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hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104663982/LeL_GNN_Learnable_Edge_Sampling_and_Line_Based_Graph_Neural_Network_for_Link_Prediction"><img alt="Research paper thumbnail of LeL-GNN: Learnable Edge Sampling and Line Based Graph Neural Network for Link Prediction" class="work-thumbnail" src="https://attachments.academia-assets.com/104332122/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/104663982/LeL_GNN_Learnable_Edge_Sampling_and_Line_Based_Graph_Neural_Network_for_Link_Prediction">LeL-GNN: Learnable Edge Sampling and Line Based Graph Neural Network for Link Prediction</a></div><div class="wp-workCard_item"><span>IEEE Access</span></div><div class="wp-workCard_item wp-workCard--actions"><span 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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="104663981"><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/104663981/Computation_Offloading_Strategy_Based_on_Multi_armed_Bandit_Learning_in_Microservice_enabled_Vehicular_Edge_Computing_Networks"><img alt="Research paper thumbnail of Computation Offloading Strategy Based on Multi-armed Bandit Learning in Microservice-enabled Vehicular Edge Computing Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104663981/Computation_Offloading_Strategy_Based_on_Multi_armed_Bandit_Learning_in_Microservice_enabled_Vehicular_Edge_Computing_Networks">Computation Offloading Strategy Based on Multi-armed Bandit Learning in Microservice-enabled Vehicular Edge Computing Networks</a></div><div class="wp-workCard_item"><span>2023 International Conference on Information Networking (ICOIN)</span><span>, Jan 11, 2023</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="104663981"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104663981"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104663981; 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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="104663980"><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/104663980/Human_Action_Recognition_A_Taxonomy_Based_Survey_Updates_and_Opportunities"><img alt="Research paper thumbnail of Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities" class="work-thumbnail" src="https://attachments.academia-assets.com/104332123/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/104663980/Human_Action_Recognition_A_Taxonomy_Based_Survey_Updates_and_Opportunities">Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities</a></div><div class="wp-workCard_item"><span>Sensors</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Human action recognition systems use data collected from a wide range of sensors to accurately id...</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">Human action recognition systems use data collected from a wide range of sensors to accurately identify and interpret human actions. One of the most challenging issues for computer vision is the automatic and precise identification of human activities. A significant increase in feature learning-based representations for action recognition has emerged in recent years, due to the widespread use of deep learning-based features. This study presents an in-depth analysis of human activity recognition that investigates recent developments in computer vision. Augmented reality, human–computer interaction, cybersecurity, home monitoring, and surveillance cameras are all examples of computer vision applications that often go in conjunction with human action detection. We give a taxonomy-based, rigorous study of human activity recognition techniques, discussing the best ways to acquire human action features, derived using RGB and depth data, as well as the latest research on deep learning and ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e599a702810567a04a41d98a3abcc52f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104332123,"asset_id":104663980,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104332123/download_file?st=MTczMjgyMTg4Myw4LjIyMi4yMDguMTQ2&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="104663980"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104663980"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104663980; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104663980]").text(description); $(".js-view-count[data-work-id=104663980]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104663980; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104663980']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 104663980, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e599a702810567a04a41d98a3abcc52f" } } $('.js-work-strip[data-work-id=104663980]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104663980,"title":"Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities","translated_title":"","metadata":{"abstract":"Human action recognition systems use data collected from a wide range of sensors to accurately identify and interpret human actions. 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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="84231053"><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/84231053/Dynamic_Task_Offloading_for_Cloud_Assisted_Vehicular_Edge_Computing_Networks_A_Non_Cooperative_Game_Theoretic_Approach"><img alt="Research paper thumbnail of Dynamic Task Offloading for Cloud-Assisted Vehicular Edge Computing Networks: A Non-Cooperative Game Theoretic Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/89326500/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/84231053/Dynamic_Task_Offloading_for_Cloud_Assisted_Vehicular_Edge_Computing_Networks_A_Non_Cooperative_Game_Theoretic_Approach">Dynamic Task Offloading for Cloud-Assisted Vehicular Edge Computing Networks: A Non-Cooperative Game Theoretic Approach</a></div><div class="wp-workCard_item"><span>Sensors</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and stora...</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">Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and storage capabilities of vehicular networks (VNs) through task offloading. In VEC, the resource-constrained vehicles offload their computing tasks to the local road-side units (RSUs) for rapid computation. However, due to the high mobility of vehicles and the overloaded problem, VEC experiences a great deal of challenges when determining a location for processing the offloaded task in real time. As a result, this degrades the quality of vehicular performance. Therefore, to deal with these above-mentioned challenges, an efficient dynamic task offloading approach based on a non-cooperative game (NGTO) is proposed in this study. In the NGTO approach, each vehicle can make its own strategy on whether a task is offloaded to a multi-access edge computing (MEC) server or a cloud server to maximize its benefits. Our proposed strategy can dynamically adjust the task-offloading probability to acquire th...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f45ade83c9ef3864d0e0e643054810b1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89326500,"asset_id":84231053,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89326500/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&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="84231053"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231053"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231053; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231053]").text(description); $(".js-view-count[data-work-id=84231053]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231053; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231053']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231053, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f45ade83c9ef3864d0e0e643054810b1" } } $('.js-work-strip[data-work-id=84231053]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231053,"title":"Dynamic Task Offloading for Cloud-Assisted Vehicular Edge Computing Networks: A Non-Cooperative Game Theoretic Approach","translated_title":"","metadata":{"abstract":"Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and storage capabilities of vehicular networks (VNs) through task offloading. 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Our proposed strategy can dynamically adjust the task-offloading probability to acquire th...","publisher":"MDPI AG","publication_name":"Sensors"},"translated_abstract":"Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and storage capabilities of vehicular networks (VNs) through task offloading. In VEC, the resource-constrained vehicles offload their computing tasks to the local road-side units (RSUs) for rapid computation. However, due to the high mobility of vehicles and the overloaded problem, VEC experiences a great deal of challenges when determining a location for processing the offloaded task in real time. As a result, this degrades the quality of vehicular performance. Therefore, to deal with these above-mentioned challenges, an efficient dynamic task offloading approach based on a non-cooperative game (NGTO) is proposed in this study. 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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="84231051"><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/84231051/gRDF_An_Efficient_Compressor_with_Reduced_Structural_Regularities_That_Utilizes_gRePair"><img alt="Research paper thumbnail of gRDF: An Efficient Compressor with Reduced Structural Regularities That Utilizes gRePair" class="work-thumbnail" src="https://attachments.academia-assets.com/89326507/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/84231051/gRDF_An_Efficient_Compressor_with_Reduced_Structural_Regularities_That_Utilizes_gRePair">gRDF: An Efficient Compressor with Reduced Structural Regularities That Utilizes gRePair</a></div><div class="wp-workCard_item"><span>Sensors</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The explosive volume of semantic data published in the Resource Description Framework (RDF) data ...