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class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="102721421" href="https://www.academia.edu/Documents/in/Video_and_Data_Transmission_over_Mobile_Networks"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://independent.academia.edu/aekbeny","location":"/aekbeny","scheme":"https","host":"independent.academia.edu","port":null,"pathname":"/aekbeny","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Video and Data Transmission over Mobile Networks"]}" data-trace="false" data-dom-id="Pill-react-component-9350c165-4aa9-4401-8bda-c7d53eae6c14"></div> <div id="Pill-react-component-9350c165-4aa9-4401-8bda-c7d53eae6c14"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="102721421" href="https://www.academia.edu/Documents/in/Information_Theory_and_coding"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Information Theory and coding"]}" data-trace="false" data-dom-id="Pill-react-component-bfda66b3-9eed-443d-ac8f-9e56caac9c21"></div> <div id="Pill-react-component-bfda66b3-9eed-443d-ac8f-9e56caac9c21"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="102721421" href="https://www.academia.edu/Documents/in/Optical_Fiber_Communications"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Optical Fiber Communications"]}" data-trace="false" data-dom-id="Pill-react-component-9eb3ca27-4e8d-4004-ac70-d386003a0df5"></div> <div id="Pill-react-component-9eb3ca27-4e8d-4004-ac70-d386003a0df5"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="102721421" href="https://www.academia.edu/Documents/in/Telecommunication"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Telecommunication"]}" data-trace="false" data-dom-id="Pill-react-component-5918c776-a25b-467c-b507-f082b99c8baa"></div> <div id="Pill-react-component-5918c776-a25b-467c-b507-f082b99c8baa"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="102721421" href="https://www.academia.edu/Documents/in/Computer_Networks"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Computer Networks"]}" data-trace="false" data-dom-id="Pill-react-component-aeec66b5-e54b-4baa-9315-12ebe5c09fb1"></div> <div id="Pill-react-component-aeec66b5-e54b-4baa-9315-12ebe5c09fb1"></div> </a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by aek beny</h3></div><div class="js-work-strip profile--work_container" data-work-id="120863205"><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/120863205/Textual_Data_Selection_based_on_Mean_Square_Difference_Probability_for_Language_Modeling"><img alt="Research paper thumbnail of Textual Data Selection based on Mean Square Difference Probability for Language Modeling" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/120863205/Textual_Data_Selection_based_on_Mean_Square_Difference_Probability_for_Language_Modeling">Textual Data Selection based on Mean Square Difference Probability for Language Modeling</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span 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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/120863204/The_Incremental_Artificial_Immune_System_for_Arabic_Handwritten_Recognition">The Incremental Artificial Immune System for Arabic Handwritten Recognition</a></div><div class="wp-workCard_item"><span>Journal of Information Technology Research</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The historical document is a treasure. The frequent use of these documents requires having a nume...</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 historical document is a treasure. The frequent use of these documents requires having a numeric copy. The use of these numeric documents requires developing techniques to facilitate their use. The search by content, the word spotting, and handwriting recognition became important points of research in document analysis. For this purpose, in this article is covered the recognition of the Arabic manuscript names extracted from the register of names of the Tunisian national archive. In the study, the authors have used several techniques for extracting knowledge, coding, and name recognition. The authors have also optimized the clonclas algorithm using the incremental principle from the i2gng algorithm. The results encourage continuing exploration.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f6ffab72bfb808ab16a26da5adbd9b5f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115881026,"asset_id":120863204,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115881026/download_file?st=MTczMjUzNzYyNyw4LjIyMi4yMDguMTQ2&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="120863204"><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="120863204"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120863204; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120863204]").text(description); $(".js-view-count[data-work-id=120863204]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120863204; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120863204']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 120863204, 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: "f6ffab72bfb808ab16a26da5adbd9b5f" } } $('.js-work-strip[data-work-id=120863204]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120863204,"title":"The Incremental Artificial Immune System for Arabic Handwritten Recognition","translated_title":"","metadata":{"abstract":"The historical document is a treasure. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="120863203"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/120863203/Recognition_of_Individuals_Based_on_Hand_Geometry"><img alt="Research paper thumbnail of Recognition of Individuals Based on Hand Geometry" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/120863203/Recognition_of_Individuals_Based_on_Hand_Geometry">Recognition of Individuals Based on Hand Geometry</a></div><div class="wp-workCard_item"><span>World Scientific Proceedings Series on Computer Engineering and Information Science</span><span>, 2012</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="120863203"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="120863203"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120863203; 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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="120736665"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/120736665/Over_fitting_avoidance_in_probabilistic_neural_networks"><img alt="Research paper thumbnail of Over-fitting avoidance in probabilistic neural networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/120736665/Over_fitting_avoidance_in_probabilistic_neural_networks">Over-fitting avoidance in probabilistic neural networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this work, a new training algorithm for probabilistic neural networks (PNN) is presented. The ...</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 work, a new training algorithm for probabilistic neural networks (PNN) is presented. The proposed algorithm addresses one of the major drawbacks of probabilistic neural networks, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the new network is compared against performance of standard probabilistic neural networks for different databases from the UCI database repository. Results show an important gain in network size and performance.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="120736665"><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="120736665"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120736665; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120736665]").text(description); $(".js-view-count[data-work-id=120736665]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120736665; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120736665']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 120736665, 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=120736665]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120736665,"title":"Over-fitting avoidance in probabilistic neural networks","translated_title":"","metadata":{"abstract":"In this work, a new training algorithm for probabilistic neural networks (PNN) is presented. 