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He received his Ph.D. in Computer Science and Engineering from Lovely Professional University, Phagwara, Punjab, India in 2021. He has received the “Young Researcher Award” and “Best Ph.D. Thesis Award” in 2022. Also, he received “The emerging Scientist Award” in 2021. He has published over 50 SCI, SCOPUS, and UGC Indexed papers. 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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 Dr. Sagar D Pande</h3></div><div class="js-work-strip profile--work_container" data-work-id="98405071"><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/98405071/Explainable_Deep_Neural_Network_Based_Analysis_on_Intrusion_Detection_Systems"><img alt="Research paper thumbnail of Explainable Deep Neural Network Based Analysis on Intrusion Detection Systems" class="work-thumbnail" src="https://attachments.academia-assets.com/99765369/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98405071/Explainable_Deep_Neural_Network_Based_Analysis_on_Intrusion_Detection_Systems">Explainable Deep Neural Network Based Analysis on Intrusion Detection Systems</a></div><div class="wp-workCard_item"><span>Computer Science</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The research on Intrusion Detection Systems (IDSs) have been increasing in recent years. Particul...</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 research on Intrusion Detection Systems (IDSs) have been increasing in recent years. Particularly, the research which are widely utilizing machine learning concepts, and it is proven that these concepts were effective with IDSs, particularly, deep neural network-based models enhanced the rate of detections of IDSs. At the same instance, the models are turning out to be very highly complex, users are unable to track down the explanations for the decisions made which indicates the necessity of identifying the explanations behind those decisions to ensure the interpretability of the framed model. In this aspect, the article deals with the proposed model that able to explain the obtained predictions. The proposed framework is a combination of a conventional intrusion detection system with the aid of a deep neural network and interpretability of the model predictions. The proposed model utilizes Shapley Additive Explanations (SHAP) that mixes with the local explainability as well as ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9f1d2f85ee0301e212f26fba03b87462" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":99765369,"asset_id":98405071,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/99765369/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98405071"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98405071"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98405071; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=98405071]").text(description); $(".js-view-count[data-work-id=98405071]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 98405071; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='98405071']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="90102199"><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/90102199/Feature_selection_and_comparison_of_classification_algorithms_for_wireless_sensor_networks"><img alt="Research paper thumbnail of Feature selection and comparison of classification algorithms for wireless sensor 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/90102199/Feature_selection_and_comparison_of_classification_algorithms_for_wireless_sensor_networks">Feature selection and comparison of classification algorithms for wireless sensor networks</a></div><div class="wp-workCard_item"><span>Journal of Ambient Intelligence and Humanized Computing</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Wireless sensor networks (WSNs) are developing at an incredible pace because of their cost-effect...</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">Wireless sensor networks (WSNs) are developing at an incredible pace because of their cost-effective solutions for applications like military and medical. WSN consists of a large number of nodes that have to suffer from constraints like limited computation capacity and limited battery capacity. There are a lot of attacks in WSNs; one of them is the distributed denial of service attack. Many studies have shown that decreasing the redundancy of relevant features from a dataset can make a model more accurate and efficient. In this paper, correlation-based feature selection, principal component analysis, linear discriminant analysis, recursive feature elimination, and univariate feature selection are used for feature selection. Results are compared after selecting features using these techniques. A novel technique for feature selection is introduced, which combines five feature selection techniques as a stack. After implementing the feature selection techniques, the model is trained with five machine learning algorithms, namely SVM, perceptron, K-nearest neighbor, stochastic gradient descent, and XGBoost. Finally, the model is evaluated with the help of K-fold cross-validation. Among all of the techniques best accuracy of 99.87% is achieved with the XGBoost classifier after selecting the best eleven features from the KDD dataset.</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="90102199"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="90102199"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 90102199; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=90102199]").text(description); $(".js-view-count[data-work-id=90102199]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 90102199; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='90102199']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=90102199]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":90102199,"title":"Feature selection and comparison of classification algorithms for wireless sensor networks","internal_url":"https://www.academia.edu/90102199/Feature_selection_and_comparison_of_classification_algorithms_for_wireless_sensor_networks","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, 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="75273559"><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/75273559/A_Review_on_Detection_of_DDOS_Attack_Using_Machine_Learning_and_Deep_Learning_Techniques"><img alt="Research paper thumbnail of A Review on Detection of DDOS Attack Using Machine Learning and Deep Learning Techniques" 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/75273559/A_Review_on_Detection_of_DDOS_Attack_Using_Machine_Learning_and_Deep_Learning_Techniques">A Review on Detection of DDOS Attack Using Machine Learning and Deep Learning Techniques</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One of the most serious threat to network security is Denial of service (DOS) attacks. When this ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">One of the most serious threat to network security is Denial of service (DOS) attacks. When this attack is performed in distributed manner it can create more disaster. Lot of research has been carried for the detection of this attack. In this various techniques has been studied and discussed. Primary focus was given to machine learning and deep learning techniques.</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="75273559"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273559"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273559; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273559]").text(description); $(".js-view-count[data-work-id=75273559]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273559; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75273559']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=75273559]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":75273559,"title":"A Review on Detection of DDOS Attack Using Machine Learning and Deep Learning Techniques","internal_url":"https://www.academia.edu/75273559/A_Review_on_Detection_of_DDOS_Attack_Using_Machine_Learning_and_Deep_Learning_Techniques","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, 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="75273558"><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/75273558/Online_Food_Requesting_Framework_for_Enhancing_Small_Scale_Business"><img alt="Research paper thumbnail of Online Food Requesting Framework for Enhancing Small Scale Business" 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/75273558/Online_Food_Requesting_Framework_for_Enhancing_Small_Scale_Business">Online Food Requesting Framework for Enhancing Small Scale Business</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper a framework for online nourishment requesting system is implemented which overcomes...</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 paper a framework for online nourishment requesting system is implemented which overcomes the drawback of the conventional framework. A simplest way to access online nourishment for cafés (hotels) as well as mess is suggested. This paper primarily focuses on enhancing small scale business in a city. Various features like payment, user history; multi-admin is added in the system.</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="75273558"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273558"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273558; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273558]").text(description); 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=75273558]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":75273558,"title":"Online Food Requesting Framework for Enhancing Small Scale Business","internal_url":"https://www.academia.edu/75273558/Online_Food_Requesting_Framework_for_Enhancing_Small_Scale_Business","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="75273557"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/75273557/A_Survey_on_Bigdata_in_Healthcare"><img alt="Research paper thumbnail of A Survey on Bigdata in Healthcare" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/75273557/A_Survey_on_Bigdata_in_Healthcare">A Survey on Bigdata in Healthcare</a></div><div class="wp-workCard_item"><span>SSRN Electronic Journal</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="75273557"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273557"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273557; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273557]").text(description); $(".js-view-count[data-work-id=75273557]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273557; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75273557']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=75273557]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":75273557,"title":"A Survey on Bigdata in Healthcare","internal_url":"https://www.academia.edu/75273557/A_Survey_on_Bigdata_in_Healthcare","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, 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="75273556"><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/75273556/A_Review_on_Essential_Resources_Utilized_for_Face_Recognition"><img alt="Research paper thumbnail of A Review on Essential Resources Utilized for Face Recognition" 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/75273556/A_Review_on_Essential_Resources_Utilized_for_Face_Recognition">A Review on Essential Resources Utilized for Face Recognition</a></div><div class="wp-workCard_item"><span>SSRN Electronic Journal</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In facial identification tasks, face alignment is the most important aspect. Utilizing localizati...</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 facial identification tasks, face alignment is the most important aspect. Utilizing localization of various face landmarks for structural face normalization, in particular, has proven to be extremely reliable, significantly enhancing recognition performance. This article presents a survey on popular repositories such as BioID face repository, XM2VTS repository, BUHMAP-DB repository, MUCT face repository, and PUT face repository and their characteristics. Along with that, the tools are essential for building applications concerning facial recognition in real-world aspects. The popular part of applications for face recognition such as localization of landmarks, estimating the facial position, and Multi-directional viewable face recognition are also discussed. Of all the existing repositories, AFLW is highly popular due to the existence of a high number of faces in real-world aspects.