</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 explosive volume of semantic data published in the Resource Description Framework (RDF) data model demands efficient management and compression with better compression ratio and runtime. Although extensive work has been carried out for compressing the RDF datasets, they do not perform well in all dimensions. However, these compressors rarely exploit the graph patterns and structural regularities of real-world datasets. Moreover, there are a variety of existing approaches that reduce the size of a graph by using a grammar-based graph compression algorithm. In this study, we introduce a novel approach named gRDF (graph repair for RDF) that uses gRePair, one of the most efficient grammar-based graph compression schemes, to compress the RDF dataset. In addition to that, we have improved the performance of HDT (header-dictionary-triple), an efficient approach for compressing the RDF datasets based on structural properties, by introducing modified HDT (M-HDT). It can detect the freque...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="18560b7c84b84ea7e3318e42cbccf083" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89326507,"asset_id":84231051,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89326507/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&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="84231051"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231051"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231051; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231051]").text(description); $(".js-view-count[data-work-id=84231051]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231051; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231051']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231051, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "18560b7c84b84ea7e3318e42cbccf083" } } $('.js-work-strip[data-work-id=84231051]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231051,"title":"gRDF: An Efficient Compressor with Reduced Structural Regularities That Utilizes gRePair","translated_title":"","metadata":{"abstract":"The explosive volume of semantic data published in the Resource Description Framework (RDF) data model demands efficient management and compression with better compression ratio and runtime. 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However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and ut...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="532d8e25caeea765f7e7f00ee606c59b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89326508,"asset_id":84231049,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89326508/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&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="84231049"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231049"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231049; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231049]").text(description); $(".js-view-count[data-work-id=84231049]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231049; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231049']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231049, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "532d8e25caeea765f7e7f00ee606c59b" } } $('.js-work-strip[data-work-id=84231049]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231049,"title":"Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks","translated_title":"","metadata":{"abstract":"Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. 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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="84231048"><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/84231048/Orchestration_Based_Task_Offloading_for_Mobile_Edge_Computing_in_Small_Cell_Networks"><img alt="Research paper thumbnail of Orchestration-Based Task Offloading for Mobile Edge Computing in Small-Cell Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84231048/Orchestration_Based_Task_Offloading_for_Mobile_Edge_Computing_in_Small_Cell_Networks">Orchestration-Based Task Offloading for Mobile Edge Computing in Small-Cell Networks</a></div><div class="wp-workCard_item"><span>Proceedings of International Joint Conference on Computational Intelligence</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">To execute computation-intensive applications and stringent latency-critical tasks at resource co...</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">To execute computation-intensive applications and stringent latency-critical tasks at resource constraints smart mobile devices, mobile edge computing (MEC) in small-cell networks is one of the leading thought, where mobile devices will offload their computation-intensive tasks to the adjacent small-cell network for faster processing. Currently, some research work has been done for combining mobile edge computing and small-cell networks together. Existing researches mostly concentrate on the user to small base station (SBS) offloading and improving the radio access performance using optimization, while the computing capability of SBS-MEC server is ignored. In order to acquire superior performance, an efficient orchestration-based task offloading for mobile edge computing in small-cell networks is proposed in this paper where edge orchestrator collects all the information from the neighboring small-cell SBS-MEC server to decide for forwarding the workloads from overloaded SBS-MEC to nearby SBS-MEC with a light workload. Simulation results affirm that orchestration-based task offloading scheme offers the best results not only by reducing the task failure but also with a smaller task completion time compared to other approaches in small-cell networks.</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="84231048"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231048"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231048; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231048]").text(description); $(".js-view-count[data-work-id=84231048]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231048; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231048']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231048, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=84231048]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231048,"title":"Orchestration-Based Task Offloading for Mobile Edge Computing in Small-Cell Networks","translated_title":"","metadata":{"abstract":"To execute computation-intensive applications and stringent latency-critical tasks at resource constraints smart mobile devices, mobile edge computing (MEC) in small-cell networks is one of the leading thought, where mobile devices will offload their computation-intensive tasks to the adjacent small-cell network for faster processing. Currently, some research work has been done for combining mobile edge computing and small-cell networks together. Existing researches mostly concentrate on the user to small base station (SBS) offloading and improving the radio access performance using optimization, while the computing capability of SBS-MEC server is ignored. In order to acquire superior performance, an efficient orchestration-based task offloading for mobile edge computing in small-cell networks is proposed in this paper where edge orchestrator collects all the information from the neighboring small-cell SBS-MEC server to decide for forwarding the workloads from overloaded SBS-MEC to nearby SBS-MEC with a light workload. Simulation results affirm that orchestration-based task offloading scheme offers the best results not only by reducing the task failure but also with a smaller task completion time compared to other approaches in small-cell networks.","publisher":"Springer Singapore","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Proceedings of International Joint Conference on Computational Intelligence"},"translated_abstract":"To execute computation-intensive applications and stringent latency-critical tasks at resource constraints smart mobile devices, mobile edge computing (MEC) in small-cell networks is one of the leading thought, where mobile devices will offload their computation-intensive tasks to the adjacent small-cell network for faster processing. Currently, some research work has been done for combining mobile edge computing and small-cell networks together. Existing researches mostly concentrate on the user to small base station (SBS) offloading and improving the radio access performance using optimization, while the computing capability of SBS-MEC server is ignored. In order to acquire superior performance, an efficient orchestration-based task offloading for mobile edge computing in small-cell networks is proposed in this paper where edge orchestrator collects all the information from the neighboring small-cell SBS-MEC server to decide for forwarding the workloads from overloaded SBS-MEC to nearby SBS-MEC with a light workload. Simulation results affirm that orchestration-based task offloading scheme offers the best results not only by reducing the task failure but also with a smaller task completion time compared to other approaches in small-cell networks.","internal_url":"https://www.academia.edu/84231048/Orchestration_Based_Task_Offloading_for_Mobile_Edge_Computing_in_Small_Cell_Networks","translated_internal_url":"","created_at":"2022-08-06T08:22:46.006-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":128920438,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Orchestration_Based_Task_Offloading_for_Mobile_Edge_Computing_in_Small_Cell_Networks","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":128920438,"first_name":"Tangina","middle_initials":null,"last_name":"Sultana","page_name":"TanginaSultana","domain_name":"khu","created_at":"2019-10-01T00:54:29.909-07:00","display_name":"Tangina Sultana","url":"https://khu.academia.edu/TanginaSultana"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2236574,"name":"Mobile Edge Computing","url":"https://www.academia.edu/Documents/in/Mobile_Edge_Computing"}],"urls":[{"id":22675098,"url":"http://link.springer.com/content/pdf/10.1007/978-981-15-3607-6_50"}]}, dispatcherData: dispatcherData }); 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They...