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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="Conference Presentations" id="Conference Presentations"><h3 class="profile--tab_heading_container">Conference Presentations by Grenze International Journal of Engineering and Technology GIJET</h3></div><div class="js-work-strip profile--work_container" data-work-id="119809427"><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/119809427/Detection_of_Cataract_and_its_Level_based_on_Deep_Learning_using_Mobile_Application"><img alt="Research paper thumbnail of Detection of Cataract and its Level based on Deep Learning using Mobile Application" class="work-thumbnail" src="https://attachments.academia-assets.com/115146963/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/119809427/Detection_of_Cataract_and_its_Level_based_on_Deep_Learning_using_Mobile_Application">Detection of Cataract and its Level based on Deep Learning using Mobile Application</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The human eye has a natural lens that refracts the incoming light rays to help us see objects. Ca...</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 human eye has a natural lens that refracts the incoming light rays to help us see<br />objects. Cataracts could be the reason why the eye's natural lens becomes cloudy. When<br />proteins in the lens start breaking down, causing cataracts, objects may appear cloudy, fuzzy,<br />or even less colorful. If Cataract is not identified and treated in the early stages, it could lead to<br />complete blindness of the eye. It is mainly observed in older age groups than the younger age<br />group, however, there are cases witnessed even in young people. We are creating a mobile<br />application using AI and deep learning that can detect the existence of cataracts to help with<br />the scarcity of ophthalmologists. With this application, patients can use their smartphones to<br />click the photograph of a patient's eye and feed the data into this AI-based system that is<br />developed using deep learning technologies. The model then determines whether the eye has a<br />nuclear sclerotic, cortical, or posterior subcapsular. The effect of this system will result in far<br />improvement in the delivery of public services, diagnosis and treatment, prioritization of<br />patients, and ultimately, the prevention of blindness. This system will demonstrate encouraging<br />outcomes.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="25ad16a68e270905b894c7c210455887" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146963,"asset_id":119809427,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146963/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119809427"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119809427"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119809427; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119809427]").text(description); $(".js-view-count[data-work-id=119809427]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119809427; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119809427']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119809427, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "25ad16a68e270905b894c7c210455887" } } $('.js-work-strip[data-work-id=119809427]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119809427,"title":"Detection of Cataract and its Level based on Deep Learning using Mobile Application","translated_title":"","metadata":{"abstract":"The human eye has a natural lens that refracts the incoming light rays to help us see\nobjects. Cataracts could be the reason why the eye's natural lens becomes cloudy. When\nproteins in the lens start breaking down, causing cataracts, objects may appear cloudy, fuzzy,\nor even less colorful. If Cataract is not identified and treated in the early stages, it could lead to\ncomplete blindness of the eye. It is mainly observed in older age groups than the younger age\ngroup, however, there are cases witnessed even in young people. We are creating a mobile\napplication using AI and deep learning that can detect the existence of cataracts to help with\nthe scarcity of ophthalmologists. With this application, patients can use their smartphones to\nclick the photograph of a patient's eye and feed the data into this AI-based system that is\ndeveloped using deep learning technologies. The model then determines whether the eye has a\nnuclear sclerotic, cortical, or posterior subcapsular. The effect of this system will result in far\nimprovement in the delivery of public services, diagnosis and treatment, prioritization of\npatients, and ultimately, the prevention of blindness. This system will demonstrate encouraging\noutcomes.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"The human eye has a natural lens that refracts the incoming light rays to help us see\nobjects. Cataracts could be the reason why the eye's natural lens becomes cloudy. When\nproteins in the lens start breaking down, causing cataracts, objects may appear cloudy, fuzzy,\nor even less colorful. If Cataract is not identified and treated in the early stages, it could lead to\ncomplete blindness of the eye. It is mainly observed in older age groups than the younger age\ngroup, however, there are cases witnessed even in young people. We are creating a mobile\napplication using AI and deep learning that can detect the existence of cataracts to help with\nthe scarcity of ophthalmologists. With this application, patients can use their smartphones to\nclick the photograph of a patient's eye and feed the data into this AI-based system that is\ndeveloped using deep learning technologies. The model then determines whether the eye has a\nnuclear sclerotic, cortical, or posterior subcapsular. The effect of this system will result in far\nimprovement in the delivery of public services, diagnosis and treatment, prioritization of\npatients, and ultimately, the prevention of blindness. This system will demonstrate encouraging\noutcomes.","internal_url":"https://www.academia.edu/119809427/Detection_of_Cataract_and_its_Level_based_on_Deep_Learning_using_Mobile_Application","translated_internal_url":"","created_at":"2024-05-22T04:53:33.263-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[],"downloadable_attachments":[{"id":115146963,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146963/thumbnails/1.jpg","file_name":"512_3_2946_2951.pdf","download_url":"https://www.academia.edu/attachments/115146963/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Detection_of_Cataract_and_its_Level_base.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146963/512_3_2946_2951-libre.pdf?1716382652=\u0026response-content-disposition=attachment%3B+filename%3DDetection_of_Cataract_and_its_Level_base.pdf\u0026Expires=1732468482\u0026Signature=K6osJCGXTPHM732OikSDLpvfgdaFmlS1XW909wCD8Cr3VfIdUgFroxmgf-f-Mlior3lDP2OQc0peHCyIvFn2yNby7pmQ~uGuBe4NUlPcIzh9-QnfHBd~bG1CdSeU8eAhGgK~r257HHbUXDR-cjq9WaJJCkdLBs8nc~NQ0V-jSEe396ihllRLGKBFW7wDg~vZjbzgoE16813hk7vzvCjXI~AlG76mRYqOc3n3-RvPlbDCN9deTADvuZ9MryIvAL8DJTw16LOMAEbPo-XfN2aQm0lb3N8bziscDjDFP-WWb9KvfWbjrA8v5TxuQF82-D-~-wzPRajqnAgf9mFtN-e1Jg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Detection_of_Cataract_and_its_Level_based_on_Deep_Learning_using_Mobile_Application","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146963,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146963/thumbnails/1.jpg","file_name":"512_3_2946_2951.pdf","download_url":"https://www.academia.edu/attachments/115146963/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Detection_of_Cataract_and_its_Level_base.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146963/512_3_2946_2951-libre.pdf?1716382652=\u0026response-content-disposition=attachment%3B+filename%3DDetection_of_Cataract_and_its_Level_base.pdf\u0026Expires=1732468482\u0026Signature=K6osJCGXTPHM732OikSDLpvfgdaFmlS1XW909wCD8Cr3VfIdUgFroxmgf-f-Mlior3lDP2OQc0peHCyIvFn2yNby7pmQ~uGuBe4NUlPcIzh9-QnfHBd~bG1CdSeU8eAhGgK~r257HHbUXDR-cjq9WaJJCkdLBs8nc~NQ0V-jSEe396ihllRLGKBFW7wDg~vZjbzgoE16813hk7vzvCjXI~AlG76mRYqOc3n3-RvPlbDCN9deTADvuZ9MryIvAL8DJTw16LOMAEbPo-XfN2aQm0lb3N8bziscDjDFP-WWb9KvfWbjrA8v5TxuQF82-D-~-wzPRajqnAgf9mFtN-e1Jg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119809304"><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/119809304/Orthogonal_Frequency_Division_Multiplexing_A_Review"><img alt="Research paper thumbnail of Orthogonal Frequency Division Multiplexing: A Review" class="work-thumbnail" src="https://attachments.academia-assets.com/115146926/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/119809304/Orthogonal_Frequency_Division_Multiplexing_A_Review">Orthogonal Frequency Division Multiplexing: A Review</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Orthogonal frequency-division multiplexing significantly reduces inter-symbol interference (I.S.I...</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">Orthogonal frequency-division multiplexing significantly reduces inter-symbol interference (I.S.I.) caused by the delayed propagation of wireless channels (OFDM). As a result, numerous standards have embraced it, and it is now utilized in various wireless systems. This paper will overview OFDM and how it can be used in wireless communications. The basics of OFDM and related modulations are covered, along with methods for enhancing OFDM's performance in wireless communications. Examples include channel estimation and signal detection, time and frequency offset estimation and correction, peak-to-average power ratio reduction PAPR, and inter-carrier interference. In wireless transmission, the fundamental concept of OFDM is gaining acceptance. OFDM is also one of the most often recommended techniques for usage in 4th Generation Wireless Systems. The cyclic extension of OFDM signals is a general principle rather than employing empty guard spaces in the frequency domain. OFDM is a modulation and multiplexing technology used in WiFi, Wi-MAX, and 3G/4G/5G mobile communication systems. A low bit rate at the subcarrier is desired to extend symbol duration and prevent multipath distortion in OFDM. This will also improve the (C.P.) interval, which is a good thing (multiple distortions and error reduction). Due to the larger C.P. interval, however, it will result in a better energy loss value. The duration of the OFDM symbol, the C.P. interval, and the permissible delay (environment) spread must all be balanced.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a60bd08a2217293e28b7c5b73dc9647a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146926,"asset_id":119809304,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146926/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119809304"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119809304"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119809304; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119809304]").text(description); $(".js-view-count[data-work-id=119809304]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119809304; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119809304']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119809304, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "a60bd08a2217293e28b7c5b73dc9647a" } } $('.js-work-strip[data-work-id=119809304]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119809304,"title":"Orthogonal Frequency Division Multiplexing: A Review","translated_title":"","metadata":{"abstract":"Orthogonal frequency-division multiplexing significantly reduces inter-symbol interference (I.S.I.) caused by the delayed propagation of wireless channels (OFDM). As a result, numerous standards have embraced it, and it is now utilized in various wireless systems. This paper will overview OFDM and how it can be used in wireless communications. The basics of OFDM and related modulations are covered, along with methods for enhancing OFDM's performance in wireless communications. Examples include channel estimation and signal detection, time and frequency offset estimation and correction, peak-to-average power ratio reduction PAPR, and inter-carrier interference. In wireless transmission, the fundamental concept of OFDM is gaining acceptance. OFDM is also one of the most often recommended techniques for usage in 4th Generation Wireless Systems. The cyclic extension of OFDM signals is a general principle rather than employing empty guard spaces in the frequency domain. OFDM is a modulation and multiplexing technology used in WiFi, Wi-MAX, and 3G/4G/5G mobile communication systems. A low bit rate at the subcarrier is desired to extend symbol duration and prevent multipath distortion in OFDM. This will also improve the (C.P.) interval, which is a good thing (multiple distortions and error reduction). Due to the larger C.P. interval, however, it will result in a better energy loss value. The duration of the OFDM symbol, the C.P. interval, and the permissible delay (environment) spread must all be balanced.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"Orthogonal frequency-division multiplexing significantly reduces inter-symbol interference (I.S.I.) caused by the delayed propagation of wireless channels (OFDM). As a result, numerous standards have embraced it, and it is now utilized in various wireless systems. This paper will overview OFDM and how it can be used in wireless communications. The basics of OFDM and related modulations are covered, along with methods for enhancing OFDM's performance in wireless communications. Examples include channel estimation and signal detection, time and frequency offset estimation and correction, peak-to-average power ratio reduction PAPR, and inter-carrier interference. In wireless transmission, the fundamental concept of OFDM is gaining acceptance. OFDM is also one of the most often recommended techniques for usage in 4th Generation Wireless Systems. The cyclic extension of OFDM signals is a general principle rather than employing empty guard spaces in the frequency domain. OFDM is a modulation and multiplexing technology used in WiFi, Wi-MAX, and 3G/4G/5G mobile communication systems. A low bit rate at the subcarrier is desired to extend symbol duration and prevent multipath distortion in OFDM. This will also improve the (C.P.) interval, which is a good thing (multiple distortions and error reduction). Due to the larger C.P. interval, however, it will result in a better energy loss value. The duration of the OFDM symbol, the C.P. interval, and the permissible delay (environment) spread must all be balanced.","internal_url":"https://www.academia.edu/119809304/Orthogonal_Frequency_Division_Multiplexing_A_Review","translated_internal_url":"","created_at":"2024-05-22T04:51:43.368-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730577,"work_id":119809304,"tagging_user_id":32919427,"tagged_user_id":263970291,"co_author_invite_id":null,"email":"y***3@gmail.com","display_order":1,"name":"YOGITA KAPSE","title":"Orthogonal Frequency Division Multiplexing: A Review"},{"id":41730578,"work_id":119809304,"tagging_user_id":32919427,"tagged_user_id":165782498,"co_author_invite_id":null,"email":"s***i@gmail.com","display_order":2,"name":"SHRIPAD MOHANI","title":"Orthogonal Frequency Division Multiplexing: A Review"}],"downloadable_attachments":[{"id":115146926,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146926/thumbnails/1.jpg","file_name":"511_1_2941_2945.pdf","download_url":"https://www.academia.edu/attachments/115146926/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Orthogonal_Frequency_Division_Multiplexi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146926/511_1_2941_2945-libre.pdf?1716382655=\u0026response-content-disposition=attachment%3B+filename%3DOrthogonal_Frequency_Division_Multiplexi.pdf\u0026Expires=1732468483\u0026Signature=YQfvg8Mha6ILQdtDOcc4OcozH19rlmqeER-SLdjJryxkVmElccKT5v4ORb6nnhvGscZyK4TacSb2zZYrHvrOvtxyUwKCeZtp1RHtXWP0MqKxC4sm4eepcsVQZ~8w1vrS~Y6eD~9~aoRBktyXy1mvJYbpLvTFQhrzVcUCR0kPHNP6PK7nC3Z6WFy6uhEoaistO-YmNEsbfkGXa0XDgx-aSTIKIACoXNrmpdX1f7ZqVetThuHiNubFhELeijpVHKviAt4cVHhJPfWl6Lhda1agZXH~DpAkgulEWir6lbeHaNJFrktGW2aKzNWvMI4aDtqKEqvDxy46PbDKEc~~nX8phA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Orthogonal_Frequency_Division_Multiplexing_A_Review","translated_slug":"","page_count":5,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146926,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146926/thumbnails/1.jpg","file_name":"511_1_2941_2945.pdf","download_url":"https://www.academia.edu/attachments/115146926/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Orthogonal_Frequency_Division_Multiplexi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146926/511_1_2941_2945-libre.pdf?1716382655=\u0026response-content-disposition=attachment%3B+filename%3DOrthogonal_Frequency_Division_Multiplexi.pdf\u0026Expires=1732468483\u0026Signature=YQfvg8Mha6ILQdtDOcc4OcozH19rlmqeER-SLdjJryxkVmElccKT5v4ORb6nnhvGscZyK4TacSb2zZYrHvrOvtxyUwKCeZtp1RHtXWP0MqKxC4sm4eepcsVQZ~8w1vrS~Y6eD~9~aoRBktyXy1mvJYbpLvTFQhrzVcUCR0kPHNP6PK7nC3Z6WFy6uhEoaistO-YmNEsbfkGXa0XDgx-aSTIKIACoXNrmpdX1f7ZqVetThuHiNubFhELeijpVHKviAt4cVHhJPfWl6Lhda1agZXH~DpAkgulEWir6lbeHaNJFrktGW2aKzNWvMI4aDtqKEqvDxy46PbDKEc~~nX8phA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119809172"><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/119809172/An_Optimal_Selection_Scheme_for_Automation_Testing_Framework_with_Quality_Assurance"><img alt="Research paper thumbnail of An Optimal Selection Scheme for Automation Testing Framework with Quality Assurance" class="work-thumbnail" src="https://attachments.academia-assets.com/115146833/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/119809172/An_Optimal_Selection_Scheme_for_Automation_Testing_Framework_with_Quality_Assurance">An Optimal Selection Scheme for Automation Testing Framework with Quality Assurance</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The world is fast changing due to digitalization and the disruption brought on by the use of digi...</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 world is fast changing due to digitalization and the disruption brought on by the use of digital technology. All IT operations require a high level of speed, and this necessitates a paradigm shift in quality assurance (QA). Digital assurance places a strong emphasis on quality at high speed and businesses want to produce high-quality goods more quickly than ever. As a result, QA teams are turning to test automation. The industry is shifting from the initial automation of regression tests to progressive automation and day one automation. Automation testing has seen a number of developments. However, it is crucial that businesses pick the appropriate automation framework because it is thought to be a key component of success. The paper considers quality assurance parameters for automation testing framework by adopting digital transformation initiatives to achieve the desired business outcome. In the paper we proposes a model of optimal testing framework selection and we'll discuss the different automation framework types and how to pick one that will assist enterprises to achieve their digital assurance objectives.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="99c6318649d441de92799aa94ed8e459" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146833,"asset_id":119809172,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146833/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119809172"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119809172"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119809172; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119809172]").text(description); $(".js-view-count[data-work-id=119809172]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119809172; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119809172']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119809172, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "99c6318649d441de92799aa94ed8e459" } } $('.js-work-strip[data-work-id=119809172]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119809172,"title":"An Optimal Selection Scheme for Automation Testing Framework with Quality Assurance","translated_title":"","metadata":{"abstract":"The world is fast changing due to digitalization and the disruption brought on by the use of digital technology. All IT operations require a high level of speed, and this necessitates a paradigm shift in quality assurance (QA). Digital assurance places a strong emphasis on quality at high speed and businesses want to produce high-quality goods more quickly than ever. As a result, QA teams are turning to test automation. The industry is shifting from the initial automation of regression tests to progressive automation and day one automation. Automation testing has seen a number of developments. However, it is crucial that businesses pick the appropriate automation framework because it is thought to be a key component of success. The paper considers quality assurance parameters for automation testing framework by adopting digital transformation initiatives to achieve the desired business outcome. In the paper we proposes a model of optimal testing framework selection and we'll discuss the different automation framework types and how to pick one that will assist enterprises to achieve their digital assurance objectives.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"The world is fast changing due to digitalization and the disruption brought on by the use of digital technology. All IT operations require a high level of speed, and this necessitates a paradigm shift in quality assurance (QA). Digital assurance places a strong emphasis on quality at high speed and businesses want to produce high-quality goods more quickly than ever. As a result, QA teams are turning to test automation. The industry is shifting from the initial automation of regression tests to progressive automation and day one automation. Automation testing has seen a number of developments. However, it is crucial that businesses pick the appropriate automation framework because it is thought to be a key component of success. The paper considers quality assurance parameters for automation testing framework by adopting digital transformation initiatives to achieve the desired business outcome. In the paper we proposes a model of optimal testing framework selection and we'll discuss the different automation framework types and how to pick one that will assist enterprises to achieve their digital assurance objectives.","internal_url":"https://www.academia.edu/119809172/An_Optimal_Selection_Scheme_for_Automation_Testing_Framework_with_Quality_Assurance","translated_internal_url":"","created_at":"2024-05-22T04:49:32.192-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730564,"work_id":119809172,"tagging_user_id":32919427,"tagged_user_id":15782117,"co_author_invite_id":null,"email":"m***1@gmail.com","affiliation":"Indian Institute of Tropical Meteorology","display_order":1,"name":"Manmeet Singh","title":"An Optimal Selection Scheme for Automation Testing Framework with Quality Assurance"},{"id":41730565,"work_id":119809172,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8154030,"email":"j***y@medicaps.ac.in","display_order":2,"name":"Jitendra Choudhary","title":"An Optimal Selection Scheme for Automation Testing Framework with Quality Assurance"},{"id":41730566,"work_id":119809172,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":5780066,"email":"l***i@gmail.com","display_order":3,"name":"Lokesh Laddhani","title":"An Optimal Selection Scheme for Automation Testing Framework with Quality Assurance"}],"downloadable_attachments":[{"id":115146833,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146833/thumbnails/1.jpg","file_name":"510_3_2935_2940.pdf","download_url":"https://www.academia.edu/attachments/115146833/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Optimal_Selection_Scheme_for_Automati.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146833/510_3_2935_2940-libre.pdf?1716378608=\u0026response-content-disposition=attachment%3B+filename%3DAn_Optimal_Selection_Scheme_for_Automati.pdf\u0026Expires=1732468483\u0026Signature=Bh3mNbqhTKlehFwyxTAl7ftOsDYU3AEMUIK9q7vEW67w8AX3pvAn2viGTqDgfTh-bZecqxBxZdJJ0-f8BxD1pin~-enKYvmG3YEUs1Wo02uO9c-YqJfyvnMYjFrD2itvBUZxGepSjnjgm~8lrvgqt8UOzjIBuhsS1I59gs~08tq4zgvamtNP~db-pMe1nPHgvkvIJ3MSnkPHBRPgeA-8qDX-Tx8yvUmwGzQxtAkPiizJwqvasFpO5PImvQxBDQM~9oVQnl0gc5ph66vbTELKgPo43teK28mS6tWhgKBEW6HMQDjLTg~WfCZXadaOzSGCKjD6kkmY2NnVoIiZw9sPmA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"An_Optimal_Selection_Scheme_for_Automation_Testing_Framework_with_Quality_Assurance","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146833,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146833/thumbnails/1.jpg","file_name":"510_3_2935_2940.pdf","download_url":"https://www.academia.edu/attachments/115146833/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Optimal_Selection_Scheme_for_Automati.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146833/510_3_2935_2940-libre.pdf?1716378608=\u0026response-content-disposition=attachment%3B+filename%3DAn_Optimal_Selection_Scheme_for_Automati.pdf\u0026Expires=1732468483\u0026Signature=Bh3mNbqhTKlehFwyxTAl7ftOsDYU3AEMUIK9q7vEW67w8AX3pvAn2viGTqDgfTh-bZecqxBxZdJJ0-f8BxD1pin~-enKYvmG3YEUs1Wo02uO9c-YqJfyvnMYjFrD2itvBUZxGepSjnjgm~8lrvgqt8UOzjIBuhsS1I59gs~08tq4zgvamtNP~db-pMe1nPHgvkvIJ3MSnkPHBRPgeA-8qDX-Tx8yvUmwGzQxtAkPiizJwqvasFpO5PImvQxBDQM~9oVQnl0gc5ph66vbTELKgPo43teK28mS6tWhgKBEW6HMQDjLTg~WfCZXadaOzSGCKjD6kkmY2NnVoIiZw9sPmA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119808898"><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/119808898/Trading_Secured_Information_in_Cloud_Utilizing_Record_Cryptography_and_Speedy_Reaction_OTP"><img alt="Research paper thumbnail of Trading Secured Information in Cloud Utilizing Record Cryptography and Speedy Reaction OTP" class="work-thumbnail" src="https://attachments.academia-assets.com/115146658/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/119808898/Trading_Secured_Information_in_Cloud_Utilizing_Record_Cryptography_and_Speedy_Reaction_OTP">Trading Secured Information in Cloud Utilizing Record Cryptography and Speedy Reaction OTP</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">CLOUD capacity is presently picking up notoriety since it offers a adaptable on-demand informatio...</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">CLOUD capacity is presently picking up notoriety since it offers a adaptable on-demand information outsourcing benefit with engaging benefits: alleviation of the burden for capacity administration, widespread information get to with area freedom, and evasion of capital use on equipment, computer program, and individual maintenances. Be that as it may, existing arrangements in conventional information outsourcing situation are incapable to at the same time meet the taking after three security prerequisites for keys outsourcing: Privacy and protection of record capacity; Data security on personality properties tied to keys; Owner controllable authorization over user's shared keys. The proposed to begin with bound together key administration system that addresses all the three objectives over. This cloud framework permits the key proprietor can perform security and controllable authorization conjointly give implemented record encryption with least data spillage. To actualize Cloud Key Bank proficiently, this demonstrate proposes a unused calculation Halter kilter Calculation. Proposed exploratory comes about and security investigation will appear the productivity and security objectives are well accomplished. To unscramble the records we utilize QR OTP for security.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a042f2f808d6a3006d7e9ddfc87afcd6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146658,"asset_id":119808898,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146658/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119808898"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119808898"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119808898; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119808898]").text(description); $(".js-view-count[data-work-id=119808898]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119808898; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119808898']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119808898, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "a042f2f808d6a3006d7e9ddfc87afcd6" } } $('.js-work-strip[data-work-id=119808898]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119808898,"title":"Trading Secured Information in Cloud Utilizing Record Cryptography and Speedy Reaction OTP","translated_title":"","metadata":{"abstract":"CLOUD capacity is presently picking up notoriety since it offers a adaptable on-demand information outsourcing benefit with engaging benefits: alleviation of the burden for capacity administration, widespread information get to with area freedom, and evasion of capital use on equipment, computer program, and individual maintenances. Be that as it may, existing arrangements in conventional information outsourcing situation are incapable to at the same time meet the taking after three security prerequisites for keys outsourcing: Privacy and protection of record capacity; Data security on personality properties tied to keys; Owner controllable authorization over user's shared keys. The proposed to begin with bound together key administration system that addresses all the three objectives over. This cloud framework permits the key proprietor can perform security and controllable authorization conjointly give implemented record encryption with least data spillage. To actualize Cloud Key Bank proficiently, this demonstrate proposes a unused calculation Halter kilter Calculation. Proposed exploratory comes about and security investigation will appear the productivity and security objectives are well accomplished. To unscramble the records we utilize QR OTP for security.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"CLOUD capacity is presently picking up notoriety since it offers a adaptable on-demand information outsourcing benefit with engaging benefits: alleviation of the burden for capacity administration, widespread information get to with area freedom, and evasion of capital use on equipment, computer program, and individual maintenances. Be that as it may, existing arrangements in conventional information outsourcing situation are incapable to at the same time meet the taking after three security prerequisites for keys outsourcing: Privacy and protection of record capacity; Data security on personality properties tied to keys; Owner controllable authorization over user's shared keys. The proposed to begin with bound together key administration system that addresses all the three objectives over. This cloud framework permits the key proprietor can perform security and controllable authorization conjointly give implemented record encryption with least data spillage. To actualize Cloud Key Bank proficiently, this demonstrate proposes a unused calculation Halter kilter Calculation. Proposed exploratory comes about and security investigation will appear the productivity and security objectives are well accomplished. To unscramble the records we utilize QR OTP for security.","internal_url":"https://www.academia.edu/119808898/Trading_Secured_Information_in_Cloud_Utilizing_Record_Cryptography_and_Speedy_Reaction_OTP","translated_internal_url":"","created_at":"2024-05-22T04:47:18.673-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730558,"work_id":119808898,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8154028,"email":"k***s@nct.ac.in","display_order":1,"name":"M. 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Various gases play a vital rol...</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">Gas sampling and sensing is a domain of great importance nowadays. Various gases play a vital role in different industries ranging from process industries to bio medical engineering. The measurement of concentration of hazardous gas components like NO 2 is in high demand and hence many researchers are attracted towards the development of NO 2 sensors on the basis of various working principles. A single sensor cannot be the best sensor for all applications. This fact leads to in the need of selection criteria for the selection of the sensor for any industrial application. This paper presents a selection criterion for the selection of NO 2 sensor based on its important features and characteristics. Considering this one can select most suitable NO 2 sensor for a typical application. This paper also presents a qualitative comparison of commercially available NO 2 sensors which can be a handy tool for someone who is interested in the measurement of NO 2 concentration for certain application.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="93e1ed9cda7dcb3143e351ae574b3598" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146568,"asset_id":119808746,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146568/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119808746"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119808746"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119808746; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119808746]").text(description); $(".js-view-count[data-work-id=119808746]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119808746; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119808746']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119808746, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "93e1ed9cda7dcb3143e351ae574b3598" } } $('.js-work-strip[data-work-id=119808746]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119808746,"title":"Qualitative Comparison of Commercial NO2 Sensors and Selection Criteria for Various Applications","translated_title":"","metadata":{"abstract":"Gas sampling and sensing is a domain of great importance nowadays. 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This paper also presents a qualitative comparison of commercially available NO 2 sensors which can be a handy tool for someone who is interested in the measurement of NO 2 concentration for certain application.