</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="75273556"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273556"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273556; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273556]").text(description); $(".js-view-count[data-work-id=75273556]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273556; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75273556']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=75273556]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":75273556,"title":"A Review on Essential Resources Utilized for Face Recognition","internal_url":"https://www.academia.edu/75273556/A_Review_on_Essential_Resources_Utilized_for_Face_Recognition","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, 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="75273555"><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/75273555/Criminal_Identification_System_using_Facial_Recognition"><img alt="Research paper thumbnail of Criminal Identification System using Facial Recognition" 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/75273555/Criminal_Identification_System_using_Facial_Recognition">Criminal Identification System using Facial Recognition</a></div><div class="wp-workCard_item"><span>SSRN Electronic Journal</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We all know that our Face is a unique and crucial part of the human body structure that identifie...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We all know that our Face is a unique and crucial part of the human body structure that identifies a person. Therefore, we can use it to trace the identity of a criminal person. With the advancement in technology, we are placed CCTV at many public places to capture the criminal’s crime. Using the previously captured faces and criminal’s images that are available in the police station, the criminal face recognition system of can be implemented. In this paper, we propose an automatic criminal identification system for Police Department to enhance and upgrade the criminal distinguishing into a more effective and efficient approach. Using technology, this idea will add plus point in the current system while bringing criminals spotting to a whole new level by automating tasks. Technology working behind it will be face recognition, from the footage captured by the CCTV cameras; our system will detect the face and recognize the criminal who is coming to that public place. The captured imag...</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="75273555"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273555"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273555; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273555]").text(description); $(".js-view-count[data-work-id=75273555]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273555; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75273555']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=75273555]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":75273555,"title":"Criminal Identification System using Facial Recognition","internal_url":"https://www.academia.edu/75273555/Criminal_Identification_System_using_Facial_Recognition","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, 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="75273554"><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/75273554/An_Information_Security_Scheme_for_Cloud_based_Environment_using_3DES_Encryption_Algorithm"><img alt="Research paper thumbnail of An Information Security Scheme for Cloud based Environment using 3DES Encryption Algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/83108670/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/75273554/An_Information_Security_Scheme_for_Cloud_based_Environment_using_3DES_Encryption_Algorithm">An Information Security Scheme for Cloud based Environment using 3DES Encryption Algorithm</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Cloud computing is the apt technology for the decade. It allows user to store large amount of dat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Cloud computing is the apt technology for the decade. It allows user to store large amount of data in cloud storage and use as and when required, from any part of the world, via any terminal equipment. While Cloud services offer flexibility, scalability and economies of scale, there have been commensurate concerns about security. On the similar terms, we have chosen to make use of a combination of authentication technique and key exchange algorithm blended with an encryption algorithm. In this project, we have proposed to make use of 3DES algorithm which is a wellknown symmetric cryptosystem and is widely used for secure data transmission, along with that we will blend it with Random Key Generator and Graphical Password to add an extra security measure. This proposed architecture of three way mechanism and the use of symmetric method of encryption make it tough for hackers to crack the security system, thereby protecting data stored in cloud. This Cipher Block Chaining system is to ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="08127d99998a900f9ca3d3136973e685" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":83108670,"asset_id":75273554,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/83108670/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="75273554"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273554"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273554; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273554]").text(description); $(".js-view-count[data-work-id=75273554]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273554; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75273554']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="75273553"><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/75273553/DDOS_Detection_Using_Machine_Learning_Technique"><img alt="Research paper thumbnail of DDOS Detection Using Machine Learning Technique" class="work-thumbnail" src="https://attachments.academia-assets.com/83108676/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/75273553/DDOS_Detection_Using_Machine_Learning_Technique">DDOS Detection Using Machine Learning Technique</a></div><div class="wp-workCard_item"><span>Recent Studies on Computational Intelligence</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Metaheuristic optimization approach has become the new framework for control synthesis. The main ...</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">Metaheuristic optimization approach has become the new framework for control synthesis. The main purposes of the control design are command (input) tracking and load (disturbance) regulating. This article proposes an optimal proportional-integral-derivative (PID) controller design for the DC motor speed control system with tracking and regulating constrained optimization by using the cuckoo search (CS), one of the most efficient population-based metaheuristic optimization techniques. The sum-squared error between the referent input and the controlled output is set as the objective function to be minimized. The rise time, the maximum overshoot, settling time and steady-state error are set as inequality constraints for tracking purpose, while the regulating time and the maximum overshoot of load regulation are set as inequality constraints for regulating purpose. Results obtained by the CS will be compared with those obtained by the conventional design method named Ziegler-Nichols (Z-N) tuning rules. From simulation results, it was found that the Z-N provides an impractical PID controller with very high gains, whereas the CS gives an optimal PID controller for DC motor speed control system satisfying the preset tracking and regulating constraints. In addition, the simulation results are confirmed by the experimental ones from the DC motor speed control system developed by analog technology.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ac6901028c27cc65721cdfa206b9d20e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":83108676,"asset_id":75273553,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/83108676/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="75273553"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273553"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273553; 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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="75273552"><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/75273552/Prediction_of_Heart_Disease_Using_a_Combination_of_Machine_Learning_and_Deep_Learning"><img alt="Research paper thumbnail of Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/83108647/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/75273552/Prediction_of_Heart_Disease_Using_a_Combination_of_Machine_Learning_and_Deep_Learning">Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning</a></div><div class="wp-workCard_item"><span>Computational Intelligence and Neuroscience</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The correct prediction of heart disease can prevent life threats, and incorrect prediction can pr...</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 correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="147e683b55c1a98dac4279f094945dc9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":83108647,"asset_id":75273552,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/83108647/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="75273552"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273552"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273552; 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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="75273551"><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/75273551/Multi_level_framework_for_anomaly_detection_in_social_networking"><img alt="Research paper thumbnail of Multi-level framework for anomaly detection in social networking" 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/75273551/Multi_level_framework_for_anomaly_detection_in_social_networking">Multi-level framework for anomaly detection in social networking</a></div><div class="wp-workCard_item"><span>Library Hi Tech</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose The purpose of this paper is to propose a structured multilevel system that will distingu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose The purpose of this paper is to propose a structured multilevel system that will distinguish the anomalies present in different online social networks (OSN). Design/methodology/approach Author first reviewed the related work, and then, the research model designed was explained. Furthermore, the details regarding Levels 1 and 2 were narrated. Findings By using the proposed technique, FScore obtained for Twitter and Facebook data set was 96.22 and 94.63, respectively. Research limitations/implications Four data sets were used for the experiment and the acquired outcomes demonstrate enhancement over the current existing frameworks. Originality/value This paper designed a multilevel framework that can be used to detect the anomalies present in the OSN.</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="75273551"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273551"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273551; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273551]").text(description); $(".js-view-count[data-work-id=75273551]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273551; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75273551']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=75273551]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":75273551,"title":"Multi-level framework for anomaly detection in social networking","internal_url":"https://www.academia.edu/75273551/Multi_level_framework_for_anomaly_detection_in_social_networking","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, 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="75273550"><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/75273550/A_Research_On_Android_Technology_With_New_Version_Naugat_7_0_7_1_"><img alt="Research paper thumbnail of A Research On Android Technology With New Version Naugat(7.0,7.1)" class="work-thumbnail" src="https://attachments.academia-assets.com/83108672/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/75273550/A_Research_On_Android_Technology_With_New_Version_Naugat_7_0_7_1_">A Research On Android Technology With New Version Naugat(7.0,7.1)</a></div><div class="wp-workCard_item"><span>IOSR Journal of Computer Engineering</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Operating System called Android 7.0. It was first released as a Android Beta Program build on Mar...</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">Operating System called Android 7.0. It was first released as a Android Beta Program build on March 9 , 2016 with factory images for current Nexus devices, which allows supported devices to be upgraded directly to the Android Nougat beta via over-the-air update. Nougat is introduced as notable changes to the operating system and its development platform also it includes the ability to display multiple apps on-screen at once in a splitscreen view with the support for inline replies to notifications, as well as an OpenJDK-based Java environment and support for the Vulkan graphics rendering API, and "seamless" system updates on supported devices.