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Results show an important gain in network size and performance.","internal_url":"https://www.academia.edu/120736665/Over_fitting_avoidance_in_probabilistic_neural_networks","translated_internal_url":"","created_at":"2024-06-08T10:35:04.915-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":102721421,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Over_fitting_avoidance_in_probabilistic_neural_networks","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":102721421,"first_name":"aek","middle_initials":null,"last_name":"beny","page_name":"aekbeny","domain_name":"independent","created_at":"2019-02-18T04:20:09.215-08:00","display_name":"aek beny","url":"https://independent.academia.edu/aekbeny"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic"},{"id":450311,"name":"Probabilistic Neural Network","url":"https://www.academia.edu/Documents/in/Probabilistic_Neural_Network"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"}],"urls":[{"id":42768102,"url":"https://doi.org/10.1109/wcitca.2015.7367037"}]}, 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="120736664"><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/120736664/Contribution_to_decision_making_in_the_big_data_industry_based_on_the_multiparametric_similarity_measure_for_Pythagorean_fuzzy_sets"><img alt="Research paper thumbnail of Contribution to decision-making in the big data industry based on the multiparametric similarity measure for Pythagorean fuzzy sets" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/120736664/Contribution_to_decision_making_in_the_big_data_industry_based_on_the_multiparametric_similarity_measure_for_Pythagorean_fuzzy_sets">Contribution to decision-making in the big data industry based on the multiparametric similarity measure for Pythagorean fuzzy sets</a></div><div class="wp-workCard_item"><span>Journal of Logic and Computation</span><span>, Jul 23, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Big Data allows analysing and assessing all human production types with its 5Vs, which are Volume...</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">Big Data allows analysing and assessing all human production types with its 5Vs, which are Volume, Velocity, Variety, Veracity and Value. Big Data is useful to improve decision-making to adjust it better to market demand, specifically selection of supplier that is an important link to optimize the logistic chain of enterprises. In this case, leadership or decider is ahead one serious complex problem, inexact and fuzzy. Pythagorean fuzzy set (PFS) is disposing the indeterminacy data by the membership and the nonmembership functions; it is a generalization of the intuitionist fuzzy set when the last set is limited. First, some results for PFSs are displaying in this study as particular cases and generalization of some binary operations. After, an improved score function of Pythagorean fuzzy number is proposed to avoid the comparison problem in practice. In addition, an existing approach exploring the combined alternatives weight to settle Pythagorean fuzzy issue by multi-parametric similarity measure is applied with the new proposed score function to selection of supplier issue with five serious criteria as a Big Data industry decision-making problem in economic environment. Finally, a comparison of the presented method with some existing approaches has been executed in the light of counterintuitive phenomena for validating its advantages.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="120736664"><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="120736664"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120736664; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120736664]").text(description); $(".js-view-count[data-work-id=120736664]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120736664; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120736664']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 120736664, 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=120736664]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120736664,"title":"Contribution to decision-making in the big data industry based on the multiparametric similarity measure for Pythagorean fuzzy sets","translated_title":"","metadata":{"abstract":"Big Data allows analysing and assessing all human production types with its 5Vs, which are Volume, Velocity, Variety, Veracity and Value. Big Data is useful to improve decision-making to adjust it better to market demand, specifically selection of supplier that is an important link to optimize the logistic chain of enterprises. In this case, leadership or decider is ahead one serious complex problem, inexact and fuzzy. Pythagorean fuzzy set (PFS) is disposing the indeterminacy data by the membership and the nonmembership functions; it is a generalization of the intuitionist fuzzy set when the last set is limited. First, some results for PFSs are displaying in this study as particular cases and generalization of some binary operations. After, an improved score function of Pythagorean fuzzy number is proposed to avoid the comparison problem in practice. In addition, an existing approach exploring the combined alternatives weight to settle Pythagorean fuzzy issue by multi-parametric similarity measure is applied with the new proposed score function to selection of supplier issue with five serious criteria as a Big Data industry decision-making problem in economic environment. Finally, a comparison of the presented method with some existing approaches has been executed in the light of counterintuitive phenomena for validating its advantages.","publisher":"Oxford University Press","publication_date":{"day":23,"month":7,"year":2022,"errors":{}},"publication_name":"Journal of Logic and Computation"},"translated_abstract":"Big Data allows analysing and assessing all human production types with its 5Vs, which are Volume, Velocity, Variety, Veracity and Value. Big Data is useful to improve decision-making to adjust it better to market demand, specifically selection of supplier that is an important link to optimize the logistic chain of enterprises. In this case, leadership or decider is ahead one serious complex problem, inexact and fuzzy. Pythagorean fuzzy set (PFS) is disposing the indeterminacy data by the membership and the nonmembership functions; it is a generalization of the intuitionist fuzzy set when the last set is limited. First, some results for PFSs are displaying in this study as particular cases and generalization of some binary operations. After, an improved score function of Pythagorean fuzzy number is proposed to avoid the comparison problem in practice. In addition, an existing approach exploring the combined alternatives weight to settle Pythagorean fuzzy issue by multi-parametric similarity measure is applied with the new proposed score function to selection of supplier issue with five serious criteria as a Big Data industry decision-making problem in economic environment. Finally, a comparison of the presented method with some existing approaches has been executed in the light of counterintuitive phenomena for validating its advantages.","internal_url":"https://www.academia.edu/120736664/Contribution_to_decision_making_in_the_big_data_industry_based_on_the_multiparametric_similarity_measure_for_Pythagorean_fuzzy_sets","translated_internal_url":"","created_at":"2024-06-08T10:35:04.647-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":102721421,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Contribution_to_decision_making_in_the_big_data_industry_based_on_the_multiparametric_similarity_measure_for_Pythagorean_fuzzy_sets","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":102721421,"first_name":"aek","middle_initials":null,"last_name":"beny","page_name":"aekbeny","domain_name":"independent","created_at":"2019-02-18T04:20:09.215-08:00","display_name":"aek beny","url":"https://independent.academia.edu/aekbeny"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":4165,"name":"Fuzzy Logic","url":"https://www.academia.edu/Documents/in/Fuzzy_Logic"},{"id":54284,"name":"Generalization","url":"https://www.academia.edu/Documents/in/Generalization"},{"id":75000,"name":"Philosophy and Religious Studies","url":"https://www.academia.edu/Documents/in/Philosophy_and_Religious_Studies"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences"}],"urls":[{"id":42768101,"url":"https://doi.org/10.1093/logcom/exac046"}]}, 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="120736663"><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/120736663/New_Contribution_to_Adaptive_Temporal_Radial_Basis_Function_Applied_on_TIMIT_Corpus"><img alt="Research paper thumbnail of New Contribution to Adaptive Temporal Radial Basis Function Applied on TIMIT Corpus" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/120736663/New_Contribution_to_Adaptive_Temporal_Radial_Basis_Function_Applied_on_TIMIT_Corpus">New Contribution to Adaptive Temporal Radial Basis Function Applied on TIMIT Corpus</a></div><div class="wp-workCard_item"><span>International Conference on Cognitive Modelling</span><span>, 2004</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Introduction A successful speech recognition system has to determine features not only present in...