporations and has published extensively in high-quality international journals, ‎book ‎chapters, and conference proceedings. His expertise is widely recognized, and he is frequently invited to ‎‎evaluate research proposals, academic programs, PhD theses, and academic promotions. Khaled has also ‎taught ‎Computer Science courses at the undergraduate and graduate levels and served as chair of ‎numerous international ‎conferences and on the editorial boards of various international journals.‎<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="suggested-academics-container"><div class="suggested-academics--header"><p class="ds2-5-body-md-bold">Related Authors</p></div><ul class="suggested-user-card-list"><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://univ-st-etienne.academia.edu/AmanZaid"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://univ-st-etienne.academia.edu/AmanZaid">Aman Zaid</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Universite Jean Monnet - Saint-Etienne</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/MouradMars1"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/MouradMars1">Mourad Mars</a></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/SonishSivarajkumar"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/SonishSivarajkumar">Sonish Sivarajkumar</a></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/EunhuiKim"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/EunhuiKim">Eunhui Kim</a></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/GhadahAlqahtani1"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/GhadahAlqahtani1">Ghadah Alqahtani</a></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/Ay%C5%9FeBa%C5%9Far3"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/Ay%C5%9FeBa%C5%9Far3">Ayşe Başar</a></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://birmingham.academia.edu/HarishTayyarMadabushi"><img class="profile-avatar u-positionAbsolute" alt="Harish Tayyar Madabushi" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" src="https://gravatar.com/avatar/2655afdf78113495036a690ee90f5220?s=200" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://birmingham.academia.edu/HarishTayyarMadabushi">Harish Tayyar Madabushi</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">The University of Birmingham</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/UsmanNaseem8"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/UsmanNaseem8">Usman Naseem</a></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://annauniv.academia.edu/IJETAEditor"><img class="profile-avatar u-positionAbsolute" alt="IJETA [Best Peer Review Open Access Journal ] -Group of ESRG journals" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/15228474/4108010/4788531/s200_ijeta.editor.gif" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://annauniv.academia.edu/IJETAEditor">IJETA [Best Peer Review Open Access Journal ] -Group of ESRG journals</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Anna University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/PriyaPriya238"><img class="profile-avatar u-positionAbsolute" alt="Priyadharshini Ravichandran" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/108014350/131463823/120879230/s200_priya.priya.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/PriyaPriya238">Priyadharshini Ravichandran</a></div></div></ul></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span><a class="ri-more-link js-profile-ri-list-card" data-click-track="profile-user-info-primary-research-interest" data-has-card-for-ri-list="30994194">View All (14)</a></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="30994194" href="https://www.academia.edu/Documents/in/Informatics"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://buid.academia.edu/KShaalan","location":"/KShaalan","scheme":"https","host":"buid.academia.edu","port":null,"pathname":"/KShaalan","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Informatics"]}" data-trace="false" data-dom-id="Pill-react-component-3933110b-b87f-48c2-84a4-435af143a4b5"></div> <div id="Pill-react-component-3933110b-b87f-48c2-84a4-435af143a4b5"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="30994194" href="https://www.academia.edu/Documents/in/Natural_Language_Processing"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Natural Language Processing"]}" data-trace="false" data-dom-id="Pill-react-component-8961ae62-507c-4e12-9c75-af4a5131f3bc"></div> <div id="Pill-react-component-8961ae62-507c-4e12-9c75-af4a5131f3bc"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="30994194" href="https://www.academia.edu/Documents/in/Arabic_NLP"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Arabic NLP"]}" data-trace="false" data-dom-id="Pill-react-component-88b3000e-1088-41ba-b2df-9d6c797646f2"></div> <div id="Pill-react-component-88b3000e-1088-41ba-b2df-9d6c797646f2"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="30994194" href="https://www.academia.edu/Documents/in/Machine_Translation"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Machine Translation"]}" data-trace="false" data-dom-id="Pill-react-component-f3642d87-f4ee-4bff-8a8a-9648cfd7c46f"></div> <div id="Pill-react-component-f3642d87-f4ee-4bff-8a8a-9648cfd7c46f"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="30994194" href="https://www.academia.edu/Documents/in/Information_Retrieval"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Information Retrieval"]}" data-trace="false" data-dom-id="Pill-react-component-d540d348-8ee6-4c24-9c29-397a05ae260a"></div> <div id="Pill-react-component-d540d348-8ee6-4c24-9c29-397a05ae260a"></div> </a></div></div><div class="external-links-container"><ul class="profile-links new-profile js-UserInfo-social"><li class="left-most js-UserInfo-social-cv" data-broccoli-component="user-info.cv-button" data-click-track="profile-user-info-cv" data-cv-filename="Prof_Khaled_Shaalan_CV.pdf" data-placement="top" data-toggle="tooltip" href="/KShaalan/CurriculumVitae"><button class="ds2-5-text-link ds2-5-text-link--small" style="font-size: 20px; letter-spacing: 0.8px"><span class="ds2-5-text-link__content">CV</span></button></li><li class="profile-profiles js-social-profiles-container"><i class="fa fa-spin fa-spinner"></i></li></ul></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Khaled Shaalan</h3></div><div class="js-work-strip profile--work_container" data-work-id="127870015"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/127870015/Transformer_Models_in_Natural_Language_Processing_A_Comprehensive_Review_and_Prospects_for_Future_Development"><img alt="Research paper thumbnail of Transformer Models in Natural Language Processing: A Comprehensive Review and Prospects for Future Development" class="work-thumbnail" src="https://attachments.academia-assets.com/121539070/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/127870015/Transformer_Models_in_Natural_Language_Processing_A_Comprehensive_Review_and_Prospects_for_Future_Development">Transformer Models in Natural Language Processing: A Comprehensive Review and Prospects for Future Development</a></div><div class="wp-workCard_item"><span>Proceedings of the 11th International Conference on Advanced Intelligent Systems and Informatics</span><span>, 2025</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Transformer-based pre-trained language models are advanced machine learning models that understan...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Transformer-based pre-trained language models are advanced machine learning models that understand and produce human language. These models are mainly based on the "Transformer" design. They have undergone substantial pre-training on large volumes of text data to understand language patterns. Notable examples include BERT, GPT, and RoBERTa. These tools have transformed NLP tasks by demonstrating exceptional performance and adaptability, facilitating knowledge transfer to specialized tasks, and addressing issues associated with training a model from the start. This systematic review examines transformer-based pre-trained language models, including architecture, pretraining techniques, and adaption approaches. This study examines the core concepts, training methods, and applications of these models to answer significant research concerns. This study examines transformer-based pre-trained models in NLP and their fine-tuning methodologies. This review sheds light on the current state of transformer-based language models and outlines potential future advances in this dynamic subject.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="10f96db4ce59304dc02500b1572ca954" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":121539070,"asset_id":127870015,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/121539070/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127870015"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127870015"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127870015; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127870015]").text(description); $(".js-view-count[data-work-id=127870015]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127870015; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127870015']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "10f96db4ce59304dc02500b1572ca954" } } $('.js-work-strip[data-work-id=127870015]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127870015,"title":"Transformer Models in Natural Language Processing: A Comprehensive Review and Prospects for Future Development","translated_title":"","metadata":{"abstract":"Transformer-based pre-trained language models are advanced machine learning models that understand and produce human language. These models are mainly based on the \"Transformer\" design. They have undergone substantial pre-training on large volumes of text data to understand language patterns. Notable examples include BERT, GPT, and RoBERTa. These tools have transformed NLP tasks by demonstrating exceptional performance and adaptability, facilitating knowledge transfer to specialized tasks, and addressing issues associated with training a model from the start. This systematic review examines transformer-based pre-trained language models, including architecture, pretraining techniques, and adaption approaches. This study examines the core concepts, training methods, and applications of these models to answer significant research concerns. This study examines transformer-based pre-trained models in NLP and their fine-tuning methodologies. This review sheds light on the current state of transformer-based language models and outlines potential future advances in this dynamic subject.","grobid_abstract":"Transformer-based pre-trained language models are advanced machine learning models that understand and produce human language. These models are mainly based on the \"Transformer\" design. They have undergone substantial pre-training on large volumes of text data to understand language patterns. Notable examples include BERT, GPT, and RoBERTa. These tools have transformed NLP tasks by demonstrating exceptional performance and adaptability, facilitating knowledge transfer to specialized tasks, and addressing issues associated with training a model from the start. This systematic review examines transformer-based pre-trained language models, including architecture, pretraining techniques, and adaption approaches. This study examines the core concepts, training methods, and applications of these models to answer significant research concerns. This study examines transformer-based pre-trained models in NLP and their fine-tuning methodologies. This review sheds light on the current state of transformer-based language models and outlines potential future advances in this dynamic subject.","publication_date":{"day":null,"month":null,"year":2025,"errors":{}},"publication_name":"Proceedings of the 11th International Conference on Advanced Intelligent Systems and Informatics","grobid_abstract_attachment_id":121539070},"translated_abstract":"Transformer-based pre-trained language models are advanced machine learning models that understand and produce human language. These models are mainly based on the \"Transformer\" design. They have undergone substantial pre-training on large volumes of text data to understand language patterns. Notable examples include BERT, GPT, and RoBERTa. These tools have transformed NLP tasks by demonstrating exceptional performance and adaptability, facilitating knowledge transfer to specialized tasks, and addressing issues associated with training a model from the start. This systematic review examines transformer-based pre-trained language models, including architecture, pretraining techniques, and adaption approaches. This study examines the core concepts, training methods, and applications of these models to answer significant research concerns. This study examines transformer-based pre-trained models in NLP and their fine-tuning methodologies. This review sheds light on the current state of transformer-based language models and outlines potential future advances in this dynamic subject.","internal_url":"https://www.academia.edu/127870015/Transformer_Models_in_Natural_Language_Processing_A_Comprehensive_Review_and_Prospects_for_Future_Development","translated_internal_url":"","created_at":"2025-02-25T07:04:08.368-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":43180709,"work_id":127870015,"tagging_user_id":30994194,"tagged_user_id":47860458,"co_author_invite_id":null,"email":"a***m@hotmail.com","display_order":1,"name":"Azza Mohamed","title":"Transformer Models in Natural Language Processing: A Comprehensive Review and Prospects for Future Development"},{"id":43180710,"work_id":127870015,"tagging_user_id":30994194,"tagged_user_id":null,"co_author_invite_id":227015,"email":"k***n@buid.ac.ae","display_order":2,"name":"Khaled Shaalan","title":"Transformer Models in Natural Language Processing: A Comprehensive Review and Prospects for Future Development"}],"downloadable_attachments":[{"id":121539070,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121539070/thumbnails/1.jpg","file_name":"AISI2025.pdf","download_url":"https://www.academia.edu/attachments/121539070/download_file","bulk_download_file_name":"Transformer_Models_in_Natural_Language_P.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121539070/AISI2025-libre.pdf?1740499771=\u0026response-content-disposition=attachment%3B+filename%3DTransformer_Models_in_Natural_Language_P.pdf\u0026Expires=1742123709\u0026Signature=cQAWT6YxMru5o0PVwXHQS8BjYA6cuXSdV3N~pQp-~X-W3tOOuQR1vmaUgh790AefAA0V7juub-TRezkaCZyv~Zb~u7wPFl6Xq7F6cHgCx7I6SX~2pKRM-07lVJhLesQpMoSJYuzOsocGwlMkEwDtd4GgpP92ji7g0dDURhVn4DLn5rnmbnajTpU9L4HvBmZegjjNiDUWZMr64MMlzLWy23lmtzQGLwczvSPI4TPpcB7VIxNIHR-6UVHyYy8wYfQ~ypX~2s8rKGKKJe3sG5yn01kP61afj1wF31K24fbmQvimuFsCQGJ2zXRvFfuPRsXABRmlNbbZYrMvXPW0a8ZpfA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Transformer_Models_in_Natural_Language_Processing_A_Comprehensive_Review_and_Prospects_for_Future_Development","translated_slug":"","page_count":10,"language":"en","content_type":"Work","summary":"Transformer-based pre-trained language models are advanced machine learning models that understand and produce human language. These models are mainly based on the \"Transformer\" design. They have undergone substantial pre-training on large volumes of text data to understand language patterns. Notable examples include BERT, GPT, and RoBERTa. These tools have transformed NLP tasks by demonstrating exceptional performance and adaptability, facilitating knowledge transfer to specialized tasks, and addressing issues associated with training a model from the start. This systematic review examines transformer-based pre-trained language models, including architecture, pretraining techniques, and adaption approaches. This study examines the core concepts, training methods, and applications of these models to answer significant research concerns. This study examines transformer-based pre-trained models in NLP and their fine-tuning methodologies. This review sheds light on the current state of transformer-based language models and outlines potential future advances in this dynamic subject.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[{"id":121539070,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121539070/thumbnails/1.jpg","file_name":"AISI2025.pdf","download_url":"https://www.academia.edu/attachments/121539070/download_file","bulk_download_file_name":"Transformer_Models_in_Natural_Language_P.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121539070/AISI2025-libre.pdf?1740499771=\u0026response-content-disposition=attachment%3B+filename%3DTransformer_Models_in_Natural_Language_P.pdf\u0026Expires=1742123709\u0026Signature=cQAWT6YxMru5o0PVwXHQS8BjYA6cuXSdV3N~pQp-~X-W3tOOuQR1vmaUgh790AefAA0V7juub-TRezkaCZyv~Zb~u7wPFl6Xq7F6cHgCx7I6SX~2pKRM-07lVJhLesQpMoSJYuzOsocGwlMkEwDtd4GgpP92ji7g0dDURhVn4DLn5rnmbnajTpU9L4HvBmZegjjNiDUWZMr64MMlzLWy23lmtzQGLwczvSPI4TPpcB7VIxNIHR-6UVHyYy8wYfQ~ypX~2s8rKGKKJe3sG5yn01kP61afj1wF31K24fbmQvimuFsCQGJ2zXRvFfuPRsXABRmlNbbZYrMvXPW0a8ZpfA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"},{"id":44293,"name":"Literature Review","url":"https://www.academia.edu/Documents/in/Literature_Review"},{"id":4288695,"name":"Transformer-based models","url":"https://www.academia.edu/Documents/in/Transformer_based_models"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127571430"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/127571430/Human_vs_Machine_A_Comparative_Study_on_the_Detection_of_AI_Generated_Content"><img alt="Research paper thumbnail of Human vs. Machine: A Comparative Study on the Detection of AI-Generated Content" class="work-thumbnail" src="https://attachments.academia-assets.com/121285703/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/127571430/Human_vs_Machine_A_Comparative_Study_on_the_Detection_of_AI_Generated_Content">Human vs. Machine: A Comparative Study on the Detection of AI-Generated Content</a></div><div class="wp-workCard_item"><span>ACM Transactions on Asian and Low-Resource Language Information Processing ‎</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The surge in advancements in large language models (LLMs) has expedited the generation of synthet...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The surge in advancements in large language models (LLMs) has expedited the generation of synthetic text imitating human writing styles. This, however, raises concerns about the potential misuse of synthetic textual data, which could compromise trust in online content. Against this backdrop, the present research aims to address the key challenges of detecting LLMs-generated texts. In this study, we used ChatGPT (v 3.5) because of its widespread and capability to comprehend and keep conversational context, allowing it to produce meaningful and contextually suitable responses. The problem revolves around the task of discerning between authentic and artificially generated textual content. To tackle this problem, we first created a dataset containing both real and DeepFake text. Subsequently, we employed transfer-learning (TL) and conducted DeepFakedetection utilizing SOTA large pre-trained LLMs. Furthermore, we conducted validation using benchmark datasets comprising unseen data samples to ensure that the model's performance reflects its ability to generalize to new data. Finally, we discussed this study's theoretical contributions, practical implications, limitations and potential avenues for future research, aiming to formulate strategies for identifying and detecting large-generative-models' produced texts. The results were promising, with accuracy ranging from 94% to 99%. The comparison between automatic detection and the human ability to detect DeepFake text revealed a significant gap in the human capacity for its identification, emphasizing an increasing need for sophisticated automated detectors. The investigation into AI-generated content detection holds central importance in the age of LLMs and technology convergence. This study is both timely and adds value to the ongoing discussion regarding the challenges associated with the pertinent theme of "DeepFake text detection", with a special focus on examining the boundaries of human detection. CCS Concepts: • Computing methodologies → Natural language processing ;</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="47c40c8e1dc6dffe61913392f875671c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":121285703,"asset_id":127571430,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/121285703/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127571430"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127571430"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127571430; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127571430]").text(description); $(".js-view-count[data-work-id=127571430]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127571430; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127571430']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "47c40c8e1dc6dffe61913392f875671c" } } $('.js-work-strip[data-work-id=127571430]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127571430,"title":"Human vs. Machine: A Comparative Study on the Detection of AI-Generated Content","translated_title":"","metadata":{"doi":"10.1145/3708889","abstract":"The surge in advancements in large language models (LLMs) has expedited the generation of synthetic text imitating human writing styles. This, however, raises concerns about the potential misuse of synthetic textual data, which could compromise trust in online content. Against this backdrop, the present research aims to address the key challenges of detecting LLMs-generated texts. In this study, we used ChatGPT (v 3.5) because of its widespread and capability to comprehend and keep conversational context, allowing it to produce meaningful and contextually suitable responses. The problem revolves around the task of discerning between authentic and artificially generated textual content. To tackle this problem, we first created a dataset containing both real and DeepFake text. Subsequently, we employed transfer-learning (TL) and conducted DeepFakedetection utilizing SOTA large pre-trained LLMs. Furthermore, we conducted validation using benchmark datasets comprising unseen data samples to ensure that the model's performance reflects its ability to generalize to new data. Finally, we discussed this study's theoretical contributions, practical implications, limitations and potential avenues for future research, aiming to formulate strategies for identifying and detecting large-generative-models' produced texts. The results were promising, with accuracy ranging from 94% to 99%. The comparison between automatic detection and the human ability to detect DeepFake text revealed a significant gap in the human capacity for its identification, emphasizing an increasing need for sophisticated automated detectors. The investigation into AI-generated content detection holds central importance in the age of LLMs and technology convergence. This study is both timely and adds value to the ongoing discussion regarding the challenges associated with the pertinent theme of \"DeepFake text detection\", with a special focus on examining the boundaries of human detection. CCS Concepts: • Computing methodologies → Natural language processing ;","grobid_abstract":"The surge in advancements in large language models (LLMs) has expedited the generation of synthetic text imitating human writing styles. This, however, raises concerns about the potential misuse of synthetic textual data, which could compromise trust in online content. Against this backdrop, the present research aims to address the key challenges of detecting LLMs-generated texts. In this study, we used ChatGPT (v 3.5) because of its widespread and capability to comprehend and keep conversational context, allowing it to produce meaningful and contextually suitable responses. The problem revolves around the task of discerning between authentic and artificially generated textual content. To tackle this problem, we first created a dataset containing both real and DeepFake text. Subsequently, we employed transfer-learning (TL) and conducted DeepFakedetection utilizing SOTA large pre-trained LLMs. Furthermore, we conducted validation using benchmark datasets comprising unseen data samples to ensure that the model's performance reflects its ability to generalize to new data. Finally, we discussed this study's theoretical contributions, practical implications, limitations and potential avenues for future research, aiming to formulate strategies for identifying and detecting large-generative-models' produced texts. The results were promising, with accuracy ranging from 94% to 99%. The comparison between automatic detection and the human ability to detect DeepFake text revealed a significant gap in the human capacity for its identification, emphasizing an increasing need for sophisticated automated detectors. The investigation into AI-generated content detection holds central importance in the age of LLMs and technology convergence. This study is both timely and adds value to the ongoing discussion regarding the challenges associated with the pertinent theme of \"DeepFake text detection\", with a special focus on examining the boundaries of human detection. CCS Concepts: • Computing methodologies → Natural language processing ;","publication_date":{"day":null,"month":null,"year":2024,"errors":{}},"publication_name":"ACM Transactions on Asian and Low-Resource Language Information Processing ‎","grobid_abstract_attachment_id":121285703},"translated_abstract":"The surge in advancements in large language models (LLMs) has expedited the generation of synthetic text imitating human writing styles. This, however, raises concerns about the potential misuse of synthetic textual data, which could compromise trust in online content. Against this backdrop, the present research aims to address the key challenges of detecting LLMs-generated texts. In this study, we used ChatGPT (v 3.5) because of its widespread and capability to comprehend and keep conversational context, allowing it to produce meaningful and contextually suitable responses. The problem revolves around the task of discerning between authentic and artificially generated textual content. To tackle this problem, we first created a dataset containing both real and DeepFake text. Subsequently, we employed transfer-learning (TL) and conducted DeepFakedetection utilizing SOTA large pre-trained LLMs. Furthermore, we conducted validation using benchmark datasets comprising unseen data samples to ensure that the model's performance reflects its ability to generalize to new data. Finally, we discussed this study's theoretical contributions, practical implications, limitations and potential avenues for future research, aiming to formulate strategies for identifying and detecting large-generative-models' produced texts. The results were promising, with accuracy ranging from 94% to 99%. The comparison between automatic detection and the human ability to detect DeepFake text revealed a significant gap in the human capacity for its identification, emphasizing an increasing need for sophisticated automated detectors. The investigation into AI-generated content detection holds central importance in the age of LLMs and technology convergence. This study is both timely and adds value to the ongoing discussion regarding the challenges associated with the pertinent theme of \"DeepFake text detection\", with a special focus on examining the boundaries of human detection. CCS Concepts: • Computing methodologies → Natural language processing ;","internal_url":"https://www.academia.edu/127571430/Human_vs_Machine_A_Comparative_Study_on_the_Detection_of_AI_Generated_Content","translated_internal_url":"","created_at":"2025-02-10T11:25:10.853-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":43104889,"work_id":127571430,"tagging_user_id":30994194,"tagged_user_id":null,"co_author_invite_id":8338852,"email":"b***l@univ-setif.dz","display_order":1,"name":"Amal Boutadjine","title":"Human vs. Machine: A Comparative Study on the Detection of AI-Generated Content"},{"id":43104890,"work_id":127571430,"tagging_user_id":30994194,"tagged_user_id":146715945,"co_author_invite_id":null,"email":"f***g@gmail.com","display_order":2,"name":"Fouzi Harrag","title":"Human vs. Machine: A Comparative Study on the Detection of AI-Generated Content"}],"downloadable_attachments":[{"id":121285703,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121285703/thumbnails/1.jpg","file_name":"3708889.pdf","download_url":"https://www.academia.edu/attachments/121285703/download_file","bulk_download_file_name":"Human_vs_Machine_A_Comparative_Study_on.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121285703/3708889-libre.pdf?1739218884=\u0026response-content-disposition=attachment%3B+filename%3DHuman_vs_Machine_A_Comparative_Study_on.pdf\u0026Expires=1742123709\u0026Signature=FmnaY0DjxVBwvw-SwReDBpRy0emnNMGnVDT4D0FXFf-01wKUi1ZeSFDIGZUPcyydhvan4lXH32BpjdB3KzliFAizNO0TwBP4CnKxZxPq0k68a44GU7Km9oe4gZyFcZcf5Ls7DkXqGuuvTjgtPDIy4Sj9xukQxSbdxKD9nGGf~ELgz8iJxfdsO0Az81L6QTg7KURqoUQcKHRfGgiYYdD-~sjvTBsgoQzYUJE-g2d~gcO4ZOhYTRWCW7RGPRwKBuNk~oiOXlcdmR6OcBAXBC4MvZ4~vXAGrDZtBHEfesiFIMie94SkooND8N2IOPP-Qu~LAyTJKP65IuUwEHxz1GrjoQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Human_vs_Machine_A_Comparative_Study_on_the_Detection_of_AI_Generated_Content","translated_slug":"","page_count":26,"language":"en","content_type":"Work","summary":"The surge in advancements in large language models (LLMs) has expedited the generation of synthetic text imitating human writing styles. This, however, raises concerns about the potential misuse of synthetic textual data, which could compromise trust in online content. Against this backdrop, the present research aims to address the key challenges of detecting LLMs-generated texts. In this study, we used ChatGPT (v 3.5) because of its widespread and capability to comprehend and keep conversational context, allowing it to produce meaningful and contextually suitable responses. The problem revolves around the task of discerning between authentic and artificially generated textual content. To tackle this problem, we first created a dataset containing both real and DeepFake text. Subsequently, we employed transfer-learning (TL) and conducted DeepFakedetection utilizing SOTA large pre-trained LLMs. Furthermore, we conducted validation using benchmark datasets comprising unseen data samples to ensure that the model's performance reflects its ability to generalize to new data. Finally, we discussed this study's theoretical contributions, practical implications, limitations and potential avenues for future research, aiming to formulate strategies for identifying and detecting large-generative-models' produced texts. The results were promising, with accuracy ranging from 94% to 99%. The comparison between automatic detection and the human ability to detect DeepFake text revealed a significant gap in the human capacity for its identification, emphasizing an increasing need for sophisticated automated detectors. The investigation into AI-generated content detection holds central importance in the age of LLMs and technology convergence. This study is both timely and adds value to the ongoing discussion regarding the challenges associated with the pertinent theme of \"DeepFake text detection\", with a special focus on examining the boundaries of human detection. CCS Concepts: • Computing methodologies → Natural language processing ;","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[{"id":121285703,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121285703/thumbnails/1.jpg","file_name":"3708889.pdf","download_url":"https://www.academia.edu/attachments/121285703/download_file","bulk_download_file_name":"Human_vs_Machine_A_Comparative_Study_on.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121285703/3708889-libre.pdf?1739218884=\u0026response-content-disposition=attachment%3B+filename%3DHuman_vs_Machine_A_Comparative_Study_on.pdf\u0026Expires=1742123709\u0026Signature=FmnaY0DjxVBwvw-SwReDBpRy0emnNMGnVDT4D0FXFf-01wKUi1ZeSFDIGZUPcyydhvan4lXH32BpjdB3KzliFAizNO0TwBP4CnKxZxPq0k68a44GU7Km9oe4gZyFcZcf5Ls7DkXqGuuvTjgtPDIy4Sj9xukQxSbdxKD9nGGf~ELgz8iJxfdsO0Az81L6QTg7KURqoUQcKHRfGgiYYdD-~sjvTBsgoQzYUJE-g2d~gcO4ZOhYTRWCW7RGPRwKBuNk~oiOXlcdmR6OcBAXBC4MvZ4~vXAGrDZtBHEfesiFIMie94SkooND8N2IOPP-Qu~LAyTJKP65IuUwEHxz1GrjoQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"},{"id":220007,"name":"Arabic Natural Language Processing","url":"https://www.academia.edu/Documents/in/Arabic_Natural_Language_Processing"},{"id":4183627,"name":"ChatGPT","url":"https://www.academia.edu/Documents/in/ChatGPT"},{"id":4201061,"name":"Generative AI","url":"https://www.academia.edu/Documents/in/Generative_AI"},{"id":4240030,"name":"Large language models","url":"https://www.academia.edu/Documents/in/Large_language_models"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436559"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/127436559/Pre_training_on_Multi_modal_for_Improved_Persona_Detection_Using_Multi_Datasets"><img alt="Research paper thumbnail of Pre-training on Multi-modal for Improved Persona Detection Using Multi Datasets" class="work-thumbnail" src="https://attachments.academia-assets.com/121168538/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/127436559/Pre_training_on_Multi_modal_for_Improved_Persona_Detection_Using_Multi_Datasets">Pre-training on Multi-modal for Improved Persona Detection Using Multi Datasets</a></div><div class="wp-workCard_item"><span>Lecture notes in civil engineering</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Persona identification helps AI-based communication systems provide personalized and situationall...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Persona identification helps AI-based communication systems provide personalized and situationally informed interactions. This paper introduces pretraining on CNN, BERT, and GPT models to improve persona detection on PMPC and ROCStories datasets. Two speakers with different personalities have dialogues in the PMPC dataset. The challenge is to match each speaker to their persona. The ROCStories dataset contains fictional character traits and activities. Our study uses transformer-based design to improve persona detection using ROCStories dataset external context. We compare our method to leading models in the field. We found that pre-training and fine-tuning on several datasets improves model performance. External context from tale collections may improve persona detection algorithms and help understand human personality and behavior. Our study found that pretraining CNN, BERT, and GPT models improves persona detection, improving user experiences and communication. The method could be used in chatbots, personalized recommendation systems, and customer support. Additionally, it can help create AI-driven communication systems with tailored, context-aware, and human-like interactions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="10b00810058a0236e61ab5f7bcc4fb2f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":121168538,"asset_id":127436559,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/121168538/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436559"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436559"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436559; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436559]").text(description); $(".js-view-count[data-work-id=127436559]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436559; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436559']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "10b00810058a0236e61ab5f7bcc4fb2f" } } $('.js-work-strip[data-work-id=127436559]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436559,"title":"Pre-training on Multi-modal for Improved Persona Detection Using Multi Datasets","translated_title":"","metadata":{"grobid_abstract":"Persona identification helps AI-based communication systems provide personalized and situationally informed interactions. This paper introduces pretraining on CNN, BERT, and GPT models to improve persona detection on PMPC and ROCStories datasets. Two speakers with different personalities have dialogues in the PMPC dataset. The challenge is to match each speaker to their persona. The ROCStories dataset contains fictional character traits and activities. Our study uses transformer-based design to improve persona detection using ROCStories dataset external context. We compare our method to leading models in the field. We found that pre-training and fine-tuning on several datasets improves model performance. External context from tale collections may improve persona detection algorithms and help understand human personality and behavior. Our study found that pretraining CNN, BERT, and GPT models improves persona detection, improving user experiences and communication. The method could be used in chatbots, personalized recommendation systems, and customer support. Additionally, it can help create AI-driven communication systems with tailored, context-aware, and human-like interactions.","publication_date":{"day":null,"month":null,"year":2024,"errors":{}},"publication_name":"Lecture notes in civil engineering","grobid_abstract_attachment_id":121168538},"translated_abstract":null,"internal_url":"https://www.academia.edu/127436559/Pre_training_on_Multi_modal_for_Improved_Persona_Detection_Using_Multi_Datasets","translated_internal_url":"","created_at":"2025-02-03T07:31:21.451-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":121168538,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168538/thumbnails/1.jpg","file_name":"978-3-031-56121-4_22.pdf","download_url":"https://www.academia.edu/attachments/121168538/download_file","bulk_download_file_name":"Pre_training_on_Multi_modal_for_Improved.