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"Gas sampling and sensing is a domain of great importance nowadays. Various gases play a vital role in different industries ranging from process industries to bio medical engineering. The measurement of concentration of hazardous gas components like NO 2 is in high demand and hence many researchers are attracted towards the development of NO 2 sensors on the basis of various working principles. A single sensor cannot be the best sensor for all applications. This fact leads to in the need of selection criteria for the selection of the sensor for any industrial application. This paper presents a selection criterion for the selection of NO 2 sensor based on its important features and characteristics. Considering this one can select most suitable NO 2 sensor for a typical application. This paper also presents a qualitative comparison of commercially available NO 2 sensors which can be a handy tool for someone who is interested in the measurement of NO 2 concentration for certain application.","internal_url":"https://www.academia.edu/119808746/Qualitative_Comparison_of_Commercial_NO2_Sensors_and_Selection_Criteria_for_Various_Applications","translated_internal_url":"","created_at":"2024-05-22T04:45:05.863-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730548,"work_id":119808746,"tagging_user_id":32919427,"tagged_user_id":218889369,"co_author_invite_id":null,"email":"v***3@gmail.com","display_order":1,"name":"Vrund Shah","title":"Qualitative Comparison of Commercial NO2 Sensors and Selection Criteria for Various Applications"}],"downloadable_attachments":[{"id":115146568,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146568/thumbnails/1.jpg","file_name":"507_3_2924_2928.pdf","download_url":"https://www.academia.edu/attachments/115146568/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Qualitative_Comparison_of_Commercial_NO2.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146568/507_3_2924_2928-libre.pdf?1716378624=\u0026response-content-disposition=attachment%3B+filename%3DQualitative_Comparison_of_Commercial_NO2.pdf\u0026Expires=1732468483\u0026Signature=aPpmNxPftoXlxkd9V-vJ6biIXmbnyIMkhgeEv~v~p6agY-mHSEnArUmHUwNPV4y9F7ufXocwzxBy5Q4CJcJpVV3en-2v-Ya0u6mU5yhNE2Bx-UJPWpzp8YmjOewJ2lm8AWB0i52NmIwL9vR0rWx6~OruvQXcu5Tw5B1hDCJRKrDpmtyZcbmI9To-b0NYEXmXhc5NQBqrx0TAmSz8BL3o0h7g7zJCHI2dEqoehONm1K0BqqWluhMFiYn9P~jcV8Nq15X6pRDbACSTBOCJdPZUA5Jp4rH1kIWujQv23Doyl3nHlME5NZ5XuVnyNsFd8bEv4~ZdP19T0MT8gzhQW1Z5UQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Qualitative_Comparison_of_Commercial_NO2_Sensors_and_Selection_Criteria_for_Various_Applications","translated_slug":"","page_count":5,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146568,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146568/thumbnails/1.jpg","file_name":"507_3_2924_2928.pdf","download_url":"https://www.academia.edu/attachments/115146568/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Qualitative_Comparison_of_Commercial_NO2.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146568/507_3_2924_2928-libre.pdf?1716378624=\u0026response-content-disposition=attachment%3B+filename%3DQualitative_Comparison_of_Commercial_NO2.pdf\u0026Expires=1732468483\u0026Signature=aPpmNxPftoXlxkd9V-vJ6biIXmbnyIMkhgeEv~v~p6agY-mHSEnArUmHUwNPV4y9F7ufXocwzxBy5Q4CJcJpVV3en-2v-Ya0u6mU5yhNE2Bx-UJPWpzp8YmjOewJ2lm8AWB0i52NmIwL9vR0rWx6~OruvQXcu5Tw5B1hDCJRKrDpmtyZcbmI9To-b0NYEXmXhc5NQBqrx0TAmSz8BL3o0h7g7zJCHI2dEqoehONm1K0BqqWluhMFiYn9P~jcV8Nq15X6pRDbACSTBOCJdPZUA5Jp4rH1kIWujQv23Doyl3nHlME5NZ5XuVnyNsFd8bEv4~ZdP19T0MT8gzhQW1Z5UQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119808623"><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/119808623/A_Classification_of_COVID_19_Cases_using_Fine_Tune_Different_Model_of_Machine_Learning"><img alt="Research paper thumbnail of A Classification of COVID-19 Cases using Fine-Tune Different Model of Machine Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/115146403/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/119808623/A_Classification_of_COVID_19_Cases_using_Fine_Tune_Different_Model_of_Machine_Learning">A Classification of COVID-19 Cases using Fine-Tune Different Model of Machine Learning</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The Machine Learning Techniques are used for finding upcoming or futuristic Prediction. It's very...</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 Machine Learning Techniques are used for finding upcoming or futuristic Prediction. It's very powerful tools in finding of future prediction. From Last decade to recent days Data mining and Machine Learning play vital roles in many Industries. In last 2-3 Years overall World is suffering from a new Virus i.e., COVID-19. The first covid case was detected in Month of December. In very less time it spread out throughout the world. In 2-3 months, this illness is declared as Pandemic by WHO (World Health Organization). The behaviour of spreading of COVID-19 was very abnormal. it was growing Exponentially. Here In this Paper as a Researcher's we are trying to find out overall Analysis of COVID-19. Here our main motto to finding How many patients gets infected, how many of them get cure in sometimes & finally How many of them goes to death. Here our main concentration is to finding the features of the COVID. We will be applying Machine Learning Algorithms like LR, SVM, DT and many more. After applying the above methods, we will find the performance measures of each one and compare with their values. Finally, we will try to Implement some new Enhancement in Existing Algorithms.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="88d1e727a5f5390c19412e89bfa41d2b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146403,"asset_id":119808623,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146403/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119808623"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119808623"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119808623; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119808623]").text(description); $(".js-view-count[data-work-id=119808623]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119808623; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119808623']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119808623, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "88d1e727a5f5390c19412e89bfa41d2b" } } $('.js-work-strip[data-work-id=119808623]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119808623,"title":"A Classification of COVID-19 Cases using Fine-Tune Different Model of Machine Learning","translated_title":"","metadata":{"abstract":"The Machine Learning Techniques are used for finding upcoming or futuristic Prediction. It's very powerful tools in finding of future prediction. From Last decade to recent days Data mining and Machine Learning play vital roles in many Industries. In last 2-3 Years overall World is suffering from a new Virus i.e., COVID-19. The first covid case was detected in Month of December. In very less time it spread out throughout the world. In 2-3 months, this illness is declared as Pandemic by WHO (World Health Organization). The behaviour of spreading of COVID-19 was very abnormal. it was growing Exponentially. Here In this Paper as a Researcher's we are trying to find out overall Analysis of COVID-19. Here our main motto to finding How many patients gets infected, how many of them get cure in sometimes \u0026 finally How many of them goes to death. Here our main concentration is to finding the features of the COVID. We will be applying Machine Learning Algorithms like LR, SVM, DT and many more. After applying the above methods, we will find the performance measures of each one and compare with their values. Finally, we will try to Implement some new Enhancement in Existing Algorithms.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"The Machine Learning Techniques are used for finding upcoming or futuristic Prediction. It's very powerful tools in finding of future prediction. From Last decade to recent days Data mining and Machine Learning play vital roles in many Industries. In last 2-3 Years overall World is suffering from a new Virus i.e., COVID-19. The first covid case was detected in Month of December. In very less time it spread out throughout the world. In 2-3 months, this illness is declared as Pandemic by WHO (World Health Organization). The behaviour of spreading of COVID-19 was very abnormal. it was growing Exponentially. Here In this Paper as a Researcher's we are trying to find out overall Analysis of COVID-19. Here our main motto to finding How many patients gets infected, how many of them get cure in sometimes \u0026 finally How many of them goes to death. Here our main concentration is to finding the features of the COVID. We will be applying Machine Learning Algorithms like LR, SVM, DT and many more. After applying the above methods, we will find the performance measures of each one and compare with their values. Finally, we will try to Implement some new Enhancement in Existing Algorithms.","internal_url":"https://www.academia.edu/119808623/A_Classification_of_COVID_19_Cases_using_Fine_Tune_Different_Model_of_Machine_Learning","translated_internal_url":"","created_at":"2024-05-22T04:43:05.440-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[],"downloadable_attachments":[{"id":115146403,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146403/thumbnails/1.jpg","file_name":"503_4_2916_2923.pdf","download_url":"https://www.academia.edu/attachments/115146403/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Classification_of_COVID_19_Cases_using.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146403/503_4_2916_2923-libre.pdf?1716378633=\u0026response-content-disposition=attachment%3B+filename%3DA_Classification_of_COVID_19_Cases_using.pdf\u0026Expires=1732468483\u0026Signature=Nax6YSryZDE3u2f7YCWjDraAAaa~XIkawAEWLoMVKT43Yj1RhW65ocY~KM9rzqSAOCGwOaBuTWyyHMIUHPzggMVdhk4B4t38vsnsBcg5alVdd7zG1iByLCg7K~nOrRqBNa1IQZml19OwtLIj~kJnxHEllRgAEBjQSLaNBEcmxKr6qYgtpVIVqazoNMIT~~losruBO5Sd4ECvuAdhSxLj3Kbd12EPL5VYqIEHN5osCh91vsy07KA5T7Ljp17DfUlMQzRZbqIu6Vmv618MmIyOcbPEQyb-Si1fUTbWU5W1tOSf8j0r~nGNdctexI~KbgV7Yc5qPw~wZTnsDmkoIOD1Ug__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Classification_of_COVID_19_Cases_using_Fine_Tune_Different_Model_of_Machine_Learning","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146403,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146403/thumbnails/1.jpg","file_name":"503_4_2916_2923.pdf","download_url":"https://www.academia.edu/attachments/115146403/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Classification_of_COVID_19_Cases_using.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146403/503_4_2916_2923-libre.pdf?1716378633=\u0026response-content-disposition=attachment%3B+filename%3DA_Classification_of_COVID_19_Cases_using.pdf\u0026Expires=1732468483\u0026Signature=Nax6YSryZDE3u2f7YCWjDraAAaa~XIkawAEWLoMVKT43Yj1RhW65ocY~KM9rzqSAOCGwOaBuTWyyHMIUHPzggMVdhk4B4t38vsnsBcg5alVdd7zG1iByLCg7K~nOrRqBNa1IQZml19OwtLIj~kJnxHEllRgAEBjQSLaNBEcmxKr6qYgtpVIVqazoNMIT~~losruBO5Sd4ECvuAdhSxLj3Kbd12EPL5VYqIEHN5osCh91vsy07KA5T7Ljp17DfUlMQzRZbqIu6Vmv618MmIyOcbPEQyb-Si1fUTbWU5W1tOSf8j0r~nGNdctexI~KbgV7Yc5qPw~wZTnsDmkoIOD1Ug__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119808414"><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/119808414/An_Innovative_Blockchain_based_System_for_Employees_in_the_Healthcare_Industry"><img alt="Research paper thumbnail of An Innovative Blockchain-based System for Employees in the Healthcare Industry" class="work-thumbnail" src="https://attachments.academia-assets.com/115146333/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/119808414/An_Innovative_Blockchain_based_System_for_Employees_in_the_Healthcare_Industry">An Innovative Blockchain-based System for Employees in the Healthcare Industry</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The pandemic caused by the COVID-19 virus has unquestionably opened our eyes to the possibility t...</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 pandemic caused by the COVID-19 virus has unquestionably opened our eyes to the possibility that we may run out of essential supplies, such as medications and medical equipment, which would result in people's deaths and increased anxiety. Insufficient funds and raw materials aren't the only factors contributing to this situation; there's also another factor that's much more discouraging. Even in the midst of these challenging circumstances, some individuals have been able to see an opportunity in the scenario that allows them to maintain and store vital equipment such as personal protective equipment kits, masks, sanitizers, and the like, and then start black-selling such items. This very problematic circumstance has arisen as a direct result of deficiencies in the supply chain's control, transparency, and traceability. Blockchain technology presents itself as a potentially useful answer to this issue. The information may be tracked and traced using blockchain technology, which can also be used to store the data in a decentralised and immutable manner, ensuring that it remains private and is protected from unauthorised users. This article discusses the many use cases for healthcare personnel as well as the various constraints of the present blockchain architecture for supply chain. [Citation needed] The shortcomings of the current blockchain framework for supply chains are then addressed via the proposal of a new framework.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="22b7b537e09c4de63ac03bb9322187eb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146333,"asset_id":119808414,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146333/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119808414"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119808414"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119808414; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119808414]").text(description); $(".js-view-count[data-work-id=119808414]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119808414; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119808414']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119808414, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "22b7b537e09c4de63ac03bb9322187eb" } } $('.js-work-strip[data-work-id=119808414]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119808414,"title":"An Innovative Blockchain-based System for Employees in the Healthcare Industry","translated_title":"","metadata":{"abstract":"The pandemic caused by the COVID-19 virus has unquestionably opened our eyes to the possibility that we may run out of essential supplies, such as medications and medical equipment, which would result in people's deaths and increased anxiety. Insufficient funds and raw materials aren't the only factors contributing to this situation; there's also another factor that's much more discouraging. Even in the midst of these challenging circumstances, some individuals have been able to see an opportunity in the scenario that allows them to maintain and store vital equipment such as personal protective equipment kits, masks, sanitizers, and the like, and then start black-selling such items. This very problematic circumstance has arisen as a direct result of deficiencies in the supply chain's control, transparency, and traceability. Blockchain technology presents itself as a potentially useful answer to this issue. The information may be tracked and traced using blockchain technology, which can also be used to store the data in a decentralised and immutable manner, ensuring that it remains private and is protected from unauthorised users. This article discusses the many use cases for healthcare personnel as well as the various constraints of the present blockchain architecture for supply chain. [Citation needed] The shortcomings of the current blockchain framework for supply chains are then addressed via the proposal of a new framework.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"The pandemic caused by the COVID-19 virus has unquestionably opened our eyes to the possibility that we may run out of essential supplies, such as medications and medical equipment, which would result in people's deaths and increased anxiety. Insufficient funds and raw materials aren't the only factors contributing to this situation; there's also another factor that's much more discouraging. Even in the midst of these challenging circumstances, some individuals have been able to see an opportunity in the scenario that allows them to maintain and store vital equipment such as personal protective equipment kits, masks, sanitizers, and the like, and then start black-selling such items. This very problematic circumstance has arisen as a direct result of deficiencies in the supply chain's control, transparency, and traceability. Blockchain technology presents itself as a potentially useful answer to this issue. The information may be tracked and traced using blockchain technology, which can also be used to store the data in a decentralised and immutable manner, ensuring that it remains private and is protected from unauthorised users. This article discusses the many use cases for healthcare personnel as well as the various constraints of the present blockchain architecture for supply chain. [Citation needed] The shortcomings of the current blockchain framework for supply chains are then addressed via the proposal of a new framework.","internal_url":"https://www.academia.edu/119808414/An_Innovative_Blockchain_based_System_for_Employees_in_the_Healthcare_Industry","translated_internal_url":"","created_at":"2024-05-22T04:40:18.398-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730524,"work_id":119808414,"tagging_user_id":32919427,"tagged_user_id":827443,"co_author_invite_id":null,"email":"a***e@gmail.com","display_order":1,"name":"Anil Turukmane","title":"An Innovative Blockchain-based System for Employees in the Healthcare Industry"}],"downloadable_attachments":[{"id":115146333,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146333/thumbnails/1.jpg","file_name":"502_2_2909_2915.pdf","download_url":"https://www.academia.edu/attachments/115146333/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Innovative_Blockchain_based_System_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146333/502_2_2909_2915-libre.pdf?1716378641=\u0026response-content-disposition=attachment%3B+filename%3DAn_Innovative_Blockchain_based_System_fo.pdf\u0026Expires=1732468484\u0026Signature=XP0QrVhJeqOb7VsXcX54IAyxS~zBdTAZilUbZOUHEJ-YCOBskFqQGblW9h8RtzHAKfsIVaC2gL~~xfC9ZEdOjt5TxL0d2OiINgbGMQ0dJlfM8cTd6W6GkctytU2SQm-pcYak64fxu1y3yt3iUKxSCD9PMR0G0aGpppzC9qjxK532QbxE-6OiP6-pfA2o~eV6t3AqtR1VVAekdEn2CKFquigSrEV1tqHuk-vv9AK5X6Xr2vmgv1orhJ6ADyMPq09fK714FwjQaRCZWGuV7fkg4MNk-PpG-umnVtzoh0h6dIVhPX3S7Tzk08H0mtQn45P5LMuRpSIF-NIF5wf4S3yCnQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"An_Innovative_Blockchain_based_System_for_Employees_in_the_Healthcare_Industry","translated_slug":"","page_count":7,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146333,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146333/thumbnails/1.jpg","file_name":"502_2_2909_2915.pdf","download_url":"https://www.academia.edu/attachments/115146333/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Innovative_Blockchain_based_System_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146333/502_2_2909_2915-libre.pdf?1716378641=\u0026response-content-disposition=attachment%3B+filename%3DAn_Innovative_Blockchain_based_System_fo.pdf\u0026Expires=1732468484\u0026Signature=XP0QrVhJeqOb7VsXcX54IAyxS~zBdTAZilUbZOUHEJ-YCOBskFqQGblW9h8RtzHAKfsIVaC2gL~~xfC9ZEdOjt5TxL0d2OiINgbGMQ0dJlfM8cTd6W6GkctytU2SQm-pcYak64fxu1y3yt3iUKxSCD9PMR0G0aGpppzC9qjxK532QbxE-6OiP6-pfA2o~eV6t3AqtR1VVAekdEn2CKFquigSrEV1tqHuk-vv9AK5X6Xr2vmgv1orhJ6ADyMPq09fK714FwjQaRCZWGuV7fkg4MNk-PpG-umnVtzoh0h6dIVhPX3S7Tzk08H0mtQn45P5LMuRpSIF-NIF5wf4S3yCnQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119808307"><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/119808307/An_Overview_of_Speaker_Recognition_Conceptual_Framework_and_CNN_based_Identification_Technique"><img alt="Research paper thumbnail of An Overview of Speaker Recognition: Conceptual Framework and CNN based Identification Technique" class="work-thumbnail" src="https://attachments.academia-assets.com/115146209/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/119808307/An_Overview_of_Speaker_Recognition_Conceptual_Framework_and_CNN_based_Identification_Technique">An Overview of Speaker Recognition: Conceptual Framework and CNN based Identification Technique</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One of the major characteristics of an individual is to recognize a human voice. He is capable to...</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">One of the major characteristics of an individual is to recognize a human voice. He is capable to do so, via digital devices or over the telephone. Acquiring this human trait, multiple technologies based on voice recognition have been developed to fulfil the purpose of biometrics and authentication. One such speech analysing technology is automatic speaker recognition (ASR) method that has been introduced to extract characteristics from the speaker's voice and identify him as a genuine source of input. The primary aim of speaker recognition is to identify and verify a person using audio signals. Hence, this has become a dominating field of research in the domain of biometrics. However, several deep learning approaches based on convolutional neural networks have been adopted to enhance the overall system of biometrics. Therefore, in this paper we review feature components on speaker recognition using CNN and feature extraction methods such as MFCC. The major advantage of using this method over traditional identification is its representation ability to extract feature inputs including audio signals and networking structures. Further, we briefly describe all the main pieces of ASR related methodologies followed by evaluation metrics to enhance the overall recognition of the system. Finally, a few relatable challenges and future expansion of speaker recognition are mentioned at the closure of this review.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cdd943a061de92f87c141919f83af63f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146209,"asset_id":119808307,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146209/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119808307"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119808307"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119808307; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119808307]").text(description); $(".js-view-count[data-work-id=119808307]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119808307; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119808307']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119808307, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "cdd943a061de92f87c141919f83af63f" } } $('.js-work-strip[data-work-id=119808307]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119808307,"title":"An Overview of Speaker Recognition: Conceptual Framework and CNN based Identification Technique","translated_title":"","metadata":{"abstract":"One of the major characteristics of an individual is to recognize a human voice. He is capable to do so, via digital devices or over the telephone. Acquiring this human trait, multiple technologies based on voice recognition have been developed to fulfil the purpose of biometrics and authentication. One such speech analysing technology is automatic speaker recognition (ASR) method that has been introduced to extract characteristics from the speaker's voice and identify him as a genuine source of input. The primary aim of speaker recognition is to identify and verify a person using audio signals. Hence, this has become a dominating field of research in the domain of biometrics. However, several deep learning approaches based on convolutional neural networks have been adopted to enhance the overall system of biometrics. Therefore, in this paper we review feature components on speaker recognition using CNN and feature extraction methods such as MFCC. The major advantage of using this method over traditional identification is its representation ability to extract feature inputs including audio signals and networking structures. Further, we briefly describe all the main pieces of ASR related methodologies followed by evaluation metrics to enhance the overall recognition of the system. Finally, a few relatable challenges and future expansion of speaker recognition are mentioned at the closure of this review.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"One of the major characteristics of an individual is to recognize a human voice. He is capable to do so, via digital devices or over the telephone. Acquiring this human trait, multiple technologies based on voice recognition have been developed to fulfil the purpose of biometrics and authentication. One such speech analysing technology is automatic speaker recognition (ASR) method that has been introduced to extract characteristics from the speaker's voice and identify him as a genuine source of input. The primary aim of speaker recognition is to identify and verify a person using audio signals. Hence, this has become a dominating field of research in the domain of biometrics. However, several deep learning approaches based on convolutional neural networks have been adopted to enhance the overall system of biometrics. Therefore, in this paper we review feature components on speaker recognition using CNN and feature extraction methods such as MFCC. The major advantage of using this method over traditional identification is its representation ability to extract feature inputs including audio signals and networking structures. Further, we briefly describe all the main pieces of ASR related methodologies followed by evaluation metrics to enhance the overall recognition of the system. Finally, a few relatable challenges and future expansion of speaker recognition are mentioned at the closure of this review.","internal_url":"https://www.academia.edu/119808307/An_Overview_of_Speaker_Recognition_Conceptual_Framework_and_CNN_based_Identification_Technique","translated_internal_url":"","created_at":"2024-05-22T04:37:37.435-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730518,"work_id":119808307,"tagging_user_id":32919427,"tagged_user_id":279999067,"co_author_invite_id":null,"email":"a***e@gmail.com","display_order":1,"name":"Avinash Dhole","title":"An Overview of Speaker Recognition: Conceptual Framework and CNN based Identification Technique"}],"downloadable_attachments":[{"id":115146209,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146209/thumbnails/1.jpg","file_name":"335_2901_2908.pdf","download_url":"https://www.academia.edu/attachments/115146209/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Overview_of_Speaker_Recognition_Conce.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146209/335_2901_2908-libre.pdf?1716378648=\u0026response-content-disposition=attachment%3B+filename%3DAn_Overview_of_Speaker_Recognition_Conce.pdf\u0026Expires=1732468484\u0026Signature=DNRSvGE0wijl4WnwHRgI6ClQpj1msBhMwAMYWSyxW1VR1dMq6SzqEjtfkh2h9pZsVwJ05E4Ew1LjyJePx40VGj~JVyAq-XqCGZNWwAREBqKn2gYzBaPkZ6RsnV~rXHcKYCtVg9XqzJt4QeD7HRkqUYJK4C8jz-Ueng23vOhWmc3jgNouuKfhT~Ju0bTf7dWEGsFelFpblb39veFGP16JP4Oa-0QWIxTc1YUwFn7MkDJWg~u57bz0ScqiplJpnWKThxhfkh7CqR-fb~jyf1cvjTmNhPqPa1XzkTjJMTEMzOq~D8hIHNIod4~Id85O6HAK0W5YOggFz~oOMcwnoaAT9g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"An_Overview_of_Speaker_Recognition_Conceptual_Framework_and_CNN_based_Identification_Technique","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146209,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146209/thumbnails/1.jpg","file_name":"335_2901_2908.pdf","download_url":"https://www.academia.edu/attachments/115146209/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Overview_of_Speaker_Recognition_Conce.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146209/335_2901_2908-libre.pdf?1716378648=\u0026response-content-disposition=attachment%3B+filename%3DAn_Overview_of_Speaker_Recognition_Conce.pdf\u0026Expires=1732468484\u0026Signature=DNRSvGE0wijl4WnwHRgI6ClQpj1msBhMwAMYWSyxW1VR1dMq6SzqEjtfkh2h9pZsVwJ05E4Ew1LjyJePx40VGj~JVyAq-XqCGZNWwAREBqKn2gYzBaPkZ6RsnV~rXHcKYCtVg9XqzJt4QeD7HRkqUYJK4C8jz-Ueng23vOhWmc3jgNouuKfhT~Ju0bTf7dWEGsFelFpblb39veFGP16JP4Oa-0QWIxTc1YUwFn7MkDJWg~u57bz0ScqiplJpnWKThxhfkh7CqR-fb~jyf1cvjTmNhPqPa1XzkTjJMTEMzOq~D8hIHNIod4~Id85O6HAK0W5YOggFz~oOMcwnoaAT9g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119808176"><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/119808176/Predicting_Dengue_using_Rule_based_Approach"><img alt="Research paper thumbnail of Predicting Dengue using Rule-based Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/115146117/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/119808176/Predicting_Dengue_using_Rule_based_Approach">Predicting Dengue using Rule-based Approach</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/IdesEditor">Grenze International Journal of Engineering and Technology GIJET</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/SakshiShejwal3">Sakshi Shejwal</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Dengue is the most common viral fever suffered by most people. It is also known as life-threateni...</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">Dengue is the most common viral fever suffered by most people. It is also known as life-threatening disease. It has taken many lives all over the world. Brisk forecast of dengue can save individual's life by warning them to take genuine conclusion and care. During the covid situation risk of dengue have reached at a larger scale with multiple symptoms. In this paper, selective symptoms are being considered to verify their impact on predicting the disease and the main motive is to predict dengue, how accurately it gives respond in advance that can save individual's life. For this a new approach has been introduced by applying Rule Fit or Rulebased algorithm and Feature transformation methods to get an accurate result which solve the interpretability and overfitting issue in each machine learning algorithm that have a huge impact on the prediction. Also results gained varies based on the selection of dataset, each algorithms results differs according to the dataset.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3267692df34fb3e0110993fcb2152490" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146117,"asset_id":119808176,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146117/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119808176"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119808176"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119808176; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119808176]").text(description); $(".js-view-count[data-work-id=119808176]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119808176; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119808176']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119808176, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "3267692df34fb3e0110993fcb2152490" } } $('.js-work-strip[data-work-id=119808176]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119808176,"title":"Predicting Dengue using Rule-based Approach","translated_title":"","metadata":{"abstract":"Dengue is the most common viral fever suffered by most people. It is also known as life-threatening disease. It has taken many lives all over the world. Brisk forecast of dengue can save individual's life by warning them to take genuine conclusion and care. During the covid situation risk of dengue have reached at a larger scale with multiple symptoms. In this paper, selective symptoms are being considered to verify their impact on predicting the disease and the main motive is to predict dengue, how accurately it gives respond in advance that can save individual's life. For this a new approach has been introduced by applying Rule Fit or Rulebased algorithm and Feature transformation methods to get an accurate result which solve the interpretability and overfitting issue in each machine learning algorithm that have a huge impact on the prediction. Also results gained varies based on the selection of dataset, each algorithms results differs according to the dataset.