</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">Introduction A successful speech recognition system has to determine features not only present in the input pattern at one point in time, but also features of input pattern changing over time ( e.g., Berthold, 1994; Benyettou, 1995). In network design, great importance must be attributed to correct choice of the number of hidden neurons, which helps avoiding problems of overfitting and contributes to reduce the time required for the training without significantly affecting the network performances (e.g., Colla &amp; Reyneri &amp; Sgarbi, 1999), but never looking to architecture adapting effect according to input. The goal to combine the approach of the RBF with the shift invariance features of the TDNN, can be get a new robust model, this is named temporal radial basis function “TRBF” (e.g., Mesbahi &amp; Benyettou, 2003), but to be more efficient, we have adapt these networks so that they come more dynamic according to their behaviour and features of the object has study. It can be goes more clearly in continuous speech. Therefore in object to obtain an Adaptive TRBF, we must adapt the TRBF networks, consequently it was necessary to develop an algorithm that permits to solve this type of problem, this algorithm is called “DOLS” which means Dynamic Orthogonal Least Square, that will be presented in this paper.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="120736663"><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="120736663"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120736663; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120736663]").text(description); $(".js-view-count[data-work-id=120736663]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120736663; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120736663']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 120736663, 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=120736663]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120736663,"title":"New Contribution to Adaptive Temporal Radial Basis Function Applied on TIMIT Corpus","translated_title":"","metadata":{"abstract":"Introduction A successful speech recognition system has to determine features not only present in the input pattern at one point in time, but also features of input pattern changing over time ( e.g., Berthold, 1994; Benyettou, 1995). In network design, great importance must be attributed to correct choice of the number of hidden neurons, which helps avoiding problems of overfitting and contributes to reduce the time required for the training without significantly affecting the network performances (e.g., Colla \u0026amp; Reyneri \u0026amp; Sgarbi, 1999), but never looking to architecture adapting effect according to input. The goal to combine the approach of the RBF with the shift invariance features of the TDNN, can be get a new robust model, this is named temporal radial basis function “TRBF” (e.g., Mesbahi \u0026amp; Benyettou, 2003), but to be more efficient, we have adapt these networks so that they come more dynamic according to their behaviour and features of the object has study. It can be goes more clearly in continuous speech. Therefore in object to obtain an Adaptive TRBF, we must adapt the TRBF networks, consequently it was necessary to develop an algorithm that permits to solve this type of problem, this algorithm is called “DOLS” which means Dynamic Orthogonal Least Square, that will be presented in this paper.","publication_date":{"day":null,"month":null,"year":2004,"errors":{}},"publication_name":"International Conference on Cognitive Modelling"},"translated_abstract":"Introduction A successful speech recognition system has to determine features not only present in the input pattern at one point in time, but also features of input pattern changing over time ( e.g., Berthold, 1994; Benyettou, 1995). In network design, great importance must be attributed to correct choice of the number of hidden neurons, which helps avoiding problems of overfitting and contributes to reduce the time required for the training without significantly affecting the network performances (e.g., Colla \u0026amp; Reyneri \u0026amp; Sgarbi, 1999), but never looking to architecture adapting effect according to input. The goal to combine the approach of the RBF with the shift invariance features of the TDNN, can be get a new robust model, this is named temporal radial basis function “TRBF” (e.g., Mesbahi \u0026amp; Benyettou, 2003), but to be more efficient, we have adapt these networks so that they come more dynamic according to their behaviour and features of the object has study. It can be goes more clearly in continuous speech. Therefore in object to obtain an Adaptive TRBF, we must adapt the TRBF networks, consequently it was necessary to develop an algorithm that permits to solve this type of problem, this algorithm is called “DOLS” which means Dynamic Orthogonal Least Square, that will be presented in this paper.","internal_url":"https://www.academia.edu/120736663/New_Contribution_to_Adaptive_Temporal_Radial_Basis_Function_Applied_on_TIMIT_Corpus","translated_internal_url":"","created_at":"2024-06-08T10:35:04.425-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":102721421,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"New_Contribution_to_Adaptive_Temporal_Radial_Basis_Function_Applied_on_TIMIT_Corpus","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":102721421,"first_name":"aek","middle_initials":null,"last_name":"beny","page_name":"aekbeny","domain_name":"independent","created_at":"2019-02-18T04:20:09.215-08:00","display_name":"aek beny","url":"https://independent.academia.edu/aekbeny"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":5751,"name":"Radial Basis Function","url":"https://www.academia.edu/Documents/in/Radial_Basis_Function"},{"id":352180,"name":"Overfitting","url":"https://www.academia.edu/Documents/in/Overfitting"}],"urls":[{"id":42768100,"url":"https://dblp.uni-trier.de/db/conf/iccm/iccm2004.html#MesbahiB04"}]}, 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="120736662"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/120736662/The_Cooperation_of_Immune_Agents_for_Intrusion_Detection_System"><img alt="Research paper thumbnail of The Cooperation of Immune Agents for Intrusion Detection System" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/120736662/The_Cooperation_of_Immune_Agents_for_Intrusion_Detection_System">The Cooperation of Immune Agents for Intrusion Detection System</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Nowadays information technology and communication has evolve, computer networks became vulnerable...</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">Nowadays information technology and communication has evolve, computer networks became vulnerable faced to new forms of threats. In this article, a new model of intrusion detection based on multi-agents system and inspired from the biological immune system is presented. We begin through a presentation of the biological immune systems, followed by immune algorithm, a model of artificial immune system which is integrated in the behavior of distributed agents on the network is proposed in order to ensure a good intrusions detection. The internal structure of the immune agents and their capacity to distinguish between self and not-self is also presented. Agents are able to achieve simultaneous treatments, they are auto-adaptable to environment evolution and have also the property of distributed coordination.