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168538/978-3-031-56121-4_22-libre.pdf?1738598721=\u0026response-content-disposition=attachment%3B+filename%3DPre_training_on_Multi_modal_for_Improved.pdf\u0026Expires=1742123709\u0026Signature=P7ytqs5pH5R6bnpKsAEpyblGDlWoptvbz6jJgNGF~z9QFGwAYTX62ztShEcwcCnnX89TLY6d7SNanUI4ruArLUjTWLTsCI82MqScHIKkb0CCAqOTBvRsZq-YDAYeuN81zEFz-TKhbu1qcBwYGwO-1VgsD0pxhVV~0g8DRwGOuxT2LfsH1kyn7rrHLbFMWf2jM7v5C3JFl3BYi5vqGmyqavnD4SJAt5xg2xaEStUU88SgLGpCyjmKMyPGKmgomtpIkdEB1TGrRf~Rj6ZfJ7bDH5qS-IWHY1Dt3wmfaLXy7fCR~DUA~LSf~Ca9Nct~bKNJKpBRTTih9Mvc~FZixWxvUA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Pre_training_on_Multi_modal_for_Improved_Persona_Detection_Using_Multi_Datasets","translated_slug":"","page_count":10,"language":"en","content_type":"Work","summary":"Persona identification helps AI-based communication systems provide personalized and situationally informed interactions. This paper introduces pretraining on CNN, BERT, and GPT models to improve persona detection on PMPC and ROCStories datasets. Two speakers with different personalities have dialogues in the PMPC dataset. The challenge is to match each speaker to their persona. The ROCStories dataset contains fictional character traits and activities. Our study uses transformer-based design to improve persona detection using ROCStories dataset external context. We compare our method to leading models in the field. We found that pre-training and fine-tuning on several datasets improves model performance. External context from tale collections may improve persona detection algorithms and help understand human personality and behavior. Our study found that pretraining CNN, BERT, and GPT models improves persona detection, improving user experiences and communication. The method could be used in chatbots, personalized recommendation systems, and customer support. Additionally, it can help create AI-driven communication systems with tailored, context-aware, and human-like interactions.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[{"id":121168538,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168538/thumbnails/1.jpg","file_name":"978-3-031-56121-4_22.pdf","download_url":"https://www.academia.edu/attachments/121168538/download_file","bulk_download_file_name":"Pre_training_on_Multi_modal_for_Improved.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168538/978-3-031-56121-4_22-libre.pdf?1738598721=\u0026response-content-disposition=attachment%3B+filename%3DPre_training_on_Multi_modal_for_Improved.pdf\u0026Expires=1742123709\u0026Signature=P7ytqs5pH5R6bnpKsAEpyblGDlWoptvbz6jJgNGF~z9QFGwAYTX62ztShEcwcCnnX89TLY6d7SNanUI4ruArLUjTWLTsCI82MqScHIKkb0CCAqOTBvRsZq-YDAYeuN81zEFz-TKhbu1qcBwYGwO-1VgsD0pxhVV~0g8DRwGOuxT2LfsH1kyn7rrHLbFMWf2jM7v5C3JFl3BYi5vqGmyqavnD4SJAt5xg2xaEStUU88SgLGpCyjmKMyPGKmgomtpIkdEB1TGrRf~Rj6ZfJ7bDH5qS-IWHY1Dt3wmfaLXy7fCR~DUA~LSf~Ca9Nct~bKNJKpBRTTih9Mvc~FZixWxvUA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":121168537,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168537/thumbnails/1.jpg","file_name":"978-3-031-56121-4_22.pdf","download_url":"https://www.academia.edu/attachments/121168537/download_file","bulk_download_file_name":"Pre_training_on_Multi_modal_for_Improved.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168537/978-3-031-56121-4_22-libre.pdf?1738598719=\u0026response-content-disposition=attachment%3B+filename%3DPre_training_on_Multi_modal_for_Improved.pdf\u0026Expires=1742123709\u0026Signature=c1okzxCgczRfxpNbCykotOUCIkt9IUsHCDkY0kZZNjo1QjhW5imJDCqOqmVoHFkRb5q1GhGUBTCuwM56XzC9rR9JbbrvSBpytsjFES8WJCHOhl3G4kt3nMrwAdZHgG3NOv-XywpfgJFWDEOnZbsiA9Uvnv2XwzffKXIpt6Xd7zl12kclkuH~tlKYuwrqgWQsHBrYzR9NKdXxm1hmmnbrj3Dc0C1TTopuoFNckRp9lZxO1ZIjmex0Mi-DPULUKOGDZwogYYFxVxExsTyWcXa8t3MUY29s~HHe8gK95LWKD4AAAY0FGqnt8XCQfxrVR8J3~4swiIAXQ2oFjNDXVDFpUg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":91971,"name":"Persona","url":"https://www.academia.edu/Documents/in/Persona"},{"id":232306,"name":"Modal","url":"https://www.academia.edu/Documents/in/Modal"}],"urls":[{"id":46723415,"url":"https://link.springer.com/content/pdf/10.1007/978-3-031-56121-4_22.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436558"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436558/Expert_systems_Useful_tools_for_enhancing_agricultural_research_and_production"><img alt="Research paper thumbnail of Expert systems: Useful tools for enhancing agricultural research and production" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436558/Expert_systems_Useful_tools_for_enhancing_agricultural_research_and_production">Expert systems: Useful tools for enhancing agricultural research and production</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436558"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436558"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436558; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436558]").text(description); $(".js-view-count[data-work-id=127436558]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436558; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436558']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436558]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436558,"title":"Expert systems: Useful tools for enhancing agricultural research and production","translated_title":"","metadata":{"publication_date":{"day":null,"month":null,"year":2002,"errors":{}}},"translated_abstract":null,"internal_url":"https://www.academia.edu/127436558/Expert_systems_Useful_tools_for_enhancing_agricultural_research_and_production","translated_internal_url":"","created_at":"2025-02-03T07:31:21.268-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Expert_systems_Useful_tools_for_enhancing_agricultural_research_and_production","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":null,"owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":10225,"name":"Agriculture","url":"https://www.academia.edu/Documents/in/Agriculture"},{"id":61714,"name":"Production economics","url":"https://www.academia.edu/Documents/in/Production_economics"},{"id":122360,"name":"Agricultural Productivity","url":"https://www.academia.edu/Documents/in/Agricultural_Productivity"}],"urls":[{"id":46723414,"url":"http://agris.fao.org/agris-search/search.do?request_locale=es\u0026recordID=QV2004000042\u0026sourceQuery=\u0026query=\u0026sortField=\u0026sortOrder=\u0026agrovocString=\u0026advQuery=\u0026centerString=\u0026enableField="}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436557"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436557/The_Future_of_E_Commerce_Systems_2030_and_Beyond"><img alt="Research paper thumbnail of The Future of E-Commerce Systems: 2030 and Beyond" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436557/The_Future_of_E_Commerce_Systems_2030_and_Beyond">The Future of E-Commerce Systems: 2030 and Beyond</a></div><div class="wp-workCard_item"><span>Studies in systems, decision and control</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The sophistication and efficiency of systems is undeniably advancing. As businesses evolve, quest...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The sophistication and efficiency of systems is undeniably advancing. As businesses evolve, questions on its past performance is beside the point but its anticipated functions and relevance in the future. With the use of software in commerce at the core of every business today, the overlook of how omnichannel transactions operates is an awe with the 4th industrial revolution and its societal impact. This chapter explains how commerce has currently evolved in the advent of technology elaborating on the current state and challenges of systems, its architecture, and the innovations of cyber physical systems in electronic commerce. It further expounds on the application of omnichannel systems in communication through the fifth-generation network, in transaction through blockchain and in composition through Social Internet of Things. We believe that this study will benefit all stakeholders in commerce from governments, supply chain organizations and consumers to understanding the forthcoming drivers of omnichannel systems in the 4th industrial revolution, its prospects, and its anticipated challenges.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436557"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436557"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436557; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436557]").text(description); $(".js-view-count[data-work-id=127436557]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436557; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436557']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436557]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436557,"title":"The Future of E-Commerce Systems: 2030 and Beyond","translated_title":"","metadata":{"abstract":"The sophistication and efficiency of systems is undeniably advancing. As businesses evolve, questions on its past performance is beside the point but its anticipated functions and relevance in the future. With the use of software in commerce at the core of every business today, the overlook of how omnichannel transactions operates is an awe with the 4th industrial revolution and its societal impact. This chapter explains how commerce has currently evolved in the advent of technology elaborating on the current state and challenges of systems, its architecture, and the innovations of cyber physical systems in electronic commerce. It further expounds on the application of omnichannel systems in communication through the fifth-generation network, in transaction through blockchain and in composition through Social Internet of Things. We believe that this study will benefit all stakeholders in commerce from governments, supply chain organizations and consumers to understanding the forthcoming drivers of omnichannel systems in the 4th industrial revolution, its prospects, and its anticipated challenges.","publisher":"Springer International Publishing","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Studies in systems, decision and control"},"translated_abstract":"The sophistication and efficiency of systems is undeniably advancing. As businesses evolve, questions on its past performance is beside the point but its anticipated functions and relevance in the future. With the use of software in commerce at the core of every business today, the overlook of how omnichannel transactions operates is an awe with the 4th industrial revolution and its societal impact. This chapter explains how commerce has currently evolved in the advent of technology elaborating on the current state and challenges of systems, its architecture, and the innovations of cyber physical systems in electronic commerce. It further expounds on the application of omnichannel systems in communication through the fifth-generation network, in transaction through blockchain and in composition through Social Internet of Things. We believe that this study will benefit all stakeholders in commerce from governments, supply chain organizations and consumers to understanding the forthcoming drivers of omnichannel systems in the 4th industrial revolution, its prospects, and its anticipated challenges.","internal_url":"https://www.academia.edu/127436557/The_Future_of_E_Commerce_Systems_2030_and_Beyond","translated_internal_url":"","created_at":"2025-02-03T07:31:21.073-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"The_Future_of_E_Commerce_Systems_2030_and_Beyond","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"The sophistication and efficiency of systems is undeniably advancing. As businesses evolve, questions on its past performance is beside the point but its anticipated functions and relevance in the future. With the use of software in commerce at the core of every business today, the overlook of how omnichannel transactions operates is an awe with the 4th industrial revolution and its societal impact. This chapter explains how commerce has currently evolved in the advent of technology elaborating on the current state and challenges of systems, its architecture, and the innovations of cyber physical systems in electronic commerce. It further expounds on the application of omnichannel systems in communication through the fifth-generation network, in transaction through blockchain and in composition through Social Internet of Things. We believe that this study will benefit all stakeholders in commerce from governments, supply chain organizations and consumers to understanding the forthcoming drivers of omnichannel systems in the 4th industrial revolution, its prospects, and its anticipated challenges.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":902819,"name":"Omnichannel","url":"https://www.academia.edu/Documents/in/Omnichannel"},{"id":1897290,"name":"Sophistication","url":"https://www.academia.edu/Documents/in/Sophistication"},{"id":3647879,"name":"Springer Ebooks","url":"https://www.academia.edu/Documents/in/Springer_Ebooks"}],"urls":[{"id":46723413,"url":"https://doi.org/10.1007/978-3-030-64987-6_18"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436556"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436556/Novel_Federated_Decision_Making_for_Distribution_of_Anti_SARS_CoV_2_Monoclonal_Antibody_to_Eligible_High_Risk_Patients"><img alt="Research paper thumbnail of Novel Federated Decision Making for Distribution of Anti-SARS-CoV-2 Monoclonal Antibody to Eligible High-Risk Patients" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436556/Novel_Federated_Decision_Making_for_Distribution_of_Anti_SARS_CoV_2_Monoclonal_Antibody_to_Eligible_High_Risk_Patients">Novel Federated Decision Making for Distribution of Anti-SARS-CoV-2 Monoclonal Antibody to Eligible High-Risk Patients</a></div><div class="wp-workCard_item"><span>International Journal of Information Technology and Decision Making</span><span>, Oct 10, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Context: When the epidemic first broke out, no specific treatment was available for severe acute ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients’ data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients’ score and dose availability without sharing the patients’ data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436556"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436556"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436556; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436556]").text(description); $(".js-view-count[data-work-id=127436556]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436556; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436556']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436556]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436556,"title":"Novel Federated Decision Making for Distribution of Anti-SARS-CoV-2 Monoclonal Antibody to Eligible High-Risk Patients","translated_title":"","metadata":{"abstract":"Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients’ data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients’ score and dose availability without sharing the patients’ data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death.","publisher":"World Scientific","publication_date":{"day":10,"month":10,"year":2022,"errors":{}},"publication_name":"International Journal of Information Technology and Decision Making"},"translated_abstract":"Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients’ data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients’ score and dose availability without sharing the patients’ data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death.","internal_url":"https://www.academia.edu/127436556/Novel_Federated_Decision_Making_for_Distribution_of_Anti_SARS_CoV_2_Monoclonal_Antibody_to_Eligible_High_Risk_Patients","translated_internal_url":"","created_at":"2025-02-03T07:31:20.863-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Novel_Federated_Decision_Making_for_Distribution_of_Anti_SARS_CoV_2_Monoclonal_Antibody_to_Eligible_High_Risk_Patients","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients’ data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients’ score and dose availability without sharing the patients’ data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":73149,"name":"Business and Management","url":"https://www.academia.edu/Documents/in/Business_and_Management"}],"urls":[{"id":46723412,"url":"https://doi.org/10.1142/s021962202250050x"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436555"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436555/Predicting_the_Road_Accidents_Severity_Using_Artificial_Neural_Network"><img alt="Research paper thumbnail of Predicting the Road Accidents Severity Using Artificial Neural Network" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436555/Predicting_the_Road_Accidents_Severity_Using_Artificial_Neural_Network">Predicting the Road Accidents Severity Using Artificial Neural Network</a></div><div class="wp-workCard_item"><span>Springer eBooks</span><span>, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436555"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436555"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436555; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436555]").text(description); $(".js-view-count[data-work-id=127436555]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436555; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436555']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436555]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436555,"title":"Predicting the Road Accidents Severity Using Artificial Neural Network","translated_title":"","metadata":{"publisher":"Springer Nature","publication_date":{"day":null,"month":null,"year":2022,"errors":{}},"publication_name":"Springer eBooks"},"translated_abstract":null,"internal_url":"https://www.academia.edu/127436555/Predicting_the_Road_Accidents_Severity_Using_Artificial_Neural_Network","translated_internal_url":"","created_at":"2025-02-03T07:31:20.659-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Predicting_the_Road_Accidents_Severity_Using_Artificial_Neural_Network","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":null,"owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":396772,"name":"Harm","url":"https://www.academia.edu/Documents/in/Harm"},{"id":3647879,"name":"Springer Ebooks","url":"https://www.academia.edu/Documents/in/Springer_Ebooks"}],"urls":[{"id":46723411,"url":"https://doi.org/10.1007/978-3-031-03918-8_22"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436554"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436554/Utilizing_Machine_Learning_to_Develop_Cloud_Based_Apprenticeship_Programs_Aligned_with_Labor_Market_Demands"><img alt="Research paper thumbnail of Utilizing Machine Learning to Develop Cloud-Based Apprenticeship Programs Aligned with Labor Market Demands" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436554/Utilizing_Machine_Learning_to_Develop_Cloud_Based_Apprenticeship_Programs_Aligned_with_Labor_Market_Demands">Utilizing Machine Learning to Develop Cloud-Based Apprenticeship Programs Aligned with Labor Market Demands</a></div><div class="wp-workCard_item"><span>2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud)/2023 IEEE 9th International Conference on Edge Computing and Scalable Cloud (EdgeCom)</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">There is a potential disparity between academic pursuits and labor market requirements. We propos...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">There is a potential disparity between academic pursuits and labor market requirements. We proposed an approach exploring the feasibility of leveraging machine learning to tailor apprenticeship programs and enhance learning outcomes. The proposed approach involves the collaboration of four key stakeholders, namely, the employer, trainer, university management, and apprentice, each with unique roles in the apprenticeship program. A machine learning algorithm is employed to customize Occupational Learning Outcomes (OLO) for each job, with the use of blockchain technology to facilitate the student credit system. The entire system is hosted on a cloud-based centralized database to enable dynamic and sustainable program modification. The paper concludes by highlighting the potential of digital technologies to transform apprenticeships and create new opportunities and challenges.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436554"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436554"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436554; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436554]").text(description); $(".js-view-count[data-work-id=127436554]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436554; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436554']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436554]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436554,"title":"Utilizing Machine Learning to Develop Cloud-Based Apprenticeship Programs Aligned with Labor Market Demands","translated_title":"","metadata":{"abstract":"There is a potential disparity between academic pursuits and labor market requirements. We proposed an approach exploring the feasibility of leveraging machine learning to tailor apprenticeship programs and enhance learning outcomes. The proposed approach involves the collaboration of four key stakeholders, namely, the employer, trainer, university management, and apprentice, each with unique roles in the apprenticeship program. A machine learning algorithm is employed to customize Occupational Learning Outcomes (OLO) for each job, with the use of blockchain technology to facilitate the student credit system. The entire system is hosted on a cloud-based centralized database to enable dynamic and sustainable program modification. The paper concludes by highlighting the potential of digital technologies to transform apprenticeships and create new opportunities and challenges.","publisher":"IEEE","publication_name":"2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud)/2023 IEEE 9th International Conference on Edge Computing and Scalable Cloud (EdgeCom)"},"translated_abstract":"There is a potential disparity between academic pursuits and labor market requirements. We proposed an approach exploring the feasibility of leveraging machine learning to tailor apprenticeship programs and enhance learning outcomes. The proposed approach involves the collaboration of four key stakeholders, namely, the employer, trainer, university management, and apprentice, each with unique roles in the apprenticeship program. A machine learning algorithm is employed to customize Occupational Learning Outcomes (OLO) for each job, with the use of blockchain technology to facilitate the student credit system. The entire system is hosted on a cloud-based centralized database to enable dynamic and sustainable program modification. The paper concludes by highlighting the potential of digital technologies to transform apprenticeships and create new opportunities and challenges.","internal_url":"https://www.academia.edu/127436554/Utilizing_Machine_Learning_to_Develop_Cloud_Based_Apprenticeship_Programs_Aligned_with_Labor_Market_Demands","translated_internal_url":"","created_at":"2025-02-03T07:31:20.291-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Utilizing_Machine_Learning_to_Develop_Cloud_Based_Apprenticeship_Programs_Aligned_with_Labor_Market_Demands","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"There is a potential disparity between academic pursuits and labor market requirements. We proposed an approach exploring the feasibility of leveraging machine learning to tailor apprenticeship programs and enhance learning outcomes. The proposed approach involves the collaboration of four key stakeholders, namely, the employer, trainer, university management, and apprentice, each with unique roles in the apprenticeship program. A machine learning algorithm is employed to customize Occupational Learning Outcomes (OLO) for each job, with the use of blockchain technology to facilitate the student credit system. The entire system is hosted on a cloud-based centralized database to enable dynamic and sustainable program modification. The paper concludes by highlighting the potential of digital technologies to transform apprenticeships and create new opportunities and challenges.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":922,"name":"Education","url":"https://www.academia.edu/Documents/in/Education"},{"id":1003,"name":"Educational Technology","url":"https://www.academia.edu/Documents/in/Educational_Technology"},{"id":2621,"name":"Higher Education","url":"https://www.academia.edu/Documents/in/Higher_Education"},{"id":26860,"name":"Cloud Computing","url":"https://www.academia.edu/Documents/in/Cloud_Computing"},{"id":741729,"name":"Trainer","url":"https://www.academia.edu/Documents/in/Trainer"},{"id":766833,"name":"Apprenticeship","url":"https://www.academia.edu/Documents/in/Apprenticeship"}],"urls":[{"id":46723410,"url":"http://xplorestaging.ieee.org/ielx7/10195194/10195273/10195358.pdf?arnumber=10195358"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436553"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436553/Automatic_Generation_of_Ancient_Poetry_Based_on_Generative_Adversarial_Network"><img alt="Research paper thumbnail of Automatic Generation of Ancient Poetry Based on Generative Adversarial Network" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436553/Automatic_Generation_of_Ancient_Poetry_Based_on_Generative_Adversarial_Network">Automatic Generation of Ancient Poetry Based on Generative Adversarial Network</a></div><div class="wp-workCard_item"><span>Business Intelligence and Information Technology</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436553"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436553"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436553; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436553]").text(description); $(".js-view-count[data-work-id=127436553]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436553; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436553']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436553]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436553,"title":"Automatic Generation of Ancient Poetry Based on Generative Adversarial Network","translated_title":"","metadata":{"publisher":"Springer Nature Singapore","publication_name":"Business Intelligence and Information Technology"},"translated_abstract":null,"internal_url":"https://www.academia.edu/127436553/Automatic_Generation_of_Ancient_Poetry_Based_on_Generative_Adversarial_Network","translated_internal_url":"","created_at":"2025-02-03T07:31:20.095-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Automatic_Generation_of_Ancient_Poetry_Based_on_Generative_Adversarial_Network","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":null,"owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":3581,"name":"Poetry","url":"https://www.academia.edu/Documents/in/Poetry"},{"id":128900,"name":"Generative grammar","url":"https://www.academia.edu/Documents/in/Generative_grammar"},{"id":140897,"name":"Encoder","url":"https://www.academia.edu/Documents/in/Encoder"},{"id":1108620,"name":"Adversarial System","url":"https://www.academia.edu/Documents/in/Adversarial_System"},{"id":4201064,"name":"Generative Adversarial Network","url":"https://www.academia.edu/Documents/in/Generative_Adversarial_Network"}],"urls":[{"id":46723409,"url":"https://link.springer.com/content/pdf/10.1007/978-981-99-3416-4_48"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436552"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436552/Towards_Creating_Public_Key_Authentication_for_IoT_Blockchain"><img alt="Research paper thumbnail of Towards Creating Public Key Authentication for IoT Blockchain" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436552/Towards_Creating_Public_Key_Authentication_for_IoT_Blockchain">Towards Creating Public Key Authentication for IoT Blockchain</a></div><div class="wp-workCard_item"><span>2019 Sixth HCT Information Technology Trends (ITT)</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Besides confidentiality and privacy, trust is an important factor for any IoT system. When a sens...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Besides confidentiality and privacy, trust is an important factor for any IoT system. When a sensor send data that is signed with its private key, the receiving nodes verify it using the public key of the sensor. Hence, it is understood that authenticating the public key of the system is part of creating trust within the system. Traditionally, trust is maintained using Public Key Infrastructure (PKI) where a centralized Certificate Authority (CA) is used for authenticating the public keys. However, a centralized system can result in single point of failure where CA can be compromised or can act maliciously. Decentralizing this system using blockchain and by automating the process of certificate authentication without the need for a central third party can overcome the abovementioned limitations. We identify the challenges in creating such a system and propose a generic framework for PKI in IoT infrastructure using blockchain that can provide the functions of a CA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436552"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436552"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436552; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436552]").text(description); $(".js-view-count[data-work-id=127436552]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436552; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436552']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436552]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436552,"title":"Towards Creating Public Key Authentication for IoT Blockchain","translated_title":"","metadata":{"abstract":"Besides confidentiality and privacy, trust is an important factor for any IoT system. When a sensor send data that is signed with its private key, the receiving nodes verify it using the public key of the sensor. Hence, it is understood that authenticating the public key of the system is part of creating trust within the system. Traditionally, trust is maintained using Public Key Infrastructure (PKI) where a centralized Certificate Authority (CA) is used for authenticating the public keys. However, a centralized system can result in single point of failure where CA can be compromised or can act maliciously. Decentralizing this system using blockchain and by automating the process of certificate authentication without the need for a central third party can overcome the abovementioned limitations. We identify the challenges in creating such a system and propose a generic framework for PKI in IoT infrastructure using blockchain that can provide the functions of a CA.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"2019 Sixth HCT Information Technology Trends (ITT)"},"translated_abstract":"Besides confidentiality and privacy, trust is an important factor for any IoT system. When a sensor send data that is signed with its private key, the receiving nodes verify it using the public key of the sensor. Hence, it is understood that authenticating the public key of the system is part of creating trust within the system. Traditionally, trust is maintained using Public Key Infrastructure (PKI) where a centralized Certificate Authority (CA) is used for authenticating the public keys. However, a centralized system can result in single point of failure where CA can be compromised or can act maliciously. Decentralizing this system using blockchain and by automating the process of certificate authentication without the need for a central third party can overcome the abovementioned limitations. We identify the challenges in creating such a system and propose a generic framework for PKI in IoT infrastructure using blockchain that can provide the functions of a CA.","internal_url":"https://www.academia.edu/127436552/Towards_Creating_Public_Key_Authentication_for_IoT_Blockchain","translated_internal_url":"","created_at":"2025-02-03T07:31:19.896-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Towards_Creating_Public_Key_Authentication_for_IoT_Blockchain","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Besides confidentiality and privacy, trust is an important factor for any IoT system. When a sensor send data that is signed with its private key, the receiving nodes verify it using the public key of the sensor. Hence, it is understood that authenticating the public key of the system is part of creating trust within the system. Traditionally, trust is maintained using Public Key Infrastructure (PKI) where a centralized Certificate Authority (CA) is used for authenticating the public keys. However, a centralized system can result in single point of failure where CA can be compromised or can act maliciously. Decentralizing this system using blockchain and by automating the process of certificate authentication without the need for a central third party can overcome the abovementioned limitations. We identify the challenges in creating such a system and propose a generic framework for PKI in IoT infrastructure using blockchain that can provide the functions of a CA.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":7719,"name":"Public Key Infrastructure","url":"https://www.academia.edu/Documents/in/Public_Key_Infrastructure"},{"id":13923,"name":"Computer Security","url":"https://www.academia.edu/Documents/in/Computer_Security"},{"id":222999,"name":"Public key cryptography","url":"https://www.academia.edu/Documents/in/Public_key_cryptography"},{"id":247799,"name":"Internet of Things (IoT)","url":"https://www.academia.edu/Documents/in/Internet_of_Things_IoT_"},{"id":263911,"name":"Certificate","url":"https://www.academia.edu/Documents/in/Certificate"},{"id":807028,"name":"emerging trends in Information Technology","url":"https://www.academia.edu/Documents/in/emerging_trends_in_Information_Technology"},{"id":1147978,"name":"Certificate Authority","url":"https://www.academia.edu/Documents/in/Certificate_Authority"},{"id":2199949,"name":"Blockchain","url":"https://www.academia.edu/Documents/in/Blockchain"}],"urls":[{"id":46723408,"url":"http://xplorestaging.ieee.org/ielx7/9066808/9075061/09075105.pdf?arnumber=9075105"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436551"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436551/An_Optimal_Consensus_Node_Selection_Process_for_IoT_Blockchain"><img alt="Research paper thumbnail of An Optimal Consensus Node Selection Process for IoT Blockchain" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436551/An_Optimal_Consensus_Node_Selection_Process_for_IoT_Blockchain">An Optimal Consensus Node Selection Process for IoT Blockchain</a></div><div class="wp-workCard_item"><span>2019 Sixth HCT Information Technology Trends (ITT)</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Blockchain and Internet of Things (IoT) are two trending technologies, when combined together can...