</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 modern competitive era, people are so much desperate to reach their goal in a short time. They are not even concerned much about their lives. When these people are driving on road, they have more attention about the time to reach the destination. As a result, they are risking their and others lives on r ad due to driver’s inattention or incompetence or drowsine ss. If driver have an assist system in their vehicle which alert him when he drives car out of lane, then he can sav e himself and others from accident. This paper propos es a video based driver assist system which alerts the d river with audio alarm and visual message about lane departure as well as can track the specific vehicle using license plate extraction. The proposed system uses camera sensor to get the real time input data of ro ad environment which is then passes through the 2D FIR filter and thresholding process. After that Hough transform and Hough line is used to detect lane mar ker and line. Kalman filter is ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="abe3bcde5dc88e8d641feda5eb411f1f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89326503,"asset_id":84231046,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89326503/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&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="84231046"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231046"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231046; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231046]").text(description); $(".js-view-count[data-work-id=84231046]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231046; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231046']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231046, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "abe3bcde5dc88e8d641feda5eb411f1f" } } $('.js-work-strip[data-work-id=84231046]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231046,"title":"Lane Departure Warning through Message and Alarm \u0026 Vehicle Position Tracking using Video Camera based Driver Assist System","translated_title":"","metadata":{"abstract":"In modern competitive era, people are so much desperate to reach their goal in a short time. They are not even concerned much about their lives. When these people are driving on road, they have more attention about the time to reach the destination. As a result, they are risking their and others lives on r ad due to driver’s inattention or incompetence or drowsine ss. If driver have an assist system in their vehicle which alert him when he drives car out of lane, then he can sav e himself and others from accident. This paper propos es a video based driver assist system which alerts the d river with audio alarm and visual message about lane departure as well as can track the specific vehicle using license plate extraction. The proposed system uses camera sensor to get the real time input data of ro ad environment which is then passes through the 2D FIR filter and thresholding process. After that Hough transform and Hough line is used to detect lane mar ker and line. Kalman filter is ...","publication_date":{"day":null,"month":null,"year":2016,"errors":{}}},"translated_abstract":"In modern competitive era, people are so much desperate to reach their goal in a short time. They are not even concerned much about their lives. When these people are driving on road, they have more attention about the time to reach the destination. As a result, they are risking their and others lives on r ad due to driver’s inattention or incompetence or drowsine ss. If driver have an assist system in their vehicle which alert him when he drives car out of lane, then he can sav e himself and others from accident. This paper propos es a video based driver assist system which alerts the d river with audio alarm and visual message about lane departure as well as can track the specific vehicle using license plate extraction. The proposed system uses camera sensor to get the real time input data of ro ad environment which is then passes through the 2D FIR filter and thresholding process. After that Hough transform and Hough line is used to detect lane mar ker and line. Kalman filter is ...","internal_url":"https://www.academia.edu/84231046/Lane_Departure_Warning_through_Message_and_Alarm_and_Vehicle_Position_Tracking_using_Video_Camera_based_Driver_Assist_System","translated_internal_url":"","created_at":"2022-08-06T08:22:45.739-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":128920438,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":89326503,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326503/thumbnails/1.jpg","file_name":"28_20IJAEMS-JUN-2016-79-Lane_20Departure_20Warning_20through_20Message_20and_20Alarm_20__20Vehicle_20Position_20Tracking_20using_20Video.pdf","download_url":"https://www.academia.edu/attachments/89326503/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Lane_Departure_Warning_through_Message_a.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326503/28_20IJAEMS-JUN-2016-79-Lane_20Departure_20Warning_20through_20Message_20and_20Alarm_20__20Vehicle_20Position_20Tracking_20using_20Video-libre.pdf?1659800610=\u0026response-content-disposition=attachment%3B+filename%3DLane_Departure_Warning_through_Message_a.pdf\u0026Expires=1732825484\u0026Signature=Nj~l3uHmWvBJJ5XPhJSi74EWSKTa1Hj~AZ9G-g5-IeasSFKasrfDMgI8uQmi275Uf995GfU~ck47amTWPeuCdPp-Ch4yq-3BetU29MLeNVOi1zRxU4Dq5l7Ez3SMtAjYn3dO8yCeSzWIPCHlrWuQYfpSW5NbQOLHsK5HIffKX6aQzfOHWvlJ68i1bSN5W-5Mp2pqhphrI~utFvaHCI0vgjZsY3Kb8EBEc7wZXtEP2oNFP4HY-FFNg8XWOU6TJVcN28X~z5uZ~TBQv5Voyg2bzzf-K3YRQO7R6qlbBY62UjZvFcSIIJSIvQn5~b24dV3OgLzhSc12aZV9XmFpz3ovuw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Lane_Departure_Warning_through_Message_and_Alarm_and_Vehicle_Position_Tracking_using_Video_Camera_based_Driver_Assist_System","translated_slug":"","page_count":5,"language":"en","content_type":"Work","owner":{"id":128920438,"first_name":"Tangina","middle_initials":null,"last_name":"Sultana","page_name":"TanginaSultana","domain_name":"khu","created_at":"2019-10-01T00:54:29.909-07:00","display_name":"Tangina Sultana","url":"https://khu.academia.edu/TanginaSultana"},"attachments":[{"id":89326503,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326503/thumbnails/1.jpg","file_name":"28_20IJAEMS-JUN-2016-79-Lane_20Departure_20Warning_20through_20Message_20and_20Alarm_20__20Vehicle_20Position_20Tracking_20using_20Video.pdf","download_url":"https://www.academia.edu/attachments/89326503/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Lane_Departure_Warning_through_Message_a.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326503/28_20IJAEMS-JUN-2016-79-Lane_20Departure_20Warning_20through_20Message_20and_20Alarm_20__20Vehicle_20Position_20Tracking_20using_20Video-libre.pdf?1659800610=\u0026response-content-disposition=attachment%3B+filename%3DLane_Departure_Warning_through_Message_a.pdf\u0026Expires=1732825484\u0026Signature=Nj~l3uHmWvBJJ5XPhJSi74EWSKTa1Hj~AZ9G-g5-IeasSFKasrfDMgI8uQmi275Uf995GfU~ck47amTWPeuCdPp-Ch4yq-3BetU29MLeNVOi1zRxU4Dq5l7Ez3SMtAjYn3dO8yCeSzWIPCHlrWuQYfpSW5NbQOLHsK5HIffKX6aQzfOHWvlJ68i1bSN5W-5Mp2pqhphrI~utFvaHCI0vgjZsY3Kb8EBEc7wZXtEP2oNFP4HY-FFNg8XWOU6TJVcN28X~z5uZ~TBQv5Voyg2bzzf-K3YRQO7R6qlbBY62UjZvFcSIIJSIvQn5~b24dV3OgLzhSc12aZV9XmFpz3ovuw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[{"id":22675097,"url":"https://ijaems.com/upload_images/issue_files/28%20IJAEMS-JUN-2016-79-Lane%20Departure%20Warning%20through%20Message%20and%20Alarm%20\u0026%20Vehicle%20Position%20Tracking%20using%20Video%20Camera%20based%20Driver%20Assist%20System.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84231045"><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/84231045/Online_Garments_Inventory_Management_System"><img alt="Research paper thumbnail of Online Garments Inventory Management System" class="work-thumbnail" src="https://attachments.academia-assets.com/89326504/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/84231045/Online_Garments_Inventory_Management_System">Online Garments Inventory Management System</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Management Systems are usually designed to enhance the efficiency of handling the information of ...</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">Management Systems are usually designed to enhance the efficiency of handling the information of any system, which is running through an inefficient procedure and is expensive, time consuming, insecure and it needs more manpower. The Online Garments Inventory Management System is a system which makes the working procedure of present manual system of Garments easier and increases its efficiency to a high degree. The main objective of this application is to automate the existing system which is manually maintaining. The whole system is accessed from different terminals of the network. There are three major sectors in this system- employee management, product inventory and raw materials inventory. In the employee management system all information of the employees are stored. In the product inventory system, the invoice and the delivery information is maintained. Raw materials inventory controls the purchase information, shipping information, total materials of stock in the garments. Th...