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="120736662"><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="120736662"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120736662; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120736662]").text(description); $(".js-view-count[data-work-id=120736662]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120736662; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120736662']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 120736662, 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=120736662]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120736662,"title":"The Cooperation of Immune Agents for Intrusion Detection System","translated_title":"","metadata":{"abstract":"Nowadays information technology and communication has evolve, computer networks became vulnerable faced to new forms of threats. In this article, a new model of intrusion detection based on multi-agents system and inspired from the biological immune system is presented. We begin through a presentation of the biological immune systems, followed by immune algorithm, a model of artificial immune system which is integrated in the behavior of distributed agents on the network is proposed in order to ensure a good intrusions detection. The internal structure of the immune agents and their capacity to distinguish between self and not-self is also presented. Agents are able to achieve simultaneous treatments, they are auto-adaptable to environment evolution and have also the property of distributed coordination.","publication_date":{"day":29,"month":12,"year":2017,"errors":{}}},"translated_abstract":"Nowadays information technology and communication has evolve, computer networks became vulnerable faced to new forms of threats. In this article, a new model of intrusion detection based on multi-agents system and inspired from the biological immune system is presented. We begin through a presentation of the biological immune systems, followed by immune algorithm, a model of artificial immune system which is integrated in the behavior of distributed agents on the network is proposed in order to ensure a good intrusions detection. The internal structure of the immune agents and their capacity to distinguish between self and not-self is also presented. Agents are able to achieve simultaneous treatments, they are auto-adaptable to environment evolution and have also the property of distributed coordination.","internal_url":"https://www.academia.edu/120736662/The_Cooperation_of_Immune_Agents_for_Intrusion_Detection_System","translated_internal_url":"","created_at":"2024-06-08T10:35:04.045-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":102721421,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"The_Cooperation_of_Immune_Agents_for_Intrusion_Detection_System","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":102721421,"first_name":"aek","middle_initials":null,"last_name":"beny","page_name":"aekbeny","domain_name":"independent","created_at":"2019-02-18T04:20:09.215-08:00","display_name":"aek beny","url":"https://independent.academia.edu/aekbeny"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":320736,"name":"Intrusion Detection System","url":"https://www.academia.edu/Documents/in/Intrusion_Detection_System"}],"urls":[{"id":42768099,"url":"https://doi.org/10.1145/3178264.3178290"}]}, 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="120736661"><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/120736661/On_Line_Arabic_Characters_Recognition_Using_Enhanced_Time_Delay_Neural_Networks"><img alt="Research paper thumbnail of On-Line Arabic Characters Recognition Using Enhanced Time Delay Neural Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/120736661/On_Line_Arabic_Characters_Recognition_Using_Enhanced_Time_Delay_Neural_Networks">On-Line Arabic Characters Recognition Using Enhanced Time Delay Neural Networks</a></div><div class="wp-workCard_item"><span>International Journal on Communications Antenna and Propagation</span><span>, Aug 31, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper concerns with the online recognition of isolated hand writing Arabic characters, throu...</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 concerns with the online recognition of isolated hand writing Arabic characters, through, the interpretation of a script presented by a pen trajectory. This technique was generally used in the electronic organizers of Personal Digital Assistant type. First of all, we have built a data base with several scripters using a graphic tablet which will be used in our application. In order to have a precise recognition of the isolated characters, it is important to model their structure the most correctly possible. In this work we present the study, the implementation and the result of the test of a particular neural network which is the Time Delay Neural Networks. We have followed a two steps approach, in the first one, the character characteristics are extracted, and in the second one, a temporal multi-layered perception is developed for a future classification. Our temporal approach with the adaptive topology, responds to the nature of the Arabic script during the acquisition phase while the use of different learning algorithms can minimize the cost function and improve recognition rates. 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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="120736652"><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/120736652/Fusion_of_Direct_Probabilistic_Multi_Class_Support_Vector_Machines_to_Enhance_Mental_Tasks_Recognition_Performance_in_BCI_Systems"><img alt="Research paper thumbnail of Fusion of Direct Probabilistic Multi-Class Support Vector Machines to Enhance Mental Tasks Recognition Performance in BCI Systems" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/120736652/Fusion_of_Direct_Probabilistic_Multi_Class_Support_Vector_Machines_to_Enhance_Mental_Tasks_Recognition_Performance_in_BCI_Systems">Fusion of Direct Probabilistic Multi-Class Support Vector Machines to Enhance Mental Tasks Recognition Performance in BCI Systems</a></div><div class="wp-workCard_item"><span>International Journal on Communications Antenna and Propagation</span><span>, Oct 31, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="120736652"><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="120736652"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120736652; 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The frequent use of these documents requires having a nume...</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 historical document is a treasure. The frequent use of these documents requires having a numeric copy. The use of these numeric documents requires developing techniques to facilitate their use. The search by content, the word spotting, and handwriting recognition became important points of research in document analysis. For this purpose, in this article is covered the recognition of the Arabic manuscript names extracted from the register of names of the Tunisian national archive. In the study, the authors have used several techniques for extracting knowledge, coding, and name recognition. The authors have also optimized the clonclas algorithm using the incremental principle from the i2gng algorithm. 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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="120736665"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/120736665/Over_fitting_avoidance_in_probabilistic_neural_networks"><img alt="Research paper thumbnail of Over-fitting avoidance in probabilistic neural networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/120736665/Over_fitting_avoidance_in_probabilistic_neural_networks">Over-fitting avoidance in probabilistic neural networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this work, a new training algorithm for probabilistic neural networks (PNN) is presented. The ...