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="dcc43f7d418b45d25c9e7796c3637d49" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":83108672,"asset_id":75273550,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/83108672/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="75273550"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273550"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273550; 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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="75273549"><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/75273549/Prevention_Mechanism_on_DDOS_Attacks_by_using_Multilevel_Filtering_of_Distributed_Firewalls"><img alt="Research paper thumbnail of Prevention Mechanism on DDOS Attacks by using Multilevel Filtering of Distributed Firewalls" 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/75273549/Prevention_Mechanism_on_DDOS_Attacks_by_using_Multilevel_Filtering_of_Distributed_Firewalls">Prevention Mechanism on DDOS Attacks by using Multilevel Filtering of Distributed Firewalls</a></div><div class="wp-workCard_item"><span>International Journal on Recent and Innovation Trends in Computing and Communication</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Citation/Export MLA Sagar D. Pande, Prof. Ajay B. Gadicha, “Prevention Mechanism on DDOS Attacks ...</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">Citation/Export MLA Sagar D. Pande, Prof. Ajay B. Gadicha, “Prevention Mechanism on DDOS Attacks by using Multilevel Filtering of Distributed Firewalls”, March 15 Volume 3 Issue 3 , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 1005 - 1008, DOI: 10.17762/ijritcc2321-8169.150323 APA Sagar D. Pande, Prof. Ajay B. Gadicha, March 15 Volume 3 Issue 3, “Prevention Mechanism on DDOS Attacks by using Multilevel Filtering of Distributed Firewalls”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 1005 - 1008, DOI: 10.17762/ijritcc2321-8169.150323</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="75273549"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273549"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273549; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273549]").text(description); $(".js-view-count[data-work-id=75273549]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273549; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75273549']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=75273549]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":75273549,"title":"Prevention Mechanism on DDOS Attacks by using Multilevel Filtering of Distributed Firewalls","internal_url":"https://www.academia.edu/75273549/Prevention_Mechanism_on_DDOS_Attacks_by_using_Multilevel_Filtering_of_Distributed_Firewalls","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, 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="75273516"><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/75273516/Res_CovNet_an_internet_of_medical_health_things_driven_COVID_19_framework_using_transfer_learning"><img alt="Research paper thumbnail of Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning" class="work-thumbnail" src="https://attachments.academia-assets.com/83108661/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/75273516/Res_CovNet_an_internet_of_medical_health_things_driven_COVID_19_framework_using_transfer_learning">Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning</a></div><div class="wp-workCard_item"><span>Neural Computing & Applications</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. ...</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">Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. There are high chances of getting false positives and false negatives identifying the COVID-19 symptoms through existing medical practices such as PCR (polymerase chain reaction) and RT-PCR (reverse transcription-polymerase chain reaction). It might lead to a community spread of the disease. The alternative of these tests can be CT (Computer Tomography) imaging or X-rays of the lungs to identify the patient with COVID-19 symptoms more accurately. Furthermore, by using feasible and usable technology to automate the identification of COVID-19, the facilities can be improved. This notion became the basic framework, Res-CovNet, of the implemented methodology, a hybrid methodology to bring different platforms into a single platform. This basic framework is incorporated into IoMT based framework, a web-based service to identify and classify various forms of pneumonia or COVID-19 utilizing ches...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="64a42a5601cc73f4da4b24a370481348" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":83108661,"asset_id":75273516,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/83108661/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="75273516"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273516"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273516; 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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="69077600"><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/69077600/Artificial_Intelligence_and_Machine_Learning_to_Assist_Climate_Change_Monitoring"><img alt="Research paper thumbnail of Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring" class="work-thumbnail" src="https://attachments.academia-assets.com/79313751/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/69077600/Artificial_Intelligence_and_Machine_Learning_to_Assist_Climate_Change_Monitoring">Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring</a></div><div class="wp-workCard_item"><span>Ai & Society</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Climate change issues societal operation, likely wanting considerable adaptation to deal with doi...</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">Climate change issues societal operation, likely wanting considerable adaptation to deal with doing well altered weather patterns. Machine learning (ML) algorithms have progressed considerably, triggering breakthroughs in some other investigation sectors, along with only lately suggested as helping climate evaluation. Though a significant volume of isolated Earth System functions are analyzed with ML techniques, much more generic phone system to find out better the whole temperature unit hasn&#39;t happened. For instance, ML is able to aid remote identification, in which complex feedbacks make characterization tough from instantaneous equation analysis or perhaps possibly visualization of sizes plus Earth System design (ESM) diagnostics. Artificial intelligence (AI) may thus build on determined climate associates to provide enhanced alerts of approaching eco-friendly functions, which includes intense events. While ESM development is actually completely necessary, a parallel concentr...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3d6cf282f358d7382edcb0fb7dead730" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":79313751,"asset_id":69077600,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/79313751/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="69077600"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="69077600"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 69077600; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=69077600]").text(description); $(".js-view-count[data-work-id=69077600]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 69077600; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='69077600']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="69077596"><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/69077596/An_IoMT_Assisted_Heart_Disease_Diagnostic_System_Using_Machine_Learning_Techniques"><img alt="Research paper thumbnail of An IoMT Assisted Heart Disease Diagnostic System Using Machine Learning Techniques" 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/69077596/An_IoMT_Assisted_Heart_Disease_Diagnostic_System_Using_Machine_Learning_Techniques">An IoMT Assisted Heart Disease Diagnostic System Using Machine Learning Techniques</a></div><div class="wp-workCard_item"><span>Cognitive Internet of Medical Things for Smart Healthcare</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Machine learning can be used across several spheres around the planet. The medical industry is no...</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">Machine learning can be used across several spheres around the planet. The medical industry is not different. Health monitoring using wearable sensor enables us to go with Internet of Medical Things (IoMT). It enables the users to obtain the real time data i.e. live monitoring for manual prediction of user’s health, using machine learning techniques. Data Generation is one of the most challenging problems which have been faced by many researchers. As the volume of obtained data is very large machine learning techniques need to be used. Machine Learning can predict the presence/absence of locomotor disorders and Heart diseases in our body. Such information, if predicted well ahead of time can provides essential insights to physicians who could subsequently schedule their treatment and diagnosis for their patients. In this paper, various machine learning algorithms have been implemented to predict the heart disease. 88.59% accuracy was obtained by using logistic regression with majority voting which is better than the existing techniques.</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="69077596"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="69077596"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 69077596; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=69077596]").text(description); $(".js-view-count[data-work-id=69077596]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 69077596; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='69077596']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=69077596]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":69077596,"title":"An IoMT Assisted Heart Disease Diagnostic System Using Machine Learning Techniques","internal_url":"https://www.academia.edu/69077596/An_IoMT_Assisted_Heart_Disease_Diagnostic_System_Using_Machine_Learning_Techniques","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, 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="62402897"><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/62402897/An_Enhanced_Secure_Deep_Learning_Algorithm_for_Fraud_Detection_in_Wireless_Communication"><img alt="Research paper thumbnail of An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication" class="work-thumbnail" src="https://attachments.academia-assets.com/75180296/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/62402897/An_Enhanced_Secure_Deep_Learning_Algorithm_for_Fraud_Detection_in_Wireless_Communication">An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication</a></div><div class="wp-workCard_item"><span>Wireless Communications and Mobile Computing</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In today’s era of technology, especially in the Internet commerce and banking, the transactions d...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service-based fraud detection, needs labelled...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5be72fc237fb310db63f77ff3172baed" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":75180296,"asset_id":62402897,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/75180296/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="62402897"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="62402897"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 62402897; 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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="56420628"><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/56420628/Recognition_and_classification_of_pomegranate_leaves_diseases_by_image_processing_and_machine_learning_techniques"><img alt="Research paper thumbnail of Recognition and classification of pomegranate leaves diseases by image processing and machine learning techniques" class="work-thumbnail" src="https://attachments.academia-assets.com/71816418/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/56420628/Recognition_and_classification_of_pomegranate_leaves_diseases_by_image_processing_and_machine_learning_techniques">Recognition and classification of pomegranate leaves diseases by image processing and machine learning techniques</a></div><div class="wp-workCard_item"><span>Computers, Materials & Continua</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Disease recognition in plants is one of the essential problems in agricultural image processing. ...</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">Disease recognition in plants is one of the essential problems in agricultural image processing. This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly. The framework utilizes image processing techniques such as image acquisition, image resizing, image enhancement, image segmentation, ROI extraction (region of interest), and feature extraction. An image dataset related to pomegranate leaf disease is utilized to implement the framework, divided into a training set and a test set. In the implementation process, techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features. An image classification will then be implemented by combining a supervised learning model with a support vector machine. The proposed framework is developed based on MATLAB with a graphical user interface. According to the experimental results, the proposed framework can achieve 98.39% accuracy for classifying diseased and healthy leaves. Moreover, the framework can achieve an accuracy of 98.07% for classifying diseases on pomegranate leaves.