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5e6552e3765578998e84e9e6778697ba" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89326504,"asset_id":84231045,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89326504/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&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="84231045"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231045"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231045; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231045]").text(description); $(".js-view-count[data-work-id=84231045]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231045; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231045']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231045, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5e6552e3765578998e84e9e6778697ba" } } $('.js-work-strip[data-work-id=84231045]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231045,"title":"Online Garments Inventory Management System","translated_title":"","metadata":{"abstract":"Management Systems are usually designed to enhance the efficiency of handling the information of any system, which is running through an inefficient procedure and is expensive, time consuming, insecure and it needs more manpower. The Online Garments Inventory Management System is a system which makes the working procedure of present manual system of Garments easier and increases its efficiency to a high degree. The main objective of this application is to automate the existing system which is manually maintaining. The whole system is accessed from different terminals of the network. There are three major sectors in this system- employee management, product inventory and raw materials inventory. In the employee management system all information of the employees are stored. In the product inventory system, the invoice and the delivery information is maintained. Raw materials inventory controls the purchase information, shipping information, total materials of stock in the garments. Th...","publication_date":{"day":null,"month":null,"year":2016,"errors":{}}},"translated_abstract":"Management Systems are usually designed to enhance the efficiency of handling the information of any system, which is running through an inefficient procedure and is expensive, time consuming, insecure and it needs more manpower. The Online Garments Inventory Management System is a system which makes the working procedure of present manual system of Garments easier and increases its efficiency to a high degree. The main objective of this application is to automate the existing system which is manually maintaining. The whole system is accessed from different terminals of the network. There are three major sectors in this system- employee management, product inventory and raw materials inventory. In the employee management system all information of the employees are stored. In the product inventory system, the invoice and the delivery information is maintained. Raw materials inventory controls the purchase information, shipping information, total materials of stock in the garments. Th...","internal_url":"https://www.academia.edu/84231045/Online_Garments_Inventory_Management_System","translated_internal_url":"","created_at":"2022-08-06T08:22:45.586-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":128920438,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":89326504,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326504/thumbnails/1.jpg","file_name":"29_20IJAEMS-JUN-2016-88-Online_20Garments_20Inventory_20Management_20System.pdf","download_url":"https://www.academia.edu/attachments/89326504/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Online_Garments_Inventory_Management_Sys.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326504/29_20IJAEMS-JUN-2016-88-Online_20Garments_20Inventory_20Management_20System-libre.pdf?1659800607=\u0026response-content-disposition=attachment%3B+filename%3DOnline_Garments_Inventory_Management_Sys.pdf\u0026Expires=1732825484\u0026Signature=NpC-3DnA4ondwnt1CM7fmF4lOgfJTN4KQkT7hFYA3mcFyZLqe9qriEyvgRU7scylRTaNo2AxH4RPmSDZeqMl34QNufp129damSyskZFN4RhaxqBeKH6dsxnTpGLLbw1wPkdF-PlS~bI~-P2jh2V5I9m9Rg6b9dVL-9U~E5QFvWC1hQzOplQJMnpcBzumdLaANodQ83s1oAZN116BnrD~jBLj37MzAxDaPrDRwxpOlrJyX1UJmn-NpGY2iA5WPD3Z3rbfZGn93NcqAVWB07zEeTp6NCJTczFluTZE1WK3qHamMQyCGWWDSQ62SSzD4ga1QU3LH-5nmMu8Z7hKtYY1ZQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Online_Garments_Inventory_Management_System","translated_slug":"","page_count":5,"language":"en","content_type":"Work","owner":{"id":128920438,"first_name":"Tangina","middle_initials":null,"last_name":"Sultana","page_name":"TanginaSultana","domain_name":"khu","created_at":"2019-10-01T00:54:29.909-07:00","display_name":"Tangina Sultana","url":"https://khu.academia.edu/TanginaSultana"},"attachments":[{"id":89326504,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326504/thumbnails/1.jpg","file_name":"29_20IJAEMS-JUN-2016-88-Online_20Garments_20Inventory_20Management_20System.pdf","download_url":"https://www.academia.edu/attachments/89326504/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Online_Garments_Inventory_Management_Sys.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326504/29_20IJAEMS-JUN-2016-88-Online_20Garments_20Inventory_20Management_20System-libre.pdf?1659800607=\u0026response-content-disposition=attachment%3B+filename%3DOnline_Garments_Inventory_Management_Sys.pdf\u0026Expires=1732825484\u0026Signature=NpC-3DnA4ondwnt1CM7fmF4lOgfJTN4KQkT7hFYA3mcFyZLqe9qriEyvgRU7scylRTaNo2AxH4RPmSDZeqMl34QNufp129damSyskZFN4RhaxqBeKH6dsxnTpGLLbw1wPkdF-PlS~bI~-P2jh2V5I9m9Rg6b9dVL-9U~E5QFvWC1hQzOplQJMnpcBzumdLaANodQ83s1oAZN116BnrD~jBLj37MzAxDaPrDRwxpOlrJyX1UJmn-NpGY2iA5WPD3Z3rbfZGn93NcqAVWB07zEeTp6NCJTczFluTZE1WK3qHamMQyCGWWDSQ62SSzD4ga1QU3LH-5nmMu8Z7hKtYY1ZQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":89326501,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326501/thumbnails/1.jpg","file_name":"29_20IJAEMS-JUN-2016-88-Online_20Garments_20Inventory_20Management_20System.pdf","download_url":"https://www.academia.edu/attachments/89326501/download_file","bulk_download_file_name":"Online_Garments_Inventory_Management_Sys.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326501/29_20IJAEMS-JUN-2016-88-Online_20Garments_20Inventory_20Management_20System-libre.pdf?1659800608=\u0026response-content-disposition=attachment%3B+filename%3DOnline_Garments_Inventory_Management_Sys.pdf\u0026Expires=1732825484\u0026Signature=Qu1G2uZTQobqa~wLqhl2kbLWJIcC~Vke~LiqH-rvJItZtcaTMcEZ44c0IkMk30WTZ-rJLvufub7j7O987Z3dwxaNR8li5jxSMsUDpYZs08yWTtkjyc1~RNaQd0BlMZyOwuaV8hNdP-O63ogYHMTkYkn99h5544DodFJV378e~~U5W552afUtNKSyb55LnTpxtR~mhuEDuqxTbfDMwGC9RRDjwCKcQzX58yjBnRcH6R97JD0AETuWoydK--F0ecHe1R06vPcGjdPsP8gGyh-YgdkU8PL71plq61NMZRT-eaS3O3y5eJJ5oT9chSTR1Rc3hBxgxOZbF2mOdYM2tvQQvw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":449,"name":"Software Engineering","url":"https://www.academia.edu/Documents/in/Software_Engineering"},{"id":117212,"name":"Inventory Management","url":"https://www.academia.edu/Documents/in/Inventory_Management"}],"urls":[{"id":22675096,"url":"https://ijaems.com/upload_images/issue_files/29%20IJAEMS-JUN-2016-88-Online%20Garments%20Inventory%20Management%20System.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84231044"><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/84231044/A_New_Approach_for_Efficient_Face_Detection_Using_BPV_Algorithm_Based_on_Mathematical_Modeling"><img alt="Research paper thumbnail of A New Approach for Efficient Face Detection Using BPV Algorithm Based on Mathematical Modeling" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84231044/A_New_Approach_for_Efficient_Face_Detection_Using_BPV_Algorithm_Based_on_Mathematical_Modeling">A New Approach for Efficient Face Detection Using BPV Algorithm Based on Mathematical Modeling</a></div><div class="wp-workCard_item"><span>Proceedings of International Joint Conference on Computational Intelligence</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper several face detection algorithms are compared on the basis of mathematical analysi...</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 several face detection algorithms are compared on the basis of mathematical analysis to find out the most efficient algorithm. At first the mathematical model of different face detection algorithms (Camshift, AdaBoost, LBP and Viola Jones algorithms) are analyzed and compared to find out the most efficient one. Mathematical results show that Viola Jones performs best result to detect the face. But in case of Viola Jones, integral image integrates the non-face region pixels with face region pixels as a result, the pixel value redundancy is occurred which degrades its efficiency. To overcome this problem, a new face detection algorithm is proposed in this paper which is named as Break Point Value (BPV) algorithm. The mathematical model of our proposed method is derived where integral images are compared with Local Binary Pattern (LBP) and the compared value is suggested as test value. If the test value is less than or equal to the BPV then the region is a face region and if it is not, the region is a non-face region. Since there is a comparison between integral image value and LBP value of the same pixel region the redundant values are reduced. Furthermore, the use of BPV helps to find out more relevant frames. Thus the proposed method is more efficient face detection process as compared to the previous processes in the field of face detection 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="84231044"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231044"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231044; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231044]").text(description); $(".js-view-count[data-work-id=84231044]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231044; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231044']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231044, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=84231044]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231044,"title":"A