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Blockchain and Internet of Things (IoT) are two trending technologies, when combined together can strengthen the security of various applications. However, security of blockchain depend on its consensus mechanism. The consensus mechanisms used by cryptocurrencies requires high computations and hence cannot be applied to IoT. Applying light weight consensus such as PBFT (Practical Byzantine Fault Tolerance) requires an authority or a protocol to select leader nodes and the nodes to be involved in consensus. However the current node selection process in many blockchain applications involves a central authority or is based on traditional round robin techniques. Hence we propose simple and efficient node selection mechanism that can perform consensus without wasting energy. Our approach uses PBFT, where the nodes to participate in the consensus are not predetermined by a central authority. Nodes are selected based on their performance in the blockchain.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436551"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436551"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436551; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436551]").text(description); $(".js-view-count[data-work-id=127436551]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436551; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436551']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436551]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436551,"title":"An Optimal Consensus Node Selection Process for IoT Blockchain","translated_title":"","metadata":{"abstract":"Blockchain and Internet of Things (IoT) are two trending technologies, when combined together can strengthen the security of various applications. However, security of blockchain depend on its consensus mechanism. The consensus mechanisms used by cryptocurrencies requires high computations and hence cannot be applied to IoT. Applying light weight consensus such as PBFT (Practical Byzantine Fault Tolerance) requires an authority or a protocol to select leader nodes and the nodes to be involved in consensus. However the current node selection process in many blockchain applications involves a central authority or is based on traditional round robin techniques. Hence we propose simple and efficient node selection mechanism that can perform consensus without wasting energy. Our approach uses PBFT, where the nodes to participate in the consensus are not predetermined by a central authority. Nodes are selected based on their performance in the blockchain.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"2019 Sixth HCT Information Technology Trends (ITT)"},"translated_abstract":"Blockchain and Internet of Things (IoT) are two trending technologies, when combined together can strengthen the security of various applications. However, security of blockchain depend on its consensus mechanism. The consensus mechanisms used by cryptocurrencies requires high computations and hence cannot be applied to IoT. Applying light weight consensus such as PBFT (Practical Byzantine Fault Tolerance) requires an authority or a protocol to select leader nodes and the nodes to be involved in consensus. However the current node selection process in many blockchain applications involves a central authority or is based on traditional round robin techniques. Hence we propose simple and efficient node selection mechanism that can perform consensus without wasting energy. Our approach uses PBFT, where the nodes to participate in the consensus are not predetermined by a central authority. Nodes are selected based on their performance in the blockchain.","internal_url":"https://www.academia.edu/127436551/An_Optimal_Consensus_Node_Selection_Process_for_IoT_Blockchain","translated_internal_url":"","created_at":"2025-02-03T07:31:19.683-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"An_Optimal_Consensus_Node_Selection_Process_for_IoT_Blockchain","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Blockchain and Internet of Things (IoT) are two trending technologies, when combined together can strengthen the security of various applications. However, security of blockchain depend on its consensus mechanism. The consensus mechanisms used by cryptocurrencies requires high computations and hence cannot be applied to IoT. Applying light weight consensus such as PBFT (Practical Byzantine Fault Tolerance) requires an authority or a protocol to select leader nodes and the nodes to be involved in consensus. However the current node selection process in many blockchain applications involves a central authority or is based on traditional round robin techniques. Hence we propose simple and efficient node selection mechanism that can perform consensus without wasting energy. Our approach uses PBFT, where the nodes to participate in the consensus are not predetermined by a central authority. Nodes are selected based on their performance in the blockchain.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing"},{"id":62540,"name":"Computer Network","url":"https://www.academia.edu/Documents/in/Computer_Network"},{"id":247799,"name":"Internet of Things (IoT)","url":"https://www.academia.edu/Documents/in/Internet_of_Things_IoT_"},{"id":740735,"name":"Byzantine Fault Tolerance","url":"https://www.academia.edu/Documents/in/Byzantine_Fault_Tolerance"},{"id":807028,"name":"emerging trends in Information Technology","url":"https://www.academia.edu/Documents/in/emerging_trends_in_Information_Technology"},{"id":2199949,"name":"Blockchain","url":"https://www.academia.edu/Documents/in/Blockchain"}],"urls":[{"id":46723407,"url":"http://xplorestaging.ieee.org/ielx7/9066808/9075061/09075069.pdf?arnumber=9075069"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436550"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436550/FCSR_Fuzzy_Continuous_Speech_Recognition_Approach_for_Identifying_Laryngeal_Pathologies_Using_New_Weighted_Spectrum_Features"><img alt="Research paper thumbnail of FCSR - Fuzzy Continuous Speech Recognition Approach for Identifying Laryngeal Pathologies Using New Weighted Spectrum Features" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436550/FCSR_Fuzzy_Continuous_Speech_Recognition_Approach_for_Identifying_Laryngeal_Pathologies_Using_New_Weighted_Spectrum_Features">FCSR - Fuzzy Continuous Speech Recognition Approach for Identifying Laryngeal Pathologies Using New Weighted Spectrum Features</a></div><div class="wp-workCard_item"><span>Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Speech processing technologies have provided distinct contributions for identifying laryngeal pat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Speech processing technologies have provided distinct contributions for identifying laryngeal pathology, in which samples of normal and pathologic voice are evaluated. In this paper, a novel Fuzzy Continuous Speech Recognition approach termed FCSR is proposed for laryngeal pathology identification. First of all, new speech weighted spectrum features based on Jacobi–Fourier Moments (JFMs) are presented for characterization of larynx pathologies. This is primarily motivated by the assumption that the energy represented by spectrogram would entirely change with some larynx pathologies like physiological pathologies, neuromuscular pathologies, while it would extremely change with normal speech. This phenomenon would extensively influence the allocation of spectrogram local energy in time axis together with frequency axis. Consequently, the JFMs computed from spectrogram local regions are utilized to characterize distribution of spectrogram local energy. Besides, a proposed multi-class fuzzy support vector machine (FSVM) model is constructed to classify larynx pathologies, where partition index maximization (PIM) clustering along with particle swarm optimization (PSO) are employed for calculating fuzzy memberships and optimizing the arguments of the kernel function of the FSVM, respectively. Eventually, the experiments legitimize the proposed approach in reference to the accuracy of the laryngeal pathology recognition.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436550"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436550"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436550; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436550]").text(description); $(".js-view-count[data-work-id=127436550]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436550; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436550']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436550]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436550,"title":"FCSR - Fuzzy Continuous Speech Recognition Approach for Identifying Laryngeal Pathologies Using New Weighted Spectrum Features","translated_title":"","metadata":{"abstract":"Speech processing technologies have provided distinct contributions for identifying laryngeal pathology, in which samples of normal and pathologic voice are evaluated. In this paper, a novel Fuzzy Continuous Speech Recognition approach termed FCSR is proposed for laryngeal pathology identification. First of all, new speech weighted spectrum features based on Jacobi–Fourier Moments (JFMs) are presented for characterization of larynx pathologies. This is primarily motivated by the assumption that the energy represented by spectrogram would entirely change with some larynx pathologies like physiological pathologies, neuromuscular pathologies, while it would extremely change with normal speech. This phenomenon would extensively influence the allocation of spectrogram local energy in time axis together with frequency axis. Consequently, the JFMs computed from spectrogram local regions are utilized to characterize distribution of spectrogram local energy. Besides, a proposed multi-class fuzzy support vector machine (FSVM) model is constructed to classify larynx pathologies, where partition index maximization (PIM) clustering along with particle swarm optimization (PSO) are employed for calculating fuzzy memberships and optimizing the arguments of the kernel function of the FSVM, respectively. Eventually, the experiments legitimize the proposed approach in reference to the accuracy of the laryngeal pathology recognition.","publisher":"Springer International Publishing","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017"},"translated_abstract":"Speech processing technologies have provided distinct contributions for identifying laryngeal pathology, in which samples of normal and pathologic voice are evaluated. In this paper, a novel Fuzzy Continuous Speech Recognition approach termed FCSR is proposed for laryngeal pathology identification. First of all, new speech weighted spectrum features based on Jacobi–Fourier Moments (JFMs) are presented for characterization of larynx pathologies. This is primarily motivated by the assumption that the energy represented by spectrogram would entirely change with some larynx pathologies like physiological pathologies, neuromuscular pathologies, while it would extremely change with normal speech. This phenomenon would extensively influence the allocation of spectrogram local energy in time axis together with frequency axis. Consequently, the JFMs computed from spectrogram local regions are utilized to characterize distribution of spectrogram local energy. Besides, a proposed multi-class fuzzy support vector machine (FSVM) model is constructed to classify larynx pathologies, where partition index maximization (PIM) clustering along with particle swarm optimization (PSO) are employed for calculating fuzzy memberships and optimizing the arguments of the kernel function of the FSVM, respectively. Eventually, the experiments legitimize the proposed approach in reference to the accuracy of the laryngeal pathology recognition.","internal_url":"https://www.academia.edu/127436550/FCSR_Fuzzy_Continuous_Speech_Recognition_Approach_for_Identifying_Laryngeal_Pathologies_Using_New_Weighted_Spectrum_Features","translated_internal_url":"","created_at":"2025-02-03T07:31:19.436-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"FCSR_Fuzzy_Continuous_Speech_Recognition_Approach_for_Identifying_Laryngeal_Pathologies_Using_New_Weighted_Spectrum_Features","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Speech processing technologies have provided distinct contributions for identifying laryngeal pathology, in which samples of normal and pathologic voice are evaluated. In this paper, a novel Fuzzy Continuous Speech Recognition approach termed FCSR is proposed for laryngeal pathology identification. First of all, new speech weighted spectrum features based on Jacobi–Fourier Moments (JFMs) are presented for characterization of larynx pathologies. This is primarily motivated by the assumption that the energy represented by spectrogram would entirely change with some larynx pathologies like physiological pathologies, neuromuscular pathologies, while it would extremely change with normal speech. This phenomenon would extensively influence the allocation of spectrogram local energy in time axis together with frequency axis. Consequently, the JFMs computed from spectrogram local regions are utilized to characterize distribution of spectrogram local energy. Besides, a proposed multi-class fuzzy support vector machine (FSVM) model is constructed to classify larynx pathologies, where partition index maximization (PIM) clustering along with particle swarm optimization (PSO) are employed for calculating fuzzy memberships and optimizing the arguments of the kernel function of the FSVM, respectively. Eventually, the experiments legitimize the proposed approach in reference to the accuracy of the laryngeal pathology recognition.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":4165,"name":"Fuzzy Logic","url":"https://www.academia.edu/Documents/in/Fuzzy_Logic"},{"id":11984,"name":"Speech Recognition","url":"https://www.academia.edu/Documents/in/Speech_Recognition"},{"id":151048,"name":"Spectrogram","url":"https://www.academia.edu/Documents/in/Spectrogram"},{"id":2887079,"name":"Larynx","url":"https://www.academia.edu/Documents/in/Larynx"}],"urls":[{"id":46723406,"url":"http://link.springer.com/content/pdf/10.1007/978-3-319-64861-3_36"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436548"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436548/Using_Arabic_Social_Media_Feeds_for_Incident_and_Emergency_Management_in_Smart_Cities"><img alt="Research paper thumbnail of Using Arabic Social Media Feeds for Incident and Emergency Management in Smart Cities" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436548/Using_Arabic_Social_Media_Feeds_for_Incident_and_Emergency_Management_in_Smart_Cities">Using Arabic Social Media Feeds for Incident and Emergency Management in Smart Cities</a></div><div class="wp-workCard_item"><span>2018 3rd International Conference on Smart and Sustainable Technologies (SpliTech)</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Research on Smart Cities tackles the challenges related to the rapid urban population growth comb...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Research on Smart Cities tackles the challenges related to the rapid urban population growth combined with resources&#39; scarcity. A key function of any Smart City initiative is to be able to continuously monitor and track a city&#39;s environment and resources so as to convert the data into intelligence for streamlining the city&#39;s operations. Social media has become one of the most popular means to allow users to communicate and share information, opinions, and sentiments about events and incidents occurring in a city. With the rapid growth and proliferation of social media platforms, there is a vast amount of user-generated content that can be used as source of information about cities. In this work, we propose the use of text mining and classification techniques to extract the intelligence needed from Arabic social media feeds, for effective incident and emergency management in smart cities. In our system, the information collected from social media feeds is processed to gen...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436548"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436548"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436548; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436548]").text(description); $(".js-view-count[data-work-id=127436548]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436548; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436548']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436548]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436548,"title":"Using Arabic Social Media Feeds for Incident and Emergency Management in Smart Cities","translated_title":"","metadata":{"abstract":"Research on Smart Cities tackles the challenges related to the rapid urban population growth combined with resources\u0026#39; scarcity. A key function of any Smart City initiative is to be able to continuously monitor and track a city\u0026#39;s environment and resources so as to convert the data into intelligence for streamlining the city\u0026#39;s operations. Social media has become one of the most popular means to allow users to communicate and share information, opinions, and sentiments about events and incidents occurring in a city. With the rapid growth and proliferation of social media platforms, there is a vast amount of user-generated content that can be used as source of information about cities. In this work, we propose the use of text mining and classification techniques to extract the intelligence needed from Arabic social media feeds, for effective incident and emergency management in smart cities. In our system, the information collected from social media feeds is processed to gen...","publisher":"2018 3rd International Conference on Smart and Sustainable Technologies (SpliTech)","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"2018 3rd International Conference on Smart and Sustainable Technologies (SpliTech)"},"translated_abstract":"Research on Smart Cities tackles the challenges related to the rapid urban population growth combined with resources\u0026#39; scarcity. A key function of any Smart City initiative is to be able to continuously monitor and track a city\u0026#39;s environment and resources so as to convert the data into intelligence for streamlining the city\u0026#39;s operations. Social media has become one of the most popular means to allow users to communicate and share information, opinions, and sentiments about events and incidents occurring in a city. With the rapid growth and proliferation of social media platforms, there is a vast amount of user-generated content that can be used as source of information about cities. In this work, we propose the use of text mining and classification techniques to extract the intelligence needed from Arabic social media feeds, for effective incident and emergency management in smart cities. In our system, the information collected from social media feeds is processed to gen...","internal_url":"https://www.academia.edu/127436548/Using_Arabic_Social_Media_Feeds_for_Incident_and_Emergency_Management_in_Smart_Cities","translated_internal_url":"","created_at":"2025-02-03T07:31:19.324-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Using_Arabic_Social_Media_Feeds_for_Incident_and_Emergency_Management_in_Smart_Cities","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Research on Smart Cities tackles the challenges related to the rapid urban population growth combined with resources\u0026#39; scarcity. A key function of any Smart City initiative is to be able to continuously monitor and track a city\u0026#39;s environment and resources so as to convert the data into intelligence for streamlining the city\u0026#39;s operations. Social media has become one of the most popular means to allow users to communicate and share information, opinions, and sentiments about events and incidents occurring in a city. With the rapid growth and proliferation of social media platforms, there is a vast amount of user-generated content that can be used as source of information about cities. In this work, we propose the use of text mining and classification techniques to extract the intelligence needed from Arabic social media feeds, for effective incident and emergency management in smart cities. In our system, the information collected from social media feeds is processed to gen...","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3324,"name":"Arabic","url":"https://www.academia.edu/Documents/in/Arabic"},{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media"},{"id":51829,"name":"Text Classification","url":"https://www.academia.edu/Documents/in/Text_Classification"},{"id":64336,"name":"Population","url":"https://www.academia.edu/Documents/in/Population"},{"id":94893,"name":"Smart City","url":"https://www.academia.edu/Documents/in/Smart_City"},{"id":581925,"name":"Scarcity","url":"https://www.academia.edu/Documents/in/Scarcity"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436547"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436547/Lean_Transformation_in_Information_Technology_The_Case_of_IT_Services_in_Financial_Firms"><img alt="Research paper thumbnail of Lean Transformation in Information Technology: The Case of IT Services in Financial Firms" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436547/Lean_Transformation_in_Information_Technology_The_Case_of_IT_Services_in_Financial_Firms">Lean Transformation in Information Technology: The Case of IT Services in Financial Firms</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In today’s competitive market, organizations have realized that high quality contributes to long-...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In today’s competitive market, organizations have realized that high quality contributes to long-term success. Lean transformation framework can be described as a set of tools and principles that positively impact quality by focusing on both eliminating waste and adding value to certain processes in the organization. This study argues the success of implementing Lean principles in IT. Since there is no such research has been conducted in the Arab world in implementing Lean in IT, this research paper is considered as a pioneer research in the field of IT quality management. The objective of this paper is to use a case-based approach to demonstrate how Lean principles and tools can help IT to enhance IT services quality, reduce cost, and improve productivity. A case study at a leading financial institution in the Arab Gulf region is examined. The study focuses on analyzing the Lean transformation stages, including current state assessment, target state design, and implementation; as w...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436547"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436547"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436547; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436547]").text(description); $(".js-view-count[data-work-id=127436547]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436547; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436547']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436547]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436547,"title":"Lean Transformation in Information Technology: The Case of IT Services in Financial Firms","translated_title":"","metadata":{"abstract":"In today’s competitive market, organizations have realized that high quality contributes to long-term success. Lean transformation framework can be described as a set of tools and principles that positively impact quality by focusing on both eliminating waste and adding value to certain processes in the organization. This study argues the success of implementing Lean principles in IT. Since there is no such research has been conducted in the Arab world in implementing Lean in IT, this research paper is considered as a pioneer research in the field of IT quality management. The objective of this paper is to use a case-based approach to demonstrate how Lean principles and tools can help IT to enhance IT services quality, reduce cost, and improve productivity. A case study at a leading financial institution in the Arab Gulf region is examined. The study focuses on analyzing the Lean transformation stages, including current state assessment, target state design, and implementation; as w...","publication_date":{"day":null,"month":null,"year":2018,"errors":{}}},"translated_abstract":"In today’s competitive market, organizations have realized that high quality contributes to long-term success. Lean transformation framework can be described as a set of tools and principles that positively impact quality by focusing on both eliminating waste and adding value to certain processes in the organization. This study argues the success of implementing Lean principles in IT. Since there is no such research has been conducted in the Arab world in implementing Lean in IT, this research paper is considered as a pioneer research in the field of IT quality management. The objective of this paper is to use a case-based approach to demonstrate how Lean principles and tools can help IT to enhance IT services quality, reduce cost, and improve productivity. A case study at a leading financial institution in the Arab Gulf region is examined. The study focuses on analyzing the Lean transformation stages, including current state assessment, target state design, and implementation; as w...","internal_url":"https://www.academia.edu/127436547/Lean_Transformation_in_Information_Technology_The_Case_of_IT_Services_in_Financial_Firms","translated_internal_url":"","created_at":"2025-02-03T07:31:19.204-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Lean_Transformation_in_Information_Technology_The_Case_of_IT_Services_in_Financial_Firms","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"In today’s competitive market, organizations have realized that high quality contributes to long-term success. Lean transformation framework can be described as a set of tools and principles that positively impact quality by focusing on both eliminating waste and adding value to certain processes in the organization. This study argues the success of implementing Lean principles in IT. Since there is no such research has been conducted in the Arab world in implementing Lean in IT, this research paper is considered as a pioneer research in the field of IT quality management. The objective of this paper is to use a case-based approach to demonstrate how Lean principles and tools can help IT to enhance IT services quality, reduce cost, and improve productivity. A case study at a leading financial institution in the Arab Gulf region is examined. The study focuses on analyzing the Lean transformation stages, including current state assessment, target state design, and implementation; as w...","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":26,"name":"Business","url":"https://www.academia.edu/Documents/in/Business"},{"id":491,"name":"Information Technology","url":"https://www.academia.edu/Documents/in/Information_Technology"},{"id":4193,"name":"Focus Groups","url":"https://www.academia.edu/Documents/in/Focus_Groups"},{"id":7854,"name":"Lean Manufacturing","url":"https://www.academia.edu/Documents/in/Lean_Manufacturing"},{"id":8968,"name":"Productivity","url":"https://www.academia.edu/Documents/in/Productivity"},{"id":9745,"name":"Quality Improvement","url":"https://www.academia.edu/Documents/in/Quality_Improvement"},{"id":17296,"name":"Lean Management","url":"https://www.academia.edu/Documents/in/Lean_Management"},{"id":94440,"name":"Process management","url":"https://www.academia.edu/Documents/in/Process_management"},{"id":96047,"name":"Case Study","url":"https://www.academia.edu/Documents/in/Case_Study"},{"id":178904,"name":"Lean Project Management","url":"https://www.academia.edu/Documents/in/Lean_Project_Management"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436546"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436546/A_Hybrid_Framework_for_Applying_Semantic_Integration_Technologies_to_Improve_Data_Quality"><img alt="Research paper thumbnail of A Hybrid Framework for Applying Semantic Integration Technologies to Improve Data Quality" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436546/A_Hybrid_Framework_for_Applying_Semantic_Integration_Technologies_to_Improve_Data_Quality">A Hybrid Framework for Applying Semantic Integration Technologies to Improve Data Quality</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This study aims to develop a new hybrid framework of semantic integration for enterprise informat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This study aims to develop a new hybrid framework of semantic integration for enterprise information system in order to improve data quality to resolve the problem from scattered data sources and rapid expansions of data. The proposed framework is based on a solid background that is inspired by previous studies. Significant and seminal research articles are reviewed based on selection criteria. A critical review is conducted in order to determine a set of qualified semantic technologies that can be used to construct a hybrid semantic integration framework. The proposed framework consists of six layers and one component as follows: source layer, translation layer, XML layer, RDF layer, inference layer, application layer, and ontology component. The proposed framework faces two challenges and one conflict; these were fixed while composing the framework. The proposed framework was examined to improve data quality for four dimensions of data quality dimensions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436546"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436546"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436546; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436546]").text(description); $(".js-view-count[data-work-id=127436546]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436546; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436546']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436546]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436546,"title":"A Hybrid Framework for Applying Semantic Integration Technologies to Improve Data Quality","translated_title":"","metadata":{"abstract":"This study aims to develop a new hybrid framework of semantic integration for enterprise information system in order to improve data quality to resolve the problem from scattered data sources and rapid expansions of data. The proposed framework is based on a solid background that is inspired by previous studies. Significant and seminal research articles are reviewed based on selection criteria. A critical review is conducted in order to determine a set of qualified semantic technologies that can be used to construct a hybrid semantic integration framework. The proposed framework consists of six layers and one component as follows: source layer, translation layer, XML layer, RDF layer, inference layer, application layer, and ontology component. The proposed framework faces two challenges and one conflict; these were fixed while composing the framework. The proposed framework was examined to improve data quality for four dimensions of data quality dimensions.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}}},"translated_abstract":"This study aims to develop a new hybrid framework of semantic integration for enterprise information system in order to improve data quality to resolve the problem from scattered data sources and rapid expansions of data. The proposed framework is based on a solid background that is inspired by previous studies. Significant and seminal research articles are reviewed based on selection criteria. A critical review is conducted in order to determine a set of qualified semantic technologies that can be used to construct a hybrid semantic integration framework. The proposed framework consists of six layers and one component as follows: source layer, translation layer, XML layer, RDF layer, inference layer, application layer, and ontology component. The proposed framework faces two challenges and one conflict; these were fixed while composing the framework. The proposed framework was examined to improve data quality for four dimensions of data quality dimensions.","internal_url":"https://www.academia.edu/127436546/A_Hybrid_Framework_for_Applying_Semantic_Integration_Technologies_to_Improve_Data_Quality","translated_internal_url":"","created_at":"2025-02-03T07:31:19.017-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_Hybrid_Framework_for_Applying_Semantic_Integration_Technologies_to_Improve_Data_Quality","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"This study aims to develop a new hybrid framework of semantic integration for enterprise information system in order to improve data quality to resolve the problem from scattered data sources and rapid expansions of data. The proposed framework is based on a solid background that is inspired by previous studies. Significant and seminal research articles are reviewed based on selection criteria. A critical review is conducted in order to determine a set of qualified semantic technologies that can be used to construct a hybrid semantic integration framework. The proposed framework consists of six layers and one component as follows: source layer, translation layer, XML layer, RDF layer, inference layer, application layer, and ontology component. The proposed framework faces two challenges and one conflict; these were fixed while composing the framework. The proposed framework was examined to improve data quality for four dimensions of data quality dimensions.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":449,"name":"Software Engineering","url":"https://www.academia.edu/Documents/in/Software_Engineering"},{"id":12512,"name":"Data Quality (Computer Science)","url":"https://www.academia.edu/Documents/in/Data_Quality_Computer_Science_"},{"id":31917,"name":"Data quality (Business)","url":"https://www.academia.edu/Documents/in/Data_quality_Business_"},{"id":137986,"name":"Data Quality","url":"https://www.academia.edu/Documents/in/Data_Quality"},{"id":361019,"name":"Semantic Integration","url":"https://www.academia.edu/Documents/in/Semantic_Integration"}],"urls":[{"id":46723405,"url":"http://journal-ik.twoa.ac.nz/index.php/ijitls/article/download/19/pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436545"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/127436545/Agent_Productivity_Measurement_in_Call_Center_Using_Machine_Learning"><img alt="Research paper thumbnail of Agent Productivity Measurement in Call Center Using Machine Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/121168563/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/127436545/Agent_Productivity_Measurement_in_Call_Center_Using_Machine_Learning">Agent Productivity Measurement in Call Center Using Machine Learning</a></div><div class="wp-workCard_item"><span>Advances in Intelligent Systems and Computing</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We present an application of sentiment analysis using natural language toolkit (NLTK) for measuri...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We present an application of sentiment analysis using natural language toolkit (NLTK) for measuring customer service representative (CSR) productivity in real estate call centers. The study describes in details the decisions made, step by step, in building an Arabic system for evaluation and measuring. The system includes transcription method, feature extraction, training process and analysis. The results are analyzed subjectively based on the original test set. The corpus consists of 7 h real estate corpus collected from three different call centers located in Egypt. We draw the baseline of productivity measurement in real estate sector.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="70f5566cfac9ef36e2576569854aa3ca" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":121168563,"asset_id":127436545,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/121168563/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436545"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436545"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436545; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436545]").text(description); $(".js-view-count[data-work-id=127436545]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436545; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436545']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "70f5566cfac9ef36e2576569854aa3ca" } } $('.js-work-strip[data-work-id=127436545]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436545,"title":"Agent Productivity Measurement in Call Center Using Machine Learning","translated_title":"","metadata":{"abstract":"We present an application of sentiment analysis using natural language toolkit (NLTK) for measuring customer service representative (CSR) productivity in real estate call centers. The study describes in details the decisions made, step by step, in building an Arabic system for evaluation and measuring. The system includes transcription method, feature extraction, training process and analysis. The results are analyzed subjectively based on the original test set. The corpus consists of 7 h real estate corpus collected from three different call centers located in Egypt. We draw the baseline of productivity measurement in real estate sector.