</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 work, a new training algorithm for probabilistic neural networks (PNN) is presented. The proposed algorithm addresses one of the major drawbacks of probabilistic neural networks, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the new network is compared against performance of standard probabilistic neural networks for different databases from the UCI database repository. Results show an important gain in network size and performance.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="120736665"><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="120736665"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120736665; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120736665]").text(description); $(".js-view-count[data-work-id=120736665]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120736665; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120736665']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 120736665, 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=120736665]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120736665,"title":"Over-fitting avoidance in probabilistic neural networks","translated_title":"","metadata":{"abstract":"In this work, a new training algorithm for probabilistic neural networks (PNN) is presented. 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Results show an important gain in network size and performance.","internal_url":"https://www.academia.edu/120736665/Over_fitting_avoidance_in_probabilistic_neural_networks","translated_internal_url":"","created_at":"2024-06-08T10:35:04.915-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":102721421,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Over_fitting_avoidance_in_probabilistic_neural_networks","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":102721421,"first_name":"aek","middle_initials":null,"last_name":"beny","page_name":"aekbeny","domain_name":"independent","created_at":"2019-02-18T04:20:09.215-08:00","display_name":"aek beny","url":"https://independent.academia.edu/aekbeny"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic"},{"id":450311,"name":"Probabilistic Neural Network","url":"https://www.academia.edu/Documents/in/Probabilistic_Neural_Network"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"}],"urls":[{"id":42768102,"url":"https://doi.org/10.1109/wcitca.2015.7367037"}]}, 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="120736664"><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/120736664/Contribution_to_decision_making_in_the_big_data_industry_based_on_the_multiparametric_similarity_measure_for_Pythagorean_fuzzy_sets"><img alt="Research paper thumbnail of Contribution to decision-making in the big data industry based on the multiparametric similarity measure for Pythagorean fuzzy sets" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/120736664/Contribution_to_decision_making_in_the_big_data_industry_based_on_the_multiparametric_similarity_measure_for_Pythagorean_fuzzy_sets">Contribution to decision-making in the big data industry based on the multiparametric similarity measure for Pythagorean fuzzy sets</a></div><div class="wp-workCard_item"><span>Journal of Logic and Computation</span><span>, Jul 23, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Big Data allows analysing and assessing all human production types with its 5Vs, which are Volume...</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">Big Data allows analysing and assessing all human production types with its 5Vs, which are Volume, Velocity, Variety, Veracity and Value. Big Data is useful to improve decision-making to adjust it better to market demand, specifically selection of supplier that is an important link to optimize the logistic chain of enterprises. In this case, leadership or decider is ahead one serious complex problem, inexact and fuzzy. Pythagorean fuzzy set (PFS) is disposing the indeterminacy data by the membership and the nonmembership functions; it is a generalization of the intuitionist fuzzy set when the last set is limited. First, some results for PFSs are displaying in this study as particular cases and generalization of some binary operations. After, an improved score function of Pythagorean fuzzy number is proposed to avoid the comparison problem in practice. In addition, an existing approach exploring the combined alternatives weight to settle Pythagorean fuzzy issue by multi-parametric similarity measure is applied with the new proposed score function to selection of supplier issue with five serious criteria as a Big Data industry decision-making problem in economic environment. Finally, a comparison of the presented method with some existing approaches has been executed in the light of counterintuitive phenomena for validating its advantages.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="120736664"><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="120736664"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120736664; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120736664]").text(description); $(".js-view-count[data-work-id=120736664]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120736664; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120736664']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 120736664, 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=120736664]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120736664,"title":"Contribution to decision-making in the big data industry based on the multiparametric similarity measure for Pythagorean fuzzy sets","translated_title":"","metadata":{"abstract":"Big Data allows analysing and assessing all human production types with its 5Vs, which are Volume, Velocity, Variety, Veracity and Value. Big Data is useful to improve decision-making to adjust it better to market demand, specifically selection of supplier that is an important link to optimize the logistic chain of enterprises. In this case, leadership or decider is ahead one serious complex problem, inexact and fuzzy. Pythagorean fuzzy set (PFS) is disposing the indeterminacy data by the membership and the nonmembership functions; it is a generalization of the intuitionist fuzzy set when the last set is limited. First, some results for PFSs are displaying in this study as particular cases and generalization of some binary operations. After, an improved score function of Pythagorean fuzzy number is proposed to avoid the comparison problem in practice. In addition, an existing approach exploring the combined alternatives weight to settle Pythagorean fuzzy issue by multi-parametric similarity measure is applied with the new proposed score function to selection of supplier issue with five serious criteria as a Big Data industry decision-making problem in economic environment. Finally, a comparison of the presented method with some existing approaches has been executed in the light of counterintuitive phenomena for validating its advantages.","publisher":"Oxford University Press","publication_date":{"day":23,"month":7,"year":2022,"errors":{}},"publication_name":"Journal of Logic and Computation"},"translated_abstract":"Big Data allows analysing and assessing all human production types with its 5Vs, which are Volume, Velocity, Variety, Veracity and Value. Big Data is useful to improve decision-making to adjust it better to market demand, specifically selection of supplier that is an important link to optimize the logistic chain of enterprises. In this case, leadership or decider is ahead one serious complex problem, inexact and fuzzy. Pythagorean fuzzy set (PFS) is disposing the indeterminacy data by the membership and the nonmembership functions; it is a generalization of the intuitionist fuzzy set when the last set is limited. First, some results for PFSs are displaying in this study as particular cases and generalization of some binary operations. After, an improved score function of Pythagorean fuzzy number is proposed to avoid the comparison problem in practice. In addition, an existing approach exploring the combined alternatives weight to settle Pythagorean fuzzy issue by multi-parametric