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d0ef682130ba4105cd7146e4a7bd71f5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":71816418,"asset_id":56420628,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/71816418/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="56420628"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="56420628"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 56420628; 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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="49845270"><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/49845270/Res_CovNet_an_internet_of_medical_health_things_driven_COVID_19_framework_using_transfer_learning"><img alt="Research paper thumbnail of Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning" class="work-thumbnail" src="https://attachments.academia-assets.com/68053389/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/49845270/Res_CovNet_an_internet_of_medical_health_things_driven_COVID_19_framework_using_transfer_learning">Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning</a></div><div class="wp-workCard_item"><span>Neural Computing and Applications, Springer</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. ...</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">Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. There are high chances of getting false positives and false negatives identifying the COVID-19 symptoms through existing medical practices such as PCR (polymerase chain reaction) and RT-PCR (reverse transcription-polymerase chain reaction). It might lead to a community spread of the disease. The alternative of these tests can be CT (Computer Tomography) imaging or X-rays of the lungs to identify the patient with COVID-19 symptoms more accurately. Furthermore, by using feasible and usable technology to automate the identification of COVID-19, the facilities can be improved. This notion became the basic framework, Res-CovNet, of the implemented methodology, a hybrid methodology to bring different platforms into a single platform. This basic framework is incorporated into IoMT based framework, a web-based service to identify and classify various forms of pneumonia or COVID-19 utilizing chest X-ray images. For the front end, the.NET framework along with C# language was utilized, MongoDB was utilized for the storage aspect, Res-CovNet was utilized for the processing aspect. Deep learning combined with the notion forms a comprehensive implementation of the framework, Res-CovNet, to classify the COVID-19 affected patients from pneumonia-affected patients as both lung imaging looks similar to the naked eye. The implemented framework, Res-CovNet, developed with the technique, transfer learning in which ResNet-50 used as a pre-trained model and then extended with classification layers. The work implemented using the data of X-ray images collected from the various trustable sources that include cases such as normal, bacterial pneumonia, viral pneumonia, and COVID-19, with the overall size of the data is about 5856. The accuracy of the model implemented is about 98.4% in identifying COVID-19 against the normal cases. The accuracy of the model is about 96.2% in the case of identifying COVID-19 against all other cases, as mentioned.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a359d17f706df38b407a58ef1024939f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":68053389,"asset_id":49845270,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/68053389/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="49845270"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="49845270"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 49845270; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a359d17f706df38b407a58ef1024939f" } } $('.js-work-strip[data-work-id=49845270]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":49845270,"title":"Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning","internal_url":"https://www.academia.edu/49845270/Res_CovNet_an_internet_of_medical_health_things_driven_COVID_19_framework_using_transfer_learning","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[{"id":68053389,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/68053389/thumbnails/1.jpg","file_name":"Resnet.pdf","download_url":"https://www.academia.edu/attachments/68053389/download_file","bulk_download_file_name":"Res_CovNet_an_internet_of_medical_health.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/68053389/Resnet-libre.pdf?1626173122=\u0026response-content-disposition=attachment%3B+filename%3DRes_CovNet_an_internet_of_medical_health.pdf\u0026Expires=1739835662\u0026Signature=Xhh8WizSH1wGRBucn8LBWh95A8-BLlUppaex-oCF3oPboLYnUGlWjBcIVFbDj3VLpBFkBmp01GqaU2ifqRZcjz9AAfI~UisAeFIfUWjxOfmI~acYYPVKKSYZQyyW0TQ2aLJCVrLsPygSprMtGgGKP1rPhrIl8Ms18iK86bvLUYiTqsysM0fBBm0E9ICvz20mCy2tZ5JxOZzYIrxJpho1xNsWI7mUXkuCWTh4fgWzH3Iw7SURFNZqMF5qyFacRQhtJ1kFWhZroJqVkjg7EhMUANTqUbOb0DPbJeFOjwofvJAiiX9QsJHGuC5Ek653tt8js40Bf6YGL9BO3LDMVbbJkA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="49845209"><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/49845209/Prediction_of_Heart_Disease_Using_a_Combination_of_Machine_Learning_and_Deep_Learning"><img alt="Research paper thumbnail of Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/68053314/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/49845209/Prediction_of_Heart_Disease_Using_a_Combination_of_Machine_Learning_and_Deep_Learning">Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning</a></div><div class="wp-workCard_item"><span>Computational Intelligence and Neuroscience,Hindawi </span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">e correct prediction of heart disease can prevent life threats, and incorrect prediction can prov...</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">e correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. e dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. e dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="90bd75f317d8155b0376077b41c93565" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":68053314,"asset_id":49845209,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/68053314/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="49845209"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="49845209"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 49845209; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="2838503" id="papers"><div class="js-work-strip profile--work_container" data-work-id="98405071"><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/98405071/Explainable_Deep_Neural_Network_Based_Analysis_on_Intrusion_Detection_Systems"><img alt="Research paper thumbnail of Explainable Deep Neural Network Based Analysis on Intrusion Detection Systems" class="work-thumbnail" src="https://attachments.academia-assets.com/99765369/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98405071/Explainable_Deep_Neural_Network_Based_Analysis_on_Intrusion_Detection_Systems">Explainable Deep Neural Network Based Analysis on Intrusion Detection Systems</a></div><div class="wp-workCard_item"><span>Computer Science</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The research on Intrusion Detection Systems (IDSs) have been increasing in recent years. Particul...</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 research on Intrusion Detection Systems (IDSs) have been increasing in recent years. Particularly, the research which are widely utilizing machine learning concepts, and it is proven that these concepts were effective with IDSs, particularly, deep neural network-based models enhanced the rate of detections of IDSs. At the same instance, the models are turning out to be very highly complex, users are unable to track down the explanations for the decisions made which indicates the necessity of identifying the explanations behind those decisions to ensure the interpretability of the framed model. In this aspect, the article deals with the proposed model that able to explain the obtained predictions. The proposed framework is a combination of a conventional intrusion detection system with the aid of a deep neural network and interpretability of the model predictions. The proposed model utilizes Shapley Additive Explanations (SHAP) that mixes with the local explainability as well as ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9f1d2f85ee0301e212f26fba03b87462" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":99765369,"asset_id":98405071,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/99765369/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98405071"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98405071"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98405071; 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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="90102199"><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/90102199/Feature_selection_and_comparison_of_classification_algorithms_for_wireless_sensor_networks"><img alt="Research paper thumbnail of Feature selection and comparison of classification algorithms for wireless sensor 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/90102199/Feature_selection_and_comparison_of_classification_algorithms_for_wireless_sensor_networks">Feature selection and comparison of classification algorithms for wireless sensor networks</a></div><div class="wp-workCard_item"><span>Journal of Ambient Intelligence and Humanized Computing</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Wireless sensor networks (WSNs) are developing at an incredible pace because of their cost-effect...</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">Wireless sensor networks (WSNs) are developing at an incredible pace because of their cost-effective solutions for applications like military and medical. WSN consists of a large number of nodes that have to suffer from constraints like limited computation capacity and limited battery capacity. There are a lot of attacks in WSNs; one of them is the distributed denial of service attack. Many studies have shown that decreasing the redundancy of relevant features from a dataset can make a model more accurate and efficient. In this paper, correlation-based feature selection, principal component analysis, linear discriminant analysis, recursive feature elimination, and univariate feature selection are used for feature selection. Results are compared after selecting features using these techniques. A novel technique for feature selection is introduced, which combines five feature selection techniques as a stack. After implementing the feature selection techniques, the model is trained with five machine learning algorithms, namely SVM, perceptron, K-nearest neighbor, stochastic gradient descent, and XGBoost. Finally, the model is evaluated with the help of K-fold cross-validation. Among all of the techniques best accuracy of 99.87% is achieved with the XGBoost classifier after selecting the best eleven features from the KDD dataset.</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="90102199"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="90102199"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 90102199; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=90102199]").text(description); $(".js-view-count[data-work-id=90102199]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 90102199; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='90102199']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=90102199]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":90102199,"title":"Feature selection and comparison of classification algorithms for wireless sensor networks","internal_url":"https://www.academia.edu/90102199/Feature_selection_and_comparison_of_classification_algorithms_for_wireless_sensor_networks","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, 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="75273559"><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/75273559/A_Review_on_Detection_of_DDOS_Attack_Using_Machine_Learning_and_Deep_Learning_Techniques"><img alt="Research paper thumbnail of A Review on Detection of DDOS Attack Using Machine Learning and Deep Learning Techniques" 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/75273559/A_Review_on_Detection_of_DDOS_Attack_Using_Machine_Learning_and_Deep_Learning_Techniques">A Review on Detection of DDOS Attack Using Machine Learning and Deep Learning Techniques</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One of the most serious threat to network security is Denial of service (DOS) attacks. When this ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">One of the most serious threat to network security is Denial of service (DOS) attacks. When this attack is performed in distributed manner it can create more disaster. Lot of research has been carried for the detection of this attack. In this various techniques has been studied and discussed. Primary focus was given to machine learning and deep learning techniques.