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"Advances in Intelligent Systems and Computing"},"translated_abstract":"We present an application of sentiment analysis using natural language toolkit (NLTK) for measuring customer service representative (CSR) productivity in real estate call centers. The study describes in details the decisions made, step by step, in building an Arabic system for evaluation and measuring. The system includes transcription method, feature extraction, training process and analysis. The results are analyzed subjectively based on the original test set. The corpus consists of 7 h real estate corpus collected from three different call centers located in Egypt. We draw the baseline of productivity measurement in real estate sector.","internal_url":"https://www.academia.edu/127436545/Agent_Productivity_Measurement_in_Call_Center_Using_Machine_Learning","translated_internal_url":"","created_at":"2025-02-03T07:31:18.778-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":121168563,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168563/thumbnails/1.jpg","file_name":"Arabic_call_center.pdf","download_url":"https://www.academia.edu/attachments/121168563/download_file","bulk_download_file_name":"Agent_Productivity_Measurement_in_Call_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168563/Arabic_call_center-libre.pdf?1738598717=\u0026response-content-disposition=attachment%3B+filename%3DAgent_Productivity_Measurement_in_Call_C.pdf\u0026Expires=1742123710\u0026Signature=YWG2xqBXGfELzUL-B1Lgpx4yNPof~2SX9e1ELd-kF50YNJ3WYAXo9WZmibPimTMNHB60Ks4mJC~TY6jX~UztAzgaOiDSR6nHjLeuauy7RGzLeh6ItDqvD06RdD8RzqCbdf-cwT0UaU6Rw3m8NfTRinq6GfECNBI~GNuLA7IEo1Y9br4BH-2UohzGxKYUVTa5hGtl6zsaMPBXPqgBVzvKKeYNb-X27rgSym8DNSBAr7p5okXRBUvg6Qwqxvo~e2aczWnf7muf2uNznGXn7qzXnKmiQyy19aoPNA9ZdkzdTtTl90m39mtm3BRKLiggDwfSviKGnIiju-v4eu0urBHKzg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Agent_Productivity_Measurement_in_Call_Center_Using_Machine_Learning","translated_slug":"","page_count":10,"language":"en","content_type":"Work","summary":"We present an application of sentiment analysis using natural language toolkit (NLTK) for measuring customer service representative (CSR) productivity in real estate call centers. The study describes in details the decisions made, step by step, in building an Arabic system for evaluation and measuring. The system includes transcription method, feature extraction, training process and analysis. The results are analyzed subjectively based on the original test set. The corpus consists of 7 h real estate corpus collected from three different call centers located in Egypt. We draw the baseline of productivity measurement in real estate sector.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[{"id":121168563,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168563/thumbnails/1.jpg","file_name":"Arabic_call_center.pdf","download_url":"https://www.academia.edu/attachments/121168563/download_file","bulk_download_file_name":"Agent_Productivity_Measurement_in_Call_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168563/Arabic_call_center-libre.pdf?1738598717=\u0026response-content-disposition=attachment%3B+filename%3DAgent_Productivity_Measurement_in_Call_C.pdf\u0026Expires=1742123710\u0026Signature=YWG2xqBXGfELzUL-B1Lgpx4yNPof~2SX9e1ELd-kF50YNJ3WYAXo9WZmibPimTMNHB60Ks4mJC~TY6jX~UztAzgaOiDSR6nHjLeuauy7RGzLeh6ItDqvD06RdD8RzqCbdf-cwT0UaU6Rw3m8NfTRinq6GfECNBI~GNuLA7IEo1Y9br4BH-2UohzGxKYUVTa5hGtl6zsaMPBXPqgBVzvKKeYNb-X27rgSym8DNSBAr7p5okXRBUvg6Qwqxvo~e2aczWnf7muf2uNznGXn7qzXnKmiQyy19aoPNA9ZdkzdTtTl90m39mtm3BRKLiggDwfSviKGnIiju-v4eu0urBHKzg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":8968,"name":"Productivity","url":"https://www.academia.edu/Documents/in/Productivity"},{"id":11984,"name":"Speech Recognition","url":"https://www.academia.edu/Documents/in/Speech_Recognition"},{"id":240981,"name":"Call Centers","url":"https://www.academia.edu/Documents/in/Call_Centers"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436544"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436544/Speculative_Work_in_Neural_Network_Forecasting_An_application_to_Egyptian_Cotton_Production"><img alt="Research paper thumbnail of Speculative Work in Neural Network Forecasting: An application to Egyptian Cotton Production" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436544/Speculative_Work_in_Neural_Network_Forecasting_An_application_to_Egyptian_Cotton_Production">Speculative Work in Neural Network Forecasting: An application to Egyptian Cotton Production</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436544"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436544"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436544; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436544]").text(description); $(".js-view-count[data-work-id=127436544]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436544; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436544']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436544]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436544,"title":"Speculative Work in Neural Network Forecasting: An application to Egyptian Cotton Production","translated_title":"","metadata":{"publication_date":{"day":null,"month":null,"year":1999,"errors":{}}},"translated_abstract":null,"internal_url":"https://www.academia.edu/127436544/Speculative_Work_in_Neural_Network_Forecasting_An_application_to_Egyptian_Cotton_Production","translated_internal_url":"","created_at":"2025-02-03T07:31:18.648-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Speculative_Work_in_Neural_Network_Forecasting_An_application_to_Egyptian_Cotton_Production","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":null,"owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":7968,"name":"Prediction","url":"https://www.academia.edu/Documents/in/Prediction"},{"id":54123,"name":"Artificial Neural Networks","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Networks"},{"id":61714,"name":"Production economics","url":"https://www.academia.edu/Documents/in/Production_economics"},{"id":112560,"name":"Effect of Drought on Cotton Production","url":"https://www.academia.edu/Documents/in/Effect_of_Drought_on_Cotton_Production"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436448"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/127436448/Enhancing_Student_Services_Machine_Learning_Chatbot_Intent_Recognition_for_High_School_Inquiries"><img alt="Research paper thumbnail of Enhancing Student Services: Machine Learning Chatbot Intent Recognition for High School Inquiries" class="work-thumbnail" src="https://attachments.academia-assets.com/121168447/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/127436448/Enhancing_Student_Services_Machine_Learning_Chatbot_Intent_Recognition_for_High_School_Inquiries">Enhancing Student Services: Machine Learning Chatbot Intent Recognition for High School Inquiries</a></div><div class="wp-workCard_item"><span>Lecture notes in civil engineering</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose -This paper aims to develop a novel chatbot to improve student services in high school by...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose -This paper aims to develop a novel chatbot to improve student services in high school by transferring students' enquiries to a particular agent, based on the enquiry type. In accordance to that, comparison between machine learning and neural network is conducted in order to identify the most accurate model to classify students' requests. Methodology -In this study we selected the data from high school students, since high school is one of the most essential stages in students' lives, as in this stage, students have the option to select their academic streams and advanced courses that can shape their careers according to their passions and interests. A new corpus is created with (1004) enquiries. The data is annotated manually based on the type of request. The label high-school-courses is assigned to the requests that are related to elective courses and standardized tests during high school. On the other hand, the label majors & universities is assigned to the questions that are related to applying to universities along with selecting the majors. Two novel classifier chatbots are developed and evaluated, where the first chatbot is developed by using a Naive Bayes Machine Learning Algorithm, while the other is developed by using Recurrent Neural Networks (RNN)-LSTM. Findings -Some features and techniques are used in both models in order to improve the performance. However, both models have conveyed a high accuracy score which exceeds (91%). The models have been validated as a pilot testing by using high school students as well as experts in education and six questions and enquiries are presented to the chatbots for the evaluation. Implications and future work -This study can add value to the team of researchers and developers to integrate such classifiers into different applications. As a result, this improves the users' services, in particular, those implemented in educational institutions. In the future, it is certain that intent recognition will be developed with the addition of a voice recognition feature which can successfully integrated into smartphones.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7d394b5a7d321e6a8b44fbd73ea5da09" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":121168447,"asset_id":127436448,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/121168447/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436448"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436448"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436448; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436448]").text(description); $(".js-view-count[data-work-id=127436448]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436448; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436448']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "7d394b5a7d321e6a8b44fbd73ea5da09" } } $('.js-work-strip[data-work-id=127436448]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436448,"title":"Enhancing Student Services: Machine Learning Chatbot Intent Recognition for High School Inquiries","translated_title":"","metadata":{"grobid_abstract":"Purpose -This paper aims to develop a novel chatbot to improve student services in high school by transferring students' enquiries to a particular agent, based on the enquiry type. In accordance to that, comparison between machine learning and neural network is conducted in order to identify the most accurate model to classify students' requests. Methodology -In this study we selected the data from high school students, since high school is one of the most essential stages in students' lives, as in this stage, students have the option to select their academic streams and advanced courses that can shape their careers according to their passions and interests. A new corpus is created with (1004) enquiries. The data is annotated manually based on the type of request. The label high-school-courses is assigned to the requests that are related to elective courses and standardized tests during high school. On the other hand, the label majors \u0026 universities is assigned to the questions that are related to applying to universities along with selecting the majors. Two novel classifier chatbots are developed and evaluated, where the first chatbot is developed by using a Naive Bayes Machine Learning Algorithm, while the other is developed by using Recurrent Neural Networks (RNN)-LSTM. Findings -Some features and techniques are used in both models in order to improve the performance. However, both models have conveyed a high accuracy score which exceeds (91%). The models have been validated as a pilot testing by using high school students as well as experts in education and six questions and enquiries are presented to the chatbots for the evaluation. Implications and future work -This study can add value to the team of researchers and developers to integrate such classifiers into different applications. As a result, this improves the users' services, in particular, those implemented in educational institutions. In the future, it is certain that intent recognition will be developed with the addition of a voice recognition feature which can successfully integrated into smartphones.","publication_date":{"day":null,"month":null,"year":2024,"errors":{}},"publication_name":"Lecture notes in civil engineering","grobid_abstract_attachment_id":121168447},"translated_abstract":null,"internal_url":"https://www.academia.edu/127436448/Enhancing_Student_Services_Machine_Learning_Chatbot_Intent_Recognition_for_High_School_Inquiries","translated_internal_url":"","created_at":"2025-02-03T07:24:08.657-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":121168447,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168447/thumbnails/1.jpg","file_name":"978-3-031-56121-4_24.pdf","download_url":"https://www.academia.edu/attachments/121168447/download_file","bulk_download_file_name":"Enhancing_Student_Services_Machine_Learn.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168447/978-3-031-56121-4_24-libre.pdf?1738598723=\u0026response-content-disposition=attachment%3B+filename%3DEnhancing_Student_Services_Machine_Learn.pdf\u0026Expires=1742097475\u0026Signature=V7jt2ndK1satW2iuDF0-5gRIdGAd0sO5S3CEY6IZ1aUyqzFzaUc~iPYq-DIX3lMsSE43LPZ0HZzB-su5UHcv5Ri8dYr7ioI6ucr2SH~ncMG8akcwxTxxwdSTS9Djzyn8nnQJEeHtKLYXmqAEX7q46xYj-FQqX-K6vBUoy~vH2LqTJwzr65q~uUyh~xBxVd~Tqh2HSPwZ6Se4RiDLRXnKatRDllH0NyT3j6WAOjrJNhsYAesBqT~g2Nn15DEm7svLPp31O9QPT7rK-zR-77UaDS0qtCZJjNJ9bZ97qkALvWDSyXh~hoBU5Mx6uxMa6OJLEl2LZczyDd11k~ixDT3v~g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Enhancing_Student_Services_Machine_Learning_Chatbot_Intent_Recognition_for_High_School_Inquiries","translated_slug":"","page_count":12,"language":"en","content_type":"Work","summary":"Purpose -This paper aims to develop a novel chatbot to improve student services in high school by transferring students' enquiries to a particular agent, based on the enquiry type. In accordance to that, comparison between machine learning and neural network is conducted in order to identify the most accurate model to classify students' requests. Methodology -In this study we selected the data from high school students, since high school is one of the most essential stages in students' lives, as in this stage, students have the option to select their academic streams and advanced courses that can shape their careers according to their passions and interests. A new corpus is created with (1004) enquiries. The data is annotated manually based on the type of request. The label high-school-courses is assigned to the requests that are related to elective courses and standardized tests during high school. On the other hand, the label majors \u0026 universities is assigned to the questions that are related to applying to universities along with selecting the majors. Two novel classifier chatbots are developed and evaluated, where the first chatbot is developed by using a Naive Bayes Machine Learning Algorithm, while the other is developed by using Recurrent Neural Networks (RNN)-LSTM. Findings -Some features and techniques are used in both models in order to improve the performance. However, both models have conveyed a high accuracy score which exceeds (91%). The models have been validated as a pilot testing by using high school students as well as experts in education and six questions and enquiries are presented to the chatbots for the evaluation. Implications and future work -This study can add value to the team of researchers and developers to integrate such classifiers into different applications. As a result, this improves the users' services, in particular, those implemented in educational institutions. In the future, it is certain that intent recognition will be developed with the addition of a voice recognition feature which can successfully integrated into smartphones.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[{"id":121168447,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168447/thumbnails/1.jpg","file_name":"978-3-031-56121-4_24.pdf","download_url":"https://www.academia.edu/attachments/121168447/download_file","bulk_download_file_name":"Enhancing_Student_Services_Machine_Learn.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168447/978-3-031-56121-4_24-libre.pdf?1738598723=\u0026response-content-disposition=attachment%3B+filename%3DEnhancing_Student_Services_Machine_Learn.pdf\u0026Expires=1742097475\u0026Signature=V7jt2ndK1satW2iuDF0-5gRIdGAd0sO5S3CEY6IZ1aUyqzFzaUc~iPYq-DIX3lMsSE43LPZ0HZzB-su5UHcv5Ri8dYr7ioI6ucr2SH~ncMG8akcwxTxxwdSTS9Djzyn8nnQJEeHtKLYXmqAEX7q46xYj-FQqX-K6vBUoy~vH2LqTJwzr65q~uUyh~xBxVd~Tqh2HSPwZ6Se4RiDLRXnKatRDllH0NyT3j6WAOjrJNhsYAesBqT~g2Nn15DEm7svLPp31O9QPT7rK-zR-77UaDS0qtCZJjNJ9bZ97qkALvWDSyXh~hoBU5Mx6uxMa6OJLEl2LZczyDd11k~ixDT3v~g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":121168446,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168446/thumbnails/1.jpg","file_name":"978-3-031-56121-4_24.pdf","download_url":"https://www.academia.edu/attachments/121168446/download_file","bulk_download_file_name":"Enhancing_Student_Services_Machine_Learn.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168446/978-3-031-56121-4_24-libre.pdf?1738598723=\u0026response-content-disposition=attachment%3B+filename%3DEnhancing_Student_Services_Machine_Learn.pdf\u0026Expires=1742097475\u0026Signature=Zjkg4maRyir8pvM61s0-fmDhkwAefdkazBsuiHs4x0NSckVv6JBIrk4wlkhwSWoNgX9oB~IW2cRfqEuoXDXwCpoXDwNxRxJnh3eGLu8roHOoeIwD0zIdjoWLT8lFKPWEF5I7A4gu8ePkRBdFV~yqVD7OLR9gLgoaJnwAnXoVibgg0fD5W4bcfmoID4ts2UzS3OwWFLmWKIxZzth6~XcbeocwUb4gzL08LQbgcDGd3~fZlf~qBGBilQOpHuaNbsoB7Kpkom7Qv~~TrR45Ff7CaU3mHEhew4KLB3LgxNoPe0mlej7smP60Z0nPVWh8JLEyY1-EDpO7jtEaTTIgX1mzjA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":81182,"name":"Deep Learning","url":"https://www.academia.edu/Documents/in/Deep_Learning"},{"id":110971,"name":"Chatbots","url":"https://www.academia.edu/Documents/in/Chatbots"},{"id":246160,"name":"Chatbot","url":"https://www.academia.edu/Documents/in/Chatbot"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"}],"urls":[{"id":46723355,"url":"https://link.springer.com/content/pdf/10.1007/978-3-031-56121-4_24.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436447"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/127436447/Nizar_Y_Habash_Introduction_to_Arabic_natural_language_processing_Synthesis_lectures_on_human_language_technologies_"><img alt="Research paper thumbnail of Nizar Y. Habash, Introduction to Arabic natural language processing (Synthesis lectures on human language technologies)" class="work-thumbnail" src="https://attachments.academia-assets.com/121168469/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/127436447/Nizar_Y_Habash_Introduction_to_Arabic_natural_language_processing_Synthesis_lectures_on_human_language_technologies_">Nizar Y. Habash, Introduction to Arabic natural language processing (Synthesis lectures on human language technologies)</a></div><div class="wp-workCard_item"><span>Machine Translation</span><span>, Dec 1, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Arabic is rich in morphology and syntax. It is normally written with optional diacritics and with...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Arabic is rich in morphology and syntax. It is normally written with optional diacritics and without the notion of capitalization. These characteristics make dealing with Arabic a challenge for both learners and researchers. My experience in the Arabic natural language processing (ANLP) research area allows me to say that the key to fostering a research or developing an application in the ANLP field lies in getting insights into the standard layer-based structure of linguistic phenomena (phonology, morphology, syntax and semantics) as well as in recognizing the interaction between them. Until now, there was no such an available introductory resource that can fulfill these requirements. For example, a simple Google search of the term "Arabic Natural Language Processing" results in research groups, research papers, tutorials and presentations, companies, and scholars. Consequently, tangible efforts had to be spent by any beginner of the ANLP field, whether a scientist, linguist, developer, or student, in going through different material which might be either irrelevant or too advanced. Therefore, the purpose and the significance of this book are clear from where it stands. Also, it is adequately classified by the publisher as belonging to the "human language technologies" series. This book gives a sufficient solid introductory background on ANLP. This makes it the first of its kind. The author has a broad background in computational linguistics, in general, and ANLP, in particular. He also has a marvelous research track record and has been very active in serving the research communities. The book is clear about its intended audience. It is the most suitable for anyone who would like to get a fundamental background about ANLP for research, study, or development purposes. It is very well written. This book amazingly takes you gradually from the ground</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ef3372372564371556dd577a543f3e8f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":121168469,"asset_id":127436447,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/121168469/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436447"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436447"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436447; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436447]").text(description); $(".js-view-count[data-work-id=127436447]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436447; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436447']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ef3372372564371556dd577a543f3e8f" } } $('.js-work-strip[data-work-id=127436447]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436447,"title":"Nizar Y. Habash, Introduction to Arabic natural language processing (Synthesis lectures on human language technologies)","translated_title":"","metadata":{"publisher":"Springer Science+Business Media","grobid_abstract":"Arabic is rich in morphology and syntax. It is normally written with optional diacritics and without the notion of capitalization. These characteristics make dealing with Arabic a challenge for both learners and researchers. My experience in the Arabic natural language processing (ANLP) research area allows me to say that the key to fostering a research or developing an application in the ANLP field lies in getting insights into the standard layer-based structure of linguistic phenomena (phonology, morphology, syntax and semantics) as well as in recognizing the interaction between them. Until now, there was no such an available introductory resource that can fulfill these requirements. For example, a simple Google search of the term \"Arabic Natural Language Processing\" results in research groups, research papers, tutorials and presentations, companies, and scholars. Consequently, tangible efforts had to be spent by any beginner of the ANLP field, whether a scientist, linguist, developer, or student, in going through different material which might be either irrelevant or too advanced. Therefore, the purpose and the significance of this book are clear from where it stands. Also, it is adequately classified by the publisher as belonging to the \"human language technologies\" series. This book gives a sufficient solid introductory background on ANLP. This makes it the first of its kind. The author has a broad background in computational linguistics, in general, and ANLP, in particular. He also has a marvelous research track record and has been very active in serving the research communities. The book is clear about its intended audience. It is the most suitable for anyone who would like to get a fundamental background about ANLP for research, study, or development purposes. It is very well written. This book amazingly takes you gradually from the ground","publication_date":{"day":1,"month":12,"year":2010,"errors":{}},"publication_name":"Machine Translation","grobid_abstract_attachment_id":121168469},"translated_abstract":null,"internal_url":"https://www.academia.edu/127436447/Nizar_Y_Habash_Introduction_to_Arabic_natural_language_processing_Synthesis_lectures_on_human_language_technologies_","translated_internal_url":"","created_at":"2025-02-03T07:24:08.477-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":121168469,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168469/thumbnails/1.jpg","file_name":"s10590-011-9087-820250203-1-r82yt9.pdf","download_url":"https://www.academia.edu/attachments/121168469/download_file","bulk_download_file_name":"Nizar_Y_Habash_Introduction_to_Arabic_na.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168469/s10590-011-9087-820250203-1-r82yt9-libre.pdf?1738598718=\u0026response-content-disposition=attachment%3B+filename%3DNizar_Y_Habash_Introduction_to_Arabic_na.pdf\u0026Expires=1742123710\u0026Signature=OfCbmCc1Q14xF0J0CtED5iCujIYAwiVJbs18gaQ2Ow7-WFx99mbBUhEm0weV8~kbC00vuguB-a5J59nFkpExb4dJtaqOsvyX2YrDXgVLko0D1iPsbFnfLxtIvqjgFVGHPoEylSFdeJrB1uNgrpAn9SYRslQ-jQuQhAZ1JP2nyg8QP7o3f-ehAsx3cgZrjcwIl4qH7s42p2omfEXTsUbInk8icXNpIpcAp9u2Yulxx0JmAai1CuZUOEu6wMSlF7CTEt8d1HBtOL6i-iIIYzZ~UqYpoG54adpSlBYb6LxmBPeH9~Pd54I5PIaA7hDKOdWacGaEpO8b65YMZkoZ-KHMXQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Nizar_Y_Habash_Introduction_to_Arabic_natural_language_processing_Synthesis_lectures_on_human_language_technologies_","translated_slug":"","page_count":5,"language":"en","content_type":"Work","summary":"Arabic is rich in morphology and syntax. It is normally written with optional diacritics and without the notion of capitalization. These characteristics make dealing with Arabic a challenge for both learners and researchers. My experience in the Arabic natural language processing (ANLP) research area allows me to say that the key to fostering a research or developing an application in the ANLP field lies in getting insights into the standard layer-based structure of linguistic phenomena (phonology, morphology, syntax and semantics) as well as in recognizing the interaction between them. Until now, there was no such an available introductory resource that can fulfill these requirements. For example, a simple Google search of the term \"Arabic Natural Language Processing\" results in research groups, research papers, tutorials and presentations, companies, and scholars. Consequently, tangible efforts had to be spent by any beginner of the ANLP field, whether a scientist, linguist, developer, or student, in going through different material which might be either irrelevant or too advanced. Therefore, the purpose and the significance of this book are clear from where it stands. Also, it is adequately classified by the publisher as belonging to the \"human language technologies\" series. This book gives a sufficient solid introductory background on ANLP. This makes it the first of its kind. The author has a broad background in computational linguistics, in general, and ANLP, in particular. He also has a marvelous research track record and has been very active in serving the research communities. The book is clear about its intended audience. It is the most suitable for anyone who would like to get a fundamental background about ANLP for research, study, or development purposes. It is very well written. This book amazingly takes you gradually from the ground","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[{"id":121168469,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168469/thumbnails/1.jpg","file_name":"s10590-011-9087-820250203-1-r82yt9.pdf","download_url":"https://www.academia.edu/attachments/121168469/download_file","bulk_download_file_name":"Nizar_Y_Habash_Introduction_to_Arabic_na.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168469/s10590-011-9087-820250203-1-r82yt9-libre.pdf?1738598718=\u0026response-content-disposition=attachment%3B+filename%3DNizar_Y_Habash_Introduction_to_Arabic_na.pdf\u0026Expires=1742123710\u0026Signature=OfCbmCc1Q14xF0J0CtED5iCujIYAwiVJbs18gaQ2Ow7-WFx99mbBUhEm0weV8~kbC00vuguB-a5J59nFkpExb4dJtaqOsvyX2YrDXgVLko0D1iPsbFnfLxtIvqjgFVGHPoEylSFdeJrB1uNgrpAn9SYRslQ-jQuQhAZ1JP2nyg8QP7o3f-ehAsx3cgZrjcwIl4qH7s42p2omfEXTsUbInk8icXNpIpcAp9u2Yulxx0JmAai1CuZUOEu6wMSlF7CTEt8d1HBtOL6i-iIIYzZ~UqYpoG54adpSlBYb6LxmBPeH9~Pd54I5PIaA7hDKOdWacGaEpO8b65YMZkoZ-KHMXQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"},{"id":3268,"name":"Computational Linguistics","url":"https://www.academia.edu/Documents/in/Computational_Linguistics"},{"id":3324,"name":"Arabic","url":"https://www.academia.edu/Documents/in/Arabic"},{"id":4696,"name":"Machine Translation","url":"https://www.academia.edu/Documents/in/Machine_Translation"},{"id":52672,"name":"Language Technology","url":"https://www.academia.edu/Documents/in/Language_Technology"},{"id":2070034,"name":"Data Format","url":"https://www.academia.edu/Documents/in/Data_Format"},{"id":3729564,"name":"Human language","url":"https://www.academia.edu/Documents/in/Human_language"}],"urls":[{"id":46723354,"url":"https://doi.org/10.1007/s10590-011-9087-8"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436446"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436446/A_Systematic_Review_of_Knowledge_Management_Integration_in_Higher_Educational_Institution_with_an_Emphasis_on_a_Blended_Learning_Environment"><img alt="Research paper thumbnail of A Systematic Review of Knowledge Management Integration in Higher Educational Institution with an Emphasis on a Blended Learning Environment" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436446/A_Systematic_Review_of_Knowledge_Management_Integration_in_Higher_Educational_Institution_with_an_Emphasis_on_a_Blended_Learning_Environment">A Systematic Review of Knowledge Management Integration in Higher Educational Institution with an Emphasis on a Blended Learning Environment</a></div><div class="wp-workCard_item"><span>Lecture notes in networks and systems</span><span>, Oct 23, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436446"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436446"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436446; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436446]").text(description); $(".js-view-count[data-work-id=127436446]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436446; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436446']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436446]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436446,"title":"A Systematic Review of Knowledge Management Integration in Higher Educational Institution with an Emphasis on a Blended Learning Environment","translated_title":"","metadata":{"publisher":"Springer International Publishing","publication_date":{"day":23,"month":10,"year":2022,"errors":{}},"publication_name":"Lecture notes in networks and systems"},"translated_abstract":null,"internal_url":"https://www.academia.edu/127436446/A_Systematic_Review_of_Knowledge_Management_Integration_in_Higher_Educational_Institution_with_an_Emphasis_on_a_Blended_Learning_Environment","translated_internal_url":"","created_at":"2025-02-03T07:24:08.291-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_Systematic_Review_of_Knowledge_Management_Integration_in_Higher_Educational_Institution_with_an_Emphasis_on_a_Blended_Learning_Environment","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":null,"owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":1241,"name":"Knowledge Management","url":"https://www.academia.edu/Documents/in/Knowledge_Management"},{"id":172331,"name":"Learning management","url":"https://www.academia.edu/Documents/in/Learning_management"}],"urls":[{"id":46723353,"url":"https://doi.org/10.1007/978-3-031-16865-9_25"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="2929639" id="papers"><div class="js-work-strip profile--work_container" data-work-id="127870015"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/127870015/Transformer_Models_in_Natural_Language_Processing_A_Comprehensive_Review_and_Prospects_for_Future_Development"><img alt="Research paper thumbnail of Transformer Models in Natural Language Processing: A Comprehensive Review and Prospects for Future Development" class="work-thumbnail" src="https://attachments.academia-assets.com/121539070/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/127870015/Transformer_Models_in_Natural_Language_Processing_A_Comprehensive_Review_and_Prospects_for_Future_Development">Transformer Models in Natural Language Processing: A Comprehensive Review and Prospects for Future Development</a></div><div class="wp-workCard_item"><span>Proceedings of the 11th International Conference on Advanced Intelligent Systems and Informatics</span><span>, 2025</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Transformer-based pre-trained language models are advanced machine learning models that understan...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Transformer-based pre-trained language models are advanced machine learning models that understand and produce human language. These models are mainly based on the "Transformer" design. They have undergone substantial pre-training on large volumes of text data to understand language patterns. Notable examples include BERT, GPT, and RoBERTa. These tools have transformed NLP tasks by demonstrating exceptional performance and adaptability, facilitating knowledge transfer to specialized tasks, and addressing issues associated with training a model from the start. This systematic review examines transformer-based pre-trained language models, including architecture, pretraining techniques, and adaption approaches. This study examines the core concepts, training methods, and applications of these models to answer significant research concerns. This study examines transformer-based pre-trained models in NLP and their fine-tuning methodologies. This review sheds light on the current state of transformer-based language models and outlines potential future advances in this dynamic subject.