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"Dengue is the most common viral fever suffered by most people. It is also known as life-threatening disease. It has taken many lives all over the world. Brisk forecast of dengue can save individual's life by warning them to take genuine conclusion and care. During the covid situation risk of dengue have reached at a larger scale with multiple symptoms. In this paper, selective symptoms are being considered to verify their impact on predicting the disease and the main motive is to predict dengue, how accurately it gives respond in advance that can save individual's life. For this a new approach has been introduced by applying Rule Fit or Rulebased algorithm and Feature transformation methods to get an accurate result which solve the interpretability and overfitting issue in each machine learning algorithm that have a huge impact on the prediction. Also results gained varies based on the selection of dataset, each algorithms results differs according to the dataset.","internal_url":"https://www.academia.edu/119808176/Predicting_Dengue_using_Rule_based_Approach","translated_internal_url":"","created_at":"2024-05-22T04:36:03.876-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730516,"work_id":119808176,"tagging_user_id":32919427,"tagged_user_id":315700423,"co_author_invite_id":8154024,"email":"s***l@mitaoe.ac.in","display_order":1,"name":"Sakshi Shejwal","title":"Predicting Dengue using Rule-based Approach"},{"id":41730517,"work_id":119808176,"tagging_user_id":32919427,"tagged_user_id":298112011,"co_author_invite_id":null,"email":"p***r@mitaoe.ac.in","display_order":2,"name":"Pramod Ganjewar","title":"Predicting Dengue using Rule-based Approach"}],"downloadable_attachments":[{"id":115146117,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146117/thumbnails/1.jpg","file_name":"334_2893_2900.pdf","download_url":"https://www.academia.edu/attachments/115146117/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Predicting_Dengue_using_Rule_based_Appro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146117/334_2893_2900-libre.pdf?1716378662=\u0026response-content-disposition=attachment%3B+filename%3DPredicting_Dengue_using_Rule_based_Appro.pdf\u0026Expires=1732468484\u0026Signature=HajxIEFAKKGyjfON~amlou3rwbBjyJCFZ2vf3g~hxzQ8~fy3m3mNAP68YD~b~rM3sTd-5J45-gx9jJ4nv0ITrznOEZzMxBKOpomxtRAZsn6SS0hsR2tlQ8hmJLBrva4MIoMoeSRL7AZa2AS274cG4rx2wwnL9tqDK9fbd~7sF-mGNbeGo9TLN4R9uMZtemZFZHunvzaUUDoBvU0XbtSlWlNULRxpKw5sgVM2ZWoccj58olWR6195zKL9FNiyUqOvuTbHxTrevKMnsAkfkpi5RpzjTROl4hk6Dseuv2Kcc4ur7xASHYd2DNFpqAQif2wQV6~Lqe8B2xnfCObGCGBkLQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Predicting_Dengue_using_Rule_based_Approach","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146117,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146117/thumbnails/1.jpg","file_name":"334_2893_2900.pdf","download_url":"https://www.academia.edu/attachments/115146117/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Predicting_Dengue_using_Rule_based_Appro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146117/334_2893_2900-libre.pdf?1716378662=\u0026response-content-disposition=attachment%3B+filename%3DPredicting_Dengue_using_Rule_based_Appro.pdf\u0026Expires=1732468484\u0026Signature=HajxIEFAKKGyjfON~amlou3rwbBjyJCFZ2vf3g~hxzQ8~fy3m3mNAP68YD~b~rM3sTd-5J45-gx9jJ4nv0ITrznOEZzMxBKOpomxtRAZsn6SS0hsR2tlQ8hmJLBrva4MIoMoeSRL7AZa2AS274cG4rx2wwnL9tqDK9fbd~7sF-mGNbeGo9TLN4R9uMZtemZFZHunvzaUUDoBvU0XbtSlWlNULRxpKw5sgVM2ZWoccj58olWR6195zKL9FNiyUqOvuTbHxTrevKMnsAkfkpi5RpzjTROl4hk6Dseuv2Kcc4ur7xASHYd2DNFpqAQif2wQV6~Lqe8B2xnfCObGCGBkLQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119808103"><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/119808103/Hybrid_Recommender_System_for_E_Learning_Platforms"><img alt="Research paper thumbnail of Hybrid Recommender System for E-Learning Platforms" class="work-thumbnail" src="https://attachments.academia-assets.com/115146062/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/119808103/Hybrid_Recommender_System_for_E_Learning_Platforms">Hybrid Recommender System for E-Learning Platforms</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Internet use has steadily increased over the past 25 years. Numerous online services are improvin...</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">Internet use has steadily increased over the past 25 years. Numerous online services are improving and simplifying life for people. E-learning is one of these services, which makes the learning process simpler. In this work, a recommendation system will be presented that will assist online course providers in automating course suggestion. By finding patterns in courses based on some attributes like field of interest, skill set, educational qualification, course content, etc. of an individual and courses, this recommendation system is built using hybrid machine learning recommendation techniques, i.e., combination of Collaborative (item based) and content-based recommendation and KNN classifiers. Lastly, the recommendations are pooled from the two blocks of recommended courses (which were discovered using a collaborative technique), similar courses (which is found using content-based finding).</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6adb9f5eeffce52269b704f4ed81903b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146062,"asset_id":119808103,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146062/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119808103"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119808103"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119808103; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119808103]").text(description); $(".js-view-count[data-work-id=119808103]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119808103; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119808103']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119808103, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "6adb9f5eeffce52269b704f4ed81903b" } } $('.js-work-strip[data-work-id=119808103]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119808103,"title":"Hybrid Recommender System for E-Learning Platforms","translated_title":"","metadata":{"abstract":"Internet use has steadily increased over the past 25 years. 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Numerous online services are improving and simplifying life for people. E-learning is one of these services, which makes the learning process simpler. In this work, a recommendation system will be presented that will assist online course providers in automating course suggestion. By finding patterns in courses based on some attributes like field of interest, skill set, educational qualification, course content, etc. of an individual and courses, this recommendation system is built using hybrid machine learning recommendation techniques, i.e., combination of Collaborative (item based) and content-based recommendation and KNN classifiers. 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It...</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 this new era of artificial intelligence and automation there is outburst in amount of data. It is challenging for a person to dig into the content and bring the light on essential data. So, for such complexity there is need of developing a perfunctory task of automatic and precise text summary of data. Automatic text summarizer reduces reading time and make selection process easier. In recent years, there has been a little change in the text summarising research trend as well. New trends have evolved that show how to improve text summarization performance and achieve high accuracy. Hence, this project mainly focuses on designing an extractive summarizer of English text/document which generate abbreviated summary with the help of different algorithms.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="acf13df73f78c27c5dd7b5b0e5044a39" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146022,"asset_id":119808045,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146022/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119808045"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119808045"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119808045; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119808045]").text(description); $(".js-view-count[data-work-id=119808045]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119808045; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119808045']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119808045, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "acf13df73f78c27c5dd7b5b0e5044a39" } } $('.js-work-strip[data-work-id=119808045]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119808045,"title":"An Automatic Extractive Text Summarization","translated_title":"","metadata":{"abstract":"In this new era of artificial intelligence and automation there is outburst in amount of data. 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Hence, this project mainly focuses on designing an extractive summarizer of English text/document which generate abbreviated summary with the help of different algorithms.","internal_url":"https://www.academia.edu/119808045/An_Automatic_Extractive_Text_Summarization","translated_internal_url":"","created_at":"2024-05-22T04:32:55.183-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730498,"work_id":119808045,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8154018,"email":"m***a@mitaoe.ac.in","display_order":1,"name":"Meet Bedhmutha","title":"An Automatic Extractive Text Summarization"},{"id":41730499,"work_id":119808045,"tagging_user_id":32919427,"tagged_user_id":315700413,"co_author_invite_id":8154019,"email":"c***v@mitaoe.ac.in","display_order":2,"name":"Chandan Yadav","title":"An Automatic Extractive Text Summarization"},{"id":41730500,"work_id":119808045,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8154020,"email":"d***i@mitaoe.ac.in","display_order":3,"name":"Devashish Dani","title":"An Automatic Extractive Text Summarization"},{"id":41730501,"work_id":119808045,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8154021,"email":"k***l@mitaoe.ac.in","display_order":4,"name":"Kshitij Patil","title":"An Automatic Extractive Text Summarization"}],"downloadable_attachments":[{"id":115146022,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146022/thumbnails/1.jpg","file_name":"332_1_2881_2886.pdf","download_url":"https://www.academia.edu/attachments/115146022/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Automatic_Extractive_Text_Summarizati.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146022/332_1_2881_2886-libre.pdf?1716378665=\u0026response-content-disposition=attachment%3B+filename%3DAn_Automatic_Extractive_Text_Summarizati.pdf\u0026Expires=1732468484\u0026Signature=T22m4ZdGNvVecrZE013F560kow414UrNR1ZtUhYQ46N0HjUVdZHk9eyfMWsTuj81punzDgwPZxeUVmmnsxtUS650iiv4bSWmScQlitT-GtF5vzYs4-QSKtnXAZn4gswKDysOGRQqycSU97oXp7JriPod85B0~FSlpaLa9aaqZrG6NiBr~uVu6Ut8kheBaNti5xDuxjr~1QRgkqLnueuXvT5bGg4iKvpqedQLH1o9h57rBjFfhfX6q98fptqlt0McexDumd~QWu0u-18o3sEMsuoQWVFtMC1r5o1inU7o7i2JwaBX6h2SGmg~CNVMVeF-V~NkPi87sL1YGXPARqJ95Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"An_Automatic_Extractive_Text_Summarization","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146022,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146022/thumbnails/1.jpg","file_name":"332_1_2881_2886.pdf","download_url":"https://www.academia.edu/attachments/115146022/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Automatic_Extractive_Text_Summarizati.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146022/332_1_2881_2886-libre.pdf?1716378665=\u0026response-content-disposition=attachment%3B+filename%3DAn_Automatic_Extractive_Text_Summarizati.pdf\u0026Expires=1732468484\u0026Signature=T22m4ZdGNvVecrZE013F560kow414UrNR1ZtUhYQ46N0HjUVdZHk9eyfMWsTuj81punzDgwPZxeUVmmnsxtUS650iiv4bSWmScQlitT-GtF5vzYs4-QSKtnXAZn4gswKDysOGRQqycSU97oXp7JriPod85B0~FSlpaLa9aaqZrG6NiBr~uVu6Ut8kheBaNti5xDuxjr~1QRgkqLnueuXvT5bGg4iKvpqedQLH1o9h57rBjFfhfX6q98fptqlt0McexDumd~QWu0u-18o3sEMsuoQWVFtMC1r5o1inU7o7i2JwaBX6h2SGmg~CNVMVeF-V~NkPi87sL1YGXPARqJ95Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119807982"><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/119807982/Hate_Speech_Detection_using_Logistic_Regression_on_Bag_of_Words_Model"><img alt="Research paper thumbnail of Hate Speech Detection using Logistic Regression on Bag of Words Model" class="work-thumbnail" src="https://attachments.academia-assets.com/115145935/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/119807982/Hate_Speech_Detection_using_Logistic_Regression_on_Bag_of_Words_Model">Hate Speech Detection using Logistic Regression on Bag of Words Model</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Hate speech is a conundrum that is spreading like wildfire. Social networks allow members to conn...</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">Hate speech is a conundrum that is spreading like wildfire. Social networks allow members to connect by a variety of common interests and share information. These channels create and also help communities engage in discussing specific topics related to said news. However, they are also used as a medium to spread hate and offensive news. News on social media spread like a forest fire. Not only social media, but other forms of online media and precedence may cause hate speech to spread. Therefore, trying to prevent it beforehand is much better than causing an outrage. The objective of this paper is to try to establish a reliable model to identify hate speech in sentences. The purpose of this project is to analyze the sentiments of speech used sentences using certain ML algorithms. The Bag of Words model and Document Frequency Inverse Term Frequency (TFIDF) are used to process the text in sentences. Then we use the Logistic Regression algorithm on our bag of words.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8d94318221a89cc6ad18576a608b3524" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115145935,"asset_id":119807982,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115145935/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119807982"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119807982"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119807982; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119807982]").text(description); $(".js-view-count[data-work-id=119807982]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119807982; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119807982']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119807982, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "8d94318221a89cc6ad18576a608b3524" } } $('.js-work-strip[data-work-id=119807982]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119807982,"title":"Hate Speech Detection using Logistic Regression on Bag of Words Model","translated_title":"","metadata":{"abstract":"Hate speech is a conundrum that is spreading like wildfire. 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Then we use the Logistic Regression algorithm on our bag of words.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"Hate speech is a conundrum that is spreading like wildfire. Social networks allow members to connect by a variety of common interests and share information. These channels create and also help communities engage in discussing specific topics related to said news. However, they are also used as a medium to spread hate and offensive news. News on social media spread like a forest fire. Not only social media, but other forms of online media and precedence may cause hate speech to spread. Therefore, trying to prevent it beforehand is much better than causing an outrage. The objective of this paper is to try to establish a reliable model to identify hate speech in sentences. The purpose of this project is to analyze the sentiments of speech used sentences using certain ML algorithms. The Bag of Words model and Document Frequency Inverse Term Frequency (TFIDF) are used to process the text in sentences. Then we use the Logistic Regression algorithm on our bag of words.","internal_url":"https://www.academia.edu/119807982/Hate_Speech_Detection_using_Logistic_Regression_on_Bag_of_Words_Model","translated_internal_url":"","created_at":"2024-05-22T04:30:45.590-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[],"downloadable_attachments":[{"id":115145935,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145935/thumbnails/1.jpg","file_name":"331_2875_2880.pdf","download_url":"https://www.academia.edu/attachments/115145935/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Hate_Speech_Detection_using_Logistic_Reg.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145935/331_2875_2880-libre.pdf?1716378668=\u0026response-content-disposition=attachment%3B+filename%3DHate_Speech_Detection_using_Logistic_Reg.pdf\u0026Expires=1732468484\u0026Signature=UZSyCI9lz0~BOg4TDLwOgamFyoGu4W7Oeh1AhgGeykHfZIId9aBPKX5ZBbiM6jcvcQFja6K~pXBb5U14eAENF53JFj9gN40sdqtvhOYvwdA3Kfk1Gqbq6hcpjEiyOruMA9OyNeJqBmKPQqjyUy5rKDjt0MhUS99QSIcoYVGjP80fea9V4YatRV0BQrB7SUysQ7kild0fUzOwEvHgo~iPXKRICIc4Qb3-Sbvso29LdFddStUJRJv60sRO~o8qqwZ6SDkk~ThX5gsOumw6azQ3kpGxwujaIaeSedh1qC7020LVcU4euLyxmD0~rrVGuXWajqhPeCmFjQ2RxFvnT4ilYg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Hate_Speech_Detection_using_Logistic_Regression_on_Bag_of_Words_Model","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115145935,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145935/thumbnails/1.jpg","file_name":"331_2875_2880.pdf","download_url":"https://www.academia.edu/attachments/115145935/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Hate_Speech_Detection_using_Logistic_Reg.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145935/331_2875_2880-libre.pdf?1716378668=\u0026response-content-disposition=attachment%3B+filename%3DHate_Speech_Detection_using_Logistic_Reg.pdf\u0026Expires=1732468484\u0026Signature=UZSyCI9lz0~BOg4TDLwOgamFyoGu4W7Oeh1AhgGeykHfZIId9aBPKX5ZBbiM6jcvcQFja6K~pXBb5U14eAENF53JFj9gN40sdqtvhOYvwdA3Kfk1Gqbq6hcpjEiyOruMA9OyNeJqBmKPQqjyUy5rKDjt0MhUS99QSIcoYVGjP80fea9V4YatRV0BQrB7SUysQ7kild0fUzOwEvHgo~iPXKRICIc4Qb3-Sbvso29LdFddStUJRJv60sRO~o8qqwZ6SDkk~ThX5gsOumw6azQ3kpGxwujaIaeSedh1qC7020LVcU4euLyxmD0~rrVGuXWajqhPeCmFjQ2RxFvnT4ilYg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119807898"><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/119807898/A_Review_Paper_on_Cyclone_Intensity_Estimation_on_INSAT_3D_IR_Imagery_using_Deep_Learning_Algorithms"><img alt="Research paper thumbnail of A Review Paper on Cyclone Intensity Estimation on INSAT 3D IR Imagery using Deep Learning Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/115145874/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/119807898/A_Review_Paper_on_Cyclone_Intensity_Estimation_on_INSAT_3D_IR_Imagery_using_Deep_Learning_Algorithms">A Review Paper on Cyclone Intensity Estimation on INSAT 3D IR Imagery using Deep Learning Algorithms</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/IdesEditor">Grenze International Journal of Engineering and Technology GIJET</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/TejasAdsare">Tejas Adsare</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Due to the various dangers involved, especially tropical cyclones, they are one of the most commo...</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">Due to the various dangers involved, especially tropical cyclones, they are one of the most common and deadly calamities in the world. The very important primary step in monitoring this destructive disaster is by estimating its intensity. The aim of this research is to determine intensity of cyclones. For the estimation of cyclones and determination of their intensity, various methods have been created. It is a difficult task which requires speed and efficiency. This paper shows comparison between various types of deep learning algorithms on infrared satellite imagery dataset for tropical cyclone intensity estimation. A side-by-side comparison research revealed that the detection of the tropical cyclone intensity evaluated via the models derived from different machine learning algorithms is significantly impacted by the use of various infrared (IR) channels.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="842448f416eab3c8288cf4c9dc4d1412" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115145874,"asset_id":119807898,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115145874/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119807898"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119807898"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119807898; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119807898]").text(description); $(".js-view-count[data-work-id=119807898]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119807898; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119807898']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119807898, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "842448f416eab3c8288cf4c9dc4d1412" } } $('.js-work-strip[data-work-id=119807898]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119807898,"title":"A Review Paper on Cyclone Intensity Estimation on INSAT 3D IR Imagery using Deep Learning Algorithms","translated_title":"","metadata":{"abstract":"Due to the various dangers involved, especially tropical cyclones, they are one of the most common and deadly calamities in the world. The very important primary step in monitoring this destructive disaster is by estimating its intensity. The aim of this research is to determine intensity of cyclones. For the estimation of cyclones and determination of their intensity, various methods have been created. It is a difficult task which requires speed and efficiency. This paper shows comparison between various types of deep learning algorithms on infrared satellite imagery dataset for tropical cyclone intensity estimation. A side-by-side comparison research revealed that the detection of the tropical cyclone intensity evaluated via the models derived from different machine learning algorithms is significantly impacted by the use of various infrared (IR) channels.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"Due to the various dangers involved, especially tropical cyclones, they are one of the most common and deadly calamities in the world. The very important primary step in monitoring this destructive disaster is by estimating its intensity. The aim of this research is to determine intensity of cyclones. For the estimation of cyclones and determination of their intensity, various methods have been created. It is a difficult task which requires speed and efficiency. This paper shows comparison between various types of deep learning algorithms on infrared satellite imagery dataset for tropical cyclone intensity estimation. A side-by-side comparison research revealed that the detection of the tropical cyclone intensity evaluated via the models derived from different machine learning algorithms is significantly impacted by the use of various infrared (IR) channels.","internal_url":"https://www.academia.edu/119807898/A_Review_Paper_on_Cyclone_Intensity_Estimation_on_INSAT_3D_IR_Imagery_using_Deep_Learning_Algorithms","translated_internal_url":"","created_at":"2024-05-22T04:28:58.419-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730476,"work_id":119807898,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8154013,"email":"k***e@gmail.com","display_order":1,"name":"Kuldeep Vayadande","title":"A Review Paper on Cyclone Intensity Estimation on INSAT 3D IR Imagery using Deep Learning 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data-click-track="profile-work-strip-title" href="https://www.academia.edu/119807803/Comparative_Study_on_Calculating_CPU_Burst_Time_using_Different_Machine_Learning_Algorithms">Comparative Study on Calculating CPU Burst Time using Different Machine Learning Algorithms</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper gives a thorough analysis of works on the subject of expert assessment of CPU burst ti...</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 paper gives a thorough analysis of works on the subject of expert assessment of CPU burst time using various machine learning algorithms. Knowing how long the CPU bursts for the processes will last is necessary for some CPU scheduling algorithms like SJF and SRTF to function. In particular, the non-preemptive SJF scheduling algorithm estimates the process that will be performed by the CPU in the least amount of burst time. One effective way of predicting CPU burst duration is an ML-based algorithm that estimates the burst-time of the processes. Throughout the study, we discovered that the effectiveness of different machinelearning approaches relies on the applications to which they are put. Our examination of the literature not only argues that these methods are competitive with conventional estimators on a single data set, but also demonstrates that they are responsive to the training data.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="03b863c17cf89fa476303b67a467c260" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115145790,"asset_id":119807803,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115145790/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119807803"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119807803"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119807803; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119807803]").text(description); $(".js-view-count[data-work-id=119807803]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119807803; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119807803']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119807803, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "03b863c17cf89fa476303b67a467c260" } } $('.js-work-strip[data-work-id=119807803]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119807803,"title":"Comparative Study on Calculating CPU Burst Time using Different Machine Learning Algorithms","translated_title":"","metadata":{"abstract":"This paper gives a thorough analysis of works on the subject of expert assessment of CPU burst time using various machine learning algorithms. 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Our examination of the literature not only argues that these methods are competitive with conventional estimators on a single data set, but also demonstrates that they are responsive to the training data.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"This paper gives a thorough analysis of works on the subject of expert assessment of CPU burst time using various machine learning algorithms. Knowing how long the CPU bursts for the processes will last is necessary for some CPU scheduling algorithms like SJF and SRTF to function. In particular, the non-preemptive SJF scheduling algorithm estimates the process that will be performed by the CPU in the least amount of burst time. One effective way of predicting CPU burst duration is an ML-based algorithm that estimates the burst-time of the processes. 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Our examination of the literature not only argues that these methods are competitive with conventional estimators on a single data set, but also demonstrates that they are responsive to the training data.","internal_url":"https://www.academia.edu/119807803/Comparative_Study_on_Calculating_CPU_Burst_Time_using_Different_Machine_Learning_Algorithms","translated_internal_url":"","created_at":"2024-05-22T04:27:17.328-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730468,"work_id":119807803,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8154012,"email":"k***e@vit.edu","display_order":1,"name":"Kuldeep Vayadande","title":"Comparative Study on Calculating CPU Burst Time using Different Machine Learning Algorithms"},{"id":41730469,"work_id":119807803,"tagging_user_id":32919427,"tagged_user_id":240068123,"co_author_invite_id":null,"email":"n***0@vit.edu","display_order":2,"name":"Agarwal Naman","title":"Comparative Study on Calculating CPU Burst Time using Different Machine Learning Algorithms"}],"downloadable_attachments":[{"id":115145790,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145790/thumbnails/1.jpg","file_name":"326_2863_2868.pdf","download_url":"https://www.academia.edu/attachments/115145790/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Comparative_Study_on_Calculating_CPU_Bur.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145790/326_2863_2868-libre.pdf?1716378682=\u0026response-content-disposition=attachment%3B+filename%3DComparative_Study_on_Calculating_CPU_Bur.pdf\u0026Expires=1732468485\u0026Signature=SQVJsRW6wmFyw1Mss4CFsbiy0NhOebDIWPIp3QoVJeUcUeCg2ogIJa6Kzad36bhrIU1dHTlJJvz4yf22wllgnzY8je~4YKITh5dXsKhBOzwZ5PjpdQRKI4AqWETmI2GiX0qR1taccLxmOqoGsGcvfDEcHLtakBIr7PkkP42s4YVIpKC8qXk0NoLzW9nmATLU-5YR969pX15JbKInQpYjfjJvfMTWlvJ0zB3vbRcwkkarbTF3C~E64bNtl1tgq1ckWnWvp6DWJP4QbmTp6w1gCGVqruWv1wu6flm4AEH0f1A7XmhWHjpp3SZIQMLTz-i3~U9-GUEiCSRjX-BMyw4TAw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Comparative_Study_on_Calculating_CPU_Burst_Time_using_Different_Machine_Learning_Algorithms","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115145790,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145790/thumbnails/1.jpg","file_name":"326_2863_2868.pdf","download_url":"https://www.academia.edu/attachments/115145790/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Comparative_Study_on_Calculating_CPU_Bur.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145790/326_2863_2868-libre.pdf?1716378682=\u0026response-content-disposition=attachment%3B+filename%3DComparative_Study_on_Calculating_CPU_Bur.pdf\u0026Expires=1732468485\u0026Signature=SQVJsRW6wmFyw1Mss4CFsbiy0NhOebDIWPIp3QoVJeUcUeCg2ogIJa6Kzad36bhrIU1dHTlJJvz4yf22wllgnzY8je~4YKITh5dXsKhBOzwZ5PjpdQRKI4AqWETmI2GiX0qR1taccLxmOqoGsGcvfDEcHLtakBIr7PkkP42s4YVIpKC8qXk0NoLzW9nmATLU-5YR969pX15JbKInQpYjfjJvfMTWlvJ0zB3vbRcwkkarbTF3C~E64bNtl1tgq1ckWnWvp6DWJP4QbmTp6w1gCGVqruWv1wu6flm4AEH0f1A7XmhWHjpp3SZIQMLTz-i3~U9-GUEiCSRjX-BMyw4TAw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[]}, dispatcherData: dispatcherData }); 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The work presented in this paper addresses the Competence Mapping problem. Semantic technologies such as Resource Description Framework and standard ontology enrich unstructured job data and enable retrieval of semantically relevant matches, as opposed to keyword based approaches.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0bc06037c45affd2ef73f6e56516575f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115145702,"asset_id":119807669,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115145702/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119807669"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119807669"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119807669; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119807669]").text(description); $(".js-view-count[data-work-id=119807669]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119807669; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119807669']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119807669, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "0bc06037c45affd2ef73f6e56516575f" } } $('.js-work-strip[data-work-id=119807669]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119807669,"title":"\"Turning Straw into Gold\", using Semantic and Big Data Technologies for Competence Mapping","translated_title":"","metadata":{"abstract":"The unstructured nature of Big Data poses several challenges to develop representations capable of handling such data. 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$(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="119807617"><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/119807617/Risk_Assessment_of_Stock_Market_Analysis_using_Time_Series_Analysis"><img alt="Research paper thumbnail of Risk Assessment of Stock Market Analysis using Time Series Analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/115145653/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/119807617/Risk_Assessment_of_Stock_Market_Analysis_using_Time_Series_Analysis">Risk Assessment of Stock Market Analysis using Time Series Analysis</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Stock market forecasting and risk assessment heavily influence investors' financial decisions. Ma...</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">Stock market forecasting and risk assessment heavily influence investors' financial decisions. Many depend on news announcements to judge the purchase or sale of risky stocks. Due to the complexity and ambiguity of natural languages, however, reliable modeling and risk management of stock market patterns derived from news releases is challenging. In contrast to past work in this field, which generally uses bag-of-words to extract tens of thousands of characteristics to construct a prediction model, this study employs a novel approach that extracts tens of thousands of features directly from the text itself. We present a Time Series Forecasting based method for financial/stock market prediction with risk assessment using a histology dataset. In particular, Time Series Forecasting is performed at the pre-processing stage to extract time period-related characteristics from financial news. Using the collected features, a time seriesbased metaheuristic CNN (MCNN) model is used to construct a prediction with risk assessment. Using a model based on MCNN, we hope to get better outcomes than previous methods in terms of speed and precision while minimizing risk. The performance is attributable to the time analysis performed during the pre-processing step since it decreases the feature dimensions substantially. Using the suggested method, we aim to enhance the accuracy of stock price forecasts based on a selection of data sets.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="97b24fe9628e66e66dffef2df3959f73" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115145653,"asset_id":119807617,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115145653/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119807617"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119807617"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119807617; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119807617]").text(description); $(".js-view-count[data-work-id=119807617]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119807617; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119807617']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119807617, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "97b24fe9628e66e66dffef2df3959f73" } } $('.js-work-strip[data-work-id=119807617]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119807617,"title":"Risk Assessment of Stock Market Analysis using Time Series Analysis","translated_title":"","metadata":{"abstract":"Stock market forecasting and risk assessment heavily influence investors' financial decisions. Many depend on news announcements to judge the purchase or sale of risky stocks. Due to the complexity and ambiguity of natural languages, however, reliable modeling and risk management of stock market patterns derived from news releases is challenging. In contrast to past work in this field, which generally uses bag-of-words to extract tens of thousands of characteristics to construct a prediction model, this study employs a novel approach that extracts tens of thousands of features directly from the text itself. We present a Time Series Forecasting based method for financial/stock market prediction with risk assessment using a histology dataset. In particular, Time Series Forecasting is performed at the pre-processing stage to extract time period-related characteristics from financial news. Using the collected features, a time seriesbased metaheuristic CNN (MCNN) model is used to construct a prediction with risk assessment. Using a model based on MCNN, we hope to get better outcomes than previous methods in terms of speed and precision while minimizing risk. The performance is attributable to the time analysis performed during the pre-processing step since it decreases the feature dimensions substantially. Using the suggested method, we aim to enhance the accuracy of stock price forecasts based on a selection of data sets.