similarity measure is applied with the new proposed score function to selection of supplier issue with five serious criteria as a Big Data industry decision-making problem in economic environment. Finally, a comparison of the presented method with some existing approaches has been executed in the light of counterintuitive phenomena for validating its advantages.","internal_url":"https://www.academia.edu/120736664/Contribution_to_decision_making_in_the_big_data_industry_based_on_the_multiparametric_similarity_measure_for_Pythagorean_fuzzy_sets","translated_internal_url":"","created_at":"2024-06-08T10:35:04.647-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":102721421,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Contribution_to_decision_making_in_the_big_data_industry_based_on_the_multiparametric_similarity_measure_for_Pythagorean_fuzzy_sets","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":102721421,"first_name":"aek","middle_initials":null,"last_name":"beny","page_name":"aekbeny","domain_name":"independent","created_at":"2019-02-18T04:20:09.215-08:00","display_name":"aek beny","url":"https://independent.academia.edu/aekbeny"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":4165,"name":"Fuzzy Logic","url":"https://www.academia.edu/Documents/in/Fuzzy_Logic"},{"id":54284,"name":"Generalization","url":"https://www.academia.edu/Documents/in/Generalization"},{"id":75000,"name":"Philosophy and Religious Studies","url":"https://www.academia.edu/Documents/in/Philosophy_and_Religious_Studies"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences"}],"urls":[{"id":42768101,"url":"https://doi.org/10.1093/logcom/exac046"}]}, 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="120736663"><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/120736663/New_Contribution_to_Adaptive_Temporal_Radial_Basis_Function_Applied_on_TIMIT_Corpus"><img alt="Research paper thumbnail of New Contribution to Adaptive Temporal Radial Basis Function Applied on TIMIT Corpus" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/120736663/New_Contribution_to_Adaptive_Temporal_Radial_Basis_Function_Applied_on_TIMIT_Corpus">New Contribution to Adaptive Temporal Radial Basis Function Applied on TIMIT Corpus</a></div><div class="wp-workCard_item"><span>International Conference on Cognitive Modelling</span><span>, 2004</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Introduction A successful speech recognition system has to determine features not only present in...</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">Introduction A successful speech recognition system has to determine features not only present in the input pattern at one point in time, but also features of input pattern changing over time ( e.g., Berthold, 1994; Benyettou, 1995). In network design, great importance must be attributed to correct choice of the number of hidden neurons, which helps avoiding problems of overfitting and contributes to reduce the time required for the training without significantly affecting the network performances (e.g., Colla &amp; Reyneri &amp; Sgarbi, 1999), but never looking to architecture adapting effect according to input. The goal to combine the approach of the RBF with the shift invariance features of the TDNN, can be get a new robust model, this is named temporal radial basis function “TRBF” (e.g., Mesbahi &amp; Benyettou, 2003), but to be more efficient, we have adapt these networks so that they come more dynamic according to their behaviour and features of the object has study. It can be goes more clearly in continuous speech. Therefore in object to obtain an Adaptive TRBF, we must adapt the TRBF networks, consequently it was necessary to develop an algorithm that permits to solve this type of problem, this algorithm is called “DOLS” which means Dynamic Orthogonal Least Square, that will be presented in this paper.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="120736663"><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="120736663"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120736663; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120736663]").text(description); $(".js-view-count[data-work-id=120736663]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120736663; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120736663']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 120736663, 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=120736663]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120736663,"title":"New Contribution to Adaptive Temporal Radial Basis Function Applied on TIMIT Corpus","translated_title":"","metadata":{"abstract":"Introduction A successful speech recognition system has to determine features not only present in the input pattern at one point in time, but also features of input pattern changing over time ( e.g., Berthold, 1994; Benyettou, 1995). In network design, great importance must be attributed to correct choice of the number of hidden neurons, which helps avoiding problems of overfitting and contributes to reduce the time required for the training without significantly affecting the network performances (e.g., Colla \u0026amp; Reyneri \u0026amp; Sgarbi, 1999), but never looking to architecture adapting effect according to input. The goal to combine the approach of the RBF with the shift invariance features of the TDNN, can be get a new robust model, this is named temporal radial basis function “TRBF” (e.g., Mesbahi \u0026amp; Benyettou, 2003), but to be more efficient, we have adapt these networks so that they come more dynamic according to their behaviour and features of the object has study. It can be goes more clearly in continuous speech. Therefore in object to obtain an Adaptive TRBF, we must adapt the TRBF networks, consequently it was necessary to develop an algorithm that permits to solve this type of problem, this algorithm is called “DOLS” which means Dynamic Orthogonal Least Square, that will be presented in this paper.","publication_date":{"day":null,"month":null,"year":2004,"errors":{}},"publication_name":"International Conference on Cognitive Modelling"},"translated_abstract":"Introduction A successful speech recognition system has to determine features not only present in the input pattern at one point in time, but also features of input pattern changing over time ( e.g., Berthold, 1994; Benyettou, 1995). In network design, great importance must be attributed to correct choice of the number of hidden neurons, which helps avoiding problems of overfitting and contributes to reduce the time required for the training without significantly affecting the network performances (e.g., Colla \u0026amp; Reyneri \u0026amp; Sgarbi, 1999), but never looking to architecture adapting effect according to input. The goal to combine the approach of the RBF with the shift invariance features of the TDNN, can be get a new robust model, this is named temporal radial basis function “TRBF” (e.g., Mesbahi \u0026amp; Benyettou, 2003), but to be more efficient, we have adapt these networks so that they come more dynamic according to their behaviour and features of the object has study. It can be goes more clearly in continuous speech. Therefore in object to obtain an Adaptive TRBF, we must adapt the TRBF networks, consequently it was necessary to develop an algorithm that permits to solve this type of problem, this algorithm is called “DOLS” which means Dynamic Orthogonal Least Square, that will be presented in this paper.","internal_url":"https://www.academia.edu/120736663/New_Contribution_to_Adaptive_Temporal_Radial_Basis_Function_Applied_on_TIMIT_Corpus","translated_internal_url":"","created_at":"2024-06-08T10:35:04.425-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":102721421,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"New_Contribution_to_Adaptive_Temporal_Radial_Basis_Function_Applied_on_TIMIT_Corpus","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":102721421,"first_name":"aek","middle_initials":null,"last_name":"beny","page_name":"aekbeny","domain_name":"independent","created_at":"2019-02-18T04:20:09.215-08:00","display_name":"aek beny","url":"https://independent.academia.edu/aekbeny"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":5751,"name":"Radial Basis Function","url":"https://www.academia.edu/Documents/in/Radial_Basis_Function"},{"id":352180,"name":"Overfitting","url":"https://www.academia.edu/Documents/in/Overfitting"}],"urls":[{"id":42768100,"url":"https://dblp.uni-trier.de/db/conf/iccm/iccm2004.html#MesbahiB04"}]}, 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="120736662"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/120736662/The_Cooperation_of_Immune_Agents_for_Intrusion_Detection_System"><img alt="Research paper thumbnail of The Cooperation of Immune Agents for Intrusion Detection System" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/120736662/The_Cooperation_of_Immune_Agents_for_Intrusion_Detection_System">The Cooperation of Immune Agents for Intrusion Detection System</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Nowadays information technology and communication has evolve, computer networks became vulnerable...