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="10f96db4ce59304dc02500b1572ca954" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":121539070,"asset_id":127870015,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/121539070/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127870015"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127870015"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127870015; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127870015]").text(description); $(".js-view-count[data-work-id=127870015]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127870015; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127870015']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "10f96db4ce59304dc02500b1572ca954" } } $('.js-work-strip[data-work-id=127870015]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127870015,"title":"Transformer Models in Natural Language Processing: A Comprehensive Review and Prospects for Future Development","translated_title":"","metadata":{"abstract":"Transformer-based pre-trained language models are advanced machine learning models that understand and produce human language. These models are mainly based on the \"Transformer\" design. They have undergone substantial pre-training on large volumes of text data to understand language patterns. Notable examples include BERT, GPT, and RoBERTa. These tools have transformed NLP tasks by demonstrating exceptional performance and adaptability, facilitating knowledge transfer to specialized tasks, and addressing issues associated with training a model from the start. This systematic review examines transformer-based pre-trained language models, including architecture, pretraining techniques, and adaption approaches. This study examines the core concepts, training methods, and applications of these models to answer significant research concerns. This study examines transformer-based pre-trained models in NLP and their fine-tuning methodologies. This review sheds light on the current state of transformer-based language models and outlines potential future advances in this dynamic subject.","grobid_abstract":"Transformer-based pre-trained language models are advanced machine learning models that understand and produce human language. These models are mainly based on the \"Transformer\" design. They have undergone substantial pre-training on large volumes of text data to understand language patterns. Notable examples include BERT, GPT, and RoBERTa. These tools have transformed NLP tasks by demonstrating exceptional performance and adaptability, facilitating knowledge transfer to specialized tasks, and addressing issues associated with training a model from the start. This systematic review examines transformer-based pre-trained language models, including architecture, pretraining techniques, and adaption approaches. This study examines the core concepts, training methods, and applications of these models to answer significant research concerns. This study examines transformer-based pre-trained models in NLP and their fine-tuning methodologies. This review sheds light on the current state of transformer-based language models and outlines potential future advances in this dynamic subject.","publication_date":{"day":null,"month":null,"year":2025,"errors":{}},"publication_name":"Proceedings of the 11th International Conference on Advanced Intelligent Systems and Informatics","grobid_abstract_attachment_id":121539070},"translated_abstract":"Transformer-based pre-trained language models are advanced machine learning models that understand and produce human language. These models are mainly based on the \"Transformer\" design. They have undergone substantial pre-training on large volumes of text data to understand language patterns. Notable examples include BERT, GPT, and RoBERTa. These tools have transformed NLP tasks by demonstrating exceptional performance and adaptability, facilitating knowledge transfer to specialized tasks, and addressing issues associated with training a model from the start. This systematic review examines transformer-based pre-trained language models, including architecture, pretraining techniques, and adaption approaches. This study examines the core concepts, training methods, and applications of these models to answer significant research concerns. This study examines transformer-based pre-trained models in NLP and their fine-tuning methodologies. This review sheds light on the current state of transformer-based language models and outlines potential future advances in this dynamic subject.","internal_url":"https://www.academia.edu/127870015/Transformer_Models_in_Natural_Language_Processing_A_Comprehensive_Review_and_Prospects_for_Future_Development","translated_internal_url":"","created_at":"2025-02-25T07:04:08.368-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":43180709,"work_id":127870015,"tagging_user_id":30994194,"tagged_user_id":47860458,"co_author_invite_id":null,"email":"a***m@hotmail.com","display_order":1,"name":"Azza Mohamed","title":"Transformer Models in Natural Language Processing: A Comprehensive Review and Prospects for Future Development"},{"id":43180710,"work_id":127870015,"tagging_user_id":30994194,"tagged_user_id":null,"co_author_invite_id":227015,"email":"k***n@buid.ac.ae","display_order":2,"name":"Khaled Shaalan","title":"Transformer Models in Natural Language Processing: A Comprehensive Review and Prospects for Future Development"}],"downloadable_attachments":[{"id":121539070,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121539070/thumbnails/1.jpg","file_name":"AISI2025.pdf","download_url":"https://www.academia.edu/attachments/121539070/download_file","bulk_download_file_name":"Transformer_Models_in_Natural_Language_P.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121539070/AISI2025-libre.pdf?1740499771=\u0026response-content-disposition=attachment%3B+filename%3DTransformer_Models_in_Natural_Language_P.pdf\u0026Expires=1742123709\u0026Signature=cQAWT6YxMru5o0PVwXHQS8BjYA6cuXSdV3N~pQp-~X-W3tOOuQR1vmaUgh790AefAA0V7juub-TRezkaCZyv~Zb~u7wPFl6Xq7F6cHgCx7I6SX~2pKRM-07lVJhLesQpMoSJYuzOsocGwlMkEwDtd4GgpP92ji7g0dDURhVn4DLn5rnmbnajTpU9L4HvBmZegjjNiDUWZMr64MMlzLWy23lmtzQGLwczvSPI4TPpcB7VIxNIHR-6UVHyYy8wYfQ~ypX~2s8rKGKKJe3sG5yn01kP61afj1wF31K24fbmQvimuFsCQGJ2zXRvFfuPRsXABRmlNbbZYrMvXPW0a8ZpfA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Transformer_Models_in_Natural_Language_Processing_A_Comprehensive_Review_and_Prospects_for_Future_Development","translated_slug":"","page_count":10,"language":"en","content_type":"Work","summary":"Transformer-based pre-trained language models are advanced machine learning models that understand and produce human language. These models are mainly based on the \"Transformer\" design. They have undergone substantial pre-training on large volumes of text data to understand language patterns. Notable examples include BERT, GPT, and RoBERTa. These tools have transformed NLP tasks by demonstrating exceptional performance and adaptability, facilitating knowledge transfer to specialized tasks, and addressing issues associated with training a model from the start. This systematic review examines transformer-based pre-trained language models, including architecture, pretraining techniques, and adaption approaches. This study examines the core concepts, training methods, and applications of these models to answer significant research concerns. This study examines transformer-based pre-trained models in NLP and their fine-tuning methodologies. This review sheds light on the current state of transformer-based language models and outlines potential future advances in this dynamic subject.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[{"id":121539070,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121539070/thumbnails/1.jpg","file_name":"AISI2025.pdf","download_url":"https://www.academia.edu/attachments/121539070/download_file","bulk_download_file_name":"Transformer_Models_in_Natural_Language_P.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121539070/AISI2025-libre.pdf?1740499771=\u0026response-content-disposition=attachment%3B+filename%3DTransformer_Models_in_Natural_Language_P.pdf\u0026Expires=1742123709\u0026Signature=cQAWT6YxMru5o0PVwXHQS8BjYA6cuXSdV3N~pQp-~X-W3tOOuQR1vmaUgh790AefAA0V7juub-TRezkaCZyv~Zb~u7wPFl6Xq7F6cHgCx7I6SX~2pKRM-07lVJhLesQpMoSJYuzOsocGwlMkEwDtd4GgpP92ji7g0dDURhVn4DLn5rnmbnajTpU9L4HvBmZegjjNiDUWZMr64MMlzLWy23lmtzQGLwczvSPI4TPpcB7VIxNIHR-6UVHyYy8wYfQ~ypX~2s8rKGKKJe3sG5yn01kP61afj1wF31K24fbmQvimuFsCQGJ2zXRvFfuPRsXABRmlNbbZYrMvXPW0a8ZpfA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"},{"id":44293,"name":"Literature Review","url":"https://www.academia.edu/Documents/in/Literature_Review"},{"id":4288695,"name":"Transformer-based models","url":"https://www.academia.edu/Documents/in/Transformer_based_models"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127571430"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/127571430/Human_vs_Machine_A_Comparative_Study_on_the_Detection_of_AI_Generated_Content"><img alt="Research paper thumbnail of Human vs. Machine: A Comparative Study on the Detection of AI-Generated Content" class="work-thumbnail" src="https://attachments.academia-assets.com/121285703/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/127571430/Human_vs_Machine_A_Comparative_Study_on_the_Detection_of_AI_Generated_Content">Human vs. Machine: A Comparative Study on the Detection of AI-Generated Content</a></div><div class="wp-workCard_item"><span>ACM Transactions on Asian and Low-Resource Language Information Processing ‎</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The surge in advancements in large language models (LLMs) has expedited the generation of synthet...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The surge in advancements in large language models (LLMs) has expedited the generation of synthetic text imitating human writing styles. This, however, raises concerns about the potential misuse of synthetic textual data, which could compromise trust in online content. Against this backdrop, the present research aims to address the key challenges of detecting LLMs-generated texts. In this study, we used ChatGPT (v 3.5) because of its widespread and capability to comprehend and keep conversational context, allowing it to produce meaningful and contextually suitable responses. The problem revolves around the task of discerning between authentic and artificially generated textual content. To tackle this problem, we first created a dataset containing both real and DeepFake text. Subsequently, we employed transfer-learning (TL) and conducted DeepFakedetection utilizing SOTA large pre-trained LLMs. Furthermore, we conducted validation using benchmark datasets comprising unseen data samples to ensure that the model's performance reflects its ability to generalize to new data. Finally, we discussed this study's theoretical contributions, practical implications, limitations and potential avenues for future research, aiming to formulate strategies for identifying and detecting large-generative-models' produced texts. The results were promising, with accuracy ranging from 94% to 99%. The comparison between automatic detection and the human ability to detect DeepFake text revealed a significant gap in the human capacity for its identification, emphasizing an increasing need for sophisticated automated detectors. The investigation into AI-generated content detection holds central importance in the age of LLMs and technology convergence. This study is both timely and adds value to the ongoing discussion regarding the challenges associated with the pertinent theme of "DeepFake text detection", with a special focus on examining the boundaries of human detection. CCS Concepts: • Computing methodologies → Natural language processing ;</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="47c40c8e1dc6dffe61913392f875671c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":121285703,"asset_id":127571430,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/121285703/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127571430"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127571430"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127571430; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127571430]").text(description); $(".js-view-count[data-work-id=127571430]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127571430; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127571430']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "47c40c8e1dc6dffe61913392f875671c" } } $('.js-work-strip[data-work-id=127571430]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127571430,"title":"Human vs. Machine: A Comparative Study on the Detection of AI-Generated Content","translated_title":"","metadata":{"doi":"10.1145/3708889","abstract":"The surge in advancements in large language models (LLMs) has expedited the generation of synthetic text imitating human writing styles. This, however, raises concerns about the potential misuse of synthetic textual data, which could compromise trust in online content. Against this backdrop, the present research aims to address the key challenges of detecting LLMs-generated texts. In this study, we used ChatGPT (v 3.5) because of its widespread and capability to comprehend and keep conversational context, allowing it to produce meaningful and contextually suitable responses. The problem revolves around the task of discerning between authentic and artificially generated textual content. To tackle this problem, we first created a dataset containing both real and DeepFake text. Subsequently, we employed transfer-learning (TL) and conducted DeepFakedetection utilizing SOTA large pre-trained LLMs. Furthermore, we conducted validation using benchmark datasets comprising unseen data samples to ensure that the model's performance reflects its ability to generalize to new data. Finally, we discussed this study's theoretical contributions, practical implications, limitations and potential avenues for future research, aiming to formulate strategies for identifying and detecting large-generative-models' produced texts. The results were promising, with accuracy ranging from 94% to 99%. The comparison between automatic detection and the human ability to detect DeepFake text revealed a significant gap in the human capacity for its identification, emphasizing an increasing need for sophisticated automated detectors. The investigation into AI-generated content detection holds central importance in the age of LLMs and technology convergence. This study is both timely and adds value to the ongoing discussion regarding the challenges associated with the pertinent theme of \"DeepFake text detection\", with a special focus on examining the boundaries of human detection. CCS Concepts: • Computing methodologies → Natural language processing ;","grobid_abstract":"The surge in advancements in large language models (LLMs) has expedited the generation of synthetic text imitating human writing styles. This, however, raises concerns about the potential misuse of synthetic textual data, which could compromise trust in online content. Against this backdrop, the present research aims to address the key challenges of detecting LLMs-generated texts. In this study, we used ChatGPT (v 3.5) because of its widespread and capability to comprehend and keep conversational context, allowing it to produce meaningful and contextually suitable responses. The problem revolves around the task of discerning between authentic and artificially generated textual content. To tackle this problem, we first created a dataset containing both real and DeepFake text. Subsequently, we employed transfer-learning (TL) and conducted DeepFakedetection utilizing SOTA large pre-trained LLMs. Furthermore, we conducted validation using benchmark datasets comprising unseen data samples to ensure that the model's performance reflects its ability to generalize to new data. Finally, we discussed this study's theoretical contributions, practical implications, limitations and potential avenues for future research, aiming to formulate strategies for identifying and detecting large-generative-models' produced texts. The results were promising, with accuracy ranging from 94% to 99%. The comparison between automatic detection and the human ability to detect DeepFake text revealed a significant gap in the human capacity for its identification, emphasizing an increasing need for sophisticated automated detectors. The investigation into AI-generated content detection holds central importance in the age of LLMs and technology convergence. This study is both timely and adds value to the ongoing discussion regarding the challenges associated with the pertinent theme of \"DeepFake text detection\", with a special focus on examining the boundaries of human detection. CCS Concepts: • Computing methodologies → Natural language processing ;","publication_date":{"day":null,"month":null,"year":2024,"errors":{}},"publication_name":"ACM Transactions on Asian and Low-Resource Language Information Processing ‎","grobid_abstract_attachment_id":121285703},"translated_abstract":"The surge in advancements in large language models (LLMs) has expedited the generation of synthetic text imitating human writing styles. This, however, raises concerns about the potential misuse of synthetic textual data, which could compromise trust in online content. Against this backdrop, the present research aims to address the key challenges of detecting LLMs-generated texts. In this study, we used ChatGPT (v 3.5) because of its widespread and capability to comprehend and keep conversational context, allowing it to produce meaningful and contextually suitable responses. The problem revolves around the task of discerning between authentic and artificially generated textual content. To tackle this problem, we first created a dataset containing both real and DeepFake text. Subsequently, we employed transfer-learning (TL) and conducted DeepFakedetection utilizing SOTA large pre-trained LLMs. Furthermore, we conducted validation using benchmark datasets comprising unseen data samples to ensure that the model's performance reflects its ability to generalize to new data. Finally, we discussed this study's theoretical contributions, practical implications, limitations and potential avenues for future research, aiming to formulate strategies for identifying and detecting large-generative-models' produced texts. The results were promising, with accuracy ranging from 94% to 99%. The comparison between automatic detection and the human ability to detect DeepFake text revealed a significant gap in the human capacity for its identification, emphasizing an increasing need for sophisticated automated detectors. The investigation into AI-generated content detection holds central importance in the age of LLMs and technology convergence. This study is both timely and adds value to the ongoing discussion regarding the challenges associated with the pertinent theme of \"DeepFake text detection\", with a special focus on examining the boundaries of human detection. CCS Concepts: • Computing methodologies → Natural language processing ;","internal_url":"https://www.academia.edu/127571430/Human_vs_Machine_A_Comparative_Study_on_the_Detection_of_AI_Generated_Content","translated_internal_url":"","created_at":"2025-02-10T11:25:10.853-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":43104889,"work_id":127571430,"tagging_user_id":30994194,"tagged_user_id":null,"co_author_invite_id":8338852,"email":"b***l@univ-setif.dz","display_order":1,"name":"Amal Boutadjine","title":"Human vs. Machine: A Comparative Study on the Detection of AI-Generated Content"},{"id":43104890,"work_id":127571430,"tagging_user_id":30994194,"tagged_user_id":146715945,"co_author_invite_id":null,"email":"f***g@gmail.com","display_order":2,"name":"Fouzi Harrag","title":"Human vs. Machine: A Comparative Study on the Detection of AI-Generated Content"}],"downloadable_attachments":[{"id":121285703,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121285703/thumbnails/1.jpg","file_name":"3708889.pdf","download_url":"https://www.academia.edu/attachments/121285703/download_file","bulk_download_file_name":"Human_vs_Machine_A_Comparative_Study_on.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121285703/3708889-libre.pdf?1739218884=\u0026response-content-disposition=attachment%3B+filename%3DHuman_vs_Machine_A_Comparative_Study_on.pdf\u0026Expires=1742123709\u0026Signature=FmnaY0DjxVBwvw-SwReDBpRy0emnNMGnVDT4D0FXFf-01wKUi1ZeSFDIGZUPcyydhvan4lXH32BpjdB3KzliFAizNO0TwBP4CnKxZxPq0k68a44GU7Km9oe4gZyFcZcf5Ls7DkXqGuuvTjgtPDIy4Sj9xukQxSbdxKD9nGGf~ELgz8iJxfdsO0Az81L6QTg7KURqoUQcKHRfGgiYYdD-~sjvTBsgoQzYUJE-g2d~gcO4ZOhYTRWCW7RGPRwKBuNk~oiOXlcdmR6OcBAXBC4MvZ4~vXAGrDZtBHEfesiFIMie94SkooND8N2IOPP-Qu~LAyTJKP65IuUwEHxz1GrjoQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Human_vs_Machine_A_Comparative_Study_on_the_Detection_of_AI_Generated_Content","translated_slug":"","page_count":26,"language":"en","content_type":"Work","summary":"The surge in advancements in large language models (LLMs) has expedited the generation of synthetic text imitating human writing styles. This, however, raises concerns about the potential misuse of synthetic textual data, which could compromise trust in online content. Against this backdrop, the present research aims to address the key challenges of detecting LLMs-generated texts. In this study, we used ChatGPT (v 3.5) because of its widespread and capability to comprehend and keep conversational context, allowing it to produce meaningful and contextually suitable responses. The problem revolves around the task of discerning between authentic and artificially generated textual content. To tackle this problem, we first created a dataset containing both real and DeepFake text. Subsequently, we employed transfer-learning (TL) and conducted DeepFakedetection utilizing SOTA large pre-trained LLMs. Furthermore, we conducted validation using benchmark datasets comprising unseen data samples to ensure that the model's performance reflects its ability to generalize to new data. Finally, we discussed this study's theoretical contributions, practical implications, limitations and potential avenues for future research, aiming to formulate strategies for identifying and detecting large-generative-models' produced texts. The results were promising, with accuracy ranging from 94% to 99%. The comparison between automatic detection and the human ability to detect DeepFake text revealed a significant gap in the human capacity for its identification, emphasizing an increasing need for sophisticated automated detectors. The investigation into AI-generated content detection holds central importance in the age of LLMs and technology convergence. This study is both timely and adds value to the ongoing discussion regarding the challenges associated with the pertinent theme of \"DeepFake text detection\", with a special focus on examining the boundaries of human detection. CCS Concepts: • Computing methodologies → Natural language processing ;","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[{"id":121285703,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121285703/thumbnails/1.jpg","file_name":"3708889.pdf","download_url":"https://www.academia.edu/attachments/121285703/download_file","bulk_download_file_name":"Human_vs_Machine_A_Comparative_Study_on.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121285703/3708889-libre.pdf?1739218884=\u0026response-content-disposition=attachment%3B+filename%3DHuman_vs_Machine_A_Comparative_Study_on.pdf\u0026Expires=1742123709\u0026Signature=FmnaY0DjxVBwvw-SwReDBpRy0emnNMGnVDT4D0FXFf-01wKUi1ZeSFDIGZUPcyydhvan4lXH32BpjdB3KzliFAizNO0TwBP4CnKxZxPq0k68a44GU7Km9oe4gZyFcZcf5Ls7DkXqGuuvTjgtPDIy4Sj9xukQxSbdxKD9nGGf~ELgz8iJxfdsO0Az81L6QTg7KURqoUQcKHRfGgiYYdD-~sjvTBsgoQzYUJE-g2d~gcO4ZOhYTRWCW7RGPRwKBuNk~oiOXlcdmR6OcBAXBC4MvZ4~vXAGrDZtBHEfesiFIMie94SkooND8N2IOPP-Qu~LAyTJKP65IuUwEHxz1GrjoQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"},{"id":220007,"name":"Arabic Natural Language Processing","url":"https://www.academia.edu/Documents/in/Arabic_Natural_Language_Processing"},{"id":4183627,"name":"ChatGPT","url":"https://www.academia.edu/Documents/in/ChatGPT"},{"id":4201061,"name":"Generative AI","url":"https://www.academia.edu/Documents/in/Generative_AI"},{"id":4240030,"name":"Large language models","url":"https://www.academia.edu/Documents/in/Large_language_models"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436559"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/127436559/Pre_training_on_Multi_modal_for_Improved_Persona_Detection_Using_Multi_Datasets"><img alt="Research paper thumbnail of Pre-training on Multi-modal for Improved Persona Detection Using Multi Datasets" class="work-thumbnail" src="https://attachments.academia-assets.com/121168538/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/127436559/Pre_training_on_Multi_modal_for_Improved_Persona_Detection_Using_Multi_Datasets">Pre-training on Multi-modal for Improved Persona Detection Using Multi Datasets</a></div><div class="wp-workCard_item"><span>Lecture notes in civil engineering</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Persona identification helps AI-based communication systems provide personalized and situationall...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Persona identification helps AI-based communication systems provide personalized and situationally informed interactions. This paper introduces pretraining on CNN, BERT, and GPT models to improve persona detection on PMPC and ROCStories datasets. Two speakers with different personalities have dialogues in the PMPC dataset. The challenge is to match each speaker to their persona. The ROCStories dataset contains fictional character traits and activities. Our study uses transformer-based design to improve persona detection using ROCStories dataset external context. We compare our method to leading models in the field. We found that pre-training and fine-tuning on several datasets improves model performance. External context from tale collections may improve persona detection algorithms and help understand human personality and behavior. Our study found that pretraining CNN, BERT, and GPT models improves persona detection, improving user experiences and communication. The method could be used in chatbots, personalized recommendation systems, and customer support. Additionally, it can help create AI-driven communication systems with tailored, context-aware, and human-like interactions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="10b00810058a0236e61ab5f7bcc4fb2f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":121168538,"asset_id":127436559,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/121168538/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436559"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436559"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436559; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436559]").text(description); $(".js-view-count[data-work-id=127436559]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436559; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436559']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "10b00810058a0236e61ab5f7bcc4fb2f" } } $('.js-work-strip[data-work-id=127436559]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436559,"title":"Pre-training on Multi-modal for Improved Persona Detection Using Multi Datasets","translated_title":"","metadata":{"grobid_abstract":"Persona identification helps AI-based communication systems provide personalized and situationally informed interactions. This paper introduces pretraining on CNN, BERT, and GPT models to improve persona detection on PMPC and ROCStories datasets. Two speakers with different personalities have dialogues in the PMPC dataset. The challenge is to match each speaker to their persona. The ROCStories dataset contains fictional character traits and activities. Our study uses transformer-based design to improve persona detection using ROCStories dataset external context. We compare our method to leading models in the field. We found that pre-training and fine-tuning on several datasets improves model performance. External context from tale collections may improve persona detection algorithms and help understand human personality and behavior. Our study found that pretraining CNN, BERT, and GPT models improves persona detection, improving user experiences and communication. The method could be used in chatbots, personalized recommendation systems, and customer support. Additionally, it can help create AI-driven communication systems with tailored, context-aware, and human-like interactions.","publication_date":{"day":null,"month":null,"year":2024,"errors":{}},"publication_name":"Lecture notes in civil engineering","grobid_abstract_attachment_id":121168538},"translated_abstract":null,"internal_url":"https://www.academia.edu/127436559/Pre_training_on_Multi_modal_for_Improved_Persona_Detection_Using_Multi_Datasets","translated_internal_url":"","created_at":"2025-02-03T07:31:21.451-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":121168538,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168538/thumbnails/1.jpg","file_name":"978-3-031-56121-4_22.pdf","download_url":"https://www.academia.edu/attachments/121168538/download_file","bulk_download_file_name":"Pre_training_on_Multi_modal_for_Improved.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168538/978-3-031-56121-4_22-libre.pdf?1738598721=\u0026response-content-disposition=attachment%3B+filename%3DPre_training_on_Multi_modal_for_Improved.pdf\u0026Expires=1742123709\u0026Signature=P7ytqs5pH5R6bnpKsAEpyblGDlWoptvbz6jJgNGF~z9QFGwAYTX62ztShEcwcCnnX89TLY6d7SNanUI4ruArLUjTWLTsCI82MqScHIKkb0CCAqOTBvRsZq-YDAYeuN81zEFz-TKhbu1qcBwYGwO-1VgsD0pxhVV~0g8DRwGOuxT2LfsH1kyn7rrHLbFMWf2jM7v5C3JFl3BYi5vqGmyqavnD4SJAt5xg2xaEStUU88SgLGpCyjmKMyPGKmgomtpIkdEB1TGrRf~Rj6ZfJ7bDH5qS-IWHY1Dt3wmfaLXy7fCR~DUA~LSf~Ca9Nct~bKNJKpBRTTih9Mvc~FZixWxvUA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Pre_training_on_Multi_modal_for_Improved_Persona_Detection_Using_Multi_Datasets","translated_slug":"","page_count":10,"language":"en","content_type":"Work","summary":"Persona identification helps AI-based communication systems provide personalized and situationally informed interactions. This paper introduces pretraining on CNN, BERT, and GPT models to improve persona detection on PMPC and ROCStories datasets. Two speakers with different personalities have dialogues in the PMPC dataset. The challenge is to match each speaker to their persona. The ROCStories dataset contains fictional character traits and activities. Our study uses transformer-based design to improve persona detection using ROCStories dataset external context. We compare our method to leading models in the field. We found that pre-training and fine-tuning on several datasets improves model performance. External context from tale collections may improve persona detection algorithms and help understand human personality and behavior. Our study found that pretraining CNN, BERT, and GPT models improves persona detection, improving user experiences and communication. The method could be used in chatbots, personalized recommendation systems, and customer support. Additionally, it can help create AI-driven communication systems with tailored, context-aware, and human-like interactions.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[{"id":121168538,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168538/thumbnails/1.jpg","file_name":"978-3-031-56121-4_22.pdf","download_url":"https://www.academia.edu/attachments/121168538/download_file","bulk_download_file_name":"Pre_training_on_Multi_modal_for_Improved.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168538/978-3-031-56121-4_22-libre.pdf?1738598721=\u0026response-content-disposition=attachment%3B+filename%3DPre_training_on_Multi_modal_for_Improved.pdf\u0026Expires=1742123709\u0026Signature=P7ytqs5pH5R6bnpKsAEpyblGDlWoptvbz6jJgNGF~z9QFGwAYTX62ztShEcwcCnnX89TLY6d7SNanUI4ruArLUjTWLTsCI82MqScHIKkb0CCAqOTBvRsZq-YDAYeuN81zEFz-TKhbu1qcBwYGwO-1VgsD0pxhVV~0g8DRwGOuxT2LfsH1kyn7rrHLbFMWf2jM7v5C3JFl3BYi5vqGmyqavnD4SJAt5xg2xaEStUU88SgLGpCyjmKMyPGKmgomtpIkdEB1TGrRf~Rj6ZfJ7bDH5qS-IWHY1Dt3wmfaLXy7fCR~DUA~LSf~Ca9Nct~bKNJKpBRTTih9Mvc~FZixWxvUA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":121168537,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168537/thumbnails/1.jpg","file_name":"978-3-031-56121-4_22.pdf","download_url":"https://www.academia.edu/attachments/121168537/download_file","bulk_download_file_name":"Pre_training_on_Multi_modal_for_Improved.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168537/978-3-031-56121-4_22-libre.pdf?1738598719=\u0026response-content-disposition=attachment%3B+filename%3DPre_training_on_Multi_modal_for_Improved.pdf\u0026Expires=1742123709\u0026Signature=c1okzxCgczRfxpNbCykotOUCIkt9IUsHCDkY0kZZNjo1QjhW5imJDCqOqmVoHFkRb5q1GhGUBTCuwM56XzC9rR9JbbrvSBpytsjFES8WJCHOhl3G4kt3nMrwAdZHgG3NOv-XywpfgJFWDEOnZbsiA9Uvnv2XwzffKXIpt6Xd7zl12kclkuH~tlKYuwrqgWQsHBrYzR9NKdXxm1hmmnbrj3Dc0C1TTopuoFNckRp9lZxO1ZIjmex0Mi-DPULUKOGDZwogYYFxVxExsTyWcXa8t3MUY29s~HHe8gK95LWKD4AAAY0FGqnt8XCQfxrVR8J3~4swiIAXQ2oFjNDXVDFpUg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":91971,"name":"Persona","url":"https://www.academia.edu/Documents/in/Persona"},{"id":232306,"name":"Modal","url":"https://www.academia.edu/Documents/in/Modal"}],"urls":[{"id":46723415,"url":"https://link.springer.com/content/pdf/10.1007/978-3-031-56121-4_22.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436558"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436558/Expert_systems_Useful_tools_for_enhancing_agricultural_research_and_production"><img alt="Research paper thumbnail of Expert systems: Useful tools for enhancing agricultural research and production" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436558/Expert_systems_Useful_tools_for_enhancing_agricultural_research_and_production">Expert systems: Useful tools for enhancing agricultural research and production</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436558"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436558"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436558; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436558]").text(description); $(".js-view-count[data-work-id=127436558]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436558; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436558']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436558]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436558,"title":"Expert systems: Useful tools for enhancing agricultural research and production","translated_title":"","metadata":{"publication_date":{"day":null,"month":null,"year":2002,"errors":{}}},"translated_abstract":null,"internal_url":"https://www.academia.edu/127436558/Expert_systems_Useful_tools_for_enhancing_agricultural_research_and_production","translated_internal_url":"","created_at":"2025-02-03T07:31:21.268-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Expert_systems_Useful_tools_for_enhancing_agricultural_research_and_production","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":null,"owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":10225,"name":"Agriculture","url":"https://www.academia.edu/Documents/in/Agriculture"},{"id":61714,"name":"Production economics","url":"https://www.academia.edu/Documents/in/Production_economics"},{"id":122360,"name":"Agricultural Productivity","url":"https://www.academia.edu/Documents/in/Agricultural_Productivity"}],"urls":[{"id":46723414,"url":"http://agris.fao.org/agris-search/search.do?request_locale=es\u0026recordID=QV2004000042\u0026sourceQuery=\u0026query=\u0026sortField=\u0026sortOrder=\u0026agrovocString=\u0026advQuery=\u0026centerString=\u0026enableField="}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436557"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436557/The_Future_of_E_Commerce_Systems_2030_and_Beyond"><img alt="Research paper thumbnail of The Future of E-Commerce Systems: 2030 and Beyond" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436557/The_Future_of_E_Commerce_Systems_2030_and_Beyond">The Future of E-Commerce Systems: 2030 and Beyond</a></div><div class="wp-workCard_item"><span>Studies in systems, decision and control</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The sophistication and efficiency of systems is undeniably advancing. As businesses evolve, quest...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The sophistication and efficiency of systems is undeniably advancing. As businesses evolve, questions on its past performance is beside the point but its anticipated functions and relevance in the future. With the use of software in commerce at the core of every business today, the overlook of how omnichannel transactions operates is an awe with the 4th industrial revolution and its societal impact. This chapter explains how commerce has currently evolved in the advent of technology elaborating on the current state and challenges of systems, its architecture, and the innovations of cyber physical systems in electronic commerce. It further expounds on the application of omnichannel systems in communication through the fifth-generation network, in transaction through blockchain and in composition through Social Internet of Things. We believe that this study will benefit all stakeholders in commerce from governments, supply chain organizations and consumers to understanding the forthcoming drivers of omnichannel systems in the 4th industrial revolution, its prospects, and its anticipated challenges.