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"Stock market forecasting and risk assessment heavily influence investors' financial decisions. Many depend on news announcements to judge the purchase or sale of risky stocks. Due to the complexity and ambiguity of natural languages, however, reliable modeling and risk management of stock market patterns derived from news releases is challenging. In contrast to past work in this field, which generally uses bag-of-words to extract tens of thousands of characteristics to construct a prediction model, this study employs a novel approach that extracts tens of thousands of features directly from the text itself. We present a Time Series Forecasting based method for financial/stock market prediction with risk assessment using a histology dataset. In particular, Time Series Forecasting is performed at the pre-processing stage to extract time period-related characteristics from financial news. Using the collected features, a time seriesbased metaheuristic CNN (MCNN) model is used to construct a prediction with risk assessment. Using a model based on MCNN, we hope to get better outcomes than previous methods in terms of speed and precision while minimizing risk. The performance is attributable to the time analysis performed during the pre-processing step since it decreases the feature dimensions substantially. Using the suggested method, we aim to enhance the accuracy of stock price forecasts based on a selection of data sets.","internal_url":"https://www.academia.edu/119807617/Risk_Assessment_of_Stock_Market_Analysis_using_Time_Series_Analysis","translated_internal_url":"","created_at":"2024-05-22T04:23:41.442-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730445,"work_id":119807617,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8154009,"email":"s***2@gmail.com","display_order":1,"name":"Sayem Patni","title":"Risk Assessment of Stock Market Analysis using Time Series Analysis"},{"id":41730446,"work_id":119807617,"tagging_user_id":32919427,"tagged_user_id":233740102,"co_author_invite_id":null,"email":"a***r@sandipuniversity.edu.in","display_order":2,"name":"Amit Gadekar","title":"Risk Assessment of Stock Market Analysis using Time Series Analysis"}],"downloadable_attachments":[{"id":115145653,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145653/thumbnails/1.jpg","file_name":"325_2847_2851.pdf","download_url":"https://www.academia.edu/attachments/115145653/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Risk_Assessment_of_Stock_Market_Analysis.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145653/325_2847_2851-libre.pdf?1716378696=\u0026response-content-disposition=attachment%3B+filename%3DRisk_Assessment_of_Stock_Market_Analysis.pdf\u0026Expires=1732468485\u0026Signature=bXg-br~kTT~LDPJYBJ7jeeQc2CGFnTbkBI8l15AoaqcWToAKuzI2xVQf3pURuWjbqg3Nfnl-n760IOR9xW2L6zW00VNwu5IiOzk35wOtqALmPM~0lJXOqLJFaLZGh1KbGh20-jbEJOobjzWkGltrK4EwmbtEiThtIQ6qxSqhVzQbGlrEuuMfc~YaOsCr4TnF3E-Dps8eqd9oG0tMNdd2wVIlxFKmZR9uD-WnyTAiZuiVZ~HX29XHjrvFO~SyxRO9xZ3ouvFq8AxyLOTjlC2440NMfgkq8jOEyKDKRwc8o9OvzuTxdIFCXN-P7jenut24uPQaG6b1pwOKScmNKS7Fsg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Risk_Assessment_of_Stock_Market_Analysis_using_Time_Series_Analysis","translated_slug":"","page_count":5,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115145653,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145653/thumbnails/1.jpg","file_name":"325_2847_2851.pdf","download_url":"https://www.academia.edu/attachments/115145653/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Risk_Assessment_of_Stock_Market_Analysis.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145653/325_2847_2851-libre.pdf?1716378696=\u0026response-content-disposition=attachment%3B+filename%3DRisk_Assessment_of_Stock_Market_Analysis.pdf\u0026Expires=1732468485\u0026Signature=bXg-br~kTT~LDPJYBJ7jeeQc2CGFnTbkBI8l15AoaqcWToAKuzI2xVQf3pURuWjbqg3Nfnl-n760IOR9xW2L6zW00VNwu5IiOzk35wOtqALmPM~0lJXOqLJFaLZGh1KbGh20-jbEJOobjzWkGltrK4EwmbtEiThtIQ6qxSqhVzQbGlrEuuMfc~YaOsCr4TnF3E-Dps8eqd9oG0tMNdd2wVIlxFKmZR9uD-WnyTAiZuiVZ~HX29XHjrvFO~SyxRO9xZ3ouvFq8AxyLOTjlC2440NMfgkq8jOEyKDKRwc8o9OvzuTxdIFCXN-P7jenut24uPQaG6b1pwOKScmNKS7Fsg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119807472"><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/119807472/A_Study_on_Various_Sentiment_Analysis_for_Mixed_Transliterated_Indigenous_Language_using_Machine_Learning_Algorithms"><img alt="Research paper thumbnail of A Study on Various Sentiment Analysis for Mixed Transliterated Indigenous Language using Machine Learning Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/115145567/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/119807472/A_Study_on_Various_Sentiment_Analysis_for_Mixed_Transliterated_Indigenous_Language_using_Machine_Learning_Algorithms">A Study on Various Sentiment Analysis for Mixed Transliterated Indigenous Language using Machine Learning Algorithms</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The evolution of Information Technology has led to the collection of large amounts of data, the v...</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 evolution of Information Technology has led to the collection of large amounts of data, the volume of which has increased to the extent that in the last two years the data produced is greater than all the data ever recorded in human history. This has necessitated the use of machines to understand, interpret and apply data, without manual involvement. Sentiment Analysis has become the area of deep research due to the necessity wrought about by the advent of social media tools such as Twitter, Facebook, WhatsApp, and so on. In this survey, we will analyze 25 literature works concentrating on machine learning approaches associated with sentiment analysis for mixed transliterated languages, bringing into light the various shortcomings of the existing methodologies. Here, the analysis of various methods will be facilitated based on several factors, such as performance metrics, year of publication and journals, achievements of the techniques in numerical evaluations, and so on. On the other hand, the analysis of the methods with respect to the merits and demerits of the methods will be presented. Lastly, the paper discusses the potential future research directions and challenges in achieving better accuracy for sentiment analysis. The motivation of the research will be described in detail through the comparative discussion of the methods.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="367f5165c15428e8f34976a7c139d31b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115145567,"asset_id":119807472,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115145567/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119807472"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119807472"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119807472; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119807472]").text(description); $(".js-view-count[data-work-id=119807472]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119807472; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119807472']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119807472, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "367f5165c15428e8f34976a7c139d31b" } } $('.js-work-strip[data-work-id=119807472]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119807472,"title":"A Study on Various Sentiment Analysis for Mixed Transliterated Indigenous Language using Machine Learning Algorithms","translated_title":"","metadata":{"abstract":"The evolution of Information Technology has led to the collection of large amounts of data, the volume of which has increased to the extent that in the last two years the data produced is greater than all the data ever recorded in human history. This has necessitated the use of machines to understand, interpret and apply data, without manual involvement. Sentiment Analysis has become the area of deep research due to the necessity wrought about by the advent of social media tools such as Twitter, Facebook, WhatsApp, and so on. In this survey, we will analyze 25 literature works concentrating on machine learning approaches associated with sentiment analysis for mixed transliterated languages, bringing into light the various shortcomings of the existing methodologies. Here, the analysis of various methods will be facilitated based on several factors, such as performance metrics, year of publication and journals, achievements of the techniques in numerical evaluations, and so on. On the other hand, the analysis of the methods with respect to the merits and demerits of the methods will be presented. Lastly, the paper discusses the potential future research directions and challenges in achieving better accuracy for sentiment analysis. The motivation of the research will be described in detail through the comparative discussion of the methods.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"The evolution of Information Technology has led to the collection of large amounts of data, the volume of which has increased to the extent that in the last two years the data produced is greater than all the data ever recorded in human history. This has necessitated the use of machines to understand, interpret and apply data, without manual involvement. Sentiment Analysis has become the area of deep research due to the necessity wrought about by the advent of social media tools such as Twitter, Facebook, WhatsApp, and so on. In this survey, we will analyze 25 literature works concentrating on machine learning approaches associated with sentiment analysis for mixed transliterated languages, bringing into light the various shortcomings of the existing methodologies. Here, the analysis of various methods will be facilitated based on several factors, such as performance metrics, year of publication and journals, achievements of the techniques in numerical evaluations, and so on. On the other hand, the analysis of the methods with respect to the merits and demerits of the methods will be presented. Lastly, the paper discusses the potential future research directions and challenges in achieving better accuracy for sentiment analysis. The motivation of the research will be described in detail through the comparative discussion of the methods.","internal_url":"https://www.academia.edu/119807472/A_Study_on_Various_Sentiment_Analysis_for_Mixed_Transliterated_Indigenous_Language_using_Machine_Learning_Algorithms","translated_internal_url":"","created_at":"2024-05-22T04:21:44.985-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[],"downloadable_attachments":[{"id":115145567,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145567/thumbnails/1.jpg","file_name":"324_1_2838_2846.pdf","download_url":"https://www.academia.edu/attachments/115145567/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Study_on_Various_Sentiment_Analysis_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145567/324_1_2838_2846-libre.pdf?1716378705=\u0026response-content-disposition=attachment%3B+filename%3DA_Study_on_Various_Sentiment_Analysis_fo.pdf\u0026Expires=1732468486\u0026Signature=VF5M1u1Y9106Encb-4bZeFE2kIu7BhTuaEt1GmSdSeSx1E3OK34P4Hjj7BleVSMBSJURBC4v73Ka5tFj5---ID4Cfwxl8XkOWbPcHvwTZsd7JtodYuIcsIHfe87f~JIg-MHvTrefBd00~XWVIZ~EDCiciCbH8dvs7lm7HMZhNmG4f4a6mju9eol35~~nkFFOZtCO1lB~Sctp6axoKyLbVEElawv6S-ZCrFvNC1MlictkezMDr6EBKNPpM1XK8rGI7FBsePBRiWJzLhqYIy5PT477RsWQRRpxSxNF7tsTHGMHVwNG7HlhYhsoARsIE4Nm1f4WLuxyoUz6RXddGReM7g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Study_on_Various_Sentiment_Analysis_for_Mixed_Transliterated_Indigenous_Language_using_Machine_Learning_Algorithms","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115145567,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145567/thumbnails/1.jpg","file_name":"324_1_2838_2846.pdf","download_url":"https://www.academia.edu/attachments/115145567/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Study_on_Various_Sentiment_Analysis_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145567/324_1_2838_2846-libre.pdf?1716378705=\u0026response-content-disposition=attachment%3B+filename%3DA_Study_on_Various_Sentiment_Analysis_fo.pdf\u0026Expires=1732468486\u0026Signature=VF5M1u1Y9106Encb-4bZeFE2kIu7BhTuaEt1GmSdSeSx1E3OK34P4Hjj7BleVSMBSJURBC4v73Ka5tFj5---ID4Cfwxl8XkOWbPcHvwTZsd7JtodYuIcsIHfe87f~JIg-MHvTrefBd00~XWVIZ~EDCiciCbH8dvs7lm7HMZhNmG4f4a6mju9eol35~~nkFFOZtCO1lB~Sctp6axoKyLbVEElawv6S-ZCrFvNC1MlictkezMDr6EBKNPpM1XK8rGI7FBsePBRiWJzLhqYIy5PT477RsWQRRpxSxNF7tsTHGMHVwNG7HlhYhsoARsIE4Nm1f4WLuxyoUz6RXddGReM7g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119807397"><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/119807397/Hybrid_Ensemble_based_Feature_Engineering_for_Detecting_Direct_DDOS_Flooding_Attack_in_Cloud"><img alt="Research paper thumbnail of Hybrid Ensemble based Feature Engineering for Detecting Direct DDOS Flooding Attack in Cloud" class="work-thumbnail" src="https://attachments.academia-assets.com/115145481/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/119807397/Hybrid_Ensemble_based_Feature_Engineering_for_Detecting_Direct_DDOS_Flooding_Attack_in_Cloud">Hybrid Ensemble based Feature Engineering for Detecting Direct DDOS Flooding Attack in Cloud</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In recent years, both the general public and commercial enterprises have grown more and more inte...</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 recent years, both the general public and commercial enterprises have grown more and more interested in using cloud services. The majority of businesses use cloud computing technologies for production operations, which draws hackers. Nowadays, one of the most popular methods used to obstruct the availability of essential internet services is Distributed Denial of Service (DDoS) floods. These attacks either totally destroy the victim or overload it with a massive amount of traffic that prevents it from carrying out normal communication. The cloud's services are fully suspended if there are any delays in identifying flooding attacks. A preprocessing stage in cloud DDoS attack defence known as feature engineering has been recognized as having the potential to improve classification accuracy and lower computing complexity. In this article, we suggest a DDoS intrusion detection system for use in cloud environments. The application of filter and wrapper-based feature selection techniques along with machine learning is proposed as a hybrid ensemble-based feature engineering strategy. The benchmark dataset, which fills in the gaps in the current datasets and comprises a wide range of direct DDoS flooding attacks, is used to assess the model. To prevent data overfitting issues, area-user-curve analysis is also assessed. When compared to previous benchmarking techniques, the evaluation and findings revealed a considerable improvement in attack detection. This framework offers a high detection rate and classification accuracy when contrasted with the existing framework. Hence, it is more suitable for protecting the cloud.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="797712078080e32df95d10a87f475141" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115145481,"asset_id":119807397,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115145481/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119807397"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119807397"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119807397; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119807397]").text(description); $(".js-view-count[data-work-id=119807397]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119807397; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119807397']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119807397, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "797712078080e32df95d10a87f475141" } } $('.js-work-strip[data-work-id=119807397]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119807397,"title":"Hybrid Ensemble based Feature Engineering for Detecting Direct DDOS Flooding Attack in Cloud","translated_title":"","metadata":{"abstract":"In recent years, both the general public and commercial enterprises have grown more and more interested in using cloud services. The majority of businesses use cloud computing technologies for production operations, which draws hackers. Nowadays, one of the most popular methods used to obstruct the availability of essential internet services is Distributed Denial of Service (DDoS) floods. These attacks either totally destroy the victim or overload it with a massive amount of traffic that prevents it from carrying out normal communication. The cloud's services are fully suspended if there are any delays in identifying flooding attacks. A preprocessing stage in cloud DDoS attack defence known as feature engineering has been recognized as having the potential to improve classification accuracy and lower computing complexity. In this article, we suggest a DDoS intrusion detection system for use in cloud environments. The application of filter and wrapper-based feature selection techniques along with machine learning is proposed as a hybrid ensemble-based feature engineering strategy. The benchmark dataset, which fills in the gaps in the current datasets and comprises a wide range of direct DDoS flooding attacks, is used to assess the model. To prevent data overfitting issues, area-user-curve analysis is also assessed. When compared to previous benchmarking techniques, the evaluation and findings revealed a considerable improvement in attack detection. This framework offers a high detection rate and classification accuracy when contrasted with the existing framework. Hence, it is more suitable for protecting the cloud.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"In recent years, both the general public and commercial enterprises have grown more and more interested in using cloud services. The majority of businesses use cloud computing technologies for production operations, which draws hackers. Nowadays, one of the most popular methods used to obstruct the availability of essential internet services is Distributed Denial of Service (DDoS) floods. These attacks either totally destroy the victim or overload it with a massive amount of traffic that prevents it from carrying out normal communication. The cloud's services are fully suspended if there are any delays in identifying flooding attacks. A preprocessing stage in cloud DDoS attack defence known as feature engineering has been recognized as having the potential to improve classification accuracy and lower computing complexity. In this article, we suggest a DDoS intrusion detection system for use in cloud environments. The application of filter and wrapper-based feature selection techniques along with machine learning is proposed as a hybrid ensemble-based feature engineering strategy. The benchmark dataset, which fills in the gaps in the current datasets and comprises a wide range of direct DDoS flooding attacks, is used to assess the model. To prevent data overfitting issues, area-user-curve analysis is also assessed. When compared to previous benchmarking techniques, the evaluation and findings revealed a considerable improvement in attack detection. This framework offers a high detection rate and classification accuracy when contrasted with the existing framework. Hence, it is more suitable for protecting the cloud.","internal_url":"https://www.academia.edu/119807397/Hybrid_Ensemble_based_Feature_Engineering_for_Detecting_Direct_DDOS_Flooding_Attack_in_Cloud","translated_internal_url":"","created_at":"2024-05-22T04:18:08.294-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730375,"work_id":119807397,"tagging_user_id":32919427,"tagged_user_id":135736264,"co_author_invite_id":null,"email":"k***m@gmail.com","display_order":1,"name":"Kalaivani Marappan","title":"Hybrid Ensemble based Feature Engineering for Detecting Direct DDOS Flooding Attack in Cloud"}],"downloadable_attachments":[{"id":115145481,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145481/thumbnails/1.jpg","file_name":"323_2_2829_2837.pdf","download_url":"https://www.academia.edu/attachments/115145481/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Hybrid_Ensemble_based_Feature_Engineerin.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145481/323_2_2829_2837-libre.pdf?1716378717=\u0026response-content-disposition=attachment%3B+filename%3DHybrid_Ensemble_based_Feature_Engineerin.pdf\u0026Expires=1732468486\u0026Signature=gc~~mz--kdF6axB4lNllji9quNL6-U6sj8EeON68NGreQ7pLdi8ZEM3R6QA67SgYNB088vXlUP~xz6kk8x7yQ-uMarMlq-3MGp9c~OFVRq-IycvhbHP3Hn5nO3sVOMe3ofd7jCliZe2qf0rvVoXrMeEKHcUj7FPPNlNU5FpB6CI3L8~S~cnBLvhdQ6DAjWhqCtHYkOtzc6ZLDFrh4DE3BF9w~~ebGk0Vc5ykZJdMgVphrhVcDmmbTzcwGnifK3K-vjKdZF0~a9KvzsNs~Ia93gQiCMFwISFcNB6SZQpnuLd1PNOThaZXMgYSsP6FHy6M-dNOA82aX5~4Ls12A-6gyA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Hybrid_Ensemble_based_Feature_Engineering_for_Detecting_Direct_DDOS_Flooding_Attack_in_Cloud","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115145481,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145481/thumbnails/1.jpg","file_name":"323_2_2829_2837.pdf","download_url":"https://www.academia.edu/attachments/115145481/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Hybrid_Ensemble_based_Feature_Engineerin.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145481/323_2_2829_2837-libre.pdf?1716378717=\u0026response-content-disposition=attachment%3B+filename%3DHybrid_Ensemble_based_Feature_Engineerin.pdf\u0026Expires=1732468486\u0026Signature=gc~~mz--kdF6axB4lNllji9quNL6-U6sj8EeON68NGreQ7pLdi8ZEM3R6QA67SgYNB088vXlUP~xz6kk8x7yQ-uMarMlq-3MGp9c~OFVRq-IycvhbHP3Hn5nO3sVOMe3ofd7jCliZe2qf0rvVoXrMeEKHcUj7FPPNlNU5FpB6CI3L8~S~cnBLvhdQ6DAjWhqCtHYkOtzc6ZLDFrh4DE3BF9w~~ebGk0Vc5ykZJdMgVphrhVcDmmbTzcwGnifK3K-vjKdZF0~a9KvzsNs~Ia93gQiCMFwISFcNB6SZQpnuLd1PNOThaZXMgYSsP6FHy6M-dNOA82aX5~4Ls12A-6gyA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[]}, dispatcherData: dispatcherData }); 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For many years, researchers in the fields of philosophy, psychology, neuroscience, and computing have examined the notions and functions of attention.This characteristic has been thoroughly investigated in deep neural networks over the past six years. In many application domains, neural attention models currently represent the cutting edge of deep learning. A thorough overview and analysis of recent advancements in neural attention models are provided in this survey. In order to identify and examine the architectures where attention has had a notable impact, we thoroughly reviewed hundreds of them in the region. Additionally, we created and made available an automated approach to aid in the growth of reviews in the field. We discuss the main applications of attention in convolutional, recurrent networks, and generative models. We also identify common subgroups of uses and applications. 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For many years, researchers in the fields of philosophy, psychology, neuroscience, and computing have examined the notions and functions of attention.This characteristic has been thoroughly investigated in deep neural networks over the past six years. In many application domains, neural attention models currently represent the cutting edge of deep learning. A thorough overview and analysis of recent advancements in neural attention models are provided in this survey. In order to identify and examine the architectures where attention has had a notable impact, we thoroughly reviewed hundreds of them in the region. Additionally, we created and made available an automated approach to aid in the growth of reviews in the field. We discuss the main applications of attention in convolutional, recurrent networks, and generative models. We also identify common subgroups of uses and applications. Additionally, we discuss the effects of attention across various application areas and how they affect.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"Attention is a fundamental component of all perceptual and cognitive processes in humans.This mechanism select, modify, and concentrate on the information that is most important to behaviour because of our limited capacity to interpret competing sources. For many years, researchers in the fields of philosophy, psychology, neuroscience, and computing have examined the notions and functions of attention.This characteristic has been thoroughly investigated in deep neural networks over the past six years. In many application domains, neural attention models currently represent the cutting edge of deep learning. A thorough overview and analysis of recent advancements in neural attention models are provided in this survey. In order to identify and examine the architectures where attention has had a notable impact, we thoroughly reviewed hundreds of them in the region. Additionally, we created and made available an automated approach to aid in the growth of reviews in the field. We discuss the main applications of attention in convolutional, recurrent networks, and generative models. We also identify common subgroups of uses and applications. Additionally, we discuss the effects of attention across various application areas and how they affect.","internal_url":"https://www.academia.edu/119807200/A_Neural_Attention_Models_Survey_for_Deep_Learning","translated_internal_url":"","created_at":"2024-05-22T04:15:35.456-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730347,"work_id":119807200,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8153997,"email":"p***u@giet.edu","display_order":1,"name":"Padma Sahu","title":"A Neural Attention Models Survey for Deep Learning"},{"id":41730348,"work_id":119807200,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8153998,"email":"s***n@giet.edu","display_order":2,"name":"Subhrajit Pradhan","title":"A Neural Attention Models Survey for Deep Learning"},{"id":41730349,"work_id":119807200,"tagging_user_id":32919427,"tagged_user_id":35789232,"co_author_invite_id":null,"email":"r***h@gmail.com","display_order":3,"name":"Ratnakar Dash","title":"A Neural Attention Models Survey for Deep Learning"}],"downloadable_attachments":[{"id":115145372,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145372/thumbnails/1.jpg","file_name":"323_1_2821_2828.pdf","download_url":"https://www.academia.edu/attachments/115145372/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Neural_Attention_Models_Survey_for_Dee.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145372/323_1_2821_2828-libre.pdf?1716378726=\u0026response-content-disposition=attachment%3B+filename%3DA_Neural_Attention_Models_Survey_for_Dee.pdf\u0026Expires=1732468486\u0026Signature=TIXFpYSul7hjbdsilQit-DtBN3JKKhJJUe-Bz51qs7DWaG2f31-lts4bQpAwfg~lpHby15O~7SvZltJNbw0nRJT2OUO~HKdJAU7bjYnuPylnNg55HicGS7Z3Y0kaAANMF3jknSqggSKnxKi0khkjRv1-Ss0wlYUEcXh7kBtsw0IQiOnjQL23T8kAQ98UGAUBjeEe7oVSn7-vVIYSXbkCc9fJYu7l0FA7658Wx9fNvpFxVkE~xpQsyrJ4CUQULcnsvPI1SnT~9CwU9X404Ugtnjuytdj00KXyG6~wjYODXmsyH9~S7nXFnRs6R4lU0~9Ui01tUARQgQ~qoE1OxDm-zA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Neural_Attention_Models_Survey_for_Deep_Learning","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115145372,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145372/thumbnails/1.jpg","file_name":"323_1_2821_2828.pdf","download_url":"https://www.academia.edu/attachments/115145372/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Neural_Attention_Models_Survey_for_Dee.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145372/323_1_2821_2828-libre.pdf?1716378726=\u0026response-content-disposition=attachment%3B+filename%3DA_Neural_Attention_Models_Survey_for_Dee.pdf\u0026Expires=1732468486\u0026Signature=TIXFpYSul7hjbdsilQit-DtBN3JKKhJJUe-Bz51qs7DWaG2f31-lts4bQpAwfg~lpHby15O~7SvZltJNbw0nRJT2OUO~HKdJAU7bjYnuPylnNg55HicGS7Z3Y0kaAANMF3jknSqggSKnxKi0khkjRv1-Ss0wlYUEcXh7kBtsw0IQiOnjQL23T8kAQ98UGAUBjeEe7oVSn7-vVIYSXbkCc9fJYu7l0FA7658Wx9fNvpFxVkE~xpQsyrJ4CUQULcnsvPI1SnT~9CwU9X404Ugtnjuytdj00KXyG6~wjYODXmsyH9~S7nXFnRs6R4lU0~9Ui01tUARQgQ~qoE1OxDm-zA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119807145"><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/119807145/Hand_Gesture_Recognition_An_Approach_of_Multiple_Object_Tracking"><img alt="Research paper thumbnail of Hand Gesture Recognition: An Approach of Multiple Object Tracking" class="work-thumbnail" src="https://attachments.academia-assets.com/115145306/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/119807145/Hand_Gesture_Recognition_An_Approach_of_Multiple_Object_Tracking">Hand Gesture Recognition: An Approach of Multiple Object Tracking</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the recent year, Object Detection and Tracking technology is one of the challenging and most e...</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 the recent year, Object Detection and Tracking technology is one of the<br />challenging and most emerging topic in Computer Vision. Multiple Object Tracking &<br />Detection (MOTD) has gained a lot of importance in various fields such as Autonomous driving,<br />Monitoring security, Surveillance, Health-care monitoring and Gesture recognition etc. Gesture<br />recognition is widely used in various fields of intelligent driving, Virtual reality, and Humancomputer<br />interaction. With the development in various technologies such as Deep Learning,<br />Artificial Intelligence and Human-Computer interaction a new revolution arise in field of<br />computer vision. We endeavor to provide a thorough review on the development of this gesture<br />recognition approach in recent decades. Here our major discussions are about the benefits and<br />limitations of existing approach where focus is on the method of feature extraction in<br />spatiotemporal structure .Various research difficulties that could be pursued will be the<br />research directions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0640859bf601f1d773185cb92389d774" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115145306,"asset_id":119807145,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115145306/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119807145"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119807145"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119807145; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119807145]").text(description); $(".js-view-count[data-work-id=119807145]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119807145; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119807145']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119807145, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "0640859bf601f1d773185cb92389d774" } } $('.js-work-strip[data-work-id=119807145]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119807145,"title":"Hand Gesture Recognition: An Approach of Multiple Object Tracking","translated_title":"","metadata":{"abstract":"In the recent year, Object Detection and Tracking technology is one of the\nchallenging and most emerging topic in Computer Vision. Multiple Object Tracking \u0026\nDetection (MOTD) has gained a lot of importance in various fields such as Autonomous driving,\nMonitoring security, Surveillance, Health-care monitoring and Gesture recognition etc. Gesture\nrecognition is widely used in various fields of intelligent driving, Virtual reality, and Humancomputer\ninteraction. With the development in various technologies such as Deep Learning,\nArtificial Intelligence and Human-Computer interaction a new revolution arise in field of\ncomputer vision. We endeavor to provide a thorough review on the development of this gesture\nrecognition approach in recent decades. Here our major discussions are about the benefits and\nlimitations of existing approach where focus is on the method of feature extraction in\nspatiotemporal structure .Various research difficulties that could be pursued will be the\nresearch directions.