New Approach for Efficient Face Detection Using BPV Algorithm Based on Mathematical Modeling","translated_title":"","metadata":{"abstract":"In this paper several face detection algorithms are compared on the basis of mathematical analysis to find out the most efficient algorithm. At first the mathematical model of different face detection algorithms (Camshift, AdaBoost, LBP and Viola Jones algorithms) are analyzed and compared to find out the most efficient one. Mathematical results show that Viola Jones performs best result to detect the face. But in case of Viola Jones, integral image integrates the non-face region pixels with face region pixels as a result, the pixel value redundancy is occurred which degrades its efficiency. To overcome this problem, a new face detection algorithm is proposed in this paper which is named as Break Point Value (BPV) algorithm. The mathematical model of our proposed method is derived where integral images are compared with Local Binary Pattern (LBP) and the compared value is suggested as test value. If the test value is less than or equal to the BPV then the region is a face region and if it is not, the region is a non-face region. Since there is a comparison between integral image value and LBP value of the same pixel region the redundant values are reduced. Furthermore, the use of BPV helps to find out more relevant frames. Thus the proposed method is more efficient face detection process as compared to the previous processes in the field of face detection system.","publisher":"Springer Singapore","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Proceedings of International Joint Conference on Computational Intelligence"},"translated_abstract":"In this paper several face detection algorithms are compared on the basis of mathematical analysis to find out the most efficient algorithm. At first the mathematical model of different face detection algorithms (Camshift, AdaBoost, LBP and Viola Jones algorithms) are analyzed and compared to find out the most efficient one. Mathematical results show that Viola Jones performs best result to detect the face. But in case of Viola Jones, integral image integrates the non-face region pixels with face region pixels as a result, the pixel value redundancy is occurred which degrades its efficiency. To overcome this problem, a new face detection algorithm is proposed in this paper which is named as Break Point Value (BPV) algorithm. The mathematical model of our proposed method is derived where integral images are compared with Local Binary Pattern (LBP) and the compared value is suggested as test value. If the test value is less than or equal to the BPV then the region is a face region and if it is not, the region is a non-face region. Since there is a comparison between integral image value and LBP value of the same pixel region the redundant values are reduced. Furthermore, the use of BPV helps to find out more relevant frames. Thus the proposed method is more efficient face detection process as compared to the previous processes in the field of face detection system.","internal_url":"https://www.academia.edu/84231044/A_New_Approach_for_Efficient_Face_Detection_Using_BPV_Algorithm_Based_on_Mathematical_Modeling","translated_internal_url":"","created_at":"2022-08-06T08:22:45.402-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":128920438,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_New_Approach_for_Efficient_Face_Detection_Using_BPV_Algorithm_Based_on_Mathematical_Modeling","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":128920438,"first_name":"Tangina","middle_initials":null,"last_name":"Sultana","page_name":"TanginaSultana","domain_name":"khu","created_at":"2019-10-01T00:54:29.909-07:00","display_name":"Tangina Sultana","url":"https://khu.academia.edu/TanginaSultana"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm"}],"urls":[{"id":22675095,"url":"http://link.springer.com/content/pdf/10.1007/978-981-13-7564-4_30"}]}, 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="84231043"><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/84231043/Collaborative_Task_Offloading_for_Overloaded_Mobile_Edge_Computing_in_Small_Cell_Networks"><img alt="Research paper thumbnail of Collaborative Task Offloading for Overloaded Mobile Edge Computing in Small-Cell Networks" class="work-thumbnail" src="https://attachments.academia-assets.com/89326515/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/84231043/Collaborative_Task_Offloading_for_Overloaded_Mobile_Edge_Computing_in_Small_Cell_Networks">Collaborative Task Offloading for Overloaded Mobile Edge Computing in Small-Cell Networks</a></div><div class="wp-workCard_item"><span>2020 International Conference on Information Networking (ICOIN)</span><span>, 2020</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c4acfd2e233fd20f9c4a1f056a8bdb29" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89326515,"asset_id":84231043,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89326515/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&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="84231043"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231043"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231043; 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MECenabled small cell network is regarded as the key technology in future 5G networks where for rapid task execution, a user offloads their tasks to the nearest small BS (SBS). The research work regarding MEC-enabled small cell network is still in its infancy. Recently, some researchers are trying to integrate MEC with small cell networks (SCNs) while ignoring the unlimited computation resource in a remote cloud and the computational capability of a single SBS-MEC server, which has the limited capacity for handling huge number of user request. To effectively handle latency-sensitive tasks and resources-hungry mobile applications in small-cell networks, two collaborative task offloading schemes of our proposed model is introduced in this paper. Our proposed collaborative model can make decision dynamically where the SBS-MEC server collaborate with mobile devices or remote cloud for executing the computation tasks. The simulation results confirm that collaborative task offloading between mobile with SBS-MEC scheme will reduce the average number of task failure more efficiently than other schemes and the collaborative task offloading between SBS-MEC with cloud scheme will provide lower task execution latency than others in small-cell networks.","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"2020 International Conference on Information Networking (ICOIN)","grobid_abstract_attachment_id":89326515},"translated_abstract":null,"internal_url":"https://www.academia.edu/84231043/Collaborative_Task_Offloading_for_Overloaded_Mobile_Edge_Computing_in_Small_Cell_Networks","translated_internal_url":"","created_at":"2022-08-06T08:22:45.197-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":128920438,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":89326515,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326515/thumbnails/1.jpg","file_name":"Collaborative_20Task_20Offloading_20for_20Overloaded_20Mobile_20Edge_20Computing_20in_20Small-Cell_20Networks.pdf","download_url":"https://www.academia.edu/attachments/89326515/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Collaborative_Task_Offloading_for_Overlo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326515/Collaborative_20Task_20Offloading_20for_20Overloaded_20Mobile_20Edge_20Computing_20in_20Small-Cell_20Networks-libre.pdf?1659800616=\u0026response-content-disposition=attachment%3B+filename%3DCollaborative_Task_Offloading_for_Overlo.pdf\u0026Expires=1732825484\u0026Signature=Bz9NeAe1ysr5p8WT1qWjNRMTr24axK~iDoiW1e4RKLhY0wOK~OMgGeK9-tjZlLiM~1CUt-OKDp7chYib1QHFV2MFCK7swVVeTyAd3nZlf2A9PGPxle1owO25UA-HfOBVjA678XLiBQUPz31wj9qU9ule~tuUHUx~k1e0nCKohJc5jRsMAKwGm6OIWYkCRUnXDBw8VkQzTtPVyI1lS7pgd2e~oNi8X2RQySdO3bVZODhHQ0hQhg2vrTN0J~ybynfDbI2uK2Mk97foulefBwOR7jtSQtoVB59IY4~RlgHj~NzFilEWOXQaPoZ1vY7Oa4SYlZ5JYhXUnxorpxeW0sKxvw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Collaborative_Task_Offloading_for_Overloaded_Mobile_Edge_Computing_in_Small_Cell_Networks","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":128920438,"first_name":"Tangina","middle_initials":null,"last_name":"Sultana","page_name":"TanginaSultana","domain_name":"khu","created_at":"2019-10-01T00:54:29.909-07:00","display_name":"Tangina Sultana","url":"https://khu.academia.edu/TanginaSultana"},"attachments":[{"id":89326515,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326515/thumbnails/1.jpg","file_name":"Collaborative_20Task_20Offloading_20for_20Overloaded_20Mobile_20Edge_20Computing_20in_20Small-Cell_20Networks.pdf","download_url":"https://www.academia.edu/attachments/89326515/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Collaborative_Task_Offloading_for_Overlo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326515/Collaborative_20Task_20Offloading_20for_20Overloaded_20Mobile_20Edge_20Computing_20in_20Small-Cell_20Networks-libre.pdf?1659800616=\u0026response-content-disposition=attachment%3B+filename%3DCollaborative_Task_Offloading_for_Overlo.pdf\u0026Expires=1732825484\u0026Signature=Bz9NeAe1ysr5p8WT1qWjNRMTr24axK~iDoiW1e4RKLhY0wOK~OMgGeK9-tjZlLiM~1CUt-OKDp7chYib1QHFV2MFCK7swVVeTyAd3nZlf2A9PGPxle1owO25UA-HfOBVjA678XLiBQUPz31wj9qU9ule~tuUHUx~k1e0nCKohJc5jRsMAKwGm6OIWYkCRUnXDBw8VkQzTtPVyI1lS7pgd2e~oNi8X2RQySdO3bVZODhHQ0hQhg2vrTN0J~ybynfDbI2uK2Mk97foulefBwOR7jtSQtoVB59IY4~RlgHj~NzFilEWOXQaPoZ1vY7Oa4SYlZ5JYhXUnxorpxeW0sKxvw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2236574,"name":"Mobile Edge