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436557"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436557"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436557; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436557]").text(description); $(".js-view-count[data-work-id=127436557]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436557; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436557']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436557]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436557,"title":"The Future of E-Commerce Systems: 2030 and Beyond","translated_title":"","metadata":{"abstract":"The sophistication and efficiency of systems is undeniably advancing. As businesses evolve, questions on its past performance is beside the point but its anticipated functions and relevance in the future. With the use of software in commerce at the core of every business today, the overlook of how omnichannel transactions operates is an awe with the 4th industrial revolution and its societal impact. This chapter explains how commerce has currently evolved in the advent of technology elaborating on the current state and challenges of systems, its architecture, and the innovations of cyber physical systems in electronic commerce. It further expounds on the application of omnichannel systems in communication through the fifth-generation network, in transaction through blockchain and in composition through Social Internet of Things. We believe that this study will benefit all stakeholders in commerce from governments, supply chain organizations and consumers to understanding the forthcoming drivers of omnichannel systems in the 4th industrial revolution, its prospects, and its anticipated challenges.","publisher":"Springer International Publishing","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Studies in systems, decision and control"},"translated_abstract":"The sophistication and efficiency of systems is undeniably advancing. As businesses evolve, questions on its past performance is beside the point but its anticipated functions and relevance in the future. With the use of software in commerce at the core of every business today, the overlook of how omnichannel transactions operates is an awe with the 4th industrial revolution and its societal impact. This chapter explains how commerce has currently evolved in the advent of technology elaborating on the current state and challenges of systems, its architecture, and the innovations of cyber physical systems in electronic commerce. It further expounds on the application of omnichannel systems in communication through the fifth-generation network, in transaction through blockchain and in composition through Social Internet of Things. We believe that this study will benefit all stakeholders in commerce from governments, supply chain organizations and consumers to understanding the forthcoming drivers of omnichannel systems in the 4th industrial revolution, its prospects, and its anticipated challenges.","internal_url":"https://www.academia.edu/127436557/The_Future_of_E_Commerce_Systems_2030_and_Beyond","translated_internal_url":"","created_at":"2025-02-03T07:31:21.073-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"The_Future_of_E_Commerce_Systems_2030_and_Beyond","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"The sophistication and efficiency of systems is undeniably advancing. As businesses evolve, questions on its past performance is beside the point but its anticipated functions and relevance in the future. With the use of software in commerce at the core of every business today, the overlook of how omnichannel transactions operates is an awe with the 4th industrial revolution and its societal impact. This chapter explains how commerce has currently evolved in the advent of technology elaborating on the current state and challenges of systems, its architecture, and the innovations of cyber physical systems in electronic commerce. It further expounds on the application of omnichannel systems in communication through the fifth-generation network, in transaction through blockchain and in composition through Social Internet of Things. We believe that this study will benefit all stakeholders in commerce from governments, supply chain organizations and consumers to understanding the forthcoming drivers of omnichannel systems in the 4th industrial revolution, its prospects, and its anticipated challenges.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":902819,"name":"Omnichannel","url":"https://www.academia.edu/Documents/in/Omnichannel"},{"id":1897290,"name":"Sophistication","url":"https://www.academia.edu/Documents/in/Sophistication"},{"id":3647879,"name":"Springer Ebooks","url":"https://www.academia.edu/Documents/in/Springer_Ebooks"}],"urls":[{"id":46723413,"url":"https://doi.org/10.1007/978-3-030-64987-6_18"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436556"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436556/Novel_Federated_Decision_Making_for_Distribution_of_Anti_SARS_CoV_2_Monoclonal_Antibody_to_Eligible_High_Risk_Patients"><img alt="Research paper thumbnail of Novel Federated Decision Making for Distribution of Anti-SARS-CoV-2 Monoclonal Antibody to Eligible High-Risk Patients" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436556/Novel_Federated_Decision_Making_for_Distribution_of_Anti_SARS_CoV_2_Monoclonal_Antibody_to_Eligible_High_Risk_Patients">Novel Federated Decision Making for Distribution of Anti-SARS-CoV-2 Monoclonal Antibody to Eligible High-Risk Patients</a></div><div class="wp-workCard_item"><span>International Journal of Information Technology and Decision Making</span><span>, Oct 10, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Context: When the epidemic first broke out, no specific treatment was available for severe acute ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients’ data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients’ score and dose availability without sharing the patients’ data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436556"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436556"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436556; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436556]").text(description); $(".js-view-count[data-work-id=127436556]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436556; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436556']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436556]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436556,"title":"Novel Federated Decision Making for Distribution of Anti-SARS-CoV-2 Monoclonal Antibody to Eligible High-Risk Patients","translated_title":"","metadata":{"abstract":"Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients’ data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients’ score and dose availability without sharing the patients’ data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death.","publisher":"World Scientific","publication_date":{"day":10,"month":10,"year":2022,"errors":{}},"publication_name":"International Journal of Information Technology and Decision Making"},"translated_abstract":"Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients’ data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients’ score and dose availability without sharing the patients’ data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death.","internal_url":"https://www.academia.edu/127436556/Novel_Federated_Decision_Making_for_Distribution_of_Anti_SARS_CoV_2_Monoclonal_Antibody_to_Eligible_High_Risk_Patients","translated_internal_url":"","created_at":"2025-02-03T07:31:20.863-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Novel_Federated_Decision_Making_for_Distribution_of_Anti_SARS_CoV_2_Monoclonal_Antibody_to_Eligible_High_Risk_Patients","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients’ data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients’ score and dose availability without sharing the patients’ data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":73149,"name":"Business and Management","url":"https://www.academia.edu/Documents/in/Business_and_Management"}],"urls":[{"id":46723412,"url":"https://doi.org/10.1142/s021962202250050x"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436555"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436555/Predicting_the_Road_Accidents_Severity_Using_Artificial_Neural_Network"><img alt="Research paper thumbnail of Predicting the Road Accidents Severity Using Artificial Neural Network" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436555/Predicting_the_Road_Accidents_Severity_Using_Artificial_Neural_Network">Predicting the Road Accidents Severity Using Artificial Neural Network</a></div><div class="wp-workCard_item"><span>Springer eBooks</span><span>, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436555"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436555"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436555; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436555]").text(description); $(".js-view-count[data-work-id=127436555]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436555; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436555']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436555]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436555,"title":"Predicting the Road Accidents Severity Using Artificial Neural Network","translated_title":"","metadata":{"publisher":"Springer Nature","publication_date":{"day":null,"month":null,"year":2022,"errors":{}},"publication_name":"Springer eBooks"},"translated_abstract":null,"internal_url":"https://www.academia.edu/127436555/Predicting_the_Road_Accidents_Severity_Using_Artificial_Neural_Network","translated_internal_url":"","created_at":"2025-02-03T07:31:20.659-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Predicting_the_Road_Accidents_Severity_Using_Artificial_Neural_Network","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":null,"owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":396772,"name":"Harm","url":"https://www.academia.edu/Documents/in/Harm"},{"id":3647879,"name":"Springer Ebooks","url":"https://www.academia.edu/Documents/in/Springer_Ebooks"}],"urls":[{"id":46723411,"url":"https://doi.org/10.1007/978-3-031-03918-8_22"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436554"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436554/Utilizing_Machine_Learning_to_Develop_Cloud_Based_Apprenticeship_Programs_Aligned_with_Labor_Market_Demands"><img alt="Research paper thumbnail of Utilizing Machine Learning to Develop Cloud-Based Apprenticeship Programs Aligned with Labor Market Demands" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436554/Utilizing_Machine_Learning_to_Develop_Cloud_Based_Apprenticeship_Programs_Aligned_with_Labor_Market_Demands">Utilizing Machine Learning to Develop Cloud-Based Apprenticeship Programs Aligned with Labor Market Demands</a></div><div class="wp-workCard_item"><span>2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud)/2023 IEEE 9th International Conference on Edge Computing and Scalable Cloud (EdgeCom)</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">There is a potential disparity between academic pursuits and labor market requirements. We propos...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">There is a potential disparity between academic pursuits and labor market requirements. We proposed an approach exploring the feasibility of leveraging machine learning to tailor apprenticeship programs and enhance learning outcomes. The proposed approach involves the collaboration of four key stakeholders, namely, the employer, trainer, university management, and apprentice, each with unique roles in the apprenticeship program. A machine learning algorithm is employed to customize Occupational Learning Outcomes (OLO) for each job, with the use of blockchain technology to facilitate the student credit system. The entire system is hosted on a cloud-based centralized database to enable dynamic and sustainable program modification. The paper concludes by highlighting the potential of digital technologies to transform apprenticeships and create new opportunities and challenges.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436554"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436554"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436554; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436554]").text(description); $(".js-view-count[data-work-id=127436554]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436554; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436554']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436554]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436554,"title":"Utilizing Machine Learning to Develop Cloud-Based Apprenticeship Programs Aligned with Labor Market Demands","translated_title":"","metadata":{"abstract":"There is a potential disparity between academic pursuits and labor market requirements. We proposed an approach exploring the feasibility of leveraging machine learning to tailor apprenticeship programs and enhance learning outcomes. The proposed approach involves the collaboration of four key stakeholders, namely, the employer, trainer, university management, and apprentice, each with unique roles in the apprenticeship program. A machine learning algorithm is employed to customize Occupational Learning Outcomes (OLO) for each job, with the use of blockchain technology to facilitate the student credit system. The entire system is hosted on a cloud-based centralized database to enable dynamic and sustainable program modification. The paper concludes by highlighting the potential of digital technologies to transform apprenticeships and create new opportunities and challenges.","publisher":"IEEE","publication_name":"2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud)/2023 IEEE 9th International Conference on Edge Computing and Scalable Cloud (EdgeCom)"},"translated_abstract":"There is a potential disparity between academic pursuits and labor market requirements. We proposed an approach exploring the feasibility of leveraging machine learning to tailor apprenticeship programs and enhance learning outcomes. The proposed approach involves the collaboration of four key stakeholders, namely, the employer, trainer, university management, and apprentice, each with unique roles in the apprenticeship program. A machine learning algorithm is employed to customize Occupational Learning Outcomes (OLO) for each job, with the use of blockchain technology to facilitate the student credit system. The entire system is hosted on a cloud-based centralized database to enable dynamic and sustainable program modification. The paper concludes by highlighting the potential of digital technologies to transform apprenticeships and create new opportunities and challenges.","internal_url":"https://www.academia.edu/127436554/Utilizing_Machine_Learning_to_Develop_Cloud_Based_Apprenticeship_Programs_Aligned_with_Labor_Market_Demands","translated_internal_url":"","created_at":"2025-02-03T07:31:20.291-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Utilizing_Machine_Learning_to_Develop_Cloud_Based_Apprenticeship_Programs_Aligned_with_Labor_Market_Demands","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"There is a potential disparity between academic pursuits and labor market requirements. We proposed an approach exploring the feasibility of leveraging machine learning to tailor apprenticeship programs and enhance learning outcomes. The proposed approach involves the collaboration of four key stakeholders, namely, the employer, trainer, university management, and apprentice, each with unique roles in the apprenticeship program. A machine learning algorithm is employed to customize Occupational Learning Outcomes (OLO) for each job, with the use of blockchain technology to facilitate the student credit system. The entire system is hosted on a cloud-based centralized database to enable dynamic and sustainable program modification. The paper concludes by highlighting the potential of digital technologies to transform apprenticeships and create new opportunities and challenges.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":922,"name":"Education","url":"https://www.academia.edu/Documents/in/Education"},{"id":1003,"name":"Educational Technology","url":"https://www.academia.edu/Documents/in/Educational_Technology"},{"id":2621,"name":"Higher Education","url":"https://www.academia.edu/Documents/in/Higher_Education"},{"id":26860,"name":"Cloud Computing","url":"https://www.academia.edu/Documents/in/Cloud_Computing"},{"id":741729,"name":"Trainer","url":"https://www.academia.edu/Documents/in/Trainer"},{"id":766833,"name":"Apprenticeship","url":"https://www.academia.edu/Documents/in/Apprenticeship"}],"urls":[{"id":46723410,"url":"http://xplorestaging.ieee.org/ielx7/10195194/10195273/10195358.pdf?arnumber=10195358"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436553"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436553/Automatic_Generation_of_Ancient_Poetry_Based_on_Generative_Adversarial_Network"><img alt="Research paper thumbnail of Automatic Generation of Ancient Poetry Based on Generative Adversarial Network" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436553/Automatic_Generation_of_Ancient_Poetry_Based_on_Generative_Adversarial_Network">Automatic Generation of Ancient Poetry Based on Generative Adversarial Network</a></div><div class="wp-workCard_item"><span>Business Intelligence and Information Technology</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436553"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436553"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436553; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436553]").text(description); $(".js-view-count[data-work-id=127436553]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436553; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436553']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436553]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436553,"title":"Automatic Generation of Ancient Poetry Based on Generative Adversarial Network","translated_title":"","metadata":{"publisher":"Springer Nature Singapore","publication_name":"Business Intelligence and Information Technology"},"translated_abstract":null,"internal_url":"https://www.academia.edu/127436553/Automatic_Generation_of_Ancient_Poetry_Based_on_Generative_Adversarial_Network","translated_internal_url":"","created_at":"2025-02-03T07:31:20.095-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Automatic_Generation_of_Ancient_Poetry_Based_on_Generative_Adversarial_Network","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":null,"owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":3581,"name":"Poetry","url":"https://www.academia.edu/Documents/in/Poetry"},{"id":128900,"name":"Generative grammar","url":"https://www.academia.edu/Documents/in/Generative_grammar"},{"id":140897,"name":"Encoder","url":"https://www.academia.edu/Documents/in/Encoder"},{"id":1108620,"name":"Adversarial System","url":"https://www.academia.edu/Documents/in/Adversarial_System"},{"id":4201064,"name":"Generative Adversarial Network","url":"https://www.academia.edu/Documents/in/Generative_Adversarial_Network"}],"urls":[{"id":46723409,"url":"https://link.springer.com/content/pdf/10.1007/978-981-99-3416-4_48"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436552"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436552/Towards_Creating_Public_Key_Authentication_for_IoT_Blockchain"><img alt="Research paper thumbnail of Towards Creating Public Key Authentication for IoT Blockchain" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436552/Towards_Creating_Public_Key_Authentication_for_IoT_Blockchain">Towards Creating Public Key Authentication for IoT Blockchain</a></div><div class="wp-workCard_item"><span>2019 Sixth HCT Information Technology Trends (ITT)</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Besides confidentiality and privacy, trust is an important factor for any IoT system. When a sens...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Besides confidentiality and privacy, trust is an important factor for any IoT system. When a sensor send data that is signed with its private key, the receiving nodes verify it using the public key of the sensor. Hence, it is understood that authenticating the public key of the system is part of creating trust within the system. Traditionally, trust is maintained using Public Key Infrastructure (PKI) where a centralized Certificate Authority (CA) is used for authenticating the public keys. However, a centralized system can result in single point of failure where CA can be compromised or can act maliciously. Decentralizing this system using blockchain and by automating the process of certificate authentication without the need for a central third party can overcome the abovementioned limitations. We identify the challenges in creating such a system and propose a generic framework for PKI in IoT infrastructure using blockchain that can provide the functions of a CA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436552"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436552"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436552; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436552]").text(description); $(".js-view-count[data-work-id=127436552]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436552; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436552']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436552]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436552,"title":"Towards Creating Public Key Authentication for IoT Blockchain","translated_title":"","metadata":{"abstract":"Besides confidentiality and privacy, trust is an important factor for any IoT system. When a sensor send data that is signed with its private key, the receiving nodes verify it using the public key of the sensor. Hence, it is understood that authenticating the public key of the system is part of creating trust within the system. Traditionally, trust is maintained using Public Key Infrastructure (PKI) where a centralized Certificate Authority (CA) is used for authenticating the public keys. However, a centralized system can result in single point of failure where CA can be compromised or can act maliciously. Decentralizing this system using blockchain and by automating the process of certificate authentication without the need for a central third party can overcome the abovementioned limitations. We identify the challenges in creating such a system and propose a generic framework for PKI in IoT infrastructure using blockchain that can provide the functions of a CA.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"2019 Sixth HCT Information Technology Trends (ITT)"},"translated_abstract":"Besides confidentiality and privacy, trust is an important factor for any IoT system. When a sensor send data that is signed with its private key, the receiving nodes verify it using the public key of the sensor. Hence, it is understood that authenticating the public key of the system is part of creating trust within the system. Traditionally, trust is maintained using Public Key Infrastructure (PKI) where a centralized Certificate Authority (CA) is used for authenticating the public keys. However, a centralized system can result in single point of failure where CA can be compromised or can act maliciously. Decentralizing this system using blockchain and by automating the process of certificate authentication without the need for a central third party can overcome the abovementioned limitations. We identify the challenges in creating such a system and propose a generic framework for PKI in IoT infrastructure using blockchain that can provide the functions of a CA.","internal_url":"https://www.academia.edu/127436552/Towards_Creating_Public_Key_Authentication_for_IoT_Blockchain","translated_internal_url":"","created_at":"2025-02-03T07:31:19.896-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Towards_Creating_Public_Key_Authentication_for_IoT_Blockchain","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Besides confidentiality and privacy, trust is an important factor for any IoT system. When a sensor send data that is signed with its private key, the receiving nodes verify it using the public key of the sensor. Hence, it is understood that authenticating the public key of the system is part of creating trust within the system. Traditionally, trust is maintained using Public Key Infrastructure (PKI) where a centralized Certificate Authority (CA) is used for authenticating the public keys. However, a centralized system can result in single point of failure where CA can be compromised or can act maliciously. Decentralizing this system using blockchain and by automating the process of certificate authentication without the need for a central third party can overcome the abovementioned limitations. We identify the challenges in creating such a system and propose a generic framework for PKI in IoT infrastructure using blockchain that can provide the functions of a CA.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":7719,"name":"Public Key Infrastructure","url":"https://www.academia.edu/Documents/in/Public_Key_Infrastructure"},{"id":13923,"name":"Computer Security","url":"https://www.academia.edu/Documents/in/Computer_Security"},{"id":222999,"name":"Public key cryptography","url":"https://www.academia.edu/Documents/in/Public_key_cryptography"},{"id":247799,"name":"Internet of Things (IoT)","url":"https://www.academia.edu/Documents/in/Internet_of_Things_IoT_"},{"id":263911,"name":"Certificate","url":"https://www.academia.edu/Documents/in/Certificate"},{"id":807028,"name":"emerging trends in Information Technology","url":"https://www.academia.edu/Documents/in/emerging_trends_in_Information_Technology"},{"id":1147978,"name":"Certificate Authority","url":"https://www.academia.edu/Documents/in/Certificate_Authority"},{"id":2199949,"name":"Blockchain","url":"https://www.academia.edu/Documents/in/Blockchain"}],"urls":[{"id":46723408,"url":"http://xplorestaging.ieee.org/ielx7/9066808/9075061/09075105.pdf?arnumber=9075105"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436551"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436551/An_Optimal_Consensus_Node_Selection_Process_for_IoT_Blockchain"><img alt="Research paper thumbnail of An Optimal Consensus Node Selection Process for IoT Blockchain" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436551/An_Optimal_Consensus_Node_Selection_Process_for_IoT_Blockchain">An Optimal Consensus Node Selection Process for IoT Blockchain</a></div><div class="wp-workCard_item"><span>2019 Sixth HCT Information Technology Trends (ITT)</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Blockchain and Internet of Things (IoT) are two trending technologies, when combined together can...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Blockchain and Internet of Things (IoT) are two trending technologies, when combined together can strengthen the security of various applications. However, security of blockchain depend on its consensus mechanism. The consensus mechanisms used by cryptocurrencies requires high computations and hence cannot be applied to IoT. Applying light weight consensus such as PBFT (Practical Byzantine Fault Tolerance) requires an authority or a protocol to select leader nodes and the nodes to be involved in consensus. However the current node selection process in many blockchain applications involves a central authority or is based on traditional round robin techniques. Hence we propose simple and efficient node selection mechanism that can perform consensus without wasting energy. Our approach uses PBFT, where the nodes to participate in the consensus are not predetermined by a central authority. Nodes are selected based on their performance in the blockchain.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436551"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436551"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436551; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436551]").text(description); $(".js-view-count[data-work-id=127436551]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436551; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436551']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436551]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436551,"title":"An Optimal Consensus Node Selection Process for IoT Blockchain","translated_title":"","metadata":{"abstract":"Blockchain and Internet of Things (IoT) are two trending technologies, when combined together can strengthen the security of various applications. However, security of blockchain depend on its consensus mechanism. The consensus mechanisms used by cryptocurrencies requires high computations and hence cannot be applied to IoT. Applying light weight consensus such as PBFT (Practical Byzantine Fault Tolerance) requires an authority or a protocol to select leader nodes and the nodes to be involved in consensus. However the current node selection process in many blockchain applications involves a central authority or is based on traditional round robin techniques. Hence we propose simple and efficient node selection mechanism that can perform consensus without wasting energy. Our approach uses PBFT, where the nodes to participate in the consensus are not predetermined by a central authority. Nodes are selected based on their performance in the blockchain.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"2019 Sixth HCT Information Technology Trends (ITT)"},"translated_abstract":"Blockchain and Internet of Things (IoT) are two trending technologies, when combined together can strengthen the security of various applications. However, security of blockchain depend on its consensus mechanism. The consensus mechanisms used by cryptocurrencies requires high computations and hence cannot be applied to IoT. Applying light weight consensus such as PBFT (Practical Byzantine Fault Tolerance) requires an authority or a protocol to select leader nodes and the nodes to be involved in consensus. However the current node selection process in many blockchain applications involves a central authority or is based on traditional round robin techniques. Hence we propose simple and efficient node selection mechanism that can perform consensus without wasting energy. Our approach uses PBFT, where the nodes to participate in the consensus are not predetermined by a central authority. Nodes are selected based on their performance in the blockchain.","internal_url":"https://www.academia.edu/127436551/An_Optimal_Consensus_Node_Selection_Process_for_IoT_Blockchain","translated_internal_url":"","created_at":"2025-02-03T07:31:19.683-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"An_Optimal_Consensus_Node_Selection_Process_for_IoT_Blockchain","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Blockchain and Internet of Things (IoT) are two trending technologies, when combined together can strengthen the security of various applications. However, security of blockchain depend on its consensus mechanism. The consensus mechanisms used by cryptocurrencies requires high computations and hence cannot be applied to IoT. Applying light weight consensus such as PBFT (Practical Byzantine Fault Tolerance) requires an authority or a protocol to select leader nodes and the nodes to be involved in consensus. However the current node selection process in many blockchain applications involves a central authority or is based on traditional round robin techniques. Hence we propose simple and efficient node selection mechanism that can perform consensus without wasting energy. Our approach uses PBFT, where the nodes to participate in the consensus are not predetermined by a central authority. Nodes are selected based on their performance in the blockchain.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing"},{"id":62540,"name":"Computer Network","url":"https://www.academia.edu/Documents/in/Computer_Network"},{"id":247799,"name":"Internet of Things (IoT)","url":"https://www.academia.edu/Documents/in/Internet_of_Things_IoT_"},{"id":740735,"name":"Byzantine Fault Tolerance","url":"https://www.academia.edu/Documents/in/Byzantine_Fault_Tolerance"},{"id":807028,"name":"emerging trends in Information Technology","url":"https://www.academia.edu/Documents/in/emerging_trends_in_Information_Technology"},{"id":2199949,"name":"Blockchain","url":"https://www.academia.edu/Documents/in/Blockchain"}],"urls":[{"id":46723407,"url":"http://xplorestaging.ieee.org/ielx7/9066808/9075061/09075069.pdf?arnumber=9075069"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436550"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436550/FCSR_Fuzzy_Continuous_Speech_Recognition_Approach_for_Identifying_Laryngeal_Pathologies_Using_New_Weighted_Spectrum_Features"><img alt="Research paper thumbnail of FCSR - Fuzzy Continuous Speech Recognition Approach for Identifying Laryngeal Pathologies Using New Weighted Spectrum Features" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436550/FCSR_Fuzzy_Continuous_Speech_Recognition_Approach_for_Identifying_Laryngeal_Pathologies_Using_New_Weighted_Spectrum_Features">FCSR - Fuzzy Continuous Speech Recognition Approach for Identifying Laryngeal Pathologies Using New Weighted Spectrum Features</a></div><div class="wp-workCard_item"><span>Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Speech processing technologies have provided distinct contributions for identifying laryngeal pat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Speech processing technologies have provided distinct contributions for identifying laryngeal pathology, in which samples of normal and pathologic voice are evaluated. In this paper, a novel Fuzzy Continuous Speech Recognition approach termed FCSR is proposed for laryngeal pathology identification. First of all, new speech weighted spectrum features based on Jacobi–Fourier Moments (JFMs) are presented for characterization of larynx pathologies. This is primarily motivated by the assumption that the energy represented by spectrogram would entirely change with some larynx pathologies like physiological pathologies, neuromuscular pathologies, while it would extremely change with normal speech. This phenomenon would extensively influence the allocation of spectrogram local energy in time axis together with frequency axis. Consequently, the JFMs computed from spectrogram local regions are utilized to characterize distribution of spectrogram local energy. Besides, a proposed multi-class fuzzy support vector machine (FSVM) model is constructed to classify larynx pathologies, where partition index maximization (PIM) clustering along with particle swarm optimization (PSO) are employed for calculating fuzzy memberships and optimizing the arguments of the kernel function of the FSVM, respectively. Eventually, the experiments legitimize the proposed approach in reference to the accuracy of the laryngeal pathology recognition.