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"In the recent year, Object Detection and Tracking technology is one of the\nchallenging and most emerging topic in Computer Vision. Multiple Object Tracking \u0026\nDetection (MOTD) has gained a lot of importance in various fields such as Autonomous driving,\nMonitoring security, Surveillance, Health-care monitoring and Gesture recognition etc. Gesture\nrecognition is widely used in various fields of intelligent driving, Virtual reality, and Humancomputer\ninteraction. With the development in various technologies such as Deep Learning,\nArtificial Intelligence and Human-Computer interaction a new revolution arise in field of\ncomputer vision. We endeavor to provide a thorough review on the development of this gesture\nrecognition approach in recent decades. Here our major discussions are about the benefits and\nlimitations of existing approach where focus is on the method of feature extraction in\nspatiotemporal structure .Various research difficulties that could be pursued will be the\nresearch directions.","internal_url":"https://www.academia.edu/119807145/Hand_Gesture_Recognition_An_Approach_of_Multiple_Object_Tracking","translated_internal_url":"","created_at":"2024-05-22T04:12:39.916-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[],"downloadable_attachments":[{"id":115145306,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145306/thumbnails/1.jpg","file_name":"322_1_2817_2820.pdf","download_url":"https://www.academia.edu/attachments/115145306/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Hand_Gesture_Recognition_An_Approach_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145306/322_1_2817_2820-libre.pdf?1716378729=\u0026response-content-disposition=attachment%3B+filename%3DHand_Gesture_Recognition_An_Approach_of.pdf\u0026Expires=1732468486\u0026Signature=GwF7CrNNT9LwjEiprVgrDdN6gPsB38YJshi8vvFUKC~4440cwhM9G3-8kwIi7-b63t2nybC2vGm7ND9R5Vep4kYaNwcKTfymKWTST3TzxAykmDhb1ee1Ur~RHrthAYVkkj6xj7wSzHzzjdkQcTVEFmAjWvlLrcFsXBuVUysdi0nvONxfMvz5qvnMmUgpz1nN0Mc4JV0uFYZvBSl6ab7Pu3fCTmRSDpJKVL-Wi97V1J09S3doPEmhMfme8A08AGFyyPJ7HuTFCJp6MP0qhsNCfOhJWInyD0dhtq6EgtEGndZLjrFArBZfHeuTQFvAavpH0q1Rof97OpKK9xIRzSfaNQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Hand_Gesture_Recognition_An_Approach_of_Multiple_Object_Tracking","translated_slug":"","page_count":4,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115145306,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145306/thumbnails/1.jpg","file_name":"322_1_2817_2820.pdf","download_url":"https://www.academia.edu/attachments/115145306/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Hand_Gesture_Recognition_An_Approach_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145306/322_1_2817_2820-libre.pdf?1716378729=\u0026response-content-disposition=attachment%3B+filename%3DHand_Gesture_Recognition_An_Approach_of.pdf\u0026Expires=1732468486\u0026Signature=GwF7CrNNT9LwjEiprVgrDdN6gPsB38YJshi8vvFUKC~4440cwhM9G3-8kwIi7-b63t2nybC2vGm7ND9R5Vep4kYaNwcKTfymKWTST3TzxAykmDhb1ee1Ur~RHrthAYVkkj6xj7wSzHzzjdkQcTVEFmAjWvlLrcFsXBuVUysdi0nvONxfMvz5qvnMmUgpz1nN0Mc4JV0uFYZvBSl6ab7Pu3fCTmRSDpJKVL-Wi97V1J09S3doPEmhMfme8A08AGFyyPJ7HuTFCJp6MP0qhsNCfOhJWInyD0dhtq6EgtEGndZLjrFArBZfHeuTQFvAavpH0q1Rof97OpKK9xIRzSfaNQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[]}, 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="8020084" id="conferencepresentations"><div class="js-work-strip profile--work_container" data-work-id="119809427"><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/119809427/Detection_of_Cataract_and_its_Level_based_on_Deep_Learning_using_Mobile_Application"><img alt="Research paper thumbnail of Detection of Cataract and its Level based on Deep Learning using Mobile Application" class="work-thumbnail" src="https://attachments.academia-assets.com/115146963/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/119809427/Detection_of_Cataract_and_its_Level_based_on_Deep_Learning_using_Mobile_Application">Detection of Cataract and its Level based on Deep Learning using Mobile Application</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The human eye has a natural lens that refracts the incoming light rays to help us see objects. Ca...</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 human eye has a natural lens that refracts the incoming light rays to help us see<br />objects. Cataracts could be the reason why the eye's natural lens becomes cloudy. When<br />proteins in the lens start breaking down, causing cataracts, objects may appear cloudy, fuzzy,<br />or even less colorful. If Cataract is not identified and treated in the early stages, it could lead to<br />complete blindness of the eye. It is mainly observed in older age groups than the younger age<br />group, however, there are cases witnessed even in young people. We are creating a mobile<br />application using AI and deep learning that can detect the existence of cataracts to help with<br />the scarcity of ophthalmologists. With this application, patients can use their smartphones to<br />click the photograph of a patient's eye and feed the data into this AI-based system that is<br />developed using deep learning technologies. The model then determines whether the eye has a<br />nuclear sclerotic, cortical, or posterior subcapsular. The effect of this system will result in far<br />improvement in the delivery of public services, diagnosis and treatment, prioritization of<br />patients, and ultimately, the prevention of blindness. This system will demonstrate encouraging<br />outcomes.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="25ad16a68e270905b894c7c210455887" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146963,"asset_id":119809427,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146963/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119809427"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119809427"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119809427; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119809427]").text(description); $(".js-view-count[data-work-id=119809427]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119809427; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119809427']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119809427, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "25ad16a68e270905b894c7c210455887" } } $('.js-work-strip[data-work-id=119809427]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119809427,"title":"Detection of Cataract and its Level based on Deep Learning using Mobile Application","translated_title":"","metadata":{"abstract":"The human eye has a natural lens that refracts the incoming light rays to help us see\nobjects. Cataracts could be the reason why the eye's natural lens becomes cloudy. When\nproteins in the lens start breaking down, causing cataracts, objects may appear cloudy, fuzzy,\nor even less colorful. If Cataract is not identified and treated in the early stages, it could lead to\ncomplete blindness of the eye. It is mainly observed in older age groups than the younger age\ngroup, however, there are cases witnessed even in young people. We are creating a mobile\napplication using AI and deep learning that can detect the existence of cataracts to help with\nthe scarcity of ophthalmologists. With this application, patients can use their smartphones to\nclick the photograph of a patient's eye and feed the data into this AI-based system that is\ndeveloped using deep learning technologies. The model then determines whether the eye has a\nnuclear sclerotic, cortical, or posterior subcapsular. The effect of this system will result in far\nimprovement in the delivery of public services, diagnosis and treatment, prioritization of\npatients, and ultimately, the prevention of blindness. This system will demonstrate encouraging\noutcomes.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"The human eye has a natural lens that refracts the incoming light rays to help us see\nobjects. Cataracts could be the reason why the eye's natural lens becomes cloudy. When\nproteins in the lens start breaking down, causing cataracts, objects may appear cloudy, fuzzy,\nor even less colorful. If Cataract is not identified and treated in the early stages, it could lead to\ncomplete blindness of the eye. It is mainly observed in older age groups than the younger age\ngroup, however, there are cases witnessed even in young people. We are creating a mobile\napplication using AI and deep learning that can detect the existence of cataracts to help with\nthe scarcity of ophthalmologists. With this application, patients can use their smartphones to\nclick the photograph of a patient's eye and feed the data into this AI-based system that is\ndeveloped using deep learning technologies. The model then determines whether the eye has a\nnuclear sclerotic, cortical, or posterior subcapsular. The effect of this system will result in far\nimprovement in the delivery of public services, diagnosis and treatment, prioritization of\npatients, and ultimately, the prevention of blindness. This system will demonstrate encouraging\noutcomes.","internal_url":"https://www.academia.edu/119809427/Detection_of_Cataract_and_its_Level_based_on_Deep_Learning_using_Mobile_Application","translated_internal_url":"","created_at":"2024-05-22T04:53:33.263-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[],"downloadable_attachments":[{"id":115146963,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146963/thumbnails/1.jpg","file_name":"512_3_2946_2951.pdf","download_url":"https://www.academia.edu/attachments/115146963/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Detection_of_Cataract_and_its_Level_base.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146963/512_3_2946_2951-libre.pdf?1716382652=\u0026response-content-disposition=attachment%3B+filename%3DDetection_of_Cataract_and_its_Level_base.pdf\u0026Expires=1732468482\u0026Signature=K6osJCGXTPHM732OikSDLpvfgdaFmlS1XW909wCD8Cr3VfIdUgFroxmgf-f-Mlior3lDP2OQc0peHCyIvFn2yNby7pmQ~uGuBe4NUlPcIzh9-QnfHBd~bG1CdSeU8eAhGgK~r257HHbUXDR-cjq9WaJJCkdLBs8nc~NQ0V-jSEe396ihllRLGKBFW7wDg~vZjbzgoE16813hk7vzvCjXI~AlG76mRYqOc3n3-RvPlbDCN9deTADvuZ9MryIvAL8DJTw16LOMAEbPo-XfN2aQm0lb3N8bziscDjDFP-WWb9KvfWbjrA8v5TxuQF82-D-~-wzPRajqnAgf9mFtN-e1Jg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Detection_of_Cataract_and_its_Level_based_on_Deep_Learning_using_Mobile_Application","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146963,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146963/thumbnails/1.jpg","file_name":"512_3_2946_2951.pdf","download_url":"https://www.academia.edu/attachments/115146963/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Detection_of_Cataract_and_its_Level_base.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146963/512_3_2946_2951-libre.pdf?1716382652=\u0026response-content-disposition=attachment%3B+filename%3DDetection_of_Cataract_and_its_Level_base.pdf\u0026Expires=1732468482\u0026Signature=K6osJCGXTPHM732OikSDLpvfgdaFmlS1XW909wCD8Cr3VfIdUgFroxmgf-f-Mlior3lDP2OQc0peHCyIvFn2yNby7pmQ~uGuBe4NUlPcIzh9-QnfHBd~bG1CdSeU8eAhGgK~r257HHbUXDR-cjq9WaJJCkdLBs8nc~NQ0V-jSEe396ihllRLGKBFW7wDg~vZjbzgoE16813hk7vzvCjXI~AlG76mRYqOc3n3-RvPlbDCN9deTADvuZ9MryIvAL8DJTw16LOMAEbPo-XfN2aQm0lb3N8bziscDjDFP-WWb9KvfWbjrA8v5TxuQF82-D-~-wzPRajqnAgf9mFtN-e1Jg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119809304"><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/119809304/Orthogonal_Frequency_Division_Multiplexing_A_Review"><img alt="Research paper thumbnail of Orthogonal Frequency Division Multiplexing: A Review" class="work-thumbnail" src="https://attachments.academia-assets.com/115146926/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/119809304/Orthogonal_Frequency_Division_Multiplexing_A_Review">Orthogonal Frequency Division Multiplexing: A Review</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Orthogonal frequency-division multiplexing significantly reduces inter-symbol interference (I.S.I...</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">Orthogonal frequency-division multiplexing significantly reduces inter-symbol interference (I.S.I.) caused by the delayed propagation of wireless channels (OFDM). As a result, numerous standards have embraced it, and it is now utilized in various wireless systems. This paper will overview OFDM and how it can be used in wireless communications. The basics of OFDM and related modulations are covered, along with methods for enhancing OFDM's performance in wireless communications. Examples include channel estimation and signal detection, time and frequency offset estimation and correction, peak-to-average power ratio reduction PAPR, and inter-carrier interference. In wireless transmission, the fundamental concept of OFDM is gaining acceptance. OFDM is also one of the most often recommended techniques for usage in 4th Generation Wireless Systems. The cyclic extension of OFDM signals is a general principle rather than employing empty guard spaces in the frequency domain. OFDM is a modulation and multiplexing technology used in WiFi, Wi-MAX, and 3G/4G/5G mobile communication systems. A low bit rate at the subcarrier is desired to extend symbol duration and prevent multipath distortion in OFDM. This will also improve the (C.P.) interval, which is a good thing (multiple distortions and error reduction). Due to the larger C.P. interval, however, it will result in a better energy loss value. The duration of the OFDM symbol, the C.P. interval, and the permissible delay (environment) spread must all be balanced.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a60bd08a2217293e28b7c5b73dc9647a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146926,"asset_id":119809304,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146926/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119809304"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119809304"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119809304; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119809304]").text(description); $(".js-view-count[data-work-id=119809304]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119809304; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119809304']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119809304, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "a60bd08a2217293e28b7c5b73dc9647a" } } $('.js-work-strip[data-work-id=119809304]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119809304,"title":"Orthogonal Frequency Division Multiplexing: A Review","translated_title":"","metadata":{"abstract":"Orthogonal frequency-division multiplexing significantly reduces inter-symbol interference (I.S.I.) caused by the delayed propagation of wireless channels (OFDM). As a result, numerous standards have embraced it, and it is now utilized in various wireless systems. This paper will overview OFDM and how it can be used in wireless communications. The basics of OFDM and related modulations are covered, along with methods for enhancing OFDM's performance in wireless communications. Examples include channel estimation and signal detection, time and frequency offset estimation and correction, peak-to-average power ratio reduction PAPR, and inter-carrier interference. In wireless transmission, the fundamental concept of OFDM is gaining acceptance. OFDM is also one of the most often recommended techniques for usage in 4th Generation Wireless Systems. The cyclic extension of OFDM signals is a general principle rather than employing empty guard spaces in the frequency domain. OFDM is a modulation and multiplexing technology used in WiFi, Wi-MAX, and 3G/4G/5G mobile communication systems. A low bit rate at the subcarrier is desired to extend symbol duration and prevent multipath distortion in OFDM. This will also improve the (C.P.) interval, which is a good thing (multiple distortions and error reduction). Due to the larger C.P. interval, however, it will result in a better energy loss value. The duration of the OFDM symbol, the C.P. interval, and the permissible delay (environment) spread must all be balanced.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"Orthogonal frequency-division multiplexing significantly reduces inter-symbol interference (I.S.I.) caused by the delayed propagation of wireless channels (OFDM). As a result, numerous standards have embraced it, and it is now utilized in various wireless systems. This paper will overview OFDM and how it can be used in wireless communications. The basics of OFDM and related modulations are covered, along with methods for enhancing OFDM's performance in wireless communications. Examples include channel estimation and signal detection, time and frequency offset estimation and correction, peak-to-average power ratio reduction PAPR, and inter-carrier interference. In wireless transmission, the fundamental concept of OFDM is gaining acceptance. OFDM is also one of the most often recommended techniques for usage in 4th Generation Wireless Systems. The cyclic extension of OFDM signals is a general principle rather than employing empty guard spaces in the frequency domain. OFDM is a modulation and multiplexing technology used in WiFi, Wi-MAX, and 3G/4G/5G mobile communication systems. A low bit rate at the subcarrier is desired to extend symbol duration and prevent multipath distortion in OFDM. This will also improve the (C.P.) interval, which is a good thing (multiple distortions and error reduction). Due to the larger C.P. interval, however, it will result in a better energy loss value. The duration of the OFDM symbol, the C.P. interval, and the permissible delay (environment) spread must all be balanced.","internal_url":"https://www.academia.edu/119809304/Orthogonal_Frequency_Division_Multiplexing_A_Review","translated_internal_url":"","created_at":"2024-05-22T04:51:43.368-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730577,"work_id":119809304,"tagging_user_id":32919427,"tagged_user_id":263970291,"co_author_invite_id":null,"email":"y***3@gmail.com","display_order":1,"name":"YOGITA KAPSE","title":"Orthogonal Frequency Division Multiplexing: A Review"},{"id":41730578,"work_id":119809304,"tagging_user_id":32919427,"tagged_user_id":165782498,"co_author_invite_id":null,"email":"s***i@gmail.com","display_order":2,"name":"SHRIPAD MOHANI","title":"Orthogonal Frequency Division Multiplexing: A Review"}],"downloadable_attachments":[{"id":115146926,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146926/thumbnails/1.jpg","file_name":"511_1_2941_2945.pdf","download_url":"https://www.academia.edu/attachments/115146926/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Orthogonal_Frequency_Division_Multiplexi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146926/511_1_2941_2945-libre.pdf?1716382655=\u0026response-content-disposition=attachment%3B+filename%3DOrthogonal_Frequency_Division_Multiplexi.pdf\u0026Expires=1732468483\u0026Signature=YQfvg8Mha6ILQdtDOcc4OcozH19rlmqeER-SLdjJryxkVmElccKT5v4ORb6nnhvGscZyK4TacSb2zZYrHvrOvtxyUwKCeZtp1RHtXWP0MqKxC4sm4eepcsVQZ~8w1vrS~Y6eD~9~aoRBktyXy1mvJYbpLvTFQhrzVcUCR0kPHNP6PK7nC3Z6WFy6uhEoaistO-YmNEsbfkGXa0XDgx-aSTIKIACoXNrmpdX1f7ZqVetThuHiNubFhELeijpVHKviAt4cVHhJPfWl6Lhda1agZXH~DpAkgulEWir6lbeHaNJFrktGW2aKzNWvMI4aDtqKEqvDxy46PbDKEc~~nX8phA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Orthogonal_Frequency_Division_Multiplexing_A_Review","translated_slug":"","page_count":5,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146926,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146926/thumbnails/1.jpg","file_name":"511_1_2941_2945.pdf","download_url":"https://www.academia.edu/attachments/115146926/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Orthogonal_Frequency_Division_Multiplexi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146926/511_1_2941_2945-libre.pdf?1716382655=\u0026response-content-disposition=attachment%3B+filename%3DOrthogonal_Frequency_Division_Multiplexi.pdf\u0026Expires=1732468483\u0026Signature=YQfvg8Mha6ILQdtDOcc4OcozH19rlmqeER-SLdjJryxkVmElccKT5v4ORb6nnhvGscZyK4TacSb2zZYrHvrOvtxyUwKCeZtp1RHtXWP0MqKxC4sm4eepcsVQZ~8w1vrS~Y6eD~9~aoRBktyXy1mvJYbpLvTFQhrzVcUCR0kPHNP6PK7nC3Z6WFy6uhEoaistO-YmNEsbfkGXa0XDgx-aSTIKIACoXNrmpdX1f7ZqVetThuHiNubFhELeijpVHKviAt4cVHhJPfWl6Lhda1agZXH~DpAkgulEWir6lbeHaNJFrktGW2aKzNWvMI4aDtqKEqvDxy46PbDKEc~~nX8phA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119809172"><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/119809172/An_Optimal_Selection_Scheme_for_Automation_Testing_Framework_with_Quality_Assurance"><img alt="Research paper thumbnail of An Optimal Selection Scheme for Automation Testing Framework with Quality Assurance" class="work-thumbnail" src="https://attachments.academia-assets.com/115146833/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/119809172/An_Optimal_Selection_Scheme_for_Automation_Testing_Framework_with_Quality_Assurance">An Optimal Selection Scheme for Automation Testing Framework with Quality Assurance</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The world is fast changing due to digitalization and the disruption brought on by the use of digi...</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 world is fast changing due to digitalization and the disruption brought on by the use of digital technology. All IT operations require a high level of speed, and this necessitates a paradigm shift in quality assurance (QA). Digital assurance places a strong emphasis on quality at high speed and businesses want to produce high-quality goods more quickly than ever. As a result, QA teams are turning to test automation. The industry is shifting from the initial automation of regression tests to progressive automation and day one automation. Automation testing has seen a number of developments. However, it is crucial that businesses pick the appropriate automation framework because it is thought to be a key component of success. The paper considers quality assurance parameters for automation testing framework by adopting digital transformation initiatives to achieve the desired business outcome. In the paper we proposes a model of optimal testing framework selection and we'll discuss the different automation framework types and how to pick one that will assist enterprises to achieve their digital assurance objectives.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="99c6318649d441de92799aa94ed8e459" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146833,"asset_id":119809172,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146833/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119809172"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119809172"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119809172; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119809172]").text(description); $(".js-view-count[data-work-id=119809172]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119809172; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119809172']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119809172, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "99c6318649d441de92799aa94ed8e459" } } $('.js-work-strip[data-work-id=119809172]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119809172,"title":"An Optimal Selection Scheme for Automation Testing Framework with Quality Assurance","translated_title":"","metadata":{"abstract":"The world is fast changing due to digitalization and the disruption brought on by the use of digital technology. 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In the paper we proposes a model of optimal testing framework selection and we'll discuss the different automation framework types and how to pick one that will assist enterprises to achieve their digital assurance objectives.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"The world is fast changing due to digitalization and the disruption brought on by the use of digital technology. All IT operations require a high level of speed, and this necessitates a paradigm shift in quality assurance (QA). Digital assurance places a strong emphasis on quality at high speed and businesses want to produce high-quality goods more quickly than ever. As a result, QA teams are turning to test automation. The industry is shifting from the initial automation of regression tests to progressive automation and day one automation. Automation testing has seen a number of developments. However, it is crucial that businesses pick the appropriate automation framework because it is thought to be a key component of success. The paper considers quality assurance parameters for automation testing framework by adopting digital transformation initiatives to achieve the desired business outcome. In the paper we proposes a model of optimal testing framework selection and we'll discuss the different automation framework types and how to pick one that will assist enterprises to achieve their digital assurance objectives.","internal_url":"https://www.academia.edu/119809172/An_Optimal_Selection_Scheme_for_Automation_Testing_Framework_with_Quality_Assurance","translated_internal_url":"","created_at":"2024-05-22T04:49:32.192-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730564,"work_id":119809172,"tagging_user_id":32919427,"tagged_user_id":15782117,"co_author_invite_id":null,"email":"m***1@gmail.com","affiliation":"Indian Institute of Tropical Meteorology","display_order":1,"name":"Manmeet Singh","title":"An Optimal Selection Scheme for Automation Testing Framework with Quality Assurance"},{"id":41730565,"work_id":119809172,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8154030,"email":"j***y@medicaps.ac.in","display_order":2,"name":"Jitendra Choudhary","title":"An Optimal Selection Scheme for Automation Testing Framework with Quality Assurance"},{"id":41730566,"work_id":119809172,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":5780066,"email":"l***i@gmail.com","display_order":3,"name":"Lokesh Laddhani","title":"An Optimal Selection Scheme for Automation Testing Framework with Quality Assurance"}],"downloadable_attachments":[{"id":115146833,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146833/thumbnails/1.jpg","file_name":"510_3_2935_2940.pdf","download_url":"https://www.academia.edu/attachments/115146833/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Optimal_Selection_Scheme_for_Automati.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146833/510_3_2935_2940-libre.pdf?1716378608=\u0026response-content-disposition=attachment%3B+filename%3DAn_Optimal_Selection_Scheme_for_Automati.pdf\u0026Expires=1732468483\u0026Signature=Bh3mNbqhTKlehFwyxTAl7ftOsDYU3AEMUIK9q7vEW67w8AX3pvAn2viGTqDgfTh-bZecqxBxZdJJ0-f8BxD1pin~-enKYvmG3YEUs1Wo02uO9c-YqJfyvnMYjFrD2itvBUZxGepSjnjgm~8lrvgqt8UOzjIBuhsS1I59gs~08tq4zgvamtNP~db-pMe1nPHgvkvIJ3MSnkPHBRPgeA-8qDX-Tx8yvUmwGzQxtAkPiizJwqvasFpO5PImvQxBDQM~9oVQnl0gc5ph66vbTELKgPo43teK28mS6tWhgKBEW6HMQDjLTg~WfCZXadaOzSGCKjD6kkmY2NnVoIiZw9sPmA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"An_Optimal_Selection_Scheme_for_Automation_Testing_Framework_with_Quality_Assurance","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146833,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146833/thumbnails/1.jpg","file_name":"510_3_2935_2940.pdf","download_url":"https://www.academia.edu/attachments/115146833/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Optimal_Selection_Scheme_for_Automati.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146833/510_3_2935_2940-libre.pdf?1716378608=\u0026response-content-disposition=attachment%3B+filename%3DAn_Optimal_Selection_Scheme_for_Automati.pdf\u0026Expires=1732468483\u0026Signature=Bh3mNbqhTKlehFwyxTAl7ftOsDYU3AEMUIK9q7vEW67w8AX3pvAn2viGTqDgfTh-bZecqxBxZdJJ0-f8BxD1pin~-enKYvmG3YEUs1Wo02uO9c-YqJfyvnMYjFrD2itvBUZxGepSjnjgm~8lrvgqt8UOzjIBuhsS1I59gs~08tq4zgvamtNP~db-pMe1nPHgvkvIJ3MSnkPHBRPgeA-8qDX-Tx8yvUmwGzQxtAkPiizJwqvasFpO5PImvQxBDQM~9oVQnl0gc5ph66vbTELKgPo43teK28mS6tWhgKBEW6HMQDjLTg~WfCZXadaOzSGCKjD6kkmY2NnVoIiZw9sPmA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119808898"><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/119808898/Trading_Secured_Information_in_Cloud_Utilizing_Record_Cryptography_and_Speedy_Reaction_OTP"><img alt="Research paper thumbnail of Trading Secured Information in Cloud Utilizing Record Cryptography and Speedy Reaction OTP" class="work-thumbnail" src="https://attachments.academia-assets.com/115146658/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/119808898/Trading_Secured_Information_in_Cloud_Utilizing_Record_Cryptography_and_Speedy_Reaction_OTP">Trading Secured Information in Cloud Utilizing Record Cryptography and Speedy Reaction OTP</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">CLOUD capacity is presently picking up notoriety since it offers a adaptable on-demand informatio...</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">CLOUD capacity is presently picking up notoriety since it offers a adaptable on-demand information outsourcing benefit with engaging benefits: alleviation of the burden for capacity administration, widespread information get to with area freedom, and evasion of capital use on equipment, computer program, and individual maintenances. Be that as it may, existing arrangements in conventional information outsourcing situation are incapable to at the same time meet the taking after three security prerequisites for keys outsourcing: Privacy and protection of record capacity; Data security on personality properties tied to keys; Owner controllable authorization over user's shared keys. The proposed to begin with bound together key administration system that addresses all the three objectives over. This cloud framework permits the key proprietor can perform security and controllable authorization conjointly give implemented record encryption with least data spillage. To actualize Cloud Key Bank proficiently, this demonstrate proposes a unused calculation Halter kilter Calculation. Proposed exploratory comes about and security investigation will appear the productivity and security objectives are well accomplished. To unscramble the records we utilize QR OTP for security.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a042f2f808d6a3006d7e9ddfc87afcd6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146658,"asset_id":119808898,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146658/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119808898"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119808898"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119808898; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119808898]").text(description); $(".js-view-count[data-work-id=119808898]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119808898; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119808898']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119808898, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "a042f2f808d6a3006d7e9ddfc87afcd6" } } $('.js-work-strip[data-work-id=119808898]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119808898,"title":"Trading Secured Information in Cloud Utilizing Record Cryptography and Speedy Reaction OTP","translated_title":"","metadata":{"abstract":"CLOUD capacity is presently picking up notoriety since it offers a adaptable on-demand information outsourcing benefit with engaging benefits: alleviation of the burden for capacity administration, widespread information get to with area freedom, and evasion of capital use on equipment, computer program, and individual maintenances. Be that as it may, existing arrangements in conventional information outsourcing situation are incapable to at the same time meet the taking after three security prerequisites for keys outsourcing: Privacy and protection of record capacity; Data security on personality properties tied to keys; Owner controllable authorization over user's shared keys. The proposed to begin with bound together key administration system that addresses all the three objectives over. This cloud framework permits the key proprietor can perform security and controllable authorization conjointly give implemented record encryption with least data spillage. To actualize Cloud Key Bank proficiently, this demonstrate proposes a unused calculation Halter kilter Calculation. Proposed exploratory comes about and security investigation will appear the productivity and security objectives are well accomplished. To unscramble the records we utilize QR OTP for security.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"CLOUD capacity is presently picking up notoriety since it offers a adaptable on-demand information outsourcing benefit with engaging benefits: alleviation of the burden for capacity administration, widespread information get to with area freedom, and evasion of capital use on equipment, computer program, and individual maintenances. Be that as it may, existing arrangements in conventional information outsourcing situation are incapable to at the same time meet the taking after three security prerequisites for keys outsourcing: Privacy and protection of record capacity; Data security on personality properties tied to keys; Owner controllable authorization over user's shared keys. The proposed to begin with bound together key administration system that addresses all the three objectives over. This cloud framework permits the key proprietor can perform security and controllable authorization conjointly give implemented record encryption with least data spillage. To actualize Cloud Key Bank proficiently, this demonstrate proposes a unused calculation Halter kilter Calculation. Proposed exploratory comes about and security investigation will appear the productivity and security objectives are well accomplished. To unscramble the records we utilize QR OTP for security.","internal_url":"https://www.academia.edu/119808898/Trading_Secured_Information_in_Cloud_Utilizing_Record_Cryptography_and_Speedy_Reaction_OTP","translated_internal_url":"","created_at":"2024-05-22T04:47:18.673-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730558,"work_id":119808898,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8154028,"email":"k***s@nct.ac.in","display_order":1,"name":"M. 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Various gases play a vital rol...</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">Gas sampling and sensing is a domain of great importance nowadays. Various gases play a vital role in different industries ranging from process industries to bio medical engineering. The measurement of concentration of hazardous gas components like NO 2 is in high demand and hence many researchers are attracted towards the development of NO 2 sensors on the basis of various working principles. A single sensor cannot be the best sensor for all applications. This fact leads to in the need of selection criteria for the selection of the sensor for any industrial application. This paper presents a selection criterion for the selection of NO 2 sensor based on its important features and characteristics. Considering this one can select most suitable NO 2 sensor for a typical application. This paper also presents a qualitative comparison of commercially available NO 2 sensors which can be a handy tool for someone who is interested in the measurement of NO 2 concentration for certain application.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="93e1ed9cda7dcb3143e351ae574b3598" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146568,"asset_id":119808746,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146568/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119808746"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119808746"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119808746; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119808746]").text(description); $(".js-view-count[data-work-id=119808746]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119808746; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119808746']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119808746, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "93e1ed9cda7dcb3143e351ae574b3598" } } $('.js-work-strip[data-work-id=119808746]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119808746,"title":"Qualitative Comparison of Commercial NO2 Sensors and Selection Criteria for Various Applications","translated_title":"","metadata":{"abstract":"Gas sampling and sensing is a domain of great importance nowadays. 