Computing","url":"https://www.academia.edu/Documents/in/Mobile_Edge_Computing"}],"urls":[{"id":22675094,"url":"http://xplorestaging.ieee.org/ielx7/8999388/9016415/09016452.pdf?arnumber=9016452"}]}, 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="84231042"><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/84231042/Efficient_Load_Management_in_Multi_Access_Edge_Computing_Using_Fuzzy_Logic"><img alt="Research paper thumbnail of Efficient Load Management in Multi-Access Edge Computing Using Fuzzy Logic" class="work-thumbnail" src="https://attachments.academia-assets.com/89326514/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/84231042/Efficient_Load_Management_in_Multi_Access_Edge_Computing_Using_Fuzzy_Logic">Efficient Load Management in Multi-Access Edge Computing Using Fuzzy Logic</a></div><div class="wp-workCard_item"><span>KIISE Transactions on Computing Practices</span><span>, 2020</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4655bc9bdffba6a6470bd6328bb27ea6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89326514,"asset_id":84231042,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89326514/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&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="84231042"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231042"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231042; 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We investigate local (up to the boundary) properties of generalized solutions to the problem in Hilbert distribution spaces that belong to the refined Sobolev scale. These spaces are parametrized with a real number and a function that varies slowly at infinity. The function parameter refines the number order of the space. We prove theorems on local regularity and a local a priori estimate of generalized solutions to the problem under investigation. These theorems are new for Sobolev spaces as well.","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"KIISE Transactions on Computing Practices","grobid_abstract_attachment_id":89326514},"translated_abstract":null,"internal_url":"https://www.academia.edu/84231042/Efficient_Load_Management_in_Multi_Access_Edge_Computing_Using_Fuzzy_Logic","translated_internal_url":"","created_at":"2022-08-06T08:22:45.059-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":128920438,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":89326514,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326514/thumbnails/1.jpg","file_name":"2006.08379.pdf","download_url":"https://www.academia.edu/attachments/89326514/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Efficient_Load_Management_in_Multi_Acces.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326514/2006.08379-libre.pdf?1659800608=\u0026response-content-disposition=attachment%3B+filename%3DEfficient_Load_Management_in_Multi_Acces.pdf\u0026Expires=1732825484\u0026Signature=Cc6lh8abglKJ4IzW6QhFaqFc4nLtdyjhjDipSGNYEwuiZYMQygGzbzhvGNDfOmCKdVqu152aKIbQECYWyvhFcQFK2XNaSxCEVpUdJ6p8E5mW251vXZ1ZZIs7tpzxx0nuWOf9zBMViLLzrBbkPjLOOGzx6P1RLBOxlBofUtnfe6BApbQ40YLlo0nX1LHq5OjUtGZ7iZ~w466x8jjdS3ZFwRv6lRJg7Qq~pFVqylMBEnzwB-8AUzCFNKBvN6fhIXvonPr~q5xrY07FAESheb7q0SuxbrpJgBY84ltDSZYJMIB~Td8JmGdB9JFQ3ny--eWa7cLC22Vs4vaA6Awk15oXSA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Efficient_Load_Management_in_Multi_Access_Edge_Computing_Using_Fuzzy_Logic","translated_slug":"","page_count":15,"language":"en","content_type":"Work","owner":{"id":128920438,"first_name":"Tangina","middle_initials":null,"last_name":"Sultana","page_name":"TanginaSultana","domain_name":"khu","created_at":"2019-10-01T00:54:29.909-07:00","display_name":"Tangina Sultana","url":"https://khu.academia.edu/TanginaSultana"},"attachments":[{"id":89326514,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326514/thumbnails/1.jpg","file_name":"2006.08379.pdf","download_url":"https://www.academia.edu/attachments/89326514/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Efficient_Load_Management_in_Multi_Acces.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326514/2006.08379-libre.pdf?1659800608=\u0026response-content-disposition=attachment%3B+filename%3DEfficient_Load_Management_in_Multi_Acces.pdf\u0026Expires=1732825484\u0026Signature=Cc6lh8abglKJ4IzW6QhFaqFc4nLtdyjhjDipSGNYEwuiZYMQygGzbzhvGNDfOmCKdVqu152aKIbQECYWyvhFcQFK2XNaSxCEVpUdJ6p8E5mW251vXZ1ZZIs7tpzxx0nuWOf9zBMViLLzrBbkPjLOOGzx6P1RLBOxlBofUtnfe6BApbQ40YLlo0nX1LHq5OjUtGZ7iZ~w466x8jjdS3ZFwRv6lRJg7Qq~pFVqylMBEnzwB-8AUzCFNKBvN6fhIXvonPr~q5xrY07FAESheb7q0SuxbrpJgBY84ltDSZYJMIB~Td8JmGdB9JFQ3ny--eWa7cLC22Vs4vaA6Awk15oXSA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84231041"><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/84231041/Expressive_Rule_Pattern_Based_Compression_with_Ranking_in_Horn_Rules_on_RDF_Style_KB"><img alt="Research paper thumbnail of Expressive Rule Pattern Based Compression with Ranking in Horn Rules on RDF Style KB" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84231041/Expressive_Rule_Pattern_Based_Compression_with_Ranking_in_Horn_Rules_on_RDF_Style_KB">Expressive Rule Pattern Based Compression with Ranking in Horn Rules on RDF Style KB</a></div><div class="wp-workCard_item"><span>2021 IEEE International Conference on Big Data and Smart Computing (BigComp)</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">With the increasing growth of linked open datasets published in the RDF format, demands for RDF c...</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">With the increasing growth of linked open datasets published in the RDF format, demands for RDF compression has been becoming extremely important. There are a number of structured compression schemes that consider the structural redundancies. A very few researches have done that focuses on compact representation. In this study, we compress the RDF datasets by using different semantic association rules learned from the RDF graphs. From the RDF datasets, our proposed system mines the logically related Horn rules for accomplishing higher compression. The system keeps the triples that match the antecedent part and deletes them while match with the head part of the rules. We have proposed the grammar based pattern system (GBS) to reduce the search space size and faster projection. Moreover, we also have proposed the ranking rules and top-k approach for effectively utilizing all the rules and decreasing the mining time. The experimental result affirms that our proposed system has achieved greater compression compared to the existing RB compression approach.</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="84231041"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231041"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231041; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231041]").text(description); $(".js-view-count[data-work-id=84231041]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231041; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231041']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231041, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=84231041]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231041,"title":"Expressive Rule Pattern Based Compression with Ranking in Horn Rules on RDF Style KB","translated_title":"","metadata":{"abstract":"With the increasing growth of linked open datasets published in the RDF format, demands for RDF compression has been becoming extremely important. There are a number of structured compression schemes that consider the structural redundancies. A very few researches have done that focuses on compact representation. In this study, we compress the RDF datasets by using different semantic association rules learned from the RDF graphs. From the RDF datasets, our proposed system mines the logically related Horn rules for accomplishing higher compression. The system keeps the triples that match the antecedent part and deletes them while match with the head part of the rules. We have proposed the grammar based pattern system (GBS) to reduce the search space size and faster projection. Moreover, we also have proposed the ranking rules and top-k approach for effectively utilizing all the rules and decreasing the mining time. The experimental result affirms that our proposed system has achieved greater compression compared to the existing RB compression approach.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"2021 IEEE International Conference on Big Data and Smart Computing (BigComp)"},"translated_abstract":"With the increasing growth of linked open datasets published in the RDF format, demands for RDF compression has been becoming extremely important. There are a number of structured compression schemes that consider the structural redundancies. A very few researches have done that focuses on compact representation. In this study, we compress the RDF datasets by using different semantic association rules learned from the RDF graphs. From the RDF datasets, our proposed system mines the logically related Horn rules for accomplishing higher compression. The system keeps the triples that match the antecedent part and deletes them while match with the head part of the rules. We have proposed the grammar based pattern system (GBS) to reduce the search space size and faster projection. Moreover, we also have proposed the ranking rules and top-k approach for effectively utilizing all the rules and decreasing the mining time. The experimental result affirms that our proposed system has achieved greater compression compared to the existing RB compression approach.","internal_url":"https://www.academia.edu/84231041/Expressive_Rule_Pattern_Based_Compression_with_Ranking_in_Horn_Rules_on_RDF_Style_KB","translated_internal_url":"","created_at":"2022-08-06T08:22:44.891-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":128920438,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Expressive_Rule_Pattern_Based_Compression_with_Ranking_in_Horn_Rules_on_RDF_Style_KB","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":128920438,"first_name":"Tangina","middle_initials":null,"last_name":"Sultana","page_name":"TanginaSultana","domain_name":"khu","created_at":"2019-10-01T00:54:29.909-07:00","display_name":"Tangina Sultana","url":"https://khu.academia.edu/TanginaSultana"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[{"id":22675093,"url":"http://xplorestaging.ieee.org/ielx7/9373068/9373070/09373279.pdf?arnumber=9373279"}]}, 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="84231040"><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/84231040/Fuzzy_Based_Collaborative_Task_Offloading_Scheme_in_the_Densely_Deployed_Small_Cell_Networks_with_Multi_Access_Edge_Computing"><img alt="Research paper thumbnail of Fuzzy Based Collaborative Task Offloading Scheme in the Densely Deployed Small-Cell Networks with Multi-Access Edge Computing" class="work-thumbnail" src="https://attachments.academia-assets.com/89326511/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/84231040/Fuzzy_Based_Collaborative_Task_Offloading_Scheme_in_the_Densely_Deployed_Small_Cell_Networks_with_Multi_Access_Edge_Computing">Fuzzy Based Collaborative Task Offloading Scheme in the Densely Deployed Small-Cell Networks with Multi-Access Edge Computing</a></div><div class="wp-workCard_item"><span>Applied Sciences</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-ac...