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436550"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436550"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436550; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436550]").text(description); $(".js-view-count[data-work-id=127436550]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436550; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436550']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436550]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436550,"title":"FCSR - Fuzzy Continuous Speech Recognition Approach for Identifying Laryngeal Pathologies Using New Weighted Spectrum Features","translated_title":"","metadata":{"abstract":"Speech processing technologies have provided distinct contributions for identifying laryngeal pathology, in which samples of normal and pathologic voice are evaluated. In this paper, a novel Fuzzy Continuous Speech Recognition approach termed FCSR is proposed for laryngeal pathology identification. First of all, new speech weighted spectrum features based on Jacobi–Fourier Moments (JFMs) are presented for characterization of larynx pathologies. This is primarily motivated by the assumption that the energy represented by spectrogram would entirely change with some larynx pathologies like physiological pathologies, neuromuscular pathologies, while it would extremely change with normal speech. This phenomenon would extensively influence the allocation of spectrogram local energy in time axis together with frequency axis. Consequently, the JFMs computed from spectrogram local regions are utilized to characterize distribution of spectrogram local energy. Besides, a proposed multi-class fuzzy support vector machine (FSVM) model is constructed to classify larynx pathologies, where partition index maximization (PIM) clustering along with particle swarm optimization (PSO) are employed for calculating fuzzy memberships and optimizing the arguments of the kernel function of the FSVM, respectively. Eventually, the experiments legitimize the proposed approach in reference to the accuracy of the laryngeal pathology recognition.","publisher":"Springer International Publishing","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017"},"translated_abstract":"Speech processing technologies have provided distinct contributions for identifying laryngeal pathology, in which samples of normal and pathologic voice are evaluated. In this paper, a novel Fuzzy Continuous Speech Recognition approach termed FCSR is proposed for laryngeal pathology identification. First of all, new speech weighted spectrum features based on Jacobi–Fourier Moments (JFMs) are presented for characterization of larynx pathologies. This is primarily motivated by the assumption that the energy represented by spectrogram would entirely change with some larynx pathologies like physiological pathologies, neuromuscular pathologies, while it would extremely change with normal speech. This phenomenon would extensively influence the allocation of spectrogram local energy in time axis together with frequency axis. Consequently, the JFMs computed from spectrogram local regions are utilized to characterize distribution of spectrogram local energy. Besides, a proposed multi-class fuzzy support vector machine (FSVM) model is constructed to classify larynx pathologies, where partition index maximization (PIM) clustering along with particle swarm optimization (PSO) are employed for calculating fuzzy memberships and optimizing the arguments of the kernel function of the FSVM, respectively. Eventually, the experiments legitimize the proposed approach in reference to the accuracy of the laryngeal pathology recognition.","internal_url":"https://www.academia.edu/127436550/FCSR_Fuzzy_Continuous_Speech_Recognition_Approach_for_Identifying_Laryngeal_Pathologies_Using_New_Weighted_Spectrum_Features","translated_internal_url":"","created_at":"2025-02-03T07:31:19.436-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"FCSR_Fuzzy_Continuous_Speech_Recognition_Approach_for_Identifying_Laryngeal_Pathologies_Using_New_Weighted_Spectrum_Features","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Speech processing technologies have provided distinct contributions for identifying laryngeal pathology, in which samples of normal and pathologic voice are evaluated. In this paper, a novel Fuzzy Continuous Speech Recognition approach termed FCSR is proposed for laryngeal pathology identification. First of all, new speech weighted spectrum features based on Jacobi–Fourier Moments (JFMs) are presented for characterization of larynx pathologies. This is primarily motivated by the assumption that the energy represented by spectrogram would entirely change with some larynx pathologies like physiological pathologies, neuromuscular pathologies, while it would extremely change with normal speech. This phenomenon would extensively influence the allocation of spectrogram local energy in time axis together with frequency axis. Consequently, the JFMs computed from spectrogram local regions are utilized to characterize distribution of spectrogram local energy. Besides, a proposed multi-class fuzzy support vector machine (FSVM) model is constructed to classify larynx pathologies, where partition index maximization (PIM) clustering along with particle swarm optimization (PSO) are employed for calculating fuzzy memberships and optimizing the arguments of the kernel function of the FSVM, respectively. Eventually, the experiments legitimize the proposed approach in reference to the accuracy of the laryngeal pathology recognition.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":4165,"name":"Fuzzy Logic","url":"https://www.academia.edu/Documents/in/Fuzzy_Logic"},{"id":11984,"name":"Speech Recognition","url":"https://www.academia.edu/Documents/in/Speech_Recognition"},{"id":151048,"name":"Spectrogram","url":"https://www.academia.edu/Documents/in/Spectrogram"},{"id":2887079,"name":"Larynx","url":"https://www.academia.edu/Documents/in/Larynx"}],"urls":[{"id":46723406,"url":"http://link.springer.com/content/pdf/10.1007/978-3-319-64861-3_36"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436548"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436548/Using_Arabic_Social_Media_Feeds_for_Incident_and_Emergency_Management_in_Smart_Cities"><img alt="Research paper thumbnail of Using Arabic Social Media Feeds for Incident and Emergency Management in Smart Cities" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436548/Using_Arabic_Social_Media_Feeds_for_Incident_and_Emergency_Management_in_Smart_Cities">Using Arabic Social Media Feeds for Incident and Emergency Management in Smart Cities</a></div><div class="wp-workCard_item"><span>2018 3rd International Conference on Smart and Sustainable Technologies (SpliTech)</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Research on Smart Cities tackles the challenges related to the rapid urban population growth comb...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Research on Smart Cities tackles the challenges related to the rapid urban population growth combined with resources&#39; scarcity. A key function of any Smart City initiative is to be able to continuously monitor and track a city&#39;s environment and resources so as to convert the data into intelligence for streamlining the city&#39;s operations. Social media has become one of the most popular means to allow users to communicate and share information, opinions, and sentiments about events and incidents occurring in a city. With the rapid growth and proliferation of social media platforms, there is a vast amount of user-generated content that can be used as source of information about cities. In this work, we propose the use of text mining and classification techniques to extract the intelligence needed from Arabic social media feeds, for effective incident and emergency management in smart cities. In our system, the information collected from social media feeds is processed to gen...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436548"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436548"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436548; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436548]").text(description); $(".js-view-count[data-work-id=127436548]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436548; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436548']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436548]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436548,"title":"Using Arabic Social Media Feeds for Incident and Emergency Management in Smart Cities","translated_title":"","metadata":{"abstract":"Research on Smart Cities tackles the challenges related to the rapid urban population growth combined with resources\u0026#39; scarcity. A key function of any Smart City initiative is to be able to continuously monitor and track a city\u0026#39;s environment and resources so as to convert the data into intelligence for streamlining the city\u0026#39;s operations. Social media has become one of the most popular means to allow users to communicate and share information, opinions, and sentiments about events and incidents occurring in a city. With the rapid growth and proliferation of social media platforms, there is a vast amount of user-generated content that can be used as source of information about cities. In this work, we propose the use of text mining and classification techniques to extract the intelligence needed from Arabic social media feeds, for effective incident and emergency management in smart cities. In our system, the information collected from social media feeds is processed to gen...","publisher":"2018 3rd International Conference on Smart and Sustainable Technologies (SpliTech)","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"2018 3rd International Conference on Smart and Sustainable Technologies (SpliTech)"},"translated_abstract":"Research on Smart Cities tackles the challenges related to the rapid urban population growth combined with resources\u0026#39; scarcity. A key function of any Smart City initiative is to be able to continuously monitor and track a city\u0026#39;s environment and resources so as to convert the data into intelligence for streamlining the city\u0026#39;s operations. Social media has become one of the most popular means to allow users to communicate and share information, opinions, and sentiments about events and incidents occurring in a city. With the rapid growth and proliferation of social media platforms, there is a vast amount of user-generated content that can be used as source of information about cities. In this work, we propose the use of text mining and classification techniques to extract the intelligence needed from Arabic social media feeds, for effective incident and emergency management in smart cities. In our system, the information collected from social media feeds is processed to gen...","internal_url":"https://www.academia.edu/127436548/Using_Arabic_Social_Media_Feeds_for_Incident_and_Emergency_Management_in_Smart_Cities","translated_internal_url":"","created_at":"2025-02-03T07:31:19.324-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Using_Arabic_Social_Media_Feeds_for_Incident_and_Emergency_Management_in_Smart_Cities","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Research on Smart Cities tackles the challenges related to the rapid urban population growth combined with resources\u0026#39; scarcity. A key function of any Smart City initiative is to be able to continuously monitor and track a city\u0026#39;s environment and resources so as to convert the data into intelligence for streamlining the city\u0026#39;s operations. Social media has become one of the most popular means to allow users to communicate and share information, opinions, and sentiments about events and incidents occurring in a city. With the rapid growth and proliferation of social media platforms, there is a vast amount of user-generated content that can be used as source of information about cities. In this work, we propose the use of text mining and classification techniques to extract the intelligence needed from Arabic social media feeds, for effective incident and emergency management in smart cities. In our system, the information collected from social media feeds is processed to gen...","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3324,"name":"Arabic","url":"https://www.academia.edu/Documents/in/Arabic"},{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media"},{"id":51829,"name":"Text Classification","url":"https://www.academia.edu/Documents/in/Text_Classification"},{"id":64336,"name":"Population","url":"https://www.academia.edu/Documents/in/Population"},{"id":94893,"name":"Smart City","url":"https://www.academia.edu/Documents/in/Smart_City"},{"id":581925,"name":"Scarcity","url":"https://www.academia.edu/Documents/in/Scarcity"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436547"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436547/Lean_Transformation_in_Information_Technology_The_Case_of_IT_Services_in_Financial_Firms"><img alt="Research paper thumbnail of Lean Transformation in Information Technology: The Case of IT Services in Financial Firms" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436547/Lean_Transformation_in_Information_Technology_The_Case_of_IT_Services_in_Financial_Firms">Lean Transformation in Information Technology: The Case of IT Services in Financial Firms</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In today’s competitive market, organizations have realized that high quality contributes to long-...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In today’s competitive market, organizations have realized that high quality contributes to long-term success. Lean transformation framework can be described as a set of tools and principles that positively impact quality by focusing on both eliminating waste and adding value to certain processes in the organization. This study argues the success of implementing Lean principles in IT. Since there is no such research has been conducted in the Arab world in implementing Lean in IT, this research paper is considered as a pioneer research in the field of IT quality management. The objective of this paper is to use a case-based approach to demonstrate how Lean principles and tools can help IT to enhance IT services quality, reduce cost, and improve productivity. A case study at a leading financial institution in the Arab Gulf region is examined. The study focuses on analyzing the Lean transformation stages, including current state assessment, target state design, and implementation; as w...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436547"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436547"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436547; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436547]").text(description); $(".js-view-count[data-work-id=127436547]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436547; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436547']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436547]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436547,"title":"Lean Transformation in Information Technology: The Case of IT Services in Financial Firms","translated_title":"","metadata":{"abstract":"In today’s competitive market, organizations have realized that high quality contributes to long-term success. Lean transformation framework can be described as a set of tools and principles that positively impact quality by focusing on both eliminating waste and adding value to certain processes in the organization. This study argues the success of implementing Lean principles in IT. Since there is no such research has been conducted in the Arab world in implementing Lean in IT, this research paper is considered as a pioneer research in the field of IT quality management. The objective of this paper is to use a case-based approach to demonstrate how Lean principles and tools can help IT to enhance IT services quality, reduce cost, and improve productivity. A case study at a leading financial institution in the Arab Gulf region is examined. The study focuses on analyzing the Lean transformation stages, including current state assessment, target state design, and implementation; as w...","publication_date":{"day":null,"month":null,"year":2018,"errors":{}}},"translated_abstract":"In today’s competitive market, organizations have realized that high quality contributes to long-term success. Lean transformation framework can be described as a set of tools and principles that positively impact quality by focusing on both eliminating waste and adding value to certain processes in the organization. This study argues the success of implementing Lean principles in IT. Since there is no such research has been conducted in the Arab world in implementing Lean in IT, this research paper is considered as a pioneer research in the field of IT quality management. The objective of this paper is to use a case-based approach to demonstrate how Lean principles and tools can help IT to enhance IT services quality, reduce cost, and improve productivity. A case study at a leading financial institution in the Arab Gulf region is examined. The study focuses on analyzing the Lean transformation stages, including current state assessment, target state design, and implementation; as w...","internal_url":"https://www.academia.edu/127436547/Lean_Transformation_in_Information_Technology_The_Case_of_IT_Services_in_Financial_Firms","translated_internal_url":"","created_at":"2025-02-03T07:31:19.204-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Lean_Transformation_in_Information_Technology_The_Case_of_IT_Services_in_Financial_Firms","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"In today’s competitive market, organizations have realized that high quality contributes to long-term success. Lean transformation framework can be described as a set of tools and principles that positively impact quality by focusing on both eliminating waste and adding value to certain processes in the organization. This study argues the success of implementing Lean principles in IT. Since there is no such research has been conducted in the Arab world in implementing Lean in IT, this research paper is considered as a pioneer research in the field of IT quality management. The objective of this paper is to use a case-based approach to demonstrate how Lean principles and tools can help IT to enhance IT services quality, reduce cost, and improve productivity. A case study at a leading financial institution in the Arab Gulf region is examined. The study focuses on analyzing the Lean transformation stages, including current state assessment, target state design, and implementation; as w...","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":26,"name":"Business","url":"https://www.academia.edu/Documents/in/Business"},{"id":491,"name":"Information Technology","url":"https://www.academia.edu/Documents/in/Information_Technology"},{"id":4193,"name":"Focus Groups","url":"https://www.academia.edu/Documents/in/Focus_Groups"},{"id":7854,"name":"Lean Manufacturing","url":"https://www.academia.edu/Documents/in/Lean_Manufacturing"},{"id":8968,"name":"Productivity","url":"https://www.academia.edu/Documents/in/Productivity"},{"id":9745,"name":"Quality Improvement","url":"https://www.academia.edu/Documents/in/Quality_Improvement"},{"id":17296,"name":"Lean Management","url":"https://www.academia.edu/Documents/in/Lean_Management"},{"id":94440,"name":"Process management","url":"https://www.academia.edu/Documents/in/Process_management"},{"id":96047,"name":"Case Study","url":"https://www.academia.edu/Documents/in/Case_Study"},{"id":178904,"name":"Lean Project Management","url":"https://www.academia.edu/Documents/in/Lean_Project_Management"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436546"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436546/A_Hybrid_Framework_for_Applying_Semantic_Integration_Technologies_to_Improve_Data_Quality"><img alt="Research paper thumbnail of A Hybrid Framework for Applying Semantic Integration Technologies to Improve Data Quality" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436546/A_Hybrid_Framework_for_Applying_Semantic_Integration_Technologies_to_Improve_Data_Quality">A Hybrid Framework for Applying Semantic Integration Technologies to Improve Data Quality</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This study aims to develop a new hybrid framework of semantic integration for enterprise informat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This study aims to develop a new hybrid framework of semantic integration for enterprise information system in order to improve data quality to resolve the problem from scattered data sources and rapid expansions of data. The proposed framework is based on a solid background that is inspired by previous studies. Significant and seminal research articles are reviewed based on selection criteria. A critical review is conducted in order to determine a set of qualified semantic technologies that can be used to construct a hybrid semantic integration framework. The proposed framework consists of six layers and one component as follows: source layer, translation layer, XML layer, RDF layer, inference layer, application layer, and ontology component. The proposed framework faces two challenges and one conflict; these were fixed while composing the framework. The proposed framework was examined to improve data quality for four dimensions of data quality dimensions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436546"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436546"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436546; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436546]").text(description); $(".js-view-count[data-work-id=127436546]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436546; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436546']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436546]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436546,"title":"A Hybrid Framework for Applying Semantic Integration Technologies to Improve Data Quality","translated_title":"","metadata":{"abstract":"This study aims to develop a new hybrid framework of semantic integration for enterprise information system in order to improve data quality to resolve the problem from scattered data sources and rapid expansions of data. The proposed framework is based on a solid background that is inspired by previous studies. Significant and seminal research articles are reviewed based on selection criteria. A critical review is conducted in order to determine a set of qualified semantic technologies that can be used to construct a hybrid semantic integration framework. The proposed framework consists of six layers and one component as follows: source layer, translation layer, XML layer, RDF layer, inference layer, application layer, and ontology component. The proposed framework faces two challenges and one conflict; these were fixed while composing the framework. The proposed framework was examined to improve data quality for four dimensions of data quality dimensions.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}}},"translated_abstract":"This study aims to develop a new hybrid framework of semantic integration for enterprise information system in order to improve data quality to resolve the problem from scattered data sources and rapid expansions of data. The proposed framework is based on a solid background that is inspired by previous studies. Significant and seminal research articles are reviewed based on selection criteria. A critical review is conducted in order to determine a set of qualified semantic technologies that can be used to construct a hybrid semantic integration framework. The proposed framework consists of six layers and one component as follows: source layer, translation layer, XML layer, RDF layer, inference layer, application layer, and ontology component. The proposed framework faces two challenges and one conflict; these were fixed while composing the framework. The proposed framework was examined to improve data quality for four dimensions of data quality dimensions.","internal_url":"https://www.academia.edu/127436546/A_Hybrid_Framework_for_Applying_Semantic_Integration_Technologies_to_Improve_Data_Quality","translated_internal_url":"","created_at":"2025-02-03T07:31:19.017-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_Hybrid_Framework_for_Applying_Semantic_Integration_Technologies_to_Improve_Data_Quality","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"This study aims to develop a new hybrid framework of semantic integration for enterprise information system in order to improve data quality to resolve the problem from scattered data sources and rapid expansions of data. The proposed framework is based on a solid background that is inspired by previous studies. Significant and seminal research articles are reviewed based on selection criteria. A critical review is conducted in order to determine a set of qualified semantic technologies that can be used to construct a hybrid semantic integration framework. The proposed framework consists of six layers and one component as follows: source layer, translation layer, XML layer, RDF layer, inference layer, application layer, and ontology component. The proposed framework faces two challenges and one conflict; these were fixed while composing the framework. The proposed framework was examined to improve data quality for four dimensions of data quality dimensions.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":449,"name":"Software Engineering","url":"https://www.academia.edu/Documents/in/Software_Engineering"},{"id":12512,"name":"Data Quality (Computer Science)","url":"https://www.academia.edu/Documents/in/Data_Quality_Computer_Science_"},{"id":31917,"name":"Data quality (Business)","url":"https://www.academia.edu/Documents/in/Data_quality_Business_"},{"id":137986,"name":"Data Quality","url":"https://www.academia.edu/Documents/in/Data_Quality"},{"id":361019,"name":"Semantic Integration","url":"https://www.academia.edu/Documents/in/Semantic_Integration"}],"urls":[{"id":46723405,"url":"http://journal-ik.twoa.ac.nz/index.php/ijitls/article/download/19/pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436545"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/127436545/Agent_Productivity_Measurement_in_Call_Center_Using_Machine_Learning"><img alt="Research paper thumbnail of Agent Productivity Measurement in Call Center Using Machine Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/121168563/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/127436545/Agent_Productivity_Measurement_in_Call_Center_Using_Machine_Learning">Agent Productivity Measurement in Call Center Using Machine Learning</a></div><div class="wp-workCard_item"><span>Advances in Intelligent Systems and Computing</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We present an application of sentiment analysis using natural language toolkit (NLTK) for measuri...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We present an application of sentiment analysis using natural language toolkit (NLTK) for measuring customer service representative (CSR) productivity in real estate call centers. The study describes in details the decisions made, step by step, in building an Arabic system for evaluation and measuring. The system includes transcription method, feature extraction, training process and analysis. The results are analyzed subjectively based on the original test set. The corpus consists of 7 h real estate corpus collected from three different call centers located in Egypt. We draw the baseline of productivity measurement in real estate sector.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="70f5566cfac9ef36e2576569854aa3ca" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":121168563,"asset_id":127436545,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/121168563/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436545"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436545"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436545; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436545]").text(description); $(".js-view-count[data-work-id=127436545]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436545; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436545']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "70f5566cfac9ef36e2576569854aa3ca" } } $('.js-work-strip[data-work-id=127436545]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436545,"title":"Agent Productivity Measurement in Call Center Using Machine Learning","translated_title":"","metadata":{"abstract":"We present an application of sentiment analysis using natural language toolkit (NLTK) for measuring customer service representative (CSR) productivity in real estate call centers. The study describes in details the decisions made, step by step, in building an Arabic system for evaluation and measuring. The system includes transcription method, feature extraction, training process and analysis. The results are analyzed subjectively based on the original test set. The corpus consists of 7 h real estate corpus collected from three different call centers located in Egypt. We draw the baseline of productivity measurement in real estate sector.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"Advances in Intelligent Systems and Computing"},"translated_abstract":"We present an application of sentiment analysis using natural language toolkit (NLTK) for measuring customer service representative (CSR) productivity in real estate call centers. The study describes in details the decisions made, step by step, in building an Arabic system for evaluation and measuring. The system includes transcription method, feature extraction, training process and analysis. The results are analyzed subjectively based on the original test set. The corpus consists of 7 h real estate corpus collected from three different call centers located in Egypt. We draw the baseline of productivity measurement in real estate sector.","internal_url":"https://www.academia.edu/127436545/Agent_Productivity_Measurement_in_Call_Center_Using_Machine_Learning","translated_internal_url":"","created_at":"2025-02-03T07:31:18.778-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":121168563,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168563/thumbnails/1.jpg","file_name":"Arabic_call_center.pdf","download_url":"https://www.academia.edu/attachments/121168563/download_file","bulk_download_file_name":"Agent_Productivity_Measurement_in_Call_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168563/Arabic_call_center-libre.pdf?1738598717=\u0026response-content-disposition=attachment%3B+filename%3DAgent_Productivity_Measurement_in_Call_C.pdf\u0026Expires=1742123710\u0026Signature=YWG2xqBXGfELzUL-B1Lgpx4yNPof~2SX9e1ELd-kF50YNJ3WYAXo9WZmibPimTMNHB60Ks4mJC~TY6jX~UztAzgaOiDSR6nHjLeuauy7RGzLeh6ItDqvD06RdD8RzqCbdf-cwT0UaU6Rw3m8NfTRinq6GfECNBI~GNuLA7IEo1Y9br4BH-2UohzGxKYUVTa5hGtl6zsaMPBXPqgBVzvKKeYNb-X27rgSym8DNSBAr7p5okXRBUvg6Qwqxvo~e2aczWnf7muf2uNznGXn7qzXnKmiQyy19aoPNA9ZdkzdTtTl90m39mtm3BRKLiggDwfSviKGnIiju-v4eu0urBHKzg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Agent_Productivity_Measurement_in_Call_Center_Using_Machine_Learning","translated_slug":"","page_count":10,"language":"en","content_type":"Work","summary":"We present an application of sentiment analysis using natural language toolkit (NLTK) for measuring customer service representative (CSR) productivity in real estate call centers. The study describes in details the decisions made, step by step, in building an Arabic system for evaluation and measuring. The system includes transcription method, feature extraction, training process and analysis. The results are analyzed subjectively based on the original test set. The corpus consists of 7 h real estate corpus collected from three different call centers located in Egypt. We draw the baseline of productivity measurement in real estate sector.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[{"id":121168563,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168563/thumbnails/1.jpg","file_name":"Arabic_call_center.pdf","download_url":"https://www.academia.edu/attachments/121168563/download_file","bulk_download_file_name":"Agent_Productivity_Measurement_in_Call_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168563/Arabic_call_center-libre.pdf?1738598717=\u0026response-content-disposition=attachment%3B+filename%3DAgent_Productivity_Measurement_in_Call_C.pdf\u0026Expires=1742123710\u0026Signature=YWG2xqBXGfELzUL-B1Lgpx4yNPof~2SX9e1ELd-kF50YNJ3WYAXo9WZmibPimTMNHB60Ks4mJC~TY6jX~UztAzgaOiDSR6nHjLeuauy7RGzLeh6ItDqvD06RdD8RzqCbdf-cwT0UaU6Rw3m8NfTRinq6GfECNBI~GNuLA7IEo1Y9br4BH-2UohzGxKYUVTa5hGtl6zsaMPBXPqgBVzvKKeYNb-X27rgSym8DNSBAr7p5okXRBUvg6Qwqxvo~e2aczWnf7muf2uNznGXn7qzXnKmiQyy19aoPNA9ZdkzdTtTl90m39mtm3BRKLiggDwfSviKGnIiju-v4eu0urBHKzg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":8968,"name":"Productivity","url":"https://www.academia.edu/Documents/in/Productivity"},{"id":11984,"name":"Speech Recognition","url":"https://www.academia.edu/Documents/in/Speech_Recognition"},{"id":240981,"name":"Call Centers","url":"https://www.academia.edu/Documents/in/Call_Centers"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436544"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436544/Speculative_Work_in_Neural_Network_Forecasting_An_application_to_Egyptian_Cotton_Production"><img alt="Research paper thumbnail of Speculative Work in Neural Network Forecasting: An application to Egyptian Cotton Production" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436544/Speculative_Work_in_Neural_Network_Forecasting_An_application_to_Egyptian_Cotton_Production">Speculative Work in Neural Network Forecasting: An application to Egyptian Cotton Production</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436544"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436544"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436544; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436544]").text(description); $(".js-view-count[data-work-id=127436544]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436544; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436544']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436544]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436544,"title":"Speculative Work in Neural Network Forecasting: An application to Egyptian Cotton Production","translated_title":"","metadata":{"publication_date":{"day":null,"month":null,"year":1999,"errors":{}}},"translated_abstract":null,"internal_url":"https://www.academia.edu/127436544/Speculative_Work_in_Neural_Network_Forecasting_An_application_to_Egyptian_Cotton_Production","translated_internal_url":"","created_at":"2025-02-03T07:31:18.648-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Speculative_Work_in_Neural_Network_Forecasting_An_application_to_Egyptian_Cotton_Production","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":null,"owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":7968,"name":"Prediction","url":"https://www.academia.edu/Documents/in/Prediction"},{"id":54123,"name":"Artificial Neural Networks","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Networks"},{"id":61714,"name":"Production economics","url":"https://www.academia.edu/Documents/in/Production_economics"},{"id":112560,"name":"Effect of Drought on Cotton Production","url":"https://www.academia.edu/Documents/in/Effect_of_Drought_on_Cotton_Production"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436448"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/127436448/Enhancing_Student_Services_Machine_Learning_Chatbot_Intent_Recognition_for_High_School_Inquiries"><img alt="Research paper thumbnail of Enhancing Student Services: Machine Learning Chatbot Intent Recognition for High School Inquiries" class="work-thumbnail" src="https://attachments.academia-assets.com/121168447/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/127436448/Enhancing_Student_Services_Machine_Learning_Chatbot_Intent_Recognition_for_High_School_Inquiries">Enhancing Student Services: Machine Learning Chatbot Intent Recognition for High School Inquiries</a></div><div class="wp-workCard_item"><span>Lecture notes in civil engineering</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose -This paper aims to develop a novel chatbot to improve student services in high school by...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose -This paper aims to develop a novel chatbot to improve student services in high school by transferring students' enquiries to a particular agent, based on the enquiry type. In accordance to that, comparison between machine learning and neural network is conducted in order to identify the most accurate model to classify students' requests. Methodology -In this study we selected the data from high school students, since high school is one of the most essential stages in students' lives, as in this stage, students have the option to select their academic streams and advanced courses that can shape their careers according to their passions and interests. A new corpus is created with (1004) enquiries. The data is annotated manually based on the type of request. The label high-school-courses is assigned to the requests that are related to elective courses and standardized tests during high school. On the other hand, the label majors & universities is assigned to the questions that are related to applying to universities along with selecting the majors. Two novel classifier chatbots are developed and evaluated, where the first chatbot is developed by using a Naive Bayes Machine Learning Algorithm, while the other is developed by using Recurrent Neural Networks (RNN)-LSTM. Findings -Some features and techniques are used in both models in order to improve the performance. However, both models have conveyed a high accuracy score which exceeds (91%). The models have been validated as a pilot testing by using high school students as well as experts in education and six questions and enquiries are presented to the chatbots for the evaluation. Implications and future work -This study can add value to the team of researchers and developers to integrate such classifiers into different applications. As a result, this improves the users' services, in particular, those implemented in educational institutions. In the future, it is certain that intent recognition will be developed with the addition of a voice recognition feature which can successfully integrated into smartphones.