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This paper also presents a qualitative comparison of commercially available NO 2 sensors which can be a handy tool for someone who is interested in the measurement of NO 2 concentration for certain application.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"Gas sampling and sensing is a domain of great importance nowadays. Various gases play a vital role in different industries ranging from process industries to bio medical engineering. The measurement of concentration of hazardous gas components like NO 2 is in high demand and hence many researchers are attracted towards the development of NO 2 sensors on the basis of various working principles. A single sensor cannot be the best sensor for all applications. This fact leads to in the need of selection criteria for the selection of the sensor for any industrial application. This paper presents a selection criterion for the selection of NO 2 sensor based on its important features and characteristics. Considering this one can select most suitable NO 2 sensor for a typical application. This paper also presents a qualitative comparison of commercially available NO 2 sensors which can be a handy tool for someone who is interested in the measurement of NO 2 concentration for certain application.","internal_url":"https://www.academia.edu/119808746/Qualitative_Comparison_of_Commercial_NO2_Sensors_and_Selection_Criteria_for_Various_Applications","translated_internal_url":"","created_at":"2024-05-22T04:45:05.863-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730548,"work_id":119808746,"tagging_user_id":32919427,"tagged_user_id":218889369,"co_author_invite_id":null,"email":"v***3@gmail.com","display_order":1,"name":"Vrund Shah","title":"Qualitative Comparison of Commercial NO2 Sensors and Selection Criteria for Various Applications"}],"downloadable_attachments":[{"id":115146568,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146568/thumbnails/1.jpg","file_name":"507_3_2924_2928.pdf","download_url":"https://www.academia.edu/attachments/115146568/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Qualitative_Comparison_of_Commercial_NO2.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146568/507_3_2924_2928-libre.pdf?1716378624=\u0026response-content-disposition=attachment%3B+filename%3DQualitative_Comparison_of_Commercial_NO2.pdf\u0026Expires=1732468483\u0026Signature=aPpmNxPftoXlxkd9V-vJ6biIXmbnyIMkhgeEv~v~p6agY-mHSEnArUmHUwNPV4y9F7ufXocwzxBy5Q4CJcJpVV3en-2v-Ya0u6mU5yhNE2Bx-UJPWpzp8YmjOewJ2lm8AWB0i52NmIwL9vR0rWx6~OruvQXcu5Tw5B1hDCJRKrDpmtyZcbmI9To-b0NYEXmXhc5NQBqrx0TAmSz8BL3o0h7g7zJCHI2dEqoehONm1K0BqqWluhMFiYn9P~jcV8Nq15X6pRDbACSTBOCJdPZUA5Jp4rH1kIWujQv23Doyl3nHlME5NZ5XuVnyNsFd8bEv4~ZdP19T0MT8gzhQW1Z5UQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Qualitative_Comparison_of_Commercial_NO2_Sensors_and_Selection_Criteria_for_Various_Applications","translated_slug":"","page_count":5,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146568,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146568/thumbnails/1.jpg","file_name":"507_3_2924_2928.pdf","download_url":"https://www.academia.edu/attachments/115146568/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Qualitative_Comparison_of_Commercial_NO2.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146568/507_3_2924_2928-libre.pdf?1716378624=\u0026response-content-disposition=attachment%3B+filename%3DQualitative_Comparison_of_Commercial_NO2.pdf\u0026Expires=1732468483\u0026Signature=aPpmNxPftoXlxkd9V-vJ6biIXmbnyIMkhgeEv~v~p6agY-mHSEnArUmHUwNPV4y9F7ufXocwzxBy5Q4CJcJpVV3en-2v-Ya0u6mU5yhNE2Bx-UJPWpzp8YmjOewJ2lm8AWB0i52NmIwL9vR0rWx6~OruvQXcu5Tw5B1hDCJRKrDpmtyZcbmI9To-b0NYEXmXhc5NQBqrx0TAmSz8BL3o0h7g7zJCHI2dEqoehONm1K0BqqWluhMFiYn9P~jcV8Nq15X6pRDbACSTBOCJdPZUA5Jp4rH1kIWujQv23Doyl3nHlME5NZ5XuVnyNsFd8bEv4~ZdP19T0MT8gzhQW1Z5UQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119808623"><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/119808623/A_Classification_of_COVID_19_Cases_using_Fine_Tune_Different_Model_of_Machine_Learning"><img alt="Research paper thumbnail of A Classification of COVID-19 Cases using Fine-Tune Different Model of Machine Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/115146403/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/119808623/A_Classification_of_COVID_19_Cases_using_Fine_Tune_Different_Model_of_Machine_Learning">A Classification of COVID-19 Cases using Fine-Tune Different Model of Machine Learning</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The Machine Learning Techniques are used for finding upcoming or futuristic Prediction. It's very...</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 Machine Learning Techniques are used for finding upcoming or futuristic Prediction. It's very powerful tools in finding of future prediction. From Last decade to recent days Data mining and Machine Learning play vital roles in many Industries. In last 2-3 Years overall World is suffering from a new Virus i.e., COVID-19. The first covid case was detected in Month of December. In very less time it spread out throughout the world. In 2-3 months, this illness is declared as Pandemic by WHO (World Health Organization). The behaviour of spreading of COVID-19 was very abnormal. it was growing Exponentially. Here In this Paper as a Researcher's we are trying to find out overall Analysis of COVID-19. Here our main motto to finding How many patients gets infected, how many of them get cure in sometimes & finally How many of them goes to death. Here our main concentration is to finding the features of the COVID. We will be applying Machine Learning Algorithms like LR, SVM, DT and many more. After applying the above methods, we will find the performance measures of each one and compare with their values. Finally, we will try to Implement some new Enhancement in Existing Algorithms.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="88d1e727a5f5390c19412e89bfa41d2b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146403,"asset_id":119808623,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146403/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119808623"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119808623"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119808623; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119808623]").text(description); $(".js-view-count[data-work-id=119808623]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119808623; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119808623']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119808623, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "88d1e727a5f5390c19412e89bfa41d2b" } } $('.js-work-strip[data-work-id=119808623]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119808623,"title":"A Classification of COVID-19 Cases using Fine-Tune Different Model of Machine Learning","translated_title":"","metadata":{"abstract":"The Machine Learning Techniques are used for finding upcoming or futuristic Prediction. It's very powerful tools in finding of future prediction. From Last decade to recent days Data mining and Machine Learning play vital roles in many Industries. In last 2-3 Years overall World is suffering from a new Virus i.e., COVID-19. The first covid case was detected in Month of December. In very less time it spread out throughout the world. In 2-3 months, this illness is declared as Pandemic by WHO (World Health Organization). The behaviour of spreading of COVID-19 was very abnormal. it was growing Exponentially. Here In this Paper as a Researcher's we are trying to find out overall Analysis of COVID-19. Here our main motto to finding How many patients gets infected, how many of them get cure in sometimes \u0026 finally How many of them goes to death. Here our main concentration is to finding the features of the COVID. We will be applying Machine Learning Algorithms like LR, SVM, DT and many more. After applying the above methods, we will find the performance measures of each one and compare with their values. Finally, we will try to Implement some new Enhancement in Existing Algorithms.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"The Machine Learning Techniques are used for finding upcoming or futuristic Prediction. It's very powerful tools in finding of future prediction. From Last decade to recent days Data mining and Machine Learning play vital roles in many Industries. In last 2-3 Years overall World is suffering from a new Virus i.e., COVID-19. The first covid case was detected in Month of December. In very less time it spread out throughout the world. In 2-3 months, this illness is declared as Pandemic by WHO (World Health Organization). The behaviour of spreading of COVID-19 was very abnormal. it was growing Exponentially. Here In this Paper as a Researcher's we are trying to find out overall Analysis of COVID-19. Here our main motto to finding How many patients gets infected, how many of them get cure in sometimes \u0026 finally How many of them goes to death. Here our main concentration is to finding the features of the COVID. We will be applying Machine Learning Algorithms like LR, SVM, DT and many more. After applying the above methods, we will find the performance measures of each one and compare with their values. Finally, we will try to Implement some new Enhancement in Existing Algorithms.","internal_url":"https://www.academia.edu/119808623/A_Classification_of_COVID_19_Cases_using_Fine_Tune_Different_Model_of_Machine_Learning","translated_internal_url":"","created_at":"2024-05-22T04:43:05.440-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[],"downloadable_attachments":[{"id":115146403,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146403/thumbnails/1.jpg","file_name":"503_4_2916_2923.pdf","download_url":"https://www.academia.edu/attachments/115146403/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Classification_of_COVID_19_Cases_using.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146403/503_4_2916_2923-libre.pdf?1716378633=\u0026response-content-disposition=attachment%3B+filename%3DA_Classification_of_COVID_19_Cases_using.pdf\u0026Expires=1732468483\u0026Signature=Nax6YSryZDE3u2f7YCWjDraAAaa~XIkawAEWLoMVKT43Yj1RhW65ocY~KM9rzqSAOCGwOaBuTWyyHMIUHPzggMVdhk4B4t38vsnsBcg5alVdd7zG1iByLCg7K~nOrRqBNa1IQZml19OwtLIj~kJnxHEllRgAEBjQSLaNBEcmxKr6qYgtpVIVqazoNMIT~~losruBO5Sd4ECvuAdhSxLj3Kbd12EPL5VYqIEHN5osCh91vsy07KA5T7Ljp17DfUlMQzRZbqIu6Vmv618MmIyOcbPEQyb-Si1fUTbWU5W1tOSf8j0r~nGNdctexI~KbgV7Yc5qPw~wZTnsDmkoIOD1Ug__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Classification_of_COVID_19_Cases_using_Fine_Tune_Different_Model_of_Machine_Learning","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146403,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146403/thumbnails/1.jpg","file_name":"503_4_2916_2923.pdf","download_url":"https://www.academia.edu/attachments/115146403/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Classification_of_COVID_19_Cases_using.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146403/503_4_2916_2923-libre.pdf?1716378633=\u0026response-content-disposition=attachment%3B+filename%3DA_Classification_of_COVID_19_Cases_using.pdf\u0026Expires=1732468483\u0026Signature=Nax6YSryZDE3u2f7YCWjDraAAaa~XIkawAEWLoMVKT43Yj1RhW65ocY~KM9rzqSAOCGwOaBuTWyyHMIUHPzggMVdhk4B4t38vsnsBcg5alVdd7zG1iByLCg7K~nOrRqBNa1IQZml19OwtLIj~kJnxHEllRgAEBjQSLaNBEcmxKr6qYgtpVIVqazoNMIT~~losruBO5Sd4ECvuAdhSxLj3Kbd12EPL5VYqIEHN5osCh91vsy07KA5T7Ljp17DfUlMQzRZbqIu6Vmv618MmIyOcbPEQyb-Si1fUTbWU5W1tOSf8j0r~nGNdctexI~KbgV7Yc5qPw~wZTnsDmkoIOD1Ug__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119808414"><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/119808414/An_Innovative_Blockchain_based_System_for_Employees_in_the_Healthcare_Industry"><img alt="Research paper thumbnail of An Innovative Blockchain-based System for Employees in the Healthcare Industry" class="work-thumbnail" src="https://attachments.academia-assets.com/115146333/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/119808414/An_Innovative_Blockchain_based_System_for_Employees_in_the_Healthcare_Industry">An Innovative Blockchain-based System for Employees in the Healthcare Industry</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The pandemic caused by the COVID-19 virus has unquestionably opened our eyes to the possibility t...</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 pandemic caused by the COVID-19 virus has unquestionably opened our eyes to the possibility that we may run out of essential supplies, such as medications and medical equipment, which would result in people's deaths and increased anxiety. Insufficient funds and raw materials aren't the only factors contributing to this situation; there's also another factor that's much more discouraging. Even in the midst of these challenging circumstances, some individuals have been able to see an opportunity in the scenario that allows them to maintain and store vital equipment such as personal protective equipment kits, masks, sanitizers, and the like, and then start black-selling such items. This very problematic circumstance has arisen as a direct result of deficiencies in the supply chain's control, transparency, and traceability. Blockchain technology presents itself as a potentially useful answer to this issue. The information may be tracked and traced using blockchain technology, which can also be used to store the data in a decentralised and immutable manner, ensuring that it remains private and is protected from unauthorised users. This article discusses the many use cases for healthcare personnel as well as the various constraints of the present blockchain architecture for supply chain. [Citation needed] The shortcomings of the current blockchain framework for supply chains are then addressed via the proposal of a new framework.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="22b7b537e09c4de63ac03bb9322187eb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146333,"asset_id":119808414,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146333/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119808414"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119808414"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119808414; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119808414]").text(description); $(".js-view-count[data-work-id=119808414]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119808414; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119808414']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119808414, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "22b7b537e09c4de63ac03bb9322187eb" } } $('.js-work-strip[data-work-id=119808414]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119808414,"title":"An Innovative Blockchain-based System for Employees in the Healthcare Industry","translated_title":"","metadata":{"abstract":"The pandemic caused by the COVID-19 virus has unquestionably opened our eyes to the possibility that we may run out of essential supplies, such as medications and medical equipment, which would result in people's deaths and increased anxiety. Insufficient funds and raw materials aren't the only factors contributing to this situation; there's also another factor that's much more discouraging. Even in the midst of these challenging circumstances, some individuals have been able to see an opportunity in the scenario that allows them to maintain and store vital equipment such as personal protective equipment kits, masks, sanitizers, and the like, and then start black-selling such items. This very problematic circumstance has arisen as a direct result of deficiencies in the supply chain's control, transparency, and traceability. Blockchain technology presents itself as a potentially useful answer to this issue. The information may be tracked and traced using blockchain technology, which can also be used to store the data in a decentralised and immutable manner, ensuring that it remains private and is protected from unauthorised users. This article discusses the many use cases for healthcare personnel as well as the various constraints of the present blockchain architecture for supply chain. [Citation needed] The shortcomings of the current blockchain framework for supply chains are then addressed via the proposal of a new framework.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"The pandemic caused by the COVID-19 virus has unquestionably opened our eyes to the possibility that we may run out of essential supplies, such as medications and medical equipment, which would result in people's deaths and increased anxiety. Insufficient funds and raw materials aren't the only factors contributing to this situation; there's also another factor that's much more discouraging. Even in the midst of these challenging circumstances, some individuals have been able to see an opportunity in the scenario that allows them to maintain and store vital equipment such as personal protective equipment kits, masks, sanitizers, and the like, and then start black-selling such items. This very problematic circumstance has arisen as a direct result of deficiencies in the supply chain's control, transparency, and traceability. Blockchain technology presents itself as a potentially useful answer to this issue. The information may be tracked and traced using blockchain technology, which can also be used to store the data in a decentralised and immutable manner, ensuring that it remains private and is protected from unauthorised users. This article discusses the many use cases for healthcare personnel as well as the various constraints of the present blockchain architecture for supply chain. [Citation needed] The shortcomings of the current blockchain framework for supply chains are then addressed via the proposal of a new framework.","internal_url":"https://www.academia.edu/119808414/An_Innovative_Blockchain_based_System_for_Employees_in_the_Healthcare_Industry","translated_internal_url":"","created_at":"2024-05-22T04:40:18.398-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730524,"work_id":119808414,"tagging_user_id":32919427,"tagged_user_id":827443,"co_author_invite_id":null,"email":"a***e@gmail.com","display_order":1,"name":"Anil Turukmane","title":"An Innovative Blockchain-based System for Employees in the Healthcare Industry"}],"downloadable_attachments":[{"id":115146333,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146333/thumbnails/1.jpg","file_name":"502_2_2909_2915.pdf","download_url":"https://www.academia.edu/attachments/115146333/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Innovative_Blockchain_based_System_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146333/502_2_2909_2915-libre.pdf?1716378641=\u0026response-content-disposition=attachment%3B+filename%3DAn_Innovative_Blockchain_based_System_fo.pdf\u0026Expires=1732468484\u0026Signature=XP0QrVhJeqOb7VsXcX54IAyxS~zBdTAZilUbZOUHEJ-YCOBskFqQGblW9h8RtzHAKfsIVaC2gL~~xfC9ZEdOjt5TxL0d2OiINgbGMQ0dJlfM8cTd6W6GkctytU2SQm-pcYak64fxu1y3yt3iUKxSCD9PMR0G0aGpppzC9qjxK532QbxE-6OiP6-pfA2o~eV6t3AqtR1VVAekdEn2CKFquigSrEV1tqHuk-vv9AK5X6Xr2vmgv1orhJ6ADyMPq09fK714FwjQaRCZWGuV7fkg4MNk-PpG-umnVtzoh0h6dIVhPX3S7Tzk08H0mtQn45P5LMuRpSIF-NIF5wf4S3yCnQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"An_Innovative_Blockchain_based_System_for_Employees_in_the_Healthcare_Industry","translated_slug":"","page_count":7,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146333,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146333/thumbnails/1.jpg","file_name":"502_2_2909_2915.pdf","download_url":"https://www.academia.edu/attachments/115146333/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Innovative_Blockchain_based_System_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146333/502_2_2909_2915-libre.pdf?1716378641=\u0026response-content-disposition=attachment%3B+filename%3DAn_Innovative_Blockchain_based_System_fo.pdf\u0026Expires=1732468484\u0026Signature=XP0QrVhJeqOb7VsXcX54IAyxS~zBdTAZilUbZOUHEJ-YCOBskFqQGblW9h8RtzHAKfsIVaC2gL~~xfC9ZEdOjt5TxL0d2OiINgbGMQ0dJlfM8cTd6W6GkctytU2SQm-pcYak64fxu1y3yt3iUKxSCD9PMR0G0aGpppzC9qjxK532QbxE-6OiP6-pfA2o~eV6t3AqtR1VVAekdEn2CKFquigSrEV1tqHuk-vv9AK5X6Xr2vmgv1orhJ6ADyMPq09fK714FwjQaRCZWGuV7fkg4MNk-PpG-umnVtzoh0h6dIVhPX3S7Tzk08H0mtQn45P5LMuRpSIF-NIF5wf4S3yCnQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119808307"><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/119808307/An_Overview_of_Speaker_Recognition_Conceptual_Framework_and_CNN_based_Identification_Technique"><img alt="Research paper thumbnail of An Overview of Speaker Recognition: Conceptual Framework and CNN based Identification Technique" class="work-thumbnail" src="https://attachments.academia-assets.com/115146209/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/119808307/An_Overview_of_Speaker_Recognition_Conceptual_Framework_and_CNN_based_Identification_Technique">An Overview of Speaker Recognition: Conceptual Framework and CNN based Identification Technique</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One of the major characteristics of an individual is to recognize a human voice. He is capable to...</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">One of the major characteristics of an individual is to recognize a human voice. He is capable to do so, via digital devices or over the telephone. Acquiring this human trait, multiple technologies based on voice recognition have been developed to fulfil the purpose of biometrics and authentication. One such speech analysing technology is automatic speaker recognition (ASR) method that has been introduced to extract characteristics from the speaker's voice and identify him as a genuine source of input. The primary aim of speaker recognition is to identify and verify a person using audio signals. Hence, this has become a dominating field of research in the domain of biometrics. However, several deep learning approaches based on convolutional neural networks have been adopted to enhance the overall system of biometrics. Therefore, in this paper we review feature components on speaker recognition using CNN and feature extraction methods such as MFCC. The major advantage of using this method over traditional identification is its representation ability to extract feature inputs including audio signals and networking structures. Further, we briefly describe all the main pieces of ASR related methodologies followed by evaluation metrics to enhance the overall recognition of the system. Finally, a few relatable challenges and future expansion of speaker recognition are mentioned at the closure of this review.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cdd943a061de92f87c141919f83af63f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146209,"asset_id":119808307,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146209/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119808307"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119808307"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119808307; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119808307]").text(description); $(".js-view-count[data-work-id=119808307]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119808307; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119808307']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119808307, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "cdd943a061de92f87c141919f83af63f" } } $('.js-work-strip[data-work-id=119808307]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119808307,"title":"An Overview of Speaker Recognition: Conceptual Framework and CNN based Identification Technique","translated_title":"","metadata":{"abstract":"One of the major characteristics of an individual is to recognize a human voice. He is capable to do so, via digital devices or over the telephone. Acquiring this human trait, multiple technologies based on voice recognition have been developed to fulfil the purpose of biometrics and authentication. One such speech analysing technology is automatic speaker recognition (ASR) method that has been introduced to extract characteristics from the speaker's voice and identify him as a genuine source of input. The primary aim of speaker recognition is to identify and verify a person using audio signals. Hence, this has become a dominating field of research in the domain of biometrics. However, several deep learning approaches based on convolutional neural networks have been adopted to enhance the overall system of biometrics. Therefore, in this paper we review feature components on speaker recognition using CNN and feature extraction methods such as MFCC. The major advantage of using this method over traditional identification is its representation ability to extract feature inputs including audio signals and networking structures. Further, we briefly describe all the main pieces of ASR related methodologies followed by evaluation metrics to enhance the overall recognition of the system. Finally, a few relatable challenges and future expansion of speaker recognition are mentioned at the closure of this review.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"One of the major characteristics of an individual is to recognize a human voice. He is capable to do so, via digital devices or over the telephone. Acquiring this human trait, multiple technologies based on voice recognition have been developed to fulfil the purpose of biometrics and authentication. One such speech analysing technology is automatic speaker recognition (ASR) method that has been introduced to extract characteristics from the speaker's voice and identify him as a genuine source of input. The primary aim of speaker recognition is to identify and verify a person using audio signals. Hence, this has become a dominating field of research in the domain of biometrics. However, several deep learning approaches based on convolutional neural networks have been adopted to enhance the overall system of biometrics. Therefore, in this paper we review feature components on speaker recognition using CNN and feature extraction methods such as MFCC. The major advantage of using this method over traditional identification is its representation ability to extract feature inputs including audio signals and networking structures. Further, we briefly describe all the main pieces of ASR related methodologies followed by evaluation metrics to enhance the overall recognition of the system. Finally, a few relatable challenges and future expansion of speaker recognition are mentioned at the closure of this review.","internal_url":"https://www.academia.edu/119808307/An_Overview_of_Speaker_Recognition_Conceptual_Framework_and_CNN_based_Identification_Technique","translated_internal_url":"","created_at":"2024-05-22T04:37:37.435-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730518,"work_id":119808307,"tagging_user_id":32919427,"tagged_user_id":279999067,"co_author_invite_id":null,"email":"a***e@gmail.com","display_order":1,"name":"Avinash Dhole","title":"An Overview of Speaker Recognition: Conceptual Framework and CNN based Identification Technique"}],"downloadable_attachments":[{"id":115146209,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146209/thumbnails/1.jpg","file_name":"335_2901_2908.pdf","download_url":"https://www.academia.edu/attachments/115146209/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Overview_of_Speaker_Recognition_Conce.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146209/335_2901_2908-libre.pdf?1716378648=\u0026response-content-disposition=attachment%3B+filename%3DAn_Overview_of_Speaker_Recognition_Conce.pdf\u0026Expires=1732468484\u0026Signature=DNRSvGE0wijl4WnwHRgI6ClQpj1msBhMwAMYWSyxW1VR1dMq6SzqEjtfkh2h9pZsVwJ05E4Ew1LjyJePx40VGj~JVyAq-XqCGZNWwAREBqKn2gYzBaPkZ6RsnV~rXHcKYCtVg9XqzJt4QeD7HRkqUYJK4C8jz-Ueng23vOhWmc3jgNouuKfhT~Ju0bTf7dWEGsFelFpblb39veFGP16JP4Oa-0QWIxTc1YUwFn7MkDJWg~u57bz0ScqiplJpnWKThxhfkh7CqR-fb~jyf1cvjTmNhPqPa1XzkTjJMTEMzOq~D8hIHNIod4~Id85O6HAK0W5YOggFz~oOMcwnoaAT9g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"An_Overview_of_Speaker_Recognition_Conceptual_Framework_and_CNN_based_Identification_Technique","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146209,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146209/thumbnails/1.jpg","file_name":"335_2901_2908.pdf","download_url":"https://www.academia.edu/attachments/115146209/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Overview_of_Speaker_Recognition_Conce.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146209/335_2901_2908-libre.pdf?1716378648=\u0026response-content-disposition=attachment%3B+filename%3DAn_Overview_of_Speaker_Recognition_Conce.pdf\u0026Expires=1732468484\u0026Signature=DNRSvGE0wijl4WnwHRgI6ClQpj1msBhMwAMYWSyxW1VR1dMq6SzqEjtfkh2h9pZsVwJ05E4Ew1LjyJePx40VGj~JVyAq-XqCGZNWwAREBqKn2gYzBaPkZ6RsnV~rXHcKYCtVg9XqzJt4QeD7HRkqUYJK4C8jz-Ueng23vOhWmc3jgNouuKfhT~Ju0bTf7dWEGsFelFpblb39veFGP16JP4Oa-0QWIxTc1YUwFn7MkDJWg~u57bz0ScqiplJpnWKThxhfkh7CqR-fb~jyf1cvjTmNhPqPa1XzkTjJMTEMzOq~D8hIHNIod4~Id85O6HAK0W5YOggFz~oOMcwnoaAT9g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119808176"><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/119808176/Predicting_Dengue_using_Rule_based_Approach"><img alt="Research paper thumbnail of Predicting Dengue using Rule-based Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/115146117/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/119808176/Predicting_Dengue_using_Rule_based_Approach">Predicting Dengue using Rule-based Approach</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/IdesEditor">Grenze International Journal of Engineering and Technology GIJET</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/SakshiShejwal3">Sakshi Shejwal</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Dengue is the most common viral fever suffered by most people. It is also known as life-threateni...</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">Dengue is the most common viral fever suffered by most people. It is also known as life-threatening disease. It has taken many lives all over the world. Brisk forecast of dengue can save individual's life by warning them to take genuine conclusion and care. During the covid situation risk of dengue have reached at a larger scale with multiple symptoms. In this paper, selective symptoms are being considered to verify their impact on predicting the disease and the main motive is to predict dengue, how accurately it gives respond in advance that can save individual's life. For this a new approach has been introduced by applying Rule Fit or Rulebased algorithm and Feature transformation methods to get an accurate result which solve the interpretability and overfitting issue in each machine learning algorithm that have a huge impact on the prediction. Also results gained varies based on the selection of dataset, each algorithms results differs according to the dataset.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3267692df34fb3e0110993fcb2152490" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146117,"asset_id":119808176,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146117/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119808176"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119808176"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119808176; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119808176]").text(description); $(".js-view-count[data-work-id=119808176]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119808176; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119808176']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119808176, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "3267692df34fb3e0110993fcb2152490" } } $('.js-work-strip[data-work-id=119808176]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119808176,"title":"Predicting Dengue using Rule-based Approach","translated_title":"","metadata":{"abstract":"Dengue is the most common viral fever suffered by most people. It is also known as life-threatening disease. It has taken many lives all over the world. Brisk forecast of dengue can save individual's life by warning them to take genuine conclusion and care. During the covid situation risk of dengue have reached at a larger scale with multiple symptoms. In this paper, selective symptoms are being considered to verify their impact on predicting the disease and the main motive is to predict dengue, how accurately it gives respond in advance that can save individual's life. For this a new approach has been introduced by applying Rule Fit or Rulebased algorithm and Feature transformation methods to get an accurate result which solve the interpretability and overfitting issue in each machine learning algorithm that have a huge impact on the prediction. Also results gained varies based on the selection of dataset, each algorithms results differs according to the dataset.