</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="75273559"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273559"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273559; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273559]").text(description); $(".js-view-count[data-work-id=75273559]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273559; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75273559']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=75273559]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":75273559,"title":"A Review on Detection of DDOS Attack Using Machine Learning and Deep Learning Techniques","internal_url":"https://www.academia.edu/75273559/A_Review_on_Detection_of_DDOS_Attack_Using_Machine_Learning_and_Deep_Learning_Techniques","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, 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="75273558"><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/75273558/Online_Food_Requesting_Framework_for_Enhancing_Small_Scale_Business"><img alt="Research paper thumbnail of Online Food Requesting Framework for Enhancing Small Scale Business" 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/75273558/Online_Food_Requesting_Framework_for_Enhancing_Small_Scale_Business">Online Food Requesting Framework for Enhancing Small Scale Business</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper a framework for online nourishment requesting system is implemented which overcomes...</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 paper a framework for online nourishment requesting system is implemented which overcomes the drawback of the conventional framework. A simplest way to access online nourishment for cafés (hotels) as well as mess is suggested. This paper primarily focuses on enhancing small scale business in a city. Various features like payment, user history; multi-admin is added in the system.</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="75273558"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273558"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273558; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273558]").text(description); $(".js-view-count[data-work-id=75273558]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273558; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75273558']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=75273558]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":75273558,"title":"Online Food Requesting Framework for Enhancing Small Scale Business","internal_url":"https://www.academia.edu/75273558/Online_Food_Requesting_Framework_for_Enhancing_Small_Scale_Business","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="75273557"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/75273557/A_Survey_on_Bigdata_in_Healthcare"><img alt="Research paper thumbnail of A Survey on Bigdata in Healthcare" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/75273557/A_Survey_on_Bigdata_in_Healthcare">A Survey on Bigdata in Healthcare</a></div><div class="wp-workCard_item"><span>SSRN Electronic Journal</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="75273557"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273557"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273557; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273557]").text(description); $(".js-view-count[data-work-id=75273557]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273557; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75273557']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=75273557]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":75273557,"title":"A Survey on Bigdata in Healthcare","internal_url":"https://www.academia.edu/75273557/A_Survey_on_Bigdata_in_Healthcare","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, 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="75273556"><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/75273556/A_Review_on_Essential_Resources_Utilized_for_Face_Recognition"><img alt="Research paper thumbnail of A Review on Essential Resources Utilized for Face Recognition" 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/75273556/A_Review_on_Essential_Resources_Utilized_for_Face_Recognition">A Review on Essential Resources Utilized for Face Recognition</a></div><div class="wp-workCard_item"><span>SSRN Electronic Journal</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In facial identification tasks, face alignment is the most important aspect. Utilizing localizati...</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 facial identification tasks, face alignment is the most important aspect. Utilizing localization of various face landmarks for structural face normalization, in particular, has proven to be extremely reliable, significantly enhancing recognition performance. This article presents a survey on popular repositories such as BioID face repository, XM2VTS repository, BUHMAP-DB repository, MUCT face repository, and PUT face repository and their characteristics. Along with that, the tools are essential for building applications concerning facial recognition in real-world aspects. The popular part of applications for face recognition such as localization of landmarks, estimating the facial position, and Multi-directional viewable face recognition are also discussed. Of all the existing repositories, AFLW is highly popular due to the existence of a high number of faces in real-world aspects.</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="75273556"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273556"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273556; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273556]").text(description); $(".js-view-count[data-work-id=75273556]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273556; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75273556']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=75273556]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":75273556,"title":"A Review on Essential Resources Utilized for Face Recognition","internal_url":"https://www.academia.edu/75273556/A_Review_on_Essential_Resources_Utilized_for_Face_Recognition","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, 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="75273555"><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/75273555/Criminal_Identification_System_using_Facial_Recognition"><img alt="Research paper thumbnail of Criminal Identification System using Facial Recognition" 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/75273555/Criminal_Identification_System_using_Facial_Recognition">Criminal Identification System using Facial Recognition</a></div><div class="wp-workCard_item"><span>SSRN Electronic Journal</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We all know that our Face is a unique and crucial part of the human body structure that identifie...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We all know that our Face is a unique and crucial part of the human body structure that identifies a person. Therefore, we can use it to trace the identity of a criminal person. With the advancement in technology, we are placed CCTV at many public places to capture the criminal’s crime. Using the previously captured faces and criminal’s images that are available in the police station, the criminal face recognition system of can be implemented. In this paper, we propose an automatic criminal identification system for Police Department to enhance and upgrade the criminal distinguishing into a more effective and efficient approach. Using technology, this idea will add plus point in the current system while bringing criminals spotting to a whole new level by automating tasks. Technology working behind it will be face recognition, from the footage captured by the CCTV cameras; our system will detect the face and recognize the criminal who is coming to that public place. The captured imag...</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="75273555"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273555"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273555; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273555]").text(description); $(".js-view-count[data-work-id=75273555]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273555; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75273555']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=75273555]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":75273555,"title":"Criminal Identification System using Facial Recognition","internal_url":"https://www.academia.edu/75273555/Criminal_Identification_System_using_Facial_Recognition","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, 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="75273554"><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/75273554/An_Information_Security_Scheme_for_Cloud_based_Environment_using_3DES_Encryption_Algorithm"><img alt="Research paper thumbnail of An Information Security Scheme for Cloud based Environment using 3DES Encryption Algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/83108670/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/75273554/An_Information_Security_Scheme_for_Cloud_based_Environment_using_3DES_Encryption_Algorithm">An Information Security Scheme for Cloud based Environment using 3DES Encryption Algorithm</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Cloud computing is the apt technology for the decade. It allows user to store large amount of dat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Cloud computing is the apt technology for the decade. It allows user to store large amount of data in cloud storage and use as and when required, from any part of the world, via any terminal equipment. While Cloud services offer flexibility, scalability and economies of scale, there have been commensurate concerns about security. On the similar terms, we have chosen to make use of a combination of authentication technique and key exchange algorithm blended with an encryption algorithm. In this project, we have proposed to make use of 3DES algorithm which is a wellknown symmetric cryptosystem and is widely used for secure data transmission, along with that we will blend it with Random Key Generator and Graphical Password to add an extra security measure. This proposed architecture of three way mechanism and the use of symmetric method of encryption make it tough for hackers to crack the security system, thereby protecting data stored in cloud. This Cipher Block Chaining system is to ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="08127d99998a900f9ca3d3136973e685" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":83108670,"asset_id":75273554,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/83108670/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="75273554"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273554"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273554; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273554]").text(description); $(".js-view-count[data-work-id=75273554]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273554; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75273554']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="75273553"><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/75273553/DDOS_Detection_Using_Machine_Learning_Technique"><img alt="Research paper thumbnail of DDOS Detection Using Machine Learning Technique" class="work-thumbnail" src="https://attachments.academia-assets.com/83108676/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/75273553/DDOS_Detection_Using_Machine_Learning_Technique">DDOS Detection Using Machine Learning Technique</a></div><div class="wp-workCard_item"><span>Recent Studies on Computational Intelligence</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Metaheuristic optimization approach has become the new framework for control synthesis. The main ...</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">Metaheuristic optimization approach has become the new framework for control synthesis. The main purposes of the control design are command (input) tracking and load (disturbance) regulating. This article proposes an optimal proportional-integral-derivative (PID) controller design for the DC motor speed control system with tracking and regulating constrained optimization by using the cuckoo search (CS), one of the most efficient population-based metaheuristic optimization techniques. The sum-squared error between the referent input and the controlled output is set as the objective function to be minimized. The rise time, the maximum overshoot, settling time and steady-state error are set as inequality constraints for tracking purpose, while the regulating time and the maximum overshoot of load regulation are set as inequality constraints for regulating purpose. Results obtained by the CS will be compared with those obtained by the conventional design method named Ziegler-Nichols (Z-N) tuning rules. From simulation results, it was found that the Z-N provides an impractical PID controller with very high gains, whereas the CS gives an optimal PID controller for DC motor speed control system satisfying the preset tracking and regulating constraints. In addition, the simulation results are confirmed by the experimental ones from the DC motor speed control system developed by analog technology.