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7d394b5a7d321e6a8b44fbd73ea5da09" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":121168447,"asset_id":127436448,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/121168447/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436448"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436448"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436448; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436448]").text(description); $(".js-view-count[data-work-id=127436448]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436448; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436448']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "7d394b5a7d321e6a8b44fbd73ea5da09" } } $('.js-work-strip[data-work-id=127436448]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436448,"title":"Enhancing Student Services: Machine Learning Chatbot Intent Recognition for High School Inquiries","translated_title":"","metadata":{"grobid_abstract":"Purpose -This paper aims to develop a novel chatbot to improve student services in high school by transferring students' enquiries to a particular agent, based on the enquiry type. In accordance to that, comparison between machine learning and neural network is conducted in order to identify the most accurate model to classify students' requests. Methodology -In this study we selected the data from high school students, since high school is one of the most essential stages in students' lives, as in this stage, students have the option to select their academic streams and advanced courses that can shape their careers according to their passions and interests. A new corpus is created with (1004) enquiries. The data is annotated manually based on the type of request. The label high-school-courses is assigned to the requests that are related to elective courses and standardized tests during high school. On the other hand, the label majors \u0026 universities is assigned to the questions that are related to applying to universities along with selecting the majors. Two novel classifier chatbots are developed and evaluated, where the first chatbot is developed by using a Naive Bayes Machine Learning Algorithm, while the other is developed by using Recurrent Neural Networks (RNN)-LSTM. Findings -Some features and techniques are used in both models in order to improve the performance. However, both models have conveyed a high accuracy score which exceeds (91%). The models have been validated as a pilot testing by using high school students as well as experts in education and six questions and enquiries are presented to the chatbots for the evaluation. Implications and future work -This study can add value to the team of researchers and developers to integrate such classifiers into different applications. As a result, this improves the users' services, in particular, those implemented in educational institutions. In the future, it is certain that intent recognition will be developed with the addition of a voice recognition feature which can successfully integrated into smartphones.","publication_date":{"day":null,"month":null,"year":2024,"errors":{}},"publication_name":"Lecture notes in civil engineering","grobid_abstract_attachment_id":121168447},"translated_abstract":null,"internal_url":"https://www.academia.edu/127436448/Enhancing_Student_Services_Machine_Learning_Chatbot_Intent_Recognition_for_High_School_Inquiries","translated_internal_url":"","created_at":"2025-02-03T07:24:08.657-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":121168447,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168447/thumbnails/1.jpg","file_name":"978-3-031-56121-4_24.pdf","download_url":"https://www.academia.edu/attachments/121168447/download_file","bulk_download_file_name":"Enhancing_Student_Services_Machine_Learn.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168447/978-3-031-56121-4_24-libre.pdf?1738598723=\u0026response-content-disposition=attachment%3B+filename%3DEnhancing_Student_Services_Machine_Learn.pdf\u0026Expires=1742097475\u0026Signature=V7jt2ndK1satW2iuDF0-5gRIdGAd0sO5S3CEY6IZ1aUyqzFzaUc~iPYq-DIX3lMsSE43LPZ0HZzB-su5UHcv5Ri8dYr7ioI6ucr2SH~ncMG8akcwxTxxwdSTS9Djzyn8nnQJEeHtKLYXmqAEX7q46xYj-FQqX-K6vBUoy~vH2LqTJwzr65q~uUyh~xBxVd~Tqh2HSPwZ6Se4RiDLRXnKatRDllH0NyT3j6WAOjrJNhsYAesBqT~g2Nn15DEm7svLPp31O9QPT7rK-zR-77UaDS0qtCZJjNJ9bZ97qkALvWDSyXh~hoBU5Mx6uxMa6OJLEl2LZczyDd11k~ixDT3v~g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Enhancing_Student_Services_Machine_Learning_Chatbot_Intent_Recognition_for_High_School_Inquiries","translated_slug":"","page_count":12,"language":"en","content_type":"Work","summary":"Purpose -This paper aims to develop a novel chatbot to improve student services in high school by transferring students' enquiries to a particular agent, based on the enquiry type. In accordance to that, comparison between machine learning and neural network is conducted in order to identify the most accurate model to classify students' requests. Methodology -In this study we selected the data from high school students, since high school is one of the most essential stages in students' lives, as in this stage, students have the option to select their academic streams and advanced courses that can shape their careers according to their passions and interests. A new corpus is created with (1004) enquiries. The data is annotated manually based on the type of request. The label high-school-courses is assigned to the requests that are related to elective courses and standardized tests during high school. On the other hand, the label majors \u0026 universities is assigned to the questions that are related to applying to universities along with selecting the majors. Two novel classifier chatbots are developed and evaluated, where the first chatbot is developed by using a Naive Bayes Machine Learning Algorithm, while the other is developed by using Recurrent Neural Networks (RNN)-LSTM. Findings -Some features and techniques are used in both models in order to improve the performance. However, both models have conveyed a high accuracy score which exceeds (91%). The models have been validated as a pilot testing by using high school students as well as experts in education and six questions and enquiries are presented to the chatbots for the evaluation. Implications and future work -This study can add value to the team of researchers and developers to integrate such classifiers into different applications. As a result, this improves the users' services, in particular, those implemented in educational institutions. In the future, it is certain that intent recognition will be developed with the addition of a voice recognition feature which can successfully integrated into smartphones.","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[{"id":121168447,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168447/thumbnails/1.jpg","file_name":"978-3-031-56121-4_24.pdf","download_url":"https://www.academia.edu/attachments/121168447/download_file","bulk_download_file_name":"Enhancing_Student_Services_Machine_Learn.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168447/978-3-031-56121-4_24-libre.pdf?1738598723=\u0026response-content-disposition=attachment%3B+filename%3DEnhancing_Student_Services_Machine_Learn.pdf\u0026Expires=1742097475\u0026Signature=V7jt2ndK1satW2iuDF0-5gRIdGAd0sO5S3CEY6IZ1aUyqzFzaUc~iPYq-DIX3lMsSE43LPZ0HZzB-su5UHcv5Ri8dYr7ioI6ucr2SH~ncMG8akcwxTxxwdSTS9Djzyn8nnQJEeHtKLYXmqAEX7q46xYj-FQqX-K6vBUoy~vH2LqTJwzr65q~uUyh~xBxVd~Tqh2HSPwZ6Se4RiDLRXnKatRDllH0NyT3j6WAOjrJNhsYAesBqT~g2Nn15DEm7svLPp31O9QPT7rK-zR-77UaDS0qtCZJjNJ9bZ97qkALvWDSyXh~hoBU5Mx6uxMa6OJLEl2LZczyDd11k~ixDT3v~g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":121168446,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168446/thumbnails/1.jpg","file_name":"978-3-031-56121-4_24.pdf","download_url":"https://www.academia.edu/attachments/121168446/download_file","bulk_download_file_name":"Enhancing_Student_Services_Machine_Learn.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168446/978-3-031-56121-4_24-libre.pdf?1738598723=\u0026response-content-disposition=attachment%3B+filename%3DEnhancing_Student_Services_Machine_Learn.pdf\u0026Expires=1742097475\u0026Signature=Zjkg4maRyir8pvM61s0-fmDhkwAefdkazBsuiHs4x0NSckVv6JBIrk4wlkhwSWoNgX9oB~IW2cRfqEuoXDXwCpoXDwNxRxJnh3eGLu8roHOoeIwD0zIdjoWLT8lFKPWEF5I7A4gu8ePkRBdFV~yqVD7OLR9gLgoaJnwAnXoVibgg0fD5W4bcfmoID4ts2UzS3OwWFLmWKIxZzth6~XcbeocwUb4gzL08LQbgcDGd3~fZlf~qBGBilQOpHuaNbsoB7Kpkom7Qv~~TrR45Ff7CaU3mHEhew4KLB3LgxNoPe0mlej7smP60Z0nPVWh8JLEyY1-EDpO7jtEaTTIgX1mzjA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":81182,"name":"Deep Learning","url":"https://www.academia.edu/Documents/in/Deep_Learning"},{"id":110971,"name":"Chatbots","url":"https://www.academia.edu/Documents/in/Chatbots"},{"id":246160,"name":"Chatbot","url":"https://www.academia.edu/Documents/in/Chatbot"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"}],"urls":[{"id":46723355,"url":"https://link.springer.com/content/pdf/10.1007/978-3-031-56121-4_24.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436447"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/127436447/Nizar_Y_Habash_Introduction_to_Arabic_natural_language_processing_Synthesis_lectures_on_human_language_technologies_"><img alt="Research paper thumbnail of Nizar Y. Habash, Introduction to Arabic natural language processing (Synthesis lectures on human language technologies)" class="work-thumbnail" src="https://attachments.academia-assets.com/121168469/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/127436447/Nizar_Y_Habash_Introduction_to_Arabic_natural_language_processing_Synthesis_lectures_on_human_language_technologies_">Nizar Y. Habash, Introduction to Arabic natural language processing (Synthesis lectures on human language technologies)</a></div><div class="wp-workCard_item"><span>Machine Translation</span><span>, Dec 1, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Arabic is rich in morphology and syntax. It is normally written with optional diacritics and with...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Arabic is rich in morphology and syntax. It is normally written with optional diacritics and without the notion of capitalization. These characteristics make dealing with Arabic a challenge for both learners and researchers. My experience in the Arabic natural language processing (ANLP) research area allows me to say that the key to fostering a research or developing an application in the ANLP field lies in getting insights into the standard layer-based structure of linguistic phenomena (phonology, morphology, syntax and semantics) as well as in recognizing the interaction between them. Until now, there was no such an available introductory resource that can fulfill these requirements. For example, a simple Google search of the term "Arabic Natural Language Processing" results in research groups, research papers, tutorials and presentations, companies, and scholars. Consequently, tangible efforts had to be spent by any beginner of the ANLP field, whether a scientist, linguist, developer, or student, in going through different material which might be either irrelevant or too advanced. Therefore, the purpose and the significance of this book are clear from where it stands. Also, it is adequately classified by the publisher as belonging to the "human language technologies" series. This book gives a sufficient solid introductory background on ANLP. This makes it the first of its kind. The author has a broad background in computational linguistics, in general, and ANLP, in particular. He also has a marvelous research track record and has been very active in serving the research communities. The book is clear about its intended audience. It is the most suitable for anyone who would like to get a fundamental background about ANLP for research, study, or development purposes. It is very well written. This book amazingly takes you gradually from the ground</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ef3372372564371556dd577a543f3e8f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":121168469,"asset_id":127436447,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/121168469/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436447"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436447"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436447; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436447]").text(description); $(".js-view-count[data-work-id=127436447]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436447; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436447']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ef3372372564371556dd577a543f3e8f" } } $('.js-work-strip[data-work-id=127436447]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436447,"title":"Nizar Y. Habash, Introduction to Arabic natural language processing (Synthesis lectures on human language technologies)","translated_title":"","metadata":{"publisher":"Springer Science+Business Media","grobid_abstract":"Arabic is rich in morphology and syntax. It is normally written with optional diacritics and without the notion of capitalization. These characteristics make dealing with Arabic a challenge for both learners and researchers. My experience in the Arabic natural language processing (ANLP) research area allows me to say that the key to fostering a research or developing an application in the ANLP field lies in getting insights into the standard layer-based structure of linguistic phenomena (phonology, morphology, syntax and semantics) as well as in recognizing the interaction between them. Until now, there was no such an available introductory resource that can fulfill these requirements. For example, a simple Google search of the term \"Arabic Natural Language Processing\" results in research groups, research papers, tutorials and presentations, companies, and scholars. Consequently, tangible efforts had to be spent by any beginner of the ANLP field, whether a scientist, linguist, developer, or student, in going through different material which might be either irrelevant or too advanced. Therefore, the purpose and the significance of this book are clear from where it stands. Also, it is adequately classified by the publisher as belonging to the \"human language technologies\" series. This book gives a sufficient solid introductory background on ANLP. This makes it the first of its kind. The author has a broad background in computational linguistics, in general, and ANLP, in particular. He also has a marvelous research track record and has been very active in serving the research communities. The book is clear about its intended audience. It is the most suitable for anyone who would like to get a fundamental background about ANLP for research, study, or development purposes. It is very well written. This book amazingly takes you gradually from the ground","publication_date":{"day":1,"month":12,"year":2010,"errors":{}},"publication_name":"Machine Translation","grobid_abstract_attachment_id":121168469},"translated_abstract":null,"internal_url":"https://www.academia.edu/127436447/Nizar_Y_Habash_Introduction_to_Arabic_natural_language_processing_Synthesis_lectures_on_human_language_technologies_","translated_internal_url":"","created_at":"2025-02-03T07:24:08.477-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":121168469,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168469/thumbnails/1.jpg","file_name":"s10590-011-9087-820250203-1-r82yt9.pdf","download_url":"https://www.academia.edu/attachments/121168469/download_file","bulk_download_file_name":"Nizar_Y_Habash_Introduction_to_Arabic_na.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168469/s10590-011-9087-820250203-1-r82yt9-libre.pdf?1738598718=\u0026response-content-disposition=attachment%3B+filename%3DNizar_Y_Habash_Introduction_to_Arabic_na.pdf\u0026Expires=1742123710\u0026Signature=OfCbmCc1Q14xF0J0CtED5iCujIYAwiVJbs18gaQ2Ow7-WFx99mbBUhEm0weV8~kbC00vuguB-a5J59nFkpExb4dJtaqOsvyX2YrDXgVLko0D1iPsbFnfLxtIvqjgFVGHPoEylSFdeJrB1uNgrpAn9SYRslQ-jQuQhAZ1JP2nyg8QP7o3f-ehAsx3cgZrjcwIl4qH7s42p2omfEXTsUbInk8icXNpIpcAp9u2Yulxx0JmAai1CuZUOEu6wMSlF7CTEt8d1HBtOL6i-iIIYzZ~UqYpoG54adpSlBYb6LxmBPeH9~Pd54I5PIaA7hDKOdWacGaEpO8b65YMZkoZ-KHMXQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Nizar_Y_Habash_Introduction_to_Arabic_natural_language_processing_Synthesis_lectures_on_human_language_technologies_","translated_slug":"","page_count":5,"language":"en","content_type":"Work","summary":"Arabic is rich in morphology and syntax. It is normally written with optional diacritics and without the notion of capitalization. These characteristics make dealing with Arabic a challenge for both learners and researchers. My experience in the Arabic natural language processing (ANLP) research area allows me to say that the key to fostering a research or developing an application in the ANLP field lies in getting insights into the standard layer-based structure of linguistic phenomena (phonology, morphology, syntax and semantics) as well as in recognizing the interaction between them. Until now, there was no such an available introductory resource that can fulfill these requirements. For example, a simple Google search of the term \"Arabic Natural Language Processing\" results in research groups, research papers, tutorials and presentations, companies, and scholars. Consequently, tangible efforts had to be spent by any beginner of the ANLP field, whether a scientist, linguist, developer, or student, in going through different material which might be either irrelevant or too advanced. Therefore, the purpose and the significance of this book are clear from where it stands. Also, it is adequately classified by the publisher as belonging to the \"human language technologies\" series. This book gives a sufficient solid introductory background on ANLP. This makes it the first of its kind. The author has a broad background in computational linguistics, in general, and ANLP, in particular. He also has a marvelous research track record and has been very active in serving the research communities. The book is clear about its intended audience. It is the most suitable for anyone who would like to get a fundamental background about ANLP for research, study, or development purposes. It is very well written. This book amazingly takes you gradually from the ground","owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[{"id":121168469,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/121168469/thumbnails/1.jpg","file_name":"s10590-011-9087-820250203-1-r82yt9.pdf","download_url":"https://www.academia.edu/attachments/121168469/download_file","bulk_download_file_name":"Nizar_Y_Habash_Introduction_to_Arabic_na.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/121168469/s10590-011-9087-820250203-1-r82yt9-libre.pdf?1738598718=\u0026response-content-disposition=attachment%3B+filename%3DNizar_Y_Habash_Introduction_to_Arabic_na.pdf\u0026Expires=1742123710\u0026Signature=OfCbmCc1Q14xF0J0CtED5iCujIYAwiVJbs18gaQ2Ow7-WFx99mbBUhEm0weV8~kbC00vuguB-a5J59nFkpExb4dJtaqOsvyX2YrDXgVLko0D1iPsbFnfLxtIvqjgFVGHPoEylSFdeJrB1uNgrpAn9SYRslQ-jQuQhAZ1JP2nyg8QP7o3f-ehAsx3cgZrjcwIl4qH7s42p2omfEXTsUbInk8icXNpIpcAp9u2Yulxx0JmAai1CuZUOEu6wMSlF7CTEt8d1HBtOL6i-iIIYzZ~UqYpoG54adpSlBYb6LxmBPeH9~Pd54I5PIaA7hDKOdWacGaEpO8b65YMZkoZ-KHMXQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"},{"id":3268,"name":"Computational Linguistics","url":"https://www.academia.edu/Documents/in/Computational_Linguistics"},{"id":3324,"name":"Arabic","url":"https://www.academia.edu/Documents/in/Arabic"},{"id":4696,"name":"Machine Translation","url":"https://www.academia.edu/Documents/in/Machine_Translation"},{"id":52672,"name":"Language Technology","url":"https://www.academia.edu/Documents/in/Language_Technology"},{"id":2070034,"name":"Data Format","url":"https://www.academia.edu/Documents/in/Data_Format"},{"id":3729564,"name":"Human language","url":"https://www.academia.edu/Documents/in/Human_language"}],"urls":[{"id":46723354,"url":"https://doi.org/10.1007/s10590-011-9087-8"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="127436446"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127436446/A_Systematic_Review_of_Knowledge_Management_Integration_in_Higher_Educational_Institution_with_an_Emphasis_on_a_Blended_Learning_Environment"><img alt="Research paper thumbnail of A Systematic Review of Knowledge Management Integration in Higher Educational Institution with an Emphasis on a Blended Learning Environment" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/127436446/A_Systematic_Review_of_Knowledge_Management_Integration_in_Higher_Educational_Institution_with_an_Emphasis_on_a_Blended_Learning_Environment">A Systematic Review of Knowledge Management Integration in Higher Educational Institution with an Emphasis on a Blended Learning Environment</a></div><div class="wp-workCard_item"><span>Lecture notes in networks and systems</span><span>, Oct 23, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127436446"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127436446"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127436446; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127436446]").text(description); $(".js-view-count[data-work-id=127436446]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 127436446; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='127436446']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=127436446]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":127436446,"title":"A Systematic Review of Knowledge Management Integration in Higher Educational Institution with an Emphasis on a Blended Learning Environment","translated_title":"","metadata":{"publisher":"Springer International Publishing","publication_date":{"day":23,"month":10,"year":2022,"errors":{}},"publication_name":"Lecture notes in networks and systems"},"translated_abstract":null,"internal_url":"https://www.academia.edu/127436446/A_Systematic_Review_of_Knowledge_Management_Integration_in_Higher_Educational_Institution_with_an_Emphasis_on_a_Blended_Learning_Environment","translated_internal_url":"","created_at":"2025-02-03T07:24:08.291-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":30994194,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_Systematic_Review_of_Knowledge_Management_Integration_in_Higher_Educational_Institution_with_an_Emphasis_on_a_Blended_Learning_Environment","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":null,"owner":{"id":30994194,"first_name":"Khaled","middle_initials":null,"last_name":"Shaalan","page_name":"KShaalan","domain_name":"buid","created_at":"2015-05-11T05:38:58.984-07:00","display_name":"Khaled Shaalan","url":"https://buid.academia.edu/KShaalan"},"attachments":[],"research_interests":[{"id":1241,"name":"Knowledge Management","url":"https://www.academia.edu/Documents/in/Knowledge_Management"},{"id":172331,"name":"Learning management","url":"https://www.academia.edu/Documents/in/Learning_management"}],"urls":[{"id":46723353,"url":"https://doi.org/10.1007/978-3-031-16865-9_25"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/google_contacts-0dfb882d836b94dbcb4a2d123d6933fc9533eda5be911641f20b4eb428429600.js"], function() { // from javascript_helper.rb $('.js-google-connect-button').click(function(e) { e.preventDefault(); GoogleContacts.authorize_and_show_contacts(); Aedu.Dismissibles.recordClickthrough("WowProfileImportContactsPrompt"); }); $('.js-update-biography-button').click(function(e) { e.preventDefault(); Aedu.Dismissibles.recordClickthrough("UpdateUserBiographyPrompt"); $.ajax({ url: $r.api_v0_profiles_update_about_path({ subdomain_param: 'api', about: "", }), type: 'PUT', success: function(response) { location.reload(); } }); }); $('.js-work-creator-button').click(function (e) { e.preventDefault(); window.location = $r.upload_funnel_document_path({ source: encodeURIComponent(""), }); }); $('.js-video-upload-button').click(function (e) { e.preventDefault(); window.location = $r.upload_funnel_video_path({ source: encodeURIComponent(""), }); }); $('.js-do-this-later-button').click(function() { $(this).closest('.js-profile-nag-panel').remove(); Aedu.Dismissibles.recordDismissal("WowProfileImportContactsPrompt"); }); $('.js-update-biography-do-this-later-button').click(function(){ $(this).closest('.js-profile-nag-panel').remove(); Aedu.Dismissibles.recordDismissal("UpdateUserBiographyPrompt"); }); $('.wow-profile-mentions-upsell--close').click(function(){ $('.wow-profile-mentions-upsell--panel').hide(); Aedu.Dismissibles.recordDismissal("WowProfileMentionsUpsell"); }); $('.wow-profile-mentions-upsell--button').click(function(){ Aedu.Dismissibles.recordClickthrough("WowProfileMentionsUpsell"); }); new WowProfile.SocialRedesignUserWorks({ initialWorksOffset: 20, allWorksOffset: 20, maxSections: 1 }) }); </script> </div></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile_edit-5ea339ee107c863779f560dd7275595239fed73f1a13d279d2b599a28c0ecd33.js","https://a.academia-assets.com/assets/add_coauthor-22174b608f9cb871d03443cafa7feac496fb50d7df2d66a53f5ee3c04ba67f53.js","https://a.academia-assets.com/assets/tab-dcac0130902f0cc2d8cb403714dd47454f11fc6fb0e99ae6a0827b06613abc20.js","https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js"], function() { // from javascript_helper.rb window.ae = window.ae || {}; window.ae.WowProfile = window.ae.WowProfile || {}; if(Aedu.User.current && Aedu.User.current.id === $viewedUser.id) { window.ae.WowProfile.current_user_edit = {}; new WowProfileEdit.EditUploadView({ el: '.js-edit-upload-button-wrapper', model: window.$current_user, }); new AddCoauthor.AddCoauthorsController(); } var userInfoView = new WowProfile.SocialRedesignUserInfo({ recaptcha_key: "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB" }); WowProfile.router = new WowProfile.Router({ userInfoView: userInfoView }); Backbone.history.start({ pushState: true, root: "/" + $viewedUser.page_name }); new WowProfile.UserWorksNav() }); </script> </div> <div class="bootstrap login"><div class="modal fade login-modal" id="login-modal"><div class="login-modal-dialog modal-dialog"><div class="modal-content"><div class="modal-header"><button class="close close" data-dismiss="modal" type="button"><span aria-hidden="true">×</span><span class="sr-only">Close</span></button><h4 class="modal-title text-center"><strong>Log In</strong></h4></div><div class="modal-body"><div class="row"><div class="col-xs-10 col-xs-offset-1"><button class="btn btn-fb btn-lg btn-block btn-v-center-content" id="login-facebook-oauth-button"><svg style="float: left; width: 19px; line-height: 1em; margin-right: .3em;" aria-hidden="true" focusable="false" data-prefix="fab" data-icon="facebook-square" class="svg-inline--fa fa-facebook-square fa-w-14" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path fill="currentColor" d="M400 32H48A48 48 0 0 0 0 80v352a48 48 0 0 0 48 48h137.25V327.69h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.27c-30.81 0-40.42 19.12-40.42 38.73V256h68.78l-11 71.69h-57.78V480H400a48 48 0 0 0 48-48V80a48 48 0 0 0-48-48z"></path></svg><small><strong>Log in</strong> with <strong>Facebook</strong></small></button><br /><button class="btn btn-google btn-lg btn-block btn-v-center-content" id="login-google-oauth-button"><svg style="float: left; width: 22px; line-height: 1em; margin-right: .3em;" aria-hidden="true" focusable="false" data-prefix="fab" data-icon="google-plus" class="svg-inline--fa fa-google-plus fa-w-16" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M256,8C119.1,8,8,119.1,8,256S119.1,504,256,504,504,392.9,504,256,392.9,8,256,8ZM185.3,380a124,124,0,0,1,0-248c31.3,0,60.1,11,83,32.3l-33.6,32.6c-13.2-12.9-31.3-19.1-49.4-19.1-42.9,0-77.2,35.5-77.2,78.1S142.3,334,185.3,334c32.6,0,64.9-19.1,70.1-53.3H185.3V238.1H302.2a109.2,109.2,0,0,1,1.9,20.7c0,70.8-47.5,121.2-118.8,121.2ZM415.5,273.8v35.5H380V273.8H344.5V238.3H380V202.8h35.5v35.5h35.2v35.5Z"></path></svg><small><strong>Log in</strong> with <strong>Google</strong></small></button><br /><style type="text/css">.sign-in-with-apple-button { width: 100%; height: 52px; border-radius: 3px; border: 1px solid black; cursor: pointer; } .sign-in-with-apple-button > div { margin: 0 auto; / This centers the Apple-rendered button horizontally }</style><script src="https://appleid.cdn-apple.com/appleauth/static/jsapi/appleid/1/en_US/appleid.auth.js" type="text/javascript"></script><div class="sign-in-with-apple-button" data-border="false" data-color="white" id="appleid-signin"><span   ="Sign Up with Apple" class="u-fs11"></span></div><script>AppleID.auth.init({ clientId: 'edu.academia.applesignon', scope: 'name email', redirectURI: 'https://www.academia.edu/sessions', state: "2cd200cfebcdfd7ba93e45f92ba238b763f32cafc5e0824b4024578513871298", });</script><script>// Hacky way of checking if on fast loswp if (window.loswp == null) { (function() { const Google = window?.Aedu?.Auth?.OauthButton?.Login?.Google; const Facebook = window?.Aedu?.Auth?.OauthButton?.Login?.Facebook; if (Google) { new Google({ el: '#login-google-oauth-button', rememberMeCheckboxId: 'remember_me', track: null }); } if (Facebook) { new Facebook({ el: '#login-facebook-oauth-button', rememberMeCheckboxId: 'remember_me', track: null }); } })(); }</script></div></div></div><div class="modal-body"><div class="row"><div class="col-xs-10 col-xs-offset-1"><div class="hr-heading login-hr-heading"><span class="hr-heading-text">or</span></div></div></div></div><div class="modal-body"><div class="row"><div class="col-xs-10 col-xs-offset-1"><form class="js-login-form" action="https://www.academia.edu/sessions" accept-charset="UTF-8" method="post"><input type="hidden" name="authenticity_token" value="xmwTXHS3FByyfU_ps08UwaJeZF4cOEW8VMWVZbelV1HjEPOIJY2maXTUuY0hpjSIc36oSeRie4kv6OPWqMyyVA" autocomplete="off" /><div class="form-group"><label class="control-label" for="login-modal-email-input" style="font-size: 14px;">Email</label><input class="form-control" id="login-modal-email-input" name="login" type="email" /></div><div class="form-group"><label class="control-label" for="login-modal-password-input" style="font-size: 14px;">Password</label><input class="form-control" id="login-modal-password-input" name="password" type="password" /></div><input type="hidden" name="post_login_redirect_url" id="post_login_redirect_url" value="https://buid.academia.edu/KShaalan" autocomplete="off" /><div class="checkbox"><label><input type="checkbox" name="remember_me" id="remember_me" value="1" checked="checked" /><small style="font-size: 12px; margin-top: 2px; display: inline-block;">Remember me on this computer</small></label></div><br><input type="submit" name="commit" value="Log In" class="btn btn-primary btn-block btn-lg js-login-submit" data-disable-with="Log In" /></br></form><script>typeof window?.Aedu?.recaptchaManagedForm === 'function' && window.Aedu.recaptchaManagedForm( document.querySelector('.js-login-form'), document.querySelector('.js-login-submit') );</script><small style="font-size: 12px;"><br />or <a data-target="#login-modal-reset-password-container" data-toggle="collapse" href="javascript:void(0)">reset password</a></small><div class="collapse" id="login-modal-reset-password-container"><br /><div class="well margin-0x"><form class="js-password-reset-form" action="https://www.academia.edu/reset_password" accept-charset="UTF-8" method="post"><input type="hidden" name="authenticity_token" value="6z0EClsT7RwOHaZ58ERP_Xb9R2fqUKqTHIh1FrY3cnzOQeTeCilfaci0UB1irW-0p92LcBIKlKZnpQOlqV6XeQ" autocomplete="off" /><p>Enter the email address you signed up with and we'll email you a reset link.</p><div class="form-group"><input class="form-control" name="email" type="email" /></div><script src="https://recaptcha.net/recaptcha/api.js" async defer></script> <script> var invisibleRecaptchaSubmit = function () { var closestForm = function (ele) { var curEle = ele.parentNode; while (curEle.nodeName !== 'FORM' && curEle.nodeName !== 'BODY'){ curEle = curEle.parentNode; } return curEle.nodeName === 'FORM' ? curEle : null }; var eles = document.getElementsByClassName('g-recaptcha'); if (eles.length > 0) { var form = closestForm(eles[0]); if (form) { form.submit(); } } }; </script> <input type="submit" data-sitekey="6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj" data-callback="invisibleRecaptchaSubmit" class="g-recaptcha btn btn-primary btn-block" value="Email me a link" value=""/> </form></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/collapse-45805421cf446ca5adf7aaa1935b08a3a8d1d9a6cc5d91a62a2a3a00b20b3e6a.js"], function() { // from javascript_helper.rb $("#login-modal-reset-password-container").on("shown.bs.collapse", function() { $(this).find("input[type=email]").focus(); }); }); </script> </div></div></div><div class="modal-footer"><div class="text-center"><small style="font-size: 12px;">Need an account? <a rel="nofollow" href="https://www.academia.edu/signup">Click here to sign up</a></small></div></div></div></div></div></div><script>// If we are on subdomain or non-bootstrapped page, redirect to login page instead of showing modal (function(){ if (typeof $ === 'undefined') return; var host = window.location.hostname; if ((host === $domain || host === "www."+$domain) && (typeof $().modal === 'function')) { $("#nav_log_in").click(function(e) { // Don't follow the link and open the modal e.preventDefault(); $("#login-modal").on('shown.bs.modal', function() { $(this).find("#login-modal-email-input").focus() }).modal('show'); }); } })()</script> <div class="bootstrap" id="footer"><div class="footer-content clearfix text-center padding-top-7x" style="width:100%;"><ul class="footer-links-secondary footer-links-wide list-inline margin-bottom-1x"><li><a href="https://www.academia.edu/about">About</a></li><li><a href="https://www.academia.edu/press">Press</a></li><li><a href="https://www.academia.edu/documents">Papers</a></li><li><a href="https://www.academia.edu/topics">Topics</a></li><li><a href="https://www.academia.edu/journals">Academia.edu Journals</a></li><li><a rel="nofollow" href="https://www.academia.edu/hiring"><svg style="width: 13px; height: 13px;" aria-hidden="true" focusable="false" data-prefix="fas" data-icon="briefcase" class="svg-inline--fa fa-briefcase fa-w-16" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M320 336c0 8.84-7.16 16-16 16h-96c-8.84 0-16-7.16-16-16v-48H0v144c0 25.6 22.4 48 48 48h416c25.6 0 48-22.4 48-48V288H320v48zm144-208h-80V80c0-25.6-22.4-48-48-48H176c-25.6 0-48 22.4-48 48v48H48c-25.6 0-48 22.4-48 48v80h512v-80c0-25.6-22.4-48-48-48zm-144 0H192V96h128v32z"></path></svg> <strong>We're Hiring!</strong></a></li><li><a rel="nofollow" href="https://support.academia.edu/hc/en-us"><svg style="width: 12px; height: 12px;" aria-hidden="true" focusable="false" data-prefix="fas" data-icon="question-circle" class="svg-inline--fa fa-question-circle fa-w-16" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M504 256c0 136.997-111.043 248-248 248S8 392.997 8 256C8 119.083 119.043 8 256 8s248 111.083 248 248zM262.655 90c-54.497 0-89.255 22.957-116.549 63.758-3.536 5.286-2.353 12.415 2.715 16.258l34.699 26.31c5.205 3.947 12.621 3.008 16.665-2.122 17.864-22.658 30.113-35.797 57.303-35.797 20.429 0 45.698 13.148 45.698 32.958 0 14.976-12.363 22.667-32.534 33.976C247.128 238.528 216 254.941 216 296v4c0 6.627 5.373 12 12 12h56c6.627 0 12-5.373 12-12v-1.333c0-28.462 83.186-29.647 83.186-106.667 0-58.002-60.165-102-116.531-102zM256 338c-25.365 0-46 20.635-46 46 0 25.364 20.635 46 46 46s46-20.636 46-46c0-25.365-20.635-46-46-46z"></path></svg> <strong>Help Center</strong></a></li></ul><ul class="footer-links-tertiary list-inline margin-bottom-1x"><li class="small">Find new research papers in:</li><li class="small"><a href="https://www.academia.edu/Documents/in/Physics">Physics</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Chemistry">Chemistry</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Biology">Biology</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Health_Sciences">Health Sciences</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Ecology">Ecology</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Earth_Sciences">Earth Sciences</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Cognitive_Science">Cognitive Science</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Mathematics">Mathematics</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a></li></ul></div></div><div class="DesignSystem" id="credit" style="width:100%;"><ul class="u-pl0x footer-links-legal list-inline"><li><a rel="nofollow" href="https://www.academia.edu/terms">Terms</a></li><li><a rel="nofollow" href="https://www.academia.edu/privacy">Privacy</a></li><li><a rel="nofollow" href="https://www.academia.edu/copyright">Copyright</a></li><li>Academia ©2025</li></ul></div><script> //<![CDATA[ window.detect_gmtoffset = true; window.Academia && window.Academia.set_gmtoffset && Academia.set_gmtoffset('/gmtoffset'); //]]> </script> <div id='overlay_background'></div> <div id='bootstrap-modal-container' class='bootstrap'></div> <div id='ds-modal-container' class='bootstrap DesignSystem'></div> <div id='full-screen-modal'></div> </div> </body> </html>