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"Dengue is the most common viral fever suffered by most people. It is also known as life-threatening disease. It has taken many lives all over the world. Brisk forecast of dengue can save individual's life by warning them to take genuine conclusion and care. During the covid situation risk of dengue have reached at a larger scale with multiple symptoms. In this paper, selective symptoms are being considered to verify their impact on predicting the disease and the main motive is to predict dengue, how accurately it gives respond in advance that can save individual's life. For this a new approach has been introduced by applying Rule Fit or Rulebased algorithm and Feature transformation methods to get an accurate result which solve the interpretability and overfitting issue in each machine learning algorithm that have a huge impact on the prediction. Also results gained varies based on the selection of dataset, each algorithms results differs according to the dataset.","internal_url":"https://www.academia.edu/119808176/Predicting_Dengue_using_Rule_based_Approach","translated_internal_url":"","created_at":"2024-05-22T04:36:03.876-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730516,"work_id":119808176,"tagging_user_id":32919427,"tagged_user_id":315700423,"co_author_invite_id":8154024,"email":"s***l@mitaoe.ac.in","display_order":1,"name":"Sakshi Shejwal","title":"Predicting Dengue using Rule-based Approach"},{"id":41730517,"work_id":119808176,"tagging_user_id":32919427,"tagged_user_id":298112011,"co_author_invite_id":null,"email":"p***r@mitaoe.ac.in","display_order":2,"name":"Pramod Ganjewar","title":"Predicting Dengue using Rule-based Approach"}],"downloadable_attachments":[{"id":115146117,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146117/thumbnails/1.jpg","file_name":"334_2893_2900.pdf","download_url":"https://www.academia.edu/attachments/115146117/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Predicting_Dengue_using_Rule_based_Appro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146117/334_2893_2900-libre.pdf?1716378662=\u0026response-content-disposition=attachment%3B+filename%3DPredicting_Dengue_using_Rule_based_Appro.pdf\u0026Expires=1732468484\u0026Signature=HajxIEFAKKGyjfON~amlou3rwbBjyJCFZ2vf3g~hxzQ8~fy3m3mNAP68YD~b~rM3sTd-5J45-gx9jJ4nv0ITrznOEZzMxBKOpomxtRAZsn6SS0hsR2tlQ8hmJLBrva4MIoMoeSRL7AZa2AS274cG4rx2wwnL9tqDK9fbd~7sF-mGNbeGo9TLN4R9uMZtemZFZHunvzaUUDoBvU0XbtSlWlNULRxpKw5sgVM2ZWoccj58olWR6195zKL9FNiyUqOvuTbHxTrevKMnsAkfkpi5RpzjTROl4hk6Dseuv2Kcc4ur7xASHYd2DNFpqAQif2wQV6~Lqe8B2xnfCObGCGBkLQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Predicting_Dengue_using_Rule_based_Approach","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146117,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146117/thumbnails/1.jpg","file_name":"334_2893_2900.pdf","download_url":"https://www.academia.edu/attachments/115146117/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Predicting_Dengue_using_Rule_based_Appro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146117/334_2893_2900-libre.pdf?1716378662=\u0026response-content-disposition=attachment%3B+filename%3DPredicting_Dengue_using_Rule_based_Appro.pdf\u0026Expires=1732468484\u0026Signature=HajxIEFAKKGyjfON~amlou3rwbBjyJCFZ2vf3g~hxzQ8~fy3m3mNAP68YD~b~rM3sTd-5J45-gx9jJ4nv0ITrznOEZzMxBKOpomxtRAZsn6SS0hsR2tlQ8hmJLBrva4MIoMoeSRL7AZa2AS274cG4rx2wwnL9tqDK9fbd~7sF-mGNbeGo9TLN4R9uMZtemZFZHunvzaUUDoBvU0XbtSlWlNULRxpKw5sgVM2ZWoccj58olWR6195zKL9FNiyUqOvuTbHxTrevKMnsAkfkpi5RpzjTROl4hk6Dseuv2Kcc4ur7xASHYd2DNFpqAQif2wQV6~Lqe8B2xnfCObGCGBkLQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119808103"><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/119808103/Hybrid_Recommender_System_for_E_Learning_Platforms"><img alt="Research paper thumbnail of Hybrid Recommender System for E-Learning Platforms" class="work-thumbnail" src="https://attachments.academia-assets.com/115146062/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/119808103/Hybrid_Recommender_System_for_E_Learning_Platforms">Hybrid Recommender System for E-Learning Platforms</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Internet use has steadily increased over the past 25 years. Numerous online services are improvin...</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">Internet use has steadily increased over the past 25 years. Numerous online services are improving and simplifying life for people. E-learning is one of these services, which makes the learning process simpler. In this work, a recommendation system will be presented that will assist online course providers in automating course suggestion. By finding patterns in courses based on some attributes like field of interest, skill set, educational qualification, course content, etc. of an individual and courses, this recommendation system is built using hybrid machine learning recommendation techniques, i.e., combination of Collaborative (item based) and content-based recommendation and KNN classifiers. Lastly, the recommendations are pooled from the two blocks of recommended courses (which were discovered using a collaborative technique), similar courses (which is found using content-based finding).</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6adb9f5eeffce52269b704f4ed81903b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146062,"asset_id":119808103,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146062/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119808103"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119808103"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119808103; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119808103]").text(description); $(".js-view-count[data-work-id=119808103]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119808103; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119808103']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119808103, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "6adb9f5eeffce52269b704f4ed81903b" } } $('.js-work-strip[data-work-id=119808103]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119808103,"title":"Hybrid Recommender System for E-Learning Platforms","translated_title":"","metadata":{"abstract":"Internet use has steadily increased over the past 25 years. 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Numerous online services are improving and simplifying life for people. E-learning is one of these services, which makes the learning process simpler. In this work, a recommendation system will be presented that will assist online course providers in automating course suggestion. By finding patterns in courses based on some attributes like field of interest, skill set, educational qualification, course content, etc. of an individual and courses, this recommendation system is built using hybrid machine learning recommendation techniques, i.e., combination of Collaborative (item based) and content-based recommendation and KNN classifiers. 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It...</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 this new era of artificial intelligence and automation there is outburst in amount of data. It is challenging for a person to dig into the content and bring the light on essential data. So, for such complexity there is need of developing a perfunctory task of automatic and precise text summary of data. Automatic text summarizer reduces reading time and make selection process easier. In recent years, there has been a little change in the text summarising research trend as well. New trends have evolved that show how to improve text summarization performance and achieve high accuracy. Hence, this project mainly focuses on designing an extractive summarizer of English text/document which generate abbreviated summary with the help of different algorithms.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="acf13df73f78c27c5dd7b5b0e5044a39" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115146022,"asset_id":119808045,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115146022/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119808045"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119808045"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119808045; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119808045]").text(description); $(".js-view-count[data-work-id=119808045]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119808045; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119808045']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119808045, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "acf13df73f78c27c5dd7b5b0e5044a39" } } $('.js-work-strip[data-work-id=119808045]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119808045,"title":"An Automatic Extractive Text Summarization","translated_title":"","metadata":{"abstract":"In this new era of artificial intelligence and automation there is outburst in amount of data. 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Hence, this project mainly focuses on designing an extractive summarizer of English text/document which generate abbreviated summary with the help of different algorithms.","internal_url":"https://www.academia.edu/119808045/An_Automatic_Extractive_Text_Summarization","translated_internal_url":"","created_at":"2024-05-22T04:32:55.183-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730498,"work_id":119808045,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8154018,"email":"m***a@mitaoe.ac.in","display_order":1,"name":"Meet Bedhmutha","title":"An Automatic Extractive Text Summarization"},{"id":41730499,"work_id":119808045,"tagging_user_id":32919427,"tagged_user_id":315700413,"co_author_invite_id":8154019,"email":"c***v@mitaoe.ac.in","display_order":2,"name":"Chandan Yadav","title":"An Automatic Extractive Text Summarization"},{"id":41730500,"work_id":119808045,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8154020,"email":"d***i@mitaoe.ac.in","display_order":3,"name":"Devashish Dani","title":"An Automatic Extractive Text Summarization"},{"id":41730501,"work_id":119808045,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8154021,"email":"k***l@mitaoe.ac.in","display_order":4,"name":"Kshitij Patil","title":"An Automatic Extractive Text Summarization"}],"downloadable_attachments":[{"id":115146022,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146022/thumbnails/1.jpg","file_name":"332_1_2881_2886.pdf","download_url":"https://www.academia.edu/attachments/115146022/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Automatic_Extractive_Text_Summarizati.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146022/332_1_2881_2886-libre.pdf?1716378665=\u0026response-content-disposition=attachment%3B+filename%3DAn_Automatic_Extractive_Text_Summarizati.pdf\u0026Expires=1732468484\u0026Signature=T22m4ZdGNvVecrZE013F560kow414UrNR1ZtUhYQ46N0HjUVdZHk9eyfMWsTuj81punzDgwPZxeUVmmnsxtUS650iiv4bSWmScQlitT-GtF5vzYs4-QSKtnXAZn4gswKDysOGRQqycSU97oXp7JriPod85B0~FSlpaLa9aaqZrG6NiBr~uVu6Ut8kheBaNti5xDuxjr~1QRgkqLnueuXvT5bGg4iKvpqedQLH1o9h57rBjFfhfX6q98fptqlt0McexDumd~QWu0u-18o3sEMsuoQWVFtMC1r5o1inU7o7i2JwaBX6h2SGmg~CNVMVeF-V~NkPi87sL1YGXPARqJ95Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"An_Automatic_Extractive_Text_Summarization","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115146022,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115146022/thumbnails/1.jpg","file_name":"332_1_2881_2886.pdf","download_url":"https://www.academia.edu/attachments/115146022/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Automatic_Extractive_Text_Summarizati.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115146022/332_1_2881_2886-libre.pdf?1716378665=\u0026response-content-disposition=attachment%3B+filename%3DAn_Automatic_Extractive_Text_Summarizati.pdf\u0026Expires=1732468484\u0026Signature=T22m4ZdGNvVecrZE013F560kow414UrNR1ZtUhYQ46N0HjUVdZHk9eyfMWsTuj81punzDgwPZxeUVmmnsxtUS650iiv4bSWmScQlitT-GtF5vzYs4-QSKtnXAZn4gswKDysOGRQqycSU97oXp7JriPod85B0~FSlpaLa9aaqZrG6NiBr~uVu6Ut8kheBaNti5xDuxjr~1QRgkqLnueuXvT5bGg4iKvpqedQLH1o9h57rBjFfhfX6q98fptqlt0McexDumd~QWu0u-18o3sEMsuoQWVFtMC1r5o1inU7o7i2JwaBX6h2SGmg~CNVMVeF-V~NkPi87sL1YGXPARqJ95Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119807982"><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/119807982/Hate_Speech_Detection_using_Logistic_Regression_on_Bag_of_Words_Model"><img alt="Research paper thumbnail of Hate Speech Detection using Logistic Regression on Bag of Words Model" class="work-thumbnail" src="https://attachments.academia-assets.com/115145935/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/119807982/Hate_Speech_Detection_using_Logistic_Regression_on_Bag_of_Words_Model">Hate Speech Detection using Logistic Regression on Bag of Words Model</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Hate speech is a conundrum that is spreading like wildfire. Social networks allow members to conn...</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">Hate speech is a conundrum that is spreading like wildfire. Social networks allow members to connect by a variety of common interests and share information. These channels create and also help communities engage in discussing specific topics related to said news. However, they are also used as a medium to spread hate and offensive news. News on social media spread like a forest fire. Not only social media, but other forms of online media and precedence may cause hate speech to spread. Therefore, trying to prevent it beforehand is much better than causing an outrage. The objective of this paper is to try to establish a reliable model to identify hate speech in sentences. The purpose of this project is to analyze the sentiments of speech used sentences using certain ML algorithms. The Bag of Words model and Document Frequency Inverse Term Frequency (TFIDF) are used to process the text in sentences. Then we use the Logistic Regression algorithm on our bag of words.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8d94318221a89cc6ad18576a608b3524" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115145935,"asset_id":119807982,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115145935/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119807982"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119807982"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119807982; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119807982]").text(description); $(".js-view-count[data-work-id=119807982]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119807982; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119807982']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119807982, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "8d94318221a89cc6ad18576a608b3524" } } $('.js-work-strip[data-work-id=119807982]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119807982,"title":"Hate Speech Detection using Logistic Regression on Bag of Words Model","translated_title":"","metadata":{"abstract":"Hate speech is a conundrum that is spreading like wildfire. 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Then we use the Logistic Regression algorithm on our bag of words.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"Hate speech is a conundrum that is spreading like wildfire. Social networks allow members to connect by a variety of common interests and share information. These channels create and also help communities engage in discussing specific topics related to said news. However, they are also used as a medium to spread hate and offensive news. News on social media spread like a forest fire. Not only social media, but other forms of online media and precedence may cause hate speech to spread. Therefore, trying to prevent it beforehand is much better than causing an outrage. The objective of this paper is to try to establish a reliable model to identify hate speech in sentences. The purpose of this project is to analyze the sentiments of speech used sentences using certain ML algorithms. The Bag of Words model and Document Frequency Inverse Term Frequency (TFIDF) are used to process the text in sentences. Then we use the Logistic Regression algorithm on our bag of words.","internal_url":"https://www.academia.edu/119807982/Hate_Speech_Detection_using_Logistic_Regression_on_Bag_of_Words_Model","translated_internal_url":"","created_at":"2024-05-22T04:30:45.590-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[],"downloadable_attachments":[{"id":115145935,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145935/thumbnails/1.jpg","file_name":"331_2875_2880.pdf","download_url":"https://www.academia.edu/attachments/115145935/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Hate_Speech_Detection_using_Logistic_Reg.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145935/331_2875_2880-libre.pdf?1716378668=\u0026response-content-disposition=attachment%3B+filename%3DHate_Speech_Detection_using_Logistic_Reg.pdf\u0026Expires=1732468484\u0026Signature=UZSyCI9lz0~BOg4TDLwOgamFyoGu4W7Oeh1AhgGeykHfZIId9aBPKX5ZBbiM6jcvcQFja6K~pXBb5U14eAENF53JFj9gN40sdqtvhOYvwdA3Kfk1Gqbq6hcpjEiyOruMA9OyNeJqBmKPQqjyUy5rKDjt0MhUS99QSIcoYVGjP80fea9V4YatRV0BQrB7SUysQ7kild0fUzOwEvHgo~iPXKRICIc4Qb3-Sbvso29LdFddStUJRJv60sRO~o8qqwZ6SDkk~ThX5gsOumw6azQ3kpGxwujaIaeSedh1qC7020LVcU4euLyxmD0~rrVGuXWajqhPeCmFjQ2RxFvnT4ilYg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Hate_Speech_Detection_using_Logistic_Regression_on_Bag_of_Words_Model","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115145935,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145935/thumbnails/1.jpg","file_name":"331_2875_2880.pdf","download_url":"https://www.academia.edu/attachments/115145935/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Hate_Speech_Detection_using_Logistic_Reg.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145935/331_2875_2880-libre.pdf?1716378668=\u0026response-content-disposition=attachment%3B+filename%3DHate_Speech_Detection_using_Logistic_Reg.pdf\u0026Expires=1732468484\u0026Signature=UZSyCI9lz0~BOg4TDLwOgamFyoGu4W7Oeh1AhgGeykHfZIId9aBPKX5ZBbiM6jcvcQFja6K~pXBb5U14eAENF53JFj9gN40sdqtvhOYvwdA3Kfk1Gqbq6hcpjEiyOruMA9OyNeJqBmKPQqjyUy5rKDjt0MhUS99QSIcoYVGjP80fea9V4YatRV0BQrB7SUysQ7kild0fUzOwEvHgo~iPXKRICIc4Qb3-Sbvso29LdFddStUJRJv60sRO~o8qqwZ6SDkk~ThX5gsOumw6azQ3kpGxwujaIaeSedh1qC7020LVcU4euLyxmD0~rrVGuXWajqhPeCmFjQ2RxFvnT4ilYg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119807898"><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/119807898/A_Review_Paper_on_Cyclone_Intensity_Estimation_on_INSAT_3D_IR_Imagery_using_Deep_Learning_Algorithms"><img alt="Research paper thumbnail of A Review Paper on Cyclone Intensity Estimation on INSAT 3D IR Imagery using Deep Learning Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/115145874/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/119807898/A_Review_Paper_on_Cyclone_Intensity_Estimation_on_INSAT_3D_IR_Imagery_using_Deep_Learning_Algorithms">A Review Paper on Cyclone Intensity Estimation on INSAT 3D IR Imagery using Deep Learning Algorithms</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/IdesEditor">Grenze International Journal of Engineering and Technology GIJET</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/TejasAdsare">Tejas Adsare</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Due to the various dangers involved, especially tropical cyclones, they are one of the most commo...</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">Due to the various dangers involved, especially tropical cyclones, they are one of the most common and deadly calamities in the world. The very important primary step in monitoring this destructive disaster is by estimating its intensity. The aim of this research is to determine intensity of cyclones. For the estimation of cyclones and determination of their intensity, various methods have been created. It is a difficult task which requires speed and efficiency. This paper shows comparison between various types of deep learning algorithms on infrared satellite imagery dataset for tropical cyclone intensity estimation. A side-by-side comparison research revealed that the detection of the tropical cyclone intensity evaluated via the models derived from different machine learning algorithms is significantly impacted by the use of various infrared (IR) channels.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="842448f416eab3c8288cf4c9dc4d1412" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115145874,"asset_id":119807898,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115145874/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119807898"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119807898"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119807898; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119807898]").text(description); $(".js-view-count[data-work-id=119807898]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119807898; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119807898']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119807898, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "842448f416eab3c8288cf4c9dc4d1412" } } $('.js-work-strip[data-work-id=119807898]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119807898,"title":"A Review Paper on Cyclone Intensity Estimation on INSAT 3D IR Imagery using Deep Learning Algorithms","translated_title":"","metadata":{"abstract":"Due to the various dangers involved, especially tropical cyclones, they are one of the most common and deadly calamities in the world. The very important primary step in monitoring this destructive disaster is by estimating its intensity. The aim of this research is to determine intensity of cyclones. For the estimation of cyclones and determination of their intensity, various methods have been created. It is a difficult task which requires speed and efficiency. This paper shows comparison between various types of deep learning algorithms on infrared satellite imagery dataset for tropical cyclone intensity estimation. A side-by-side comparison research revealed that the detection of the tropical cyclone intensity evaluated via the models derived from different machine learning algorithms is significantly impacted by the use of various infrared (IR) channels.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"Due to the various dangers involved, especially tropical cyclones, they are one of the most common and deadly calamities in the world. The very important primary step in monitoring this destructive disaster is by estimating its intensity. The aim of this research is to determine intensity of cyclones. For the estimation of cyclones and determination of their intensity, various methods have been created. It is a difficult task which requires speed and efficiency. This paper shows comparison between various types of deep learning algorithms on infrared satellite imagery dataset for tropical cyclone intensity estimation. A side-by-side comparison research revealed that the detection of the tropical cyclone intensity evaluated via the models derived from different machine learning algorithms is significantly impacted by the use of various infrared (IR) channels.","internal_url":"https://www.academia.edu/119807898/A_Review_Paper_on_Cyclone_Intensity_Estimation_on_INSAT_3D_IR_Imagery_using_Deep_Learning_Algorithms","translated_internal_url":"","created_at":"2024-05-22T04:28:58.419-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730476,"work_id":119807898,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8154013,"email":"k***e@gmail.com","display_order":1,"name":"Kuldeep Vayadande","title":"A Review Paper on Cyclone Intensity Estimation on INSAT 3D IR Imagery using Deep Learning 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data-click-track="profile-work-strip-title" href="https://www.academia.edu/119807803/Comparative_Study_on_Calculating_CPU_Burst_Time_using_Different_Machine_Learning_Algorithms">Comparative Study on Calculating CPU Burst Time using Different Machine Learning Algorithms</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper gives a thorough analysis of works on the subject of expert assessment of CPU burst ti...</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 paper gives a thorough analysis of works on the subject of expert assessment of CPU burst time using various machine learning algorithms. Knowing how long the CPU bursts for the processes will last is necessary for some CPU scheduling algorithms like SJF and SRTF to function. In particular, the non-preemptive SJF scheduling algorithm estimates the process that will be performed by the CPU in the least amount of burst time. One effective way of predicting CPU burst duration is an ML-based algorithm that estimates the burst-time of the processes. Throughout the study, we discovered that the effectiveness of different machinelearning approaches relies on the applications to which they are put. Our examination of the literature not only argues that these methods are competitive with conventional estimators on a single data set, but also demonstrates that they are responsive to the training data.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="03b863c17cf89fa476303b67a467c260" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115145790,"asset_id":119807803,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115145790/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119807803"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119807803"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119807803; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119807803]").text(description); $(".js-view-count[data-work-id=119807803]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119807803; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119807803']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119807803, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "03b863c17cf89fa476303b67a467c260" } } $('.js-work-strip[data-work-id=119807803]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119807803,"title":"Comparative Study on Calculating CPU Burst Time using Different Machine Learning Algorithms","translated_title":"","metadata":{"abstract":"This paper gives a thorough analysis of works on the subject of expert assessment of CPU burst time using various machine learning algorithms. 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Our examination of the literature not only argues that these methods are competitive with conventional estimators on a single data set, but also demonstrates that they are responsive to the training data.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"This paper gives a thorough analysis of works on the subject of expert assessment of CPU burst time using various machine learning algorithms. Knowing how long the CPU bursts for the processes will last is necessary for some CPU scheduling algorithms like SJF and SRTF to function. In particular, the non-preemptive SJF scheduling algorithm estimates the process that will be performed by the CPU in the least amount of burst time. One effective way of predicting CPU burst duration is an ML-based algorithm that estimates the burst-time of the processes. 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Our examination of the literature not only argues that these methods are competitive with conventional estimators on a single data set, but also demonstrates that they are responsive to the training data.","internal_url":"https://www.academia.edu/119807803/Comparative_Study_on_Calculating_CPU_Burst_Time_using_Different_Machine_Learning_Algorithms","translated_internal_url":"","created_at":"2024-05-22T04:27:17.328-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730468,"work_id":119807803,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8154012,"email":"k***e@vit.edu","display_order":1,"name":"Kuldeep Vayadande","title":"Comparative Study on Calculating CPU Burst Time using Different Machine Learning Algorithms"},{"id":41730469,"work_id":119807803,"tagging_user_id":32919427,"tagged_user_id":240068123,"co_author_invite_id":null,"email":"n***0@vit.edu","display_order":2,"name":"Agarwal Naman","title":"Comparative Study on Calculating CPU Burst Time using Different Machine Learning Algorithms"}],"downloadable_attachments":[{"id":115145790,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145790/thumbnails/1.jpg","file_name":"326_2863_2868.pdf","download_url":"https://www.academia.edu/attachments/115145790/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Comparative_Study_on_Calculating_CPU_Bur.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145790/326_2863_2868-libre.pdf?1716378682=\u0026response-content-disposition=attachment%3B+filename%3DComparative_Study_on_Calculating_CPU_Bur.pdf\u0026Expires=1732468485\u0026Signature=SQVJsRW6wmFyw1Mss4CFsbiy0NhOebDIWPIp3QoVJeUcUeCg2ogIJa6Kzad36bhrIU1dHTlJJvz4yf22wllgnzY8je~4YKITh5dXsKhBOzwZ5PjpdQRKI4AqWETmI2GiX0qR1taccLxmOqoGsGcvfDEcHLtakBIr7PkkP42s4YVIpKC8qXk0NoLzW9nmATLU-5YR969pX15JbKInQpYjfjJvfMTWlvJ0zB3vbRcwkkarbTF3C~E64bNtl1tgq1ckWnWvp6DWJP4QbmTp6w1gCGVqruWv1wu6flm4AEH0f1A7XmhWHjpp3SZIQMLTz-i3~U9-GUEiCSRjX-BMyw4TAw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Comparative_Study_on_Calculating_CPU_Burst_Time_using_Different_Machine_Learning_Algorithms","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115145790,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145790/thumbnails/1.jpg","file_name":"326_2863_2868.pdf","download_url":"https://www.academia.edu/attachments/115145790/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Comparative_Study_on_Calculating_CPU_Bur.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145790/326_2863_2868-libre.pdf?1716378682=\u0026response-content-disposition=attachment%3B+filename%3DComparative_Study_on_Calculating_CPU_Bur.pdf\u0026Expires=1732468485\u0026Signature=SQVJsRW6wmFyw1Mss4CFsbiy0NhOebDIWPIp3QoVJeUcUeCg2ogIJa6Kzad36bhrIU1dHTlJJvz4yf22wllgnzY8je~4YKITh5dXsKhBOzwZ5PjpdQRKI4AqWETmI2GiX0qR1taccLxmOqoGsGcvfDEcHLtakBIr7PkkP42s4YVIpKC8qXk0NoLzW9nmATLU-5YR969pX15JbKInQpYjfjJvfMTWlvJ0zB3vbRcwkkarbTF3C~E64bNtl1tgq1ckWnWvp6DWJP4QbmTp6w1gCGVqruWv1wu6flm4AEH0f1A7XmhWHjpp3SZIQMLTz-i3~U9-GUEiCSRjX-BMyw4TAw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[]}, dispatcherData: dispatcherData }); 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The work presented in this paper addresses the Competence Mapping problem. Semantic technologies such as Resource Description Framework and standard ontology enrich unstructured job data and enable retrieval of semantically relevant matches, as opposed to keyword based approaches.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0bc06037c45affd2ef73f6e56516575f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115145702,"asset_id":119807669,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115145702/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119807669"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119807669"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119807669; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119807669]").text(description); $(".js-view-count[data-work-id=119807669]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119807669; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119807669']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119807669, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "0bc06037c45affd2ef73f6e56516575f" } } $('.js-work-strip[data-work-id=119807669]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119807669,"title":"\"Turning Straw into Gold\", using Semantic and Big Data Technologies for Competence Mapping","translated_title":"","metadata":{"abstract":"The unstructured nature of Big Data poses several challenges to develop representations capable of handling such data. 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$(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="119807617"><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/119807617/Risk_Assessment_of_Stock_Market_Analysis_using_Time_Series_Analysis"><img alt="Research paper thumbnail of Risk Assessment of Stock Market Analysis using Time Series Analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/115145653/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/119807617/Risk_Assessment_of_Stock_Market_Analysis_using_Time_Series_Analysis">Risk Assessment of Stock Market Analysis using Time Series Analysis</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Stock market forecasting and risk assessment heavily influence investors' financial decisions. Ma...</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">Stock market forecasting and risk assessment heavily influence investors' financial decisions. Many depend on news announcements to judge the purchase or sale of risky stocks. Due to the complexity and ambiguity of natural languages, however, reliable modeling and risk management of stock market patterns derived from news releases is challenging. In contrast to past work in this field, which generally uses bag-of-words to extract tens of thousands of characteristics to construct a prediction model, this study employs a novel approach that extracts tens of thousands of features directly from the text itself. We present a Time Series Forecasting based method for financial/stock market prediction with risk assessment using a histology dataset. In particular, Time Series Forecasting is performed at the pre-processing stage to extract time period-related characteristics from financial news. Using the collected features, a time seriesbased metaheuristic CNN (MCNN) model is used to construct a prediction with risk assessment. Using a model based on MCNN, we hope to get better outcomes than previous methods in terms of speed and precision while minimizing risk. The performance is attributable to the time analysis performed during the pre-processing step since it decreases the feature dimensions substantially. Using the suggested method, we aim to enhance the accuracy of stock price forecasts based on a selection of data sets.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="97b24fe9628e66e66dffef2df3959f73" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115145653,"asset_id":119807617,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115145653/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119807617"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119807617"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119807617; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119807617]").text(description); $(".js-view-count[data-work-id=119807617]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119807617; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119807617']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119807617, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "97b24fe9628e66e66dffef2df3959f73" } } $('.js-work-strip[data-work-id=119807617]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119807617,"title":"Risk Assessment of Stock Market Analysis using Time Series Analysis","translated_title":"","metadata":{"abstract":"Stock market forecasting and risk assessment heavily influence investors' financial decisions. Many depend on news announcements to judge the purchase or sale of risky stocks. Due to the complexity and ambiguity of natural languages, however, reliable modeling and risk management of stock market patterns derived from news releases is challenging. In contrast to past work in this field, which generally uses bag-of-words to extract tens of thousands of characteristics to construct a prediction model, this study employs a novel approach that extracts tens of thousands of features directly from the text itself. We present a Time Series Forecasting based method for financial/stock market prediction with risk assessment using a histology dataset. In particular, Time Series Forecasting is performed at the pre-processing stage to extract time period-related characteristics from financial news. Using the collected features, a time seriesbased metaheuristic CNN (MCNN) model is used to construct a prediction with risk assessment. Using a model based on MCNN, we hope to get better outcomes than previous methods in terms of speed and precision while minimizing risk. The performance is attributable to the time analysis performed during the pre-processing step since it decreases the feature dimensions substantially. Using the suggested method, we aim to enhance the accuracy of stock price forecasts based on a selection of data sets.