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ac6901028c27cc65721cdfa206b9d20e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":83108676,"asset_id":75273553,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/83108676/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="75273553"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273553"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273553; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273553]").text(description); $(".js-view-count[data-work-id=75273553]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273553; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75273553']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="75273552"><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/75273552/Prediction_of_Heart_Disease_Using_a_Combination_of_Machine_Learning_and_Deep_Learning"><img alt="Research paper thumbnail of Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/83108647/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/75273552/Prediction_of_Heart_Disease_Using_a_Combination_of_Machine_Learning_and_Deep_Learning">Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning</a></div><div class="wp-workCard_item"><span>Computational Intelligence and Neuroscience</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The correct prediction of heart disease can prevent life threats, and incorrect prediction can pr...</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 correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="147e683b55c1a98dac4279f094945dc9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":83108647,"asset_id":75273552,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/83108647/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="75273552"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273552"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273552; 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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="75273551"><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/75273551/Multi_level_framework_for_anomaly_detection_in_social_networking"><img alt="Research paper thumbnail of Multi-level framework for anomaly detection in social networking" 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/75273551/Multi_level_framework_for_anomaly_detection_in_social_networking">Multi-level framework for anomaly detection in social networking</a></div><div class="wp-workCard_item"><span>Library Hi Tech</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose The purpose of this paper is to propose a structured multilevel system that will distingu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose The purpose of this paper is to propose a structured multilevel system that will distinguish the anomalies present in different online social networks (OSN). Design/methodology/approach Author first reviewed the related work, and then, the research model designed was explained. Furthermore, the details regarding Levels 1 and 2 were narrated. Findings By using the proposed technique, FScore obtained for Twitter and Facebook data set was 96.22 and 94.63, respectively. Research limitations/implications Four data sets were used for the experiment and the acquired outcomes demonstrate enhancement over the current existing frameworks. Originality/value This paper designed a multilevel framework that can be used to detect the anomalies present in the OSN.</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="75273551"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273551"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273551; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273551]").text(description); $(".js-view-count[data-work-id=75273551]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273551; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75273551']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=75273551]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":75273551,"title":"Multi-level framework for anomaly detection in social networking","internal_url":"https://www.academia.edu/75273551/Multi_level_framework_for_anomaly_detection_in_social_networking","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, 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="75273550"><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/75273550/A_Research_On_Android_Technology_With_New_Version_Naugat_7_0_7_1_"><img alt="Research paper thumbnail of A Research On Android Technology With New Version Naugat(7.0,7.1)" class="work-thumbnail" src="https://attachments.academia-assets.com/83108672/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/75273550/A_Research_On_Android_Technology_With_New_Version_Naugat_7_0_7_1_">A Research On Android Technology With New Version Naugat(7.0,7.1)</a></div><div class="wp-workCard_item"><span>IOSR Journal of Computer Engineering</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Operating System called Android 7.0. It was first released as a Android Beta Program build on Mar...</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">Operating System called Android 7.0. It was first released as a Android Beta Program build on March 9 , 2016 with factory images for current Nexus devices, which allows supported devices to be upgraded directly to the Android Nougat beta via over-the-air update. Nougat is introduced as notable changes to the operating system and its development platform also it includes the ability to display multiple apps on-screen at once in a splitscreen view with the support for inline replies to notifications, as well as an OpenJDK-based Java environment and support for the Vulkan graphics rendering API, and "seamless" system updates on supported devices.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="dcc43f7d418b45d25c9e7796c3637d49" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":83108672,"asset_id":75273550,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/83108672/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="75273550"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273550"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273550; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273550]").text(description); $(".js-view-count[data-work-id=75273550]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273550; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75273550']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="75273549"><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/75273549/Prevention_Mechanism_on_DDOS_Attacks_by_using_Multilevel_Filtering_of_Distributed_Firewalls"><img alt="Research paper thumbnail of Prevention Mechanism on DDOS Attacks by using Multilevel Filtering of Distributed Firewalls" 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/75273549/Prevention_Mechanism_on_DDOS_Attacks_by_using_Multilevel_Filtering_of_Distributed_Firewalls">Prevention Mechanism on DDOS Attacks by using Multilevel Filtering of Distributed Firewalls</a></div><div class="wp-workCard_item"><span>International Journal on Recent and Innovation Trends in Computing and Communication</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Citation/Export MLA Sagar D. Pande, Prof. Ajay B. Gadicha, “Prevention Mechanism on DDOS Attacks ...</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">Citation/Export MLA Sagar D. Pande, Prof. Ajay B. Gadicha, “Prevention Mechanism on DDOS Attacks by using Multilevel Filtering of Distributed Firewalls”, March 15 Volume 3 Issue 3 , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 1005 - 1008, DOI: 10.17762/ijritcc2321-8169.150323 APA Sagar D. Pande, Prof. Ajay B. Gadicha, March 15 Volume 3 Issue 3, “Prevention Mechanism on DDOS Attacks by using Multilevel Filtering of Distributed Firewalls”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 1005 - 1008, DOI: 10.17762/ijritcc2321-8169.150323</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="75273549"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273549"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273549; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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There are high chances of getting false positives and false negatives identifying the COVID-19 symptoms through existing medical practices such as PCR (polymerase chain reaction) and RT-PCR (reverse transcription-polymerase chain reaction). It might lead to a community spread of the disease. The alternative of these tests can be CT (Computer Tomography) imaging or X-rays of the lungs to identify the patient with COVID-19 symptoms more accurately. Furthermore, by using feasible and usable technology to automate the identification of COVID-19, the facilities can be improved. This notion became the basic framework, Res-CovNet, of the implemented methodology, a hybrid methodology to bring different platforms into a single platform. This basic framework is incorporated into IoMT based framework, a web-based service to identify and classify various forms of pneumonia or COVID-19 utilizing ches...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="64a42a5601cc73f4da4b24a370481348" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":83108661,"asset_id":75273516,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/83108661/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="75273516"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75273516"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273516; 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$(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="69077600"><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/69077600/Artificial_Intelligence_and_Machine_Learning_to_Assist_Climate_Change_Monitoring"><img alt="Research paper thumbnail of Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring" class="work-thumbnail" src="https://attachments.academia-assets.com/79313751/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/69077600/Artificial_Intelligence_and_Machine_Learning_to_Assist_Climate_Change_Monitoring">Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring</a></div><div class="wp-workCard_item"><span>Ai & Society</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Climate change issues societal operation, likely wanting considerable adaptation to deal with doi...</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">Climate change issues societal operation, likely wanting considerable adaptation to deal with doing well altered weather patterns. Machine learning (ML) algorithms have progressed considerably, triggering breakthroughs in some other investigation sectors, along with only lately suggested as helping climate evaluation. Though a significant volume of isolated Earth System functions are analyzed with ML techniques, much more generic phone system to find out better the whole temperature unit hasn&#39;t happened. For instance, ML is able to aid remote identification, in which complex feedbacks make characterization tough from instantaneous equation analysis or perhaps possibly visualization of sizes plus Earth System design (ESM) diagnostics. Artificial intelligence (AI) may thus build on determined climate associates to provide enhanced alerts of approaching eco-friendly functions, which includes intense events. While ESM development is actually completely necessary, a parallel concentr...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3d6cf282f358d7382edcb0fb7dead730" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":79313751,"asset_id":69077600,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/79313751/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="69077600"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="69077600"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 69077600; 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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="69077596"><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/69077596/An_IoMT_Assisted_Heart_Disease_Diagnostic_System_Using_Machine_Learning_Techniques"><img alt="Research paper thumbnail of An IoMT Assisted Heart Disease Diagnostic System Using Machine Learning Techniques" 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/69077596/An_IoMT_Assisted_Heart_Disease_Diagnostic_System_Using_Machine_Learning_Techniques">An IoMT Assisted Heart Disease Diagnostic System Using Machine Learning Techniques</a></div><div class="wp-workCard_item"><span>Cognitive Internet of Medical Things for Smart Healthcare</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Machine learning can be used across several spheres around the planet. The medical industry is no...