</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">Nowadays information technology and communication has evolve, computer networks became vulnerable faced to new forms of threats. In this article, a new model of intrusion detection based on multi-agents system and inspired from the biological immune system is presented. We begin through a presentation of the biological immune systems, followed by immune algorithm, a model of artificial immune system which is integrated in the behavior of distributed agents on the network is proposed in order to ensure a good intrusions detection. The internal structure of the immune agents and their capacity to distinguish between self and not-self is also presented. Agents are able to achieve simultaneous treatments, they are auto-adaptable to environment evolution and have also the property of distributed coordination.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="120736662"><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="120736662"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120736662; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120736662]").text(description); $(".js-view-count[data-work-id=120736662]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120736662; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120736662']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 120736662, 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=120736662]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120736662,"title":"The Cooperation of Immune Agents for Intrusion Detection System","translated_title":"","metadata":{"abstract":"Nowadays information technology and communication has evolve, computer networks became vulnerable faced to new forms of threats. In this article, a new model of intrusion detection based on multi-agents system and inspired from the biological immune system is presented. We begin through a presentation of the biological immune systems, followed by immune algorithm, a model of artificial immune system which is integrated in the behavior of distributed agents on the network is proposed in order to ensure a good intrusions detection. The internal structure of the immune agents and their capacity to distinguish between self and not-self is also presented. Agents are able to achieve simultaneous treatments, they are auto-adaptable to environment evolution and have also the property of distributed coordination.","publication_date":{"day":29,"month":12,"year":2017,"errors":{}}},"translated_abstract":"Nowadays information technology and communication has evolve, computer networks became vulnerable faced to new forms of threats. In this article, a new model of intrusion detection based on multi-agents system and inspired from the biological immune system is presented. We begin through a presentation of the biological immune systems, followed by immune algorithm, a model of artificial immune system which is integrated in the behavior of distributed agents on the network is proposed in order to ensure a good intrusions detection. The internal structure of the immune agents and their capacity to distinguish between self and not-self is also presented. Agents are able to achieve simultaneous treatments, they are auto-adaptable to environment evolution and have also the property of distributed coordination.","internal_url":"https://www.academia.edu/120736662/The_Cooperation_of_Immune_Agents_for_Intrusion_Detection_System","translated_internal_url":"","created_at":"2024-06-08T10:35:04.045-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":102721421,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"The_Cooperation_of_Immune_Agents_for_Intrusion_Detection_System","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":102721421,"first_name":"aek","middle_initials":null,"last_name":"beny","page_name":"aekbeny","domain_name":"independent","created_at":"2019-02-18T04:20:09.215-08:00","display_name":"aek beny","url":"https://independent.academia.edu/aekbeny"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":320736,"name":"Intrusion Detection System","url":"https://www.academia.edu/Documents/in/Intrusion_Detection_System"}],"urls":[{"id":42768099,"url":"https://doi.org/10.1145/3178264.3178290"}]}, 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="120736661"><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/120736661/On_Line_Arabic_Characters_Recognition_Using_Enhanced_Time_Delay_Neural_Networks"><img alt="Research paper thumbnail of On-Line Arabic Characters Recognition Using Enhanced Time Delay Neural Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/120736661/On_Line_Arabic_Characters_Recognition_Using_Enhanced_Time_Delay_Neural_Networks">On-Line Arabic Characters Recognition Using Enhanced Time Delay Neural Networks</a></div><div class="wp-workCard_item"><span>International Journal on Communications Antenna and Propagation</span><span>, Aug 31, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper concerns with the online recognition of isolated hand writing Arabic characters, throu...</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 concerns with the online recognition of isolated hand writing Arabic characters, through, the interpretation of a script presented by a pen trajectory. This technique was generally used in the electronic organizers of Personal Digital Assistant type. First of all, we have built a data base with several scripters using a graphic tablet which will be used in our application. In order to have a precise recognition of the isolated characters, it is important to model their structure the most correctly possible. In this work we present the study, the implementation and the result of the test of a particular neural network which is the Time Delay Neural Networks. We have followed a two steps approach, in the first one, the character characteristics are extracted, and in the second one, a temporal multi-layered perception is developed for a future classification. Our temporal approach with the adaptive topology, responds to the nature of the Arabic script during the acquisition phase while the use of different learning algorithms can minimize the cost function and improve recognition rates. The parameterization of these two parts will allow us to analyze the impact of the neural network topology on the results of character recognition rates.