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"Stock market forecasting and risk assessment heavily influence investors' financial decisions. Many depend on news announcements to judge the purchase or sale of risky stocks. Due to the complexity and ambiguity of natural languages, however, reliable modeling and risk management of stock market patterns derived from news releases is challenging. In contrast to past work in this field, which generally uses bag-of-words to extract tens of thousands of characteristics to construct a prediction model, this study employs a novel approach that extracts tens of thousands of features directly from the text itself. We present a Time Series Forecasting based method for financial/stock market prediction with risk assessment using a histology dataset. In particular, Time Series Forecasting is performed at the pre-processing stage to extract time period-related characteristics from financial news. Using the collected features, a time seriesbased metaheuristic CNN (MCNN) model is used to construct a prediction with risk assessment. Using a model based on MCNN, we hope to get better outcomes than previous methods in terms of speed and precision while minimizing risk. The performance is attributable to the time analysis performed during the pre-processing step since it decreases the feature dimensions substantially. Using the suggested method, we aim to enhance the accuracy of stock price forecasts based on a selection of data sets.","internal_url":"https://www.academia.edu/119807617/Risk_Assessment_of_Stock_Market_Analysis_using_Time_Series_Analysis","translated_internal_url":"","created_at":"2024-05-22T04:23:41.442-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730445,"work_id":119807617,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8154009,"email":"s***2@gmail.com","display_order":1,"name":"Sayem Patni","title":"Risk Assessment of Stock Market Analysis using Time Series Analysis"},{"id":41730446,"work_id":119807617,"tagging_user_id":32919427,"tagged_user_id":233740102,"co_author_invite_id":null,"email":"a***r@sandipuniversity.edu.in","display_order":2,"name":"Amit Gadekar","title":"Risk Assessment of Stock Market Analysis using Time Series Analysis"}],"downloadable_attachments":[{"id":115145653,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145653/thumbnails/1.jpg","file_name":"325_2847_2851.pdf","download_url":"https://www.academia.edu/attachments/115145653/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Risk_Assessment_of_Stock_Market_Analysis.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145653/325_2847_2851-libre.pdf?1716378696=\u0026response-content-disposition=attachment%3B+filename%3DRisk_Assessment_of_Stock_Market_Analysis.pdf\u0026Expires=1732468485\u0026Signature=bXg-br~kTT~LDPJYBJ7jeeQc2CGFnTbkBI8l15AoaqcWToAKuzI2xVQf3pURuWjbqg3Nfnl-n760IOR9xW2L6zW00VNwu5IiOzk35wOtqALmPM~0lJXOqLJFaLZGh1KbGh20-jbEJOobjzWkGltrK4EwmbtEiThtIQ6qxSqhVzQbGlrEuuMfc~YaOsCr4TnF3E-Dps8eqd9oG0tMNdd2wVIlxFKmZR9uD-WnyTAiZuiVZ~HX29XHjrvFO~SyxRO9xZ3ouvFq8AxyLOTjlC2440NMfgkq8jOEyKDKRwc8o9OvzuTxdIFCXN-P7jenut24uPQaG6b1pwOKScmNKS7Fsg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Risk_Assessment_of_Stock_Market_Analysis_using_Time_Series_Analysis","translated_slug":"","page_count":5,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115145653,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145653/thumbnails/1.jpg","file_name":"325_2847_2851.pdf","download_url":"https://www.academia.edu/attachments/115145653/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Risk_Assessment_of_Stock_Market_Analysis.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145653/325_2847_2851-libre.pdf?1716378696=\u0026response-content-disposition=attachment%3B+filename%3DRisk_Assessment_of_Stock_Market_Analysis.pdf\u0026Expires=1732468485\u0026Signature=bXg-br~kTT~LDPJYBJ7jeeQc2CGFnTbkBI8l15AoaqcWToAKuzI2xVQf3pURuWjbqg3Nfnl-n760IOR9xW2L6zW00VNwu5IiOzk35wOtqALmPM~0lJXOqLJFaLZGh1KbGh20-jbEJOobjzWkGltrK4EwmbtEiThtIQ6qxSqhVzQbGlrEuuMfc~YaOsCr4TnF3E-Dps8eqd9oG0tMNdd2wVIlxFKmZR9uD-WnyTAiZuiVZ~HX29XHjrvFO~SyxRO9xZ3ouvFq8AxyLOTjlC2440NMfgkq8jOEyKDKRwc8o9OvzuTxdIFCXN-P7jenut24uPQaG6b1pwOKScmNKS7Fsg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119807472"><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/119807472/A_Study_on_Various_Sentiment_Analysis_for_Mixed_Transliterated_Indigenous_Language_using_Machine_Learning_Algorithms"><img alt="Research paper thumbnail of A Study on Various Sentiment Analysis for Mixed Transliterated Indigenous Language using Machine Learning Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/115145567/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/119807472/A_Study_on_Various_Sentiment_Analysis_for_Mixed_Transliterated_Indigenous_Language_using_Machine_Learning_Algorithms">A Study on Various Sentiment Analysis for Mixed Transliterated Indigenous Language using Machine Learning Algorithms</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The evolution of Information Technology has led to the collection of large amounts of data, the v...</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 evolution of Information Technology has led to the collection of large amounts of data, the volume of which has increased to the extent that in the last two years the data produced is greater than all the data ever recorded in human history. This has necessitated the use of machines to understand, interpret and apply data, without manual involvement. Sentiment Analysis has become the area of deep research due to the necessity wrought about by the advent of social media tools such as Twitter, Facebook, WhatsApp, and so on. In this survey, we will analyze 25 literature works concentrating on machine learning approaches associated with sentiment analysis for mixed transliterated languages, bringing into light the various shortcomings of the existing methodologies. Here, the analysis of various methods will be facilitated based on several factors, such as performance metrics, year of publication and journals, achievements of the techniques in numerical evaluations, and so on. On the other hand, the analysis of the methods with respect to the merits and demerits of the methods will be presented. Lastly, the paper discusses the potential future research directions and challenges in achieving better accuracy for sentiment analysis. The motivation of the research will be described in detail through the comparative discussion of the methods.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="367f5165c15428e8f34976a7c139d31b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115145567,"asset_id":119807472,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115145567/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119807472"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119807472"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119807472; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119807472]").text(description); $(".js-view-count[data-work-id=119807472]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119807472; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119807472']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119807472, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "367f5165c15428e8f34976a7c139d31b" } } $('.js-work-strip[data-work-id=119807472]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119807472,"title":"A Study on Various Sentiment Analysis for Mixed Transliterated Indigenous Language using Machine Learning Algorithms","translated_title":"","metadata":{"abstract":"The evolution of Information Technology has led to the collection of large amounts of data, the volume of which has increased to the extent that in the last two years the data produced is greater than all the data ever recorded in human history. This has necessitated the use of machines to understand, interpret and apply data, without manual involvement. Sentiment Analysis has become the area of deep research due to the necessity wrought about by the advent of social media tools such as Twitter, Facebook, WhatsApp, and so on. In this survey, we will analyze 25 literature works concentrating on machine learning approaches associated with sentiment analysis for mixed transliterated languages, bringing into light the various shortcomings of the existing methodologies. Here, the analysis of various methods will be facilitated based on several factors, such as performance metrics, year of publication and journals, achievements of the techniques in numerical evaluations, and so on. On the other hand, the analysis of the methods with respect to the merits and demerits of the methods will be presented. Lastly, the paper discusses the potential future research directions and challenges in achieving better accuracy for sentiment analysis. The motivation of the research will be described in detail through the comparative discussion of the methods.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"The evolution of Information Technology has led to the collection of large amounts of data, the volume of which has increased to the extent that in the last two years the data produced is greater than all the data ever recorded in human history. This has necessitated the use of machines to understand, interpret and apply data, without manual involvement. Sentiment Analysis has become the area of deep research due to the necessity wrought about by the advent of social media tools such as Twitter, Facebook, WhatsApp, and so on. In this survey, we will analyze 25 literature works concentrating on machine learning approaches associated with sentiment analysis for mixed transliterated languages, bringing into light the various shortcomings of the existing methodologies. Here, the analysis of various methods will be facilitated based on several factors, such as performance metrics, year of publication and journals, achievements of the techniques in numerical evaluations, and so on. On the other hand, the analysis of the methods with respect to the merits and demerits of the methods will be presented. Lastly, the paper discusses the potential future research directions and challenges in achieving better accuracy for sentiment analysis. The motivation of the research will be described in detail through the comparative discussion of the methods.","internal_url":"https://www.academia.edu/119807472/A_Study_on_Various_Sentiment_Analysis_for_Mixed_Transliterated_Indigenous_Language_using_Machine_Learning_Algorithms","translated_internal_url":"","created_at":"2024-05-22T04:21:44.985-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[],"downloadable_attachments":[{"id":115145567,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145567/thumbnails/1.jpg","file_name":"324_1_2838_2846.pdf","download_url":"https://www.academia.edu/attachments/115145567/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Study_on_Various_Sentiment_Analysis_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145567/324_1_2838_2846-libre.pdf?1716378705=\u0026response-content-disposition=attachment%3B+filename%3DA_Study_on_Various_Sentiment_Analysis_fo.pdf\u0026Expires=1732468486\u0026Signature=VF5M1u1Y9106Encb-4bZeFE2kIu7BhTuaEt1GmSdSeSx1E3OK34P4Hjj7BleVSMBSJURBC4v73Ka5tFj5---ID4Cfwxl8XkOWbPcHvwTZsd7JtodYuIcsIHfe87f~JIg-MHvTrefBd00~XWVIZ~EDCiciCbH8dvs7lm7HMZhNmG4f4a6mju9eol35~~nkFFOZtCO1lB~Sctp6axoKyLbVEElawv6S-ZCrFvNC1MlictkezMDr6EBKNPpM1XK8rGI7FBsePBRiWJzLhqYIy5PT477RsWQRRpxSxNF7tsTHGMHVwNG7HlhYhsoARsIE4Nm1f4WLuxyoUz6RXddGReM7g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Study_on_Various_Sentiment_Analysis_for_Mixed_Transliterated_Indigenous_Language_using_Machine_Learning_Algorithms","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115145567,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145567/thumbnails/1.jpg","file_name":"324_1_2838_2846.pdf","download_url":"https://www.academia.edu/attachments/115145567/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Study_on_Various_Sentiment_Analysis_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145567/324_1_2838_2846-libre.pdf?1716378705=\u0026response-content-disposition=attachment%3B+filename%3DA_Study_on_Various_Sentiment_Analysis_fo.pdf\u0026Expires=1732468486\u0026Signature=VF5M1u1Y9106Encb-4bZeFE2kIu7BhTuaEt1GmSdSeSx1E3OK34P4Hjj7BleVSMBSJURBC4v73Ka5tFj5---ID4Cfwxl8XkOWbPcHvwTZsd7JtodYuIcsIHfe87f~JIg-MHvTrefBd00~XWVIZ~EDCiciCbH8dvs7lm7HMZhNmG4f4a6mju9eol35~~nkFFOZtCO1lB~Sctp6axoKyLbVEElawv6S-ZCrFvNC1MlictkezMDr6EBKNPpM1XK8rGI7FBsePBRiWJzLhqYIy5PT477RsWQRRpxSxNF7tsTHGMHVwNG7HlhYhsoARsIE4Nm1f4WLuxyoUz6RXddGReM7g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119807397"><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/119807397/Hybrid_Ensemble_based_Feature_Engineering_for_Detecting_Direct_DDOS_Flooding_Attack_in_Cloud"><img alt="Research paper thumbnail of Hybrid Ensemble based Feature Engineering for Detecting Direct DDOS Flooding Attack in Cloud" class="work-thumbnail" src="https://attachments.academia-assets.com/115145481/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/119807397/Hybrid_Ensemble_based_Feature_Engineering_for_Detecting_Direct_DDOS_Flooding_Attack_in_Cloud">Hybrid Ensemble based Feature Engineering for Detecting Direct DDOS Flooding Attack in Cloud</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In recent years, both the general public and commercial enterprises have grown more and more inte...</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 recent years, both the general public and commercial enterprises have grown more and more interested in using cloud services. The majority of businesses use cloud computing technologies for production operations, which draws hackers. Nowadays, one of the most popular methods used to obstruct the availability of essential internet services is Distributed Denial of Service (DDoS) floods. These attacks either totally destroy the victim or overload it with a massive amount of traffic that prevents it from carrying out normal communication. The cloud's services are fully suspended if there are any delays in identifying flooding attacks. A preprocessing stage in cloud DDoS attack defence known as feature engineering has been recognized as having the potential to improve classification accuracy and lower computing complexity. In this article, we suggest a DDoS intrusion detection system for use in cloud environments. The application of filter and wrapper-based feature selection techniques along with machine learning is proposed as a hybrid ensemble-based feature engineering strategy. The benchmark dataset, which fills in the gaps in the current datasets and comprises a wide range of direct DDoS flooding attacks, is used to assess the model. To prevent data overfitting issues, area-user-curve analysis is also assessed. When compared to previous benchmarking techniques, the evaluation and findings revealed a considerable improvement in attack detection. This framework offers a high detection rate and classification accuracy when contrasted with the existing framework. Hence, it is more suitable for protecting the cloud.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="797712078080e32df95d10a87f475141" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115145481,"asset_id":119807397,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115145481/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119807397"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119807397"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119807397; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119807397]").text(description); $(".js-view-count[data-work-id=119807397]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119807397; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119807397']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119807397, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "797712078080e32df95d10a87f475141" } } $('.js-work-strip[data-work-id=119807397]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119807397,"title":"Hybrid Ensemble based Feature Engineering for Detecting Direct DDOS Flooding Attack in Cloud","translated_title":"","metadata":{"abstract":"In recent years, both the general public and commercial enterprises have grown more and more interested in using cloud services. The majority of businesses use cloud computing technologies for production operations, which draws hackers. Nowadays, one of the most popular methods used to obstruct the availability of essential internet services is Distributed Denial of Service (DDoS) floods. These attacks either totally destroy the victim or overload it with a massive amount of traffic that prevents it from carrying out normal communication. The cloud's services are fully suspended if there are any delays in identifying flooding attacks. A preprocessing stage in cloud DDoS attack defence known as feature engineering has been recognized as having the potential to improve classification accuracy and lower computing complexity. In this article, we suggest a DDoS intrusion detection system for use in cloud environments. The application of filter and wrapper-based feature selection techniques along with machine learning is proposed as a hybrid ensemble-based feature engineering strategy. The benchmark dataset, which fills in the gaps in the current datasets and comprises a wide range of direct DDoS flooding attacks, is used to assess the model. To prevent data overfitting issues, area-user-curve analysis is also assessed. When compared to previous benchmarking techniques, the evaluation and findings revealed a considerable improvement in attack detection. This framework offers a high detection rate and classification accuracy when contrasted with the existing framework. Hence, it is more suitable for protecting the cloud.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"In recent years, both the general public and commercial enterprises have grown more and more interested in using cloud services. The majority of businesses use cloud computing technologies for production operations, which draws hackers. Nowadays, one of the most popular methods used to obstruct the availability of essential internet services is Distributed Denial of Service (DDoS) floods. These attacks either totally destroy the victim or overload it with a massive amount of traffic that prevents it from carrying out normal communication. The cloud's services are fully suspended if there are any delays in identifying flooding attacks. A preprocessing stage in cloud DDoS attack defence known as feature engineering has been recognized as having the potential to improve classification accuracy and lower computing complexity. In this article, we suggest a DDoS intrusion detection system for use in cloud environments. The application of filter and wrapper-based feature selection techniques along with machine learning is proposed as a hybrid ensemble-based feature engineering strategy. The benchmark dataset, which fills in the gaps in the current datasets and comprises a wide range of direct DDoS flooding attacks, is used to assess the model. To prevent data overfitting issues, area-user-curve analysis is also assessed. When compared to previous benchmarking techniques, the evaluation and findings revealed a considerable improvement in attack detection. This framework offers a high detection rate and classification accuracy when contrasted with the existing framework. Hence, it is more suitable for protecting the cloud.","internal_url":"https://www.academia.edu/119807397/Hybrid_Ensemble_based_Feature_Engineering_for_Detecting_Direct_DDOS_Flooding_Attack_in_Cloud","translated_internal_url":"","created_at":"2024-05-22T04:18:08.294-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730375,"work_id":119807397,"tagging_user_id":32919427,"tagged_user_id":135736264,"co_author_invite_id":null,"email":"k***m@gmail.com","display_order":1,"name":"Kalaivani Marappan","title":"Hybrid Ensemble based Feature Engineering for Detecting Direct DDOS Flooding Attack in Cloud"}],"downloadable_attachments":[{"id":115145481,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145481/thumbnails/1.jpg","file_name":"323_2_2829_2837.pdf","download_url":"https://www.academia.edu/attachments/115145481/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Hybrid_Ensemble_based_Feature_Engineerin.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145481/323_2_2829_2837-libre.pdf?1716378717=\u0026response-content-disposition=attachment%3B+filename%3DHybrid_Ensemble_based_Feature_Engineerin.pdf\u0026Expires=1732468486\u0026Signature=gc~~mz--kdF6axB4lNllji9quNL6-U6sj8EeON68NGreQ7pLdi8ZEM3R6QA67SgYNB088vXlUP~xz6kk8x7yQ-uMarMlq-3MGp9c~OFVRq-IycvhbHP3Hn5nO3sVOMe3ofd7jCliZe2qf0rvVoXrMeEKHcUj7FPPNlNU5FpB6CI3L8~S~cnBLvhdQ6DAjWhqCtHYkOtzc6ZLDFrh4DE3BF9w~~ebGk0Vc5ykZJdMgVphrhVcDmmbTzcwGnifK3K-vjKdZF0~a9KvzsNs~Ia93gQiCMFwISFcNB6SZQpnuLd1PNOThaZXMgYSsP6FHy6M-dNOA82aX5~4Ls12A-6gyA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Hybrid_Ensemble_based_Feature_Engineering_for_Detecting_Direct_DDOS_Flooding_Attack_in_Cloud","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115145481,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145481/thumbnails/1.jpg","file_name":"323_2_2829_2837.pdf","download_url":"https://www.academia.edu/attachments/115145481/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Hybrid_Ensemble_based_Feature_Engineerin.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145481/323_2_2829_2837-libre.pdf?1716378717=\u0026response-content-disposition=attachment%3B+filename%3DHybrid_Ensemble_based_Feature_Engineerin.pdf\u0026Expires=1732468486\u0026Signature=gc~~mz--kdF6axB4lNllji9quNL6-U6sj8EeON68NGreQ7pLdi8ZEM3R6QA67SgYNB088vXlUP~xz6kk8x7yQ-uMarMlq-3MGp9c~OFVRq-IycvhbHP3Hn5nO3sVOMe3ofd7jCliZe2qf0rvVoXrMeEKHcUj7FPPNlNU5FpB6CI3L8~S~cnBLvhdQ6DAjWhqCtHYkOtzc6ZLDFrh4DE3BF9w~~ebGk0Vc5ykZJdMgVphrhVcDmmbTzcwGnifK3K-vjKdZF0~a9KvzsNs~Ia93gQiCMFwISFcNB6SZQpnuLd1PNOThaZXMgYSsP6FHy6M-dNOA82aX5~4Ls12A-6gyA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[]}, dispatcherData: dispatcherData }); 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For many years, researchers in the fields of philosophy, psychology, neuroscience, and computing have examined the notions and functions of attention.This characteristic has been thoroughly investigated in deep neural networks over the past six years. In many application domains, neural attention models currently represent the cutting edge of deep learning. A thorough overview and analysis of recent advancements in neural attention models are provided in this survey. In order to identify and examine the architectures where attention has had a notable impact, we thoroughly reviewed hundreds of them in the region. Additionally, we created and made available an automated approach to aid in the growth of reviews in the field. We discuss the main applications of attention in convolutional, recurrent networks, and generative models. We also identify common subgroups of uses and applications. 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For many years, researchers in the fields of philosophy, psychology, neuroscience, and computing have examined the notions and functions of attention.This characteristic has been thoroughly investigated in deep neural networks over the past six years. In many application domains, neural attention models currently represent the cutting edge of deep learning. A thorough overview and analysis of recent advancements in neural attention models are provided in this survey. In order to identify and examine the architectures where attention has had a notable impact, we thoroughly reviewed hundreds of them in the region. Additionally, we created and made available an automated approach to aid in the growth of reviews in the field. We discuss the main applications of attention in convolutional, recurrent networks, and generative models. We also identify common subgroups of uses and applications. Additionally, we discuss the effects of attention across various application areas and how they affect.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"Attention is a fundamental component of all perceptual and cognitive processes in humans.This mechanism select, modify, and concentrate on the information that is most important to behaviour because of our limited capacity to interpret competing sources. For many years, researchers in the fields of philosophy, psychology, neuroscience, and computing have examined the notions and functions of attention.This characteristic has been thoroughly investigated in deep neural networks over the past six years. In many application domains, neural attention models currently represent the cutting edge of deep learning. A thorough overview and analysis of recent advancements in neural attention models are provided in this survey. In order to identify and examine the architectures where attention has had a notable impact, we thoroughly reviewed hundreds of them in the region. Additionally, we created and made available an automated approach to aid in the growth of reviews in the field. We discuss the main applications of attention in convolutional, recurrent networks, and generative models. We also identify common subgroups of uses and applications. Additionally, we discuss the effects of attention across various application areas and how they affect.","internal_url":"https://www.academia.edu/119807200/A_Neural_Attention_Models_Survey_for_Deep_Learning","translated_internal_url":"","created_at":"2024-05-22T04:15:35.456-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32919427,"coauthors_can_edit":true,"document_type":"conference_presentation","co_author_tags":[{"id":41730347,"work_id":119807200,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8153997,"email":"p***u@giet.edu","display_order":1,"name":"Padma Sahu","title":"A Neural Attention Models Survey for Deep Learning"},{"id":41730348,"work_id":119807200,"tagging_user_id":32919427,"tagged_user_id":null,"co_author_invite_id":8153998,"email":"s***n@giet.edu","display_order":2,"name":"Subhrajit Pradhan","title":"A Neural Attention Models Survey for Deep Learning"},{"id":41730349,"work_id":119807200,"tagging_user_id":32919427,"tagged_user_id":35789232,"co_author_invite_id":null,"email":"r***h@gmail.com","display_order":3,"name":"Ratnakar Dash","title":"A Neural Attention Models Survey for Deep Learning"}],"downloadable_attachments":[{"id":115145372,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145372/thumbnails/1.jpg","file_name":"323_1_2821_2828.pdf","download_url":"https://www.academia.edu/attachments/115145372/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Neural_Attention_Models_Survey_for_Dee.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145372/323_1_2821_2828-libre.pdf?1716378726=\u0026response-content-disposition=attachment%3B+filename%3DA_Neural_Attention_Models_Survey_for_Dee.pdf\u0026Expires=1732468486\u0026Signature=TIXFpYSul7hjbdsilQit-DtBN3JKKhJJUe-Bz51qs7DWaG2f31-lts4bQpAwfg~lpHby15O~7SvZltJNbw0nRJT2OUO~HKdJAU7bjYnuPylnNg55HicGS7Z3Y0kaAANMF3jknSqggSKnxKi0khkjRv1-Ss0wlYUEcXh7kBtsw0IQiOnjQL23T8kAQ98UGAUBjeEe7oVSn7-vVIYSXbkCc9fJYu7l0FA7658Wx9fNvpFxVkE~xpQsyrJ4CUQULcnsvPI1SnT~9CwU9X404Ugtnjuytdj00KXyG6~wjYODXmsyH9~S7nXFnRs6R4lU0~9Ui01tUARQgQ~qoE1OxDm-zA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Neural_Attention_Models_Survey_for_Deep_Learning","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":32919427,"first_name":"Grenze International Journal of Engineering and Technology","middle_initials":"","last_name":"GIJET","page_name":"IdesEditor","domain_name":"independent","created_at":"2015-07-08T20:53:07.213-07:00","display_name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"},"attachments":[{"id":115145372,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115145372/thumbnails/1.jpg","file_name":"323_1_2821_2828.pdf","download_url":"https://www.academia.edu/attachments/115145372/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Neural_Attention_Models_Survey_for_Dee.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115145372/323_1_2821_2828-libre.pdf?1716378726=\u0026response-content-disposition=attachment%3B+filename%3DA_Neural_Attention_Models_Survey_for_Dee.pdf\u0026Expires=1732468486\u0026Signature=TIXFpYSul7hjbdsilQit-DtBN3JKKhJJUe-Bz51qs7DWaG2f31-lts4bQpAwfg~lpHby15O~7SvZltJNbw0nRJT2OUO~HKdJAU7bjYnuPylnNg55HicGS7Z3Y0kaAANMF3jknSqggSKnxKi0khkjRv1-Ss0wlYUEcXh7kBtsw0IQiOnjQL23T8kAQ98UGAUBjeEe7oVSn7-vVIYSXbkCc9fJYu7l0FA7658Wx9fNvpFxVkE~xpQsyrJ4CUQULcnsvPI1SnT~9CwU9X404Ugtnjuytdj00KXyG6~wjYODXmsyH9~S7nXFnRs6R4lU0~9Ui01tUARQgQ~qoE1OxDm-zA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"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="119807145"><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/119807145/Hand_Gesture_Recognition_An_Approach_of_Multiple_Object_Tracking"><img alt="Research paper thumbnail of Hand Gesture Recognition: An Approach of Multiple Object Tracking" class="work-thumbnail" src="https://attachments.academia-assets.com/115145306/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/119807145/Hand_Gesture_Recognition_An_Approach_of_Multiple_Object_Tracking">Hand Gesture Recognition: An Approach of Multiple Object Tracking</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the recent year, Object Detection and Tracking technology is one of the challenging and most e...</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 the recent year, Object Detection and Tracking technology is one of the<br />challenging and most emerging topic in Computer Vision. Multiple Object Tracking &<br />Detection (MOTD) has gained a lot of importance in various fields such as Autonomous driving,<br />Monitoring security, Surveillance, Health-care monitoring and Gesture recognition etc. Gesture<br />recognition is widely used in various fields of intelligent driving, Virtual reality, and Humancomputer<br />interaction. With the development in various technologies such as Deep Learning,<br />Artificial Intelligence and Human-Computer interaction a new revolution arise in field of<br />computer vision. We endeavor to provide a thorough review on the development of this gesture<br />recognition approach in recent decades. Here our major discussions are about the benefits and<br />limitations of existing approach where focus is on the method of feature extraction in<br />spatiotemporal structure .Various research difficulties that could be pursued will be the<br />research directions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0640859bf601f1d773185cb92389d774" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115145306,"asset_id":119807145,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115145306/download_file?st=MTczMjQ4MjA2NCw4LjIyMi4yMDguMTQ2&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="119807145"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="119807145"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 119807145; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=119807145]").text(description); $(".js-view-count[data-work-id=119807145]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 119807145; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='119807145']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 119807145, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "0640859bf601f1d773185cb92389d774" } } $('.js-work-strip[data-work-id=119807145]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":119807145,"title":"Hand Gesture Recognition: An Approach of Multiple Object Tracking","translated_title":"","metadata":{"abstract":"In the recent year, Object Detection and Tracking technology is one of the\nchallenging and most emerging topic in Computer Vision. Multiple Object Tracking \u0026\nDetection (MOTD) has gained a lot of importance in various fields such as Autonomous driving,\nMonitoring security, Surveillance, Health-care monitoring and Gesture recognition etc. Gesture\nrecognition is widely used in various fields of intelligent driving, Virtual reality, and Humancomputer\ninteraction. With the development in various technologies such as Deep Learning,\nArtificial Intelligence and Human-Computer interaction a new revolution arise in field of\ncomputer vision. We endeavor to provide a thorough review on the development of this gesture\nrecognition approach in recent decades. Here our major discussions are about the benefits and\nlimitations of existing approach where focus is on the method of feature extraction in\nspatiotemporal structure .Various research difficulties that could be pursued will be the\nresearch directions.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}}},"translated_abstract":"In the recent year, Object Detection and Tracking technology is one of the\nchallenging and most emerging topic in Computer Vision. Multiple Object Tracking \u0026\nDetection (MOTD) has gained a lot of importance in various fields such as Autonomous driving,\nMonitoring security, Surveillance, Health-care monitoring and Gesture recognition etc. Gesture\nrecognition is widely used in various fields of intelligent driving, Virtual reality, and Humancomputer\ninteraction. With the development in various technologies such as Deep Learning,\nArtificial Intelligence and Human-Computer interaction a new revolution arise in field of\ncomputer vision. We endeavor to provide a thorough review on the development of this gesture\nrecognition approach in recent decades. 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