</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">Machine learning can be used across several spheres around the planet. The medical industry is not different. Health monitoring using wearable sensor enables us to go with Internet of Medical Things (IoMT). It enables the users to obtain the real time data i.e. live monitoring for manual prediction of user’s health, using machine learning techniques. Data Generation is one of the most challenging problems which have been faced by many researchers. As the volume of obtained data is very large machine learning techniques need to be used. Machine Learning can predict the presence/absence of locomotor disorders and Heart diseases in our body. Such information, if predicted well ahead of time can provides essential insights to physicians who could subsequently schedule their treatment and diagnosis for their patients. In this paper, various machine learning algorithms have been implemented to predict the heart disease. 88.59% accuracy was obtained by using logistic regression with majority voting which is better than the existing techniques.</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="69077596"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="69077596"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 69077596; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=69077596]").text(description); $(".js-view-count[data-work-id=69077596]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 69077596; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='69077596']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=69077596]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":69077596,"title":"An IoMT Assisted Heart Disease Diagnostic System Using Machine Learning Techniques","internal_url":"https://www.academia.edu/69077596/An_IoMT_Assisted_Heart_Disease_Diagnostic_System_Using_Machine_Learning_Techniques","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[]}, 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="62402897"><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/62402897/An_Enhanced_Secure_Deep_Learning_Algorithm_for_Fraud_Detection_in_Wireless_Communication"><img alt="Research paper thumbnail of An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication" class="work-thumbnail" src="https://attachments.academia-assets.com/75180296/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/62402897/An_Enhanced_Secure_Deep_Learning_Algorithm_for_Fraud_Detection_in_Wireless_Communication">An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication</a></div><div class="wp-workCard_item"><span>Wireless Communications and Mobile Computing</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In today’s era of technology, especially in the Internet commerce and banking, the transactions d...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service-based fraud detection, needs labelled...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5be72fc237fb310db63f77ff3172baed" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":75180296,"asset_id":62402897,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/75180296/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="62402897"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="62402897"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 62402897; 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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="56420628"><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/56420628/Recognition_and_classification_of_pomegranate_leaves_diseases_by_image_processing_and_machine_learning_techniques"><img alt="Research paper thumbnail of Recognition and classification of pomegranate leaves diseases by image processing and machine learning techniques" class="work-thumbnail" src="https://attachments.academia-assets.com/71816418/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/56420628/Recognition_and_classification_of_pomegranate_leaves_diseases_by_image_processing_and_machine_learning_techniques">Recognition and classification of pomegranate leaves diseases by image processing and machine learning techniques</a></div><div class="wp-workCard_item"><span>Computers, Materials & Continua</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Disease recognition in plants is one of the essential problems in agricultural image processing. ...</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">Disease recognition in plants is one of the essential problems in agricultural image processing. This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly. The framework utilizes image processing techniques such as image acquisition, image resizing, image enhancement, image segmentation, ROI extraction (region of interest), and feature extraction. An image dataset related to pomegranate leaf disease is utilized to implement the framework, divided into a training set and a test set. In the implementation process, techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features. An image classification will then be implemented by combining a supervised learning model with a support vector machine. The proposed framework is developed based on MATLAB with a graphical user interface. According to the experimental results, the proposed framework can achieve 98.39% accuracy for classifying diseased and healthy leaves. Moreover, the framework can achieve an accuracy of 98.07% for classifying diseases on pomegranate leaves.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d0ef682130ba4105cd7146e4a7bd71f5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":71816418,"asset_id":56420628,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/71816418/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="56420628"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="56420628"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 56420628; 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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="49845270"><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/49845270/Res_CovNet_an_internet_of_medical_health_things_driven_COVID_19_framework_using_transfer_learning"><img alt="Research paper thumbnail of Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning" class="work-thumbnail" src="https://attachments.academia-assets.com/68053389/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/49845270/Res_CovNet_an_internet_of_medical_health_things_driven_COVID_19_framework_using_transfer_learning">Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning</a></div><div class="wp-workCard_item"><span>Neural Computing and Applications, Springer</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. ...</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">Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. There are high chances of getting false positives and false negatives identifying the COVID-19 symptoms through existing medical practices such as PCR (polymerase chain reaction) and RT-PCR (reverse transcription-polymerase chain reaction). It might lead to a community spread of the disease. The alternative of these tests can be CT (Computer Tomography) imaging or X-rays of the lungs to identify the patient with COVID-19 symptoms more accurately. Furthermore, by using feasible and usable technology to automate the identification of COVID-19, the facilities can be improved. This notion became the basic framework, Res-CovNet, of the implemented methodology, a hybrid methodology to bring different platforms into a single platform. This basic framework is incorporated into IoMT based framework, a web-based service to identify and classify various forms of pneumonia or COVID-19 utilizing chest X-ray images. For the front end, the.NET framework along with C# language was utilized, MongoDB was utilized for the storage aspect, Res-CovNet was utilized for the processing aspect. Deep learning combined with the notion forms a comprehensive implementation of the framework, Res-CovNet, to classify the COVID-19 affected patients from pneumonia-affected patients as both lung imaging looks similar to the naked eye. The implemented framework, Res-CovNet, developed with the technique, transfer learning in which ResNet-50 used as a pre-trained model and then extended with classification layers. The work implemented using the data of X-ray images collected from the various trustable sources that include cases such as normal, bacterial pneumonia, viral pneumonia, and COVID-19, with the overall size of the data is about 5856. The accuracy of the model implemented is about 98.4% in identifying COVID-19 against the normal cases. The accuracy of the model is about 96.2% in the case of identifying COVID-19 against all other cases, as mentioned.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a359d17f706df38b407a58ef1024939f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":68053389,"asset_id":49845270,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/68053389/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="49845270"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="49845270"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 49845270; 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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="49845209"><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/49845209/Prediction_of_Heart_Disease_Using_a_Combination_of_Machine_Learning_and_Deep_Learning"><img alt="Research paper thumbnail of Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/68053314/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/49845209/Prediction_of_Heart_Disease_Using_a_Combination_of_Machine_Learning_and_Deep_Learning">Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning</a></div><div class="wp-workCard_item"><span>Computational Intelligence and Neuroscience,Hindawi </span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">e correct prediction of heart disease can prevent life threats, and incorrect prediction can prov...</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">e correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. e dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. e dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="90bd75f317d8155b0376077b41c93565" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":68053314,"asset_id":49845209,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/68053314/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="49845209"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="49845209"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 49845209; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="4449988" id="volume3issue3"><div class="js-work-strip profile--work_container" data-work-id="20422593"><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/20422593/Prevention_Mechanism_on_DDOS_Attacks_by_using_Multilevel_Filtering_of_Distributed_Firewalls"><img alt="Research paper thumbnail of Prevention Mechanism on DDOS Attacks by using Multilevel Filtering of Distributed Firewalls" class="work-thumbnail" src="https://attachments.academia-assets.com/41360425/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20422593/Prevention_Mechanism_on_DDOS_Attacks_by_using_Multilevel_Filtering_of_Distributed_Firewalls">Prevention Mechanism on DDOS Attacks by using Multilevel Filtering of Distributed Firewalls</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://vit.academia.edu/DrSagarPande">Dr. Sagar D Pande</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://ijritcc.academia.edu/ijritcc">International Journal IJRITCC</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Citation/Export MLA Sagar D. Pande, Prof. Ajay B. Gadicha, “Prevention Mechanism on DDOS Attacks...</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">Citation/Export<br />MLA<br /><br />Sagar D. Pande, Prof. Ajay B. Gadicha, “Prevention Mechanism on DDOS Attacks by using Multilevel Filtering of Distributed Firewalls”, March 15 Volume 3 Issue 3 , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 1005 - 1008, DOI: 10.17762/ijritcc2321-8169.150323<br />APA<br /><br />Sagar D. Pande, Prof. Ajay B. Gadicha, March 15 Volume 3 Issue 3, “Prevention Mechanism on DDOS Attacks by using Multilevel Filtering of Distributed Firewalls”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 1005 - 1008, DOI: 10.17762/ijritcc2321-8169.150323</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3cd943a6fc84184b64f53a72ba9ab71b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41360425,"asset_id":20422593,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41360425/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20422593"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20422593"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20422593; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="10716055" id="books"><div class="js-work-strip profile--work_container" data-work-id="44320094"><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/44320094/DDOS_Detection_Using_Machine_Learning_Technique"><img alt="Research paper thumbnail of DDOS Detection Using Machine Learning Technique" class="work-thumbnail" src="https://attachments.academia-assets.com/64708026/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44320094/DDOS_Detection_Using_Machine_Learning_Technique">DDOS Detection Using Machine Learning Technique</a></div><div class="wp-workCard_item"><span>Recent Studies on Computational Intelligence, Studies in Computational Intelligence</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Numerous attacks are performed on network infrastructures. These include attacks on network avail...