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="120736661"><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="120736661"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120736661; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120736661]").text(description); $(".js-view-count[data-work-id=120736661]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120736661; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120736661']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 120736661, 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=120736661]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120736661,"title":"On-Line Arabic Characters Recognition Using Enhanced Time Delay Neural Networks","translated_title":"","metadata":{"abstract":"This paper concerns with the online recognition of isolated hand writing Arabic characters, through, the interpretation of a script presented by a pen trajectory. This technique was generally used in the electronic organizers of Personal Digital Assistant type. First of all, we have built a data base with several scripters using a graphic tablet which will be used in our application. In order to have a precise recognition of the isolated characters, it is important to model their structure the most correctly possible. In this work we present the study, the implementation and the result of the test of a particular neural network which is the Time Delay Neural Networks. We have followed a two steps approach, in the first one, the character characteristics are extracted, and in the second one, a temporal multi-layered perception is developed for a future classification. Our temporal approach with the adaptive topology, responds to the nature of the Arabic script during the acquisition phase while the use of different learning algorithms can minimize the cost function and improve recognition rates. The parameterization of these two parts will allow us to analyze the impact of the neural network topology on the results of character recognition rates.","publisher":"Praise Worthy Prize, s.r.l.","publication_date":{"day":31,"month":8,"year":2017,"errors":{}},"publication_name":"International Journal on Communications Antenna and Propagation"},"translated_abstract":"This paper concerns with the online recognition of isolated hand writing Arabic characters, through, the interpretation of a script presented by a pen trajectory. This technique was generally used in the electronic organizers of Personal Digital Assistant type. First of all, we have built a data base with several scripters using a graphic tablet which will be used in our application. In order to have a precise recognition of the isolated characters, it is important to model their structure the most correctly possible. In this work we present the study, the implementation and the result of the test of a particular neural network which is the Time Delay Neural Networks. We have followed a two steps approach, in the first one, the character characteristics are extracted, and in the second one, a temporal multi-layered perception is developed for a future classification. Our temporal approach with the adaptive topology, responds to the nature of the Arabic script during the acquisition phase while the use of different learning algorithms can minimize the cost function and improve recognition rates. The parameterization of these two parts will allow us to analyze the impact of the neural network topology on the results of character recognition rates.","internal_url":"https://www.academia.edu/120736661/On_Line_Arabic_Characters_Recognition_Using_Enhanced_Time_Delay_Neural_Networks","translated_internal_url":"","created_at":"2024-06-08T10:35:03.826-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":102721421,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"On_Line_Arabic_Characters_Recognition_Using_Enhanced_Time_Delay_Neural_Networks","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":102721421,"first_name":"aek","middle_initials":null,"last_name":"beny","page_name":"aekbeny","domain_name":"independent","created_at":"2019-02-18T04:20:09.215-08:00","display_name":"aek beny","url":"https://independent.academia.edu/aekbeny"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3324,"name":"Arabic","url":"https://www.academia.edu/Documents/in/Arabic"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"}],"urls":[{"id":42768098,"url":"https://doi.org/10.15866/irecap.v7i4.13204"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="120736660"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/120736660/The_Multi_Agents_Immune_System_for_Network_Intrusions_Detection_MAISID_"><img alt="Research paper thumbnail of The Multi-Agents Immune System for Network Intrusions Detection (MAISID)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/120736660/The_Multi_Agents_Immune_System_for_Network_Intrusions_Detection_MAISID_">The Multi-Agents Immune System for Network Intrusions Detection (MAISID)</a></div><div class="wp-workCard_item"><span>Oriental journal of computer science and technology</span><span>, Dec 4, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="120736660"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="120736660"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120736660; 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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="120736655"><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/120736655/Control_of_Nosocomial_Infections_by_Data_Mining_TI_Journals"><img alt="Research paper thumbnail of Control of Nosocomial Infections by Data Mining - TI Journals" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/120736655/Control_of_Nosocomial_Infections_by_Data_Mining_TI_Journals">Control of Nosocomial Infections by Data Mining - TI Journals</a></div><div class="wp-workCard_item"><span>World Applied Programming</span><span>, Nov 23, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract: These last 15 years have been rich in publishing high quality scientific studies evalua...</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">Abstract: These last 15 years have been rich in publishing high quality scientific studies evaluating the effectiveness of measures to prevent nosocomial infections, particularly in intensive care unit (ICU), comparison of the results of these studies and practices in intensive care units can now to better define a program for preventing nosocomial infections to develop in these services. Focused on managing the risk of infection and prevention of nosocomial infections, our study, using tools that use data mining methods, together proposals for how well resuscitation. Among the techniques we use in data mining classification, neural networks and decision trees that also use the description used for prevention or for the unsupervised classification and clustering, we estimate we have for the rules Association. These techniques are used with several algorithms that give different results and which are distinguished from each other.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="120736655"><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="120736655"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120736655; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120736655]").text(description); $(".js-view-count[data-work-id=120736655]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120736655; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120736655']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 120736655, 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=120736655]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120736655,"title":"Control of Nosocomial Infections by Data Mining - TI Journals","translated_title":"","metadata":{"abstract":"Abstract: These last 15 years have been rich in publishing high quality scientific studies evaluating the effectiveness of measures to prevent nosocomial infections, particularly in intensive care unit (ICU), comparison of the results of these studies and practices in intensive care units can now to better define a program for preventing nosocomial infections to develop in these services. 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The final phase is important because it can provide a trained connectionist architecture with explanation power and validate its output decisions. Moreover, it can be used to refine and maintain the initial knowledge acquired from domain experts. In this paper, we present three rule-extraction techniques. The first technique extracts a set of binary rules from any type of neural network. The other two techniques are specific to feedforward networks, with a single hidden layer of sigmoidal units. Technique 2 extracts partial rules that represent the most important embedded knowledge with an adjustable level of detail, while the third technique provides a more comprehensive and universal approach. A rule-evaluation technique, which orders extracted rules based on three performance measures, is then proposed. The three techniques area applied to the iris and breast cancer data sets. 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