</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">Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="58f06b893e38870e787b34aedf393ef2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89326511,"asset_id":84231040,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89326511/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&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="84231040"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231040"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231040; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231040]").text(description); $(".js-view-count[data-work-id=84231040]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231040; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231040']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231040, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "58f06b893e38870e787b34aedf393ef2" } } $('.js-work-strip[data-work-id=84231040]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231040,"title":"Fuzzy Based Collaborative Task Offloading Scheme in the Densely Deployed Small-Cell Networks with Multi-Access Edge Computing","translated_title":"","metadata":{"abstract":"Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. 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In FCTO, the delay sensitivity of the ...","publisher":"MDPI AG","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Applied Sciences"},"translated_abstract":"Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. 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data-work-id="104663982"><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/104663982/LeL_GNN_Learnable_Edge_Sampling_and_Line_Based_Graph_Neural_Network_for_Link_Prediction"><img alt="Research paper thumbnail of LeL-GNN: Learnable Edge Sampling and Line Based Graph Neural Network for Link Prediction" class="work-thumbnail" src="https://attachments.academia-assets.com/104332122/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/104663982/LeL_GNN_Learnable_Edge_Sampling_and_Line_Based_Graph_Neural_Network_for_Link_Prediction">LeL-GNN: Learnable Edge Sampling and Line Based Graph Neural Network for Link Prediction</a></div><div class="wp-workCard_item"><span>IEEE 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WowProfile.WorkStripView({ el: this, workJSON: {"id":104663982,"title":"LeL-GNN: Learnable Edge Sampling and Line Based Graph Neural Network for Link Prediction","translated_title":"","metadata":{"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","grobid_abstract":"Graph neural networks lose a lot of their computing power when more network layers are added. As a result, the majority of existing graph neural networks have a shallow depth of learning. Oversmoothing and information loss are two of the key issues that restrict graph neural networks from going deeper. As network depth goes up, the embeddings of all the nodes eventually converge on the same value, which separates output representations from input vectors and causes over-smoothing. Moreover, layers of graph pooling are required in a deep learning model to retrieve specified features for prediction, which results in some degree of information loss. In this research, we present a new and multi-scale approach for overcoming these constraints by using concepts from graph theory, namely learnable edge sampling and line graphs. An edge-sampling mechanism that selects a particular number of edges through a learning parameter before training reduces oversmoothing, and the issue of information loss is alleviated using a line graph technique that converts the original graph into a similar line graph. Our method of edge sampling preserves the core spectral features of the graph without affecting its fundamental structure. Our suggested technique outperforms state-of-the-art models on publicly available datasets of diverse applications while having minimal constraints and great training skills. 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One of the most challenging issues for computer vision is the automatic and precise identification of human activities. A significant increase in feature learning-based representations for action recognition has emerged in recent years, due to the widespread use of deep learning-based features. This study presents an in-depth analysis of human activity recognition that investigates recent developments in computer vision. Augmented reality, human–computer interaction, cybersecurity, home monitoring, and surveillance cameras are all examples of computer vision applications that often go in conjunction with human action detection. We give a taxonomy-based, rigorous study of human activity recognition techniques, discussing the best ways to acquire human action features, derived using RGB and depth data, as well as the latest research on deep learning and ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e599a702810567a04a41d98a3abcc52f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104332123,"asset_id":104663980,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104332123/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4Myw4LjIyMi4yMDguMTQ2&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="104663980"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104663980"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104663980; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104663980]").text(description); $(".js-view-count[data-work-id=104663980]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104663980; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104663980']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 104663980, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e599a702810567a04a41d98a3abcc52f" } } $('.js-work-strip[data-work-id=104663980]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104663980,"title":"Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities","translated_title":"","metadata":{"abstract":"Human action recognition systems use data collected from a wide range of sensors to accurately identify and interpret human actions. 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We give a taxonomy-based, rigorous study of human activity recognition techniques, discussing the best ways to acquire human action features, derived using RGB and depth data, as well as the latest research on deep learning and ...","publisher":"MDPI AG","publication_name":"Sensors"},"translated_abstract":"Human action recognition systems use data collected from a wide range of sensors to accurately identify and interpret human actions. One of the most challenging issues for computer vision is the automatic and precise identification of human activities. A significant increase in feature learning-based representations for action recognition has emerged in recent years, due to the widespread use of deep learning-based features. This study presents an in-depth analysis of human activity recognition that investigates recent developments in computer vision. 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Chief among them were the Afro-Martinican intellectuals and sisters Jane and Paulette Nardal. It was here that Jane Nardal published her now famous essay \"Internationalisme noir,\" introducing the idea of \"black internationalism\" into popular parlance. Nardal documented a new understanding of blackness and collectivity amid post-World War I globalization. Just as wartime had broken down barriers among Europeans and white Americans, so too had it fostered the \"sentiment\" among black people from the around the world that they \"belong[ed] to one and the same race.\" 1 Introducing and reifying terms such as \"Afro Latino\" and \"African American\" into French and English vernaculars, Nardal focused on black people's efforts to rhetorically and ideologically link the African diaspora while also reconciling these new identities with the \"ancient traditions\" of Africa. 2 The result: one of the first efforts to define black internationalism as an ideology, worldview, and political practice in a moment in which black people the world over were trying to negotiate the modernizing world and their place in it. As Nardal's work offered rich and ideologically fertile ground, contemporaneous male writers and thinkers quickly usurped the ideas and lexicon of black internationalism. Her contemporaries-most notably Martinican writer Aimé Césaire and Senegalese poet and politician Léopold Senghor-gained global fame for their redefinition of black colonial subjectivity through what is now known as the Negritude movement. Moreover, they created a \"conspicuously masculine genealogy of their critical consciousness\" that credited black American Harlem Renaissance writers and intellectuals such as Langston Hughes, Claude McKay, and W. E. B. Du Bois as the movement's intellectual progenitors. 