</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">Numerous attacks are performed on network infrastructures. These include attacks on network availability, confidentiality and integrity. Distributed denial-of-service (DDoS) attack is a persistent attack which affects the availability of the network. Command and Control (C & C) mechanism is used to perform such kind of attack. Various researchers have proposed different methods based on machine learning technique to detect these attacks. In this paper, DDoS attack was performed using ping of death technique and detected using machine learning technique by using WEKA tool. NSL-KDD dataset was used in this experiment. Random forest algorithm was used to perform classification of the normal and attack samples. 99.76% of the samples were correctly classified.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6f4bd97b2e7f0d557141ed8c042b171d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64708026,"asset_id":44320094,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64708026/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44320094"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44320094"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44320094; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="11455661" id="conferencepresentations"><div class="js-work-strip profile--work_container" data-work-id="97048117"><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/97048117/Customized_Deep_Learning_Technique_for_Vehicle_Detection_along_with_Speed_Estimation"><img alt="Research paper thumbnail of Customized Deep Learning Technique for Vehicle Detection along with Speed Estimation" class="work-thumbnail" src="https://attachments.academia-assets.com/98777846/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/97048117/Customized_Deep_Learning_Technique_for_Vehicle_Detection_along_with_Speed_Estimation">Customized Deep Learning Technique for Vehicle Detection along with Speed Estimation</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/IdesEditor">Grenze International Journal of Engineering and Technology GIJET</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://vit.academia.edu/DrSagarPande">Dr. Sagar D Pande</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The method proposed over here in this paper is a vehicle speed estimation technique on moving veh...</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 method proposed over here in this paper is a vehicle speed estimation technique on moving vehicle under the cctv camera surveillance. For real-time vehicle detection, the YOLO (You Only Look Once) technique is employed, and the centroid approach is used to estimate vehicle speed. The video frame is converted to grayscale so that it may be processed by the computer as 0 and 1. The brightness of the scale is represented by each number. Then, by looking at these statistics, we train the YOLO Convolutional Neural Network to learn to identify the final detection. YOLO reframes object recognition as a single regression issue by taking the entire image and going directly from image pixels to bounding box coordinates and class probabilities. The next step is to compute bounding boxes (boxes that encompass the objects) using IoU (Intersect over Union) and NMS (non-maximum suppression). The IoU indicates how closely the machine's predicted bounding box fits the bounding box of the real item. However, because of the process, a problem of over-identification with a specific object arises. NMS ensures that the best cell is found among all these bounding boxes. Rather than concluding that a single car in the image has numerous causes, NMS chooses the boxes with the highest likelihood of determining the same vehicle. The vehicle centroid values are calculated after the cars have been detected. The distance traveled by vehicle is calculated using the centroid value. The speed of the vehicle is calculated after sorting out the distance that has been covered by the vehicle. YOLO is an effective and efficient strategy that epitomizes the spirit of machine learning in the suggested methodology for our vehicle recognition and speed estimation system. YOLO initially trains with 416*416 photographs, then retrains for 30 epochs at a 10-3 learning rate using 416*416 images. After training, the classifier has a top-one accuracy of 99.4% and a top-five accuracy of 99.3%.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7a3e33516297e4d462d27bd83716240f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":98777846,"asset_id":97048117,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/98777846/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="97048117"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="97048117"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 97048117; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=97048117]").text(description); $(".js-view-count[data-work-id=97048117]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 97048117; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='97048117']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="50047813"><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/50047813/Criminal_Identification_System_using_Facial_Recognition"><img alt="Research paper thumbnail of Criminal Identification System using Facial Recognition" class="work-thumbnail" src="https://attachments.academia-assets.com/68177474/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/50047813/Criminal_Identification_System_using_Facial_Recognition">Criminal Identification System using Facial Recognition</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We all know that our Face is a unique and crucial part of the human body structure that identifie...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We all know that our Face is a unique and crucial part of the human body structure that identifies a person. Therefore, we can use it to trace the identity of a criminal person. With the advancement in technology, we are placed CCTV at many public places to capture the criminal's crime. Using the previously captured faces and criminal's images that are available in the police station, the criminal face recognition system of can be implemented. In this paper, we propose an automatic criminal identification system for Police Department to enhance and upgrade the criminal distinguishing into a more effective and efficient approach. Using technology, this idea will add plus point in the current system while bringing criminals spotting to a whole new level by automating tasks. Technology working behind it will be face recognition, from the footage captured by the CCTV cameras; our system will detect the face and recognize the criminal who is coming to that public place. The captured images of the person coming to that public place get compared with the criminal data we have in our database. If any person's face from public place matches, the system will display their image on the system screen and will give the message with their name that the criminal is found and present in this public place. This system matching more than 80% of the captured images with database images.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2d8b0e0a43acfcbb5929d8341d782c71" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":68177474,"asset_id":50047813,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/68177474/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="50047813"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="50047813"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50047813; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2d8b0e0a43acfcbb5929d8341d782c71" } } $('.js-work-strip[data-work-id=50047813]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":50047813,"title":"Criminal Identification System using Facial Recognition","internal_url":"https://www.academia.edu/50047813/Criminal_Identification_System_using_Facial_Recognition","owner_id":29451838,"coauthors_can_edit":true,"owner":{"id":29451838,"first_name":"Dr. Sagar","middle_initials":"D","last_name":"Pande","page_name":"DrSagarPande","domain_name":"vit","created_at":"2015-04-11T23:37:30.332-07:00","display_name":"Dr. Sagar D Pande","url":"https://vit.academia.edu/DrSagarPande"},"attachments":[{"id":68177474,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/68177474/thumbnails/1.jpg","file_name":"SSRN_id3884827.pdf","download_url":"https://www.academia.edu/attachments/68177474/download_file","bulk_download_file_name":"Criminal_Identification_System_using_Fac.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/68177474/SSRN_id3884827-libre.pdf?1626629259=\u0026response-content-disposition=attachment%3B+filename%3DCriminal_Identification_System_using_Fac.pdf\u0026Expires=1739835662\u0026Signature=IspJGxGPS-60DBYWZzDJVnqqXF6w3qItwlgC5Jz2IKiyrYHhaySiD0YOANYShdrbBAdb94scogsJaU~FEKvcMkDJCcZI3zhSY4iuwFJ2kMrpMkCgz8e0Oukwj8AXao1q80kZZUW9DKEWykRT3fbtaiDcpGanu7b0pBLHuzXQd4~37JAyexSzNjt11N3mhID7G7XosYRfYurvXt2~TWWHc9yay~2itP61OT7YGqrQHQqug4T33H2AnPSyYqIlku8v8CecBMif9bdyDLFDS7tigfXaDt59n1KTRJsaQT35~HwoKLetqCB8tXwS43s-F1bO6VfSrOlDhpMXuPoRLnzW1w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="50047796"><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/50047796/A_Review_on_Essential_Resources_Utilized_for_Face_Recognition"><img alt="Research paper thumbnail of A Review on Essential Resources Utilized for Face Recognition" class="work-thumbnail" src="https://attachments.academia-assets.com/68177465/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/50047796/A_Review_on_Essential_Resources_Utilized_for_Face_Recognition">A Review on Essential Resources Utilized for Face Recognition</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In facial identification tasks, face alignment is the most important aspect. Utilizing localizati...</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 facial identification tasks, face alignment is the most important aspect. Utilizing localization of various face landmarks for structural face normalization, in particular, has proven to be extremely reliable, significantly enhancing recognition performance. This article presents a survey on popular repositories such as BioID face repository, XM2VTS repository, BUHMAP-DB repository, MUCT face repository, and PUT face repository and their characteristics. Along with that, the tools are essential for building applications concerning facial recognition in real-world aspects. The popular part of applications for face recognition such as localization of landmarks, estimating the facial position, and Multi-directional viewable face recognition are also discussed. Of all the existing repositories, AFLW is highly popular due to the existence of a high number of faces in real-world aspects.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d26d765728f7c969f1a99661a41f5a7b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":68177465,"asset_id":50047796,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/68177465/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="50047796"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="50047796"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50047796; 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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="50047767"><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/50047767/A_Survey_on_Bigdata_in_Healthcare"><img alt="Research paper thumbnail of A Survey on Bigdata in Healthcare" class="work-thumbnail" src="https://attachments.academia-assets.com/68177447/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/50047767/A_Survey_on_Bigdata_in_Healthcare">A Survey on Bigdata in Healthcare</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Big data refers to vast volumes of data that can be used to solve problems. It has piqued people'...</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 refers to vast volumes of data that can be used to solve problems. It has piqued people's imagination over the last two decades due to the enormous promise it holds. Big data is generated, stored, and analyzed by a variety of public and private sector sectors to enhance the services they offer. Hospital reports, patient history records, medical test outcomes, and IoT technologies are all examples of big data providers in the healthcare sector. Biomedical science often provides a vast volume of big data that is important to public health. To extract useful data from this information, it must be properly managed and analyzed. Alternatively, discovering a solution by studying large data is akin is very hard. Each of these phases in managing big data comes with its own set of problems that can only be solved by utilizing high-end computational strategies for big data analytics. As a result, healthcare facilities must be properly prepared with sufficient technology to systematically produce and interpret big data to offer relevant strategies for enhancing public health. Big data that is managed, analyzed, and interpreted effectively will shift the paradigm by opening new doors for contemporary healthcare applications. It seems that a variety of sectors, like the healthcare sector, are working hard to transform this opportunity into quality facilities and economic benefits. 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