3 Despite her","publication_name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","grobid_abstract_attachment_id":89326518},"translated_abstract":null,"internal_url":"https://www.academia.edu/84231054/Edge_Orchestration_Based_Computation_Peer_Offloading_in_MEC_Enabled_Networks_A_Fuzzy_Logic_Approach","translated_internal_url":"","created_at":"2022-08-06T08:22:46.922-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":128920438,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":89326518,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326518/thumbnails/1.jpg","file_name":"S1479244321000081.pdf","download_url":"https://www.academia.edu/attachments/89326518/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Edge_Orchestration_Based_Computation_Pee.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326518/S1479244321000081-libre.pdf?1659800607=\u0026response-content-disposition=attachment%3B+filename%3DEdge_Orchestration_Based_Computation_Pee.pdf\u0026Expires=1732825483\u0026Signature=bdbcgd38sgWuVQrynTeKiwizafdF8Ih5WCAeHaFMNYpInCo5ix8oQ2-nO78ZqFMtWF73GhR~l3n0dzwYRA~JwTwwRfaFb2JVjXvnrII9HHUvATdyFCtCAQa9RxCBMVx9gIUK6piUF0a~KF-cxQtxjXAGrwY0GQLS2XGNuO1rrqOf4QBlmNA5-CPmbeaDrDMpoxiVkc2F9R7Fow~9rggtx9~flNyz8HgIjUfO6Ib8CG8Mv4WzBNXdrRxa-P7PZdC5anMwaYBSW4wSQgVjj1O0f5STQxUdWMpvQK-y7XCLkVPZUG5x-CHoGdZWLJpITj43rsMRNZDfSszexEIXWz8GhA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Edge_Orchestration_Based_Computation_Peer_Offloading_in_MEC_Enabled_Networks_A_Fuzzy_Logic_Approach","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":128920438,"first_name":"Tangina","middle_initials":null,"last_name":"Sultana","page_name":"TanginaSultana","domain_name":"khu","created_at":"2019-10-01T00:54:29.909-07:00","display_name":"Tangina Sultana","url":"https://khu.academia.edu/TanginaSultana"},"attachments":[{"id":89326518,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326518/thumbnails/1.jpg","file_name":"S1479244321000081.pdf","download_url":"https://www.academia.edu/attachments/89326518/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Edge_Orchestration_Based_Computation_Pee.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326518/S1479244321000081-libre.pdf?1659800607=\u0026response-content-disposition=attachment%3B+filename%3DEdge_Orchestration_Based_Computation_Pee.pdf\u0026Expires=1732825483\u0026Signature=bdbcgd38sgWuVQrynTeKiwizafdF8Ih5WCAeHaFMNYpInCo5ix8oQ2-nO78ZqFMtWF73GhR~l3n0dzwYRA~JwTwwRfaFb2JVjXvnrII9HHUvATdyFCtCAQa9RxCBMVx9gIUK6piUF0a~KF-cxQtxjXAGrwY0GQLS2XGNuO1rrqOf4QBlmNA5-CPmbeaDrDMpoxiVkc2F9R7Fow~9rggtx9~flNyz8HgIjUfO6Ib8CG8Mv4WzBNXdrRxa-P7PZdC5anMwaYBSW4wSQgVjj1O0f5STQxUdWMpvQK-y7XCLkVPZUG5x-CHoGdZWLJpITj43rsMRNZDfSszexEIXWz8GhA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":111436,"name":"IEEE","url":"https://www.academia.edu/Documents/in/IEEE"}],"urls":[{"id":22675102,"url":"http://xplorestaging.ieee.org/ielx7/9377321/9377322/09377327.pdf?arnumber=9377327"}]}, 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="84231053"><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/84231053/Dynamic_Task_Offloading_for_Cloud_Assisted_Vehicular_Edge_Computing_Networks_A_Non_Cooperative_Game_Theoretic_Approach"><img alt="Research paper thumbnail of Dynamic Task Offloading for Cloud-Assisted Vehicular Edge Computing Networks: A Non-Cooperative Game Theoretic Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/89326500/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/84231053/Dynamic_Task_Offloading_for_Cloud_Assisted_Vehicular_Edge_Computing_Networks_A_Non_Cooperative_Game_Theoretic_Approach">Dynamic Task Offloading for Cloud-Assisted Vehicular Edge Computing Networks: A Non-Cooperative Game Theoretic Approach</a></div><div class="wp-workCard_item"><span>Sensors</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and stora...</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">Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and storage capabilities of vehicular networks (VNs) through task offloading. In VEC, the resource-constrained vehicles offload their computing tasks to the local road-side units (RSUs) for rapid computation. However, due to the high mobility of vehicles and the overloaded problem, VEC experiences a great deal of challenges when determining a location for processing the offloaded task in real time. As a result, this degrades the quality of vehicular performance. Therefore, to deal with these above-mentioned challenges, an efficient dynamic task offloading approach based on a non-cooperative game (NGTO) is proposed in this study. In the NGTO approach, each vehicle can make its own strategy on whether a task is offloaded to a multi-access edge computing (MEC) server or a cloud server to maximize its benefits. Our proposed strategy can dynamically adjust the task-offloading probability to acquire th...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f45ade83c9ef3864d0e0e643054810b1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89326500,"asset_id":84231053,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89326500/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&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="84231053"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231053"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231053; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231053]").text(description); $(".js-view-count[data-work-id=84231053]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231053; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231053']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231053, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f45ade83c9ef3864d0e0e643054810b1" } } $('.js-work-strip[data-work-id=84231053]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231053,"title":"Dynamic Task Offloading for Cloud-Assisted Vehicular Edge Computing Networks: A Non-Cooperative Game Theoretic Approach","translated_title":"","metadata":{"abstract":"Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and storage capabilities of vehicular networks (VNs) through task offloading. In VEC, the resource-constrained vehicles offload their computing tasks to the local road-side units (RSUs) for rapid computation. However, due to the high mobility of vehicles and the overloaded problem, VEC experiences a great deal of challenges when determining a location for processing the offloaded task in real time. As a result, this degrades the quality of vehicular performance. Therefore, to deal with these above-mentioned challenges, an efficient dynamic task offloading approach based on a non-cooperative game (NGTO) is proposed in this study. In the NGTO approach, each vehicle can make its own strategy on whether a task is offloaded to a multi-access edge computing (MEC) server or a cloud server to maximize its benefits. Our proposed strategy can dynamically adjust the task-offloading probability to acquire th...","publisher":"MDPI AG","publication_name":"Sensors"},"translated_abstract":"Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and storage capabilities of vehicular networks (VNs) through task offloading. In VEC, the resource-constrained vehicles offload their computing tasks to the local road-side units (RSUs) for rapid computation. However, due to the high mobility of vehicles and the overloaded problem, VEC experiences a great deal of challenges when determining a location for processing the offloaded task in real time. As a result, this degrades the quality of vehicular performance. Therefore, to deal with these above-mentioned challenges, an efficient dynamic task offloading approach based on a non-cooperative game (NGTO) is proposed in this study. In the NGTO approach, each vehicle can make its own strategy on whether a task is offloaded to a multi-access edge computing (MEC) server or a cloud server to maximize its benefits. Our proposed strategy can dynamically adjust the task-offloading probability to acquire th...","internal_url":"https://www.academia.edu/84231053/Dynamic_Task_Offloading_for_Cloud_Assisted_Vehicular_Edge_Computing_Networks_A_Non_Cooperative_Game_Theoretic_Approach","translated_internal_url":"","created_at":"2022-08-06T08:22:46.733-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":128920438,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":89326500,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326500/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/89326500/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Dynamic_Task_Offloading_for_Cloud_Assist.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326500/pdf-libre.pdf?1659800640=\u0026response-content-disposition=attachment%3B+filename%3DDynamic_Task_Offloading_for_Cloud_Assist.pdf\u0026Expires=1732825483\u0026Signature=a33Zye2C9n~l4NBQE26KfWZu-RhpIE8ouTtoDMW-3PLaEusTYPlpvZclXUJXtx4UTaKhjumK-W~lHlHVEt21ZUXXfyfr2Dm5vcGLF-FmDYg8picRDG7HyS1B8yRL~en0xVVnLoEwdK4DVFsfNzzWSaq8uWCbv3Tgiqqa2fN-Ap62njSWBy25Gvci1qmPebsSNWlkCl1i3me8JXAOf-Vysuii7UTKPw9BwPGlRsCP4vH69Q41gjkSLrFL7m287xqPfXbrva8YExoBfhsTRLvms5q9AUPfoRvsKecVEjX4JjyvRiFJH~SoJfkwOUqJ~Rrb3rCEfUwnjz9vwfTyL08ezQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Dynamic_Task_Offloading_for_Cloud_Assisted_Vehicular_Edge_Computing_Networks_A_Non_Cooperative_Game_Theoretic_Approach","translated_slug":"","page_count":18,"language":"en","content_type":"Work","owner":{"id":128920438,"first_name":"Tangina","middle_initials":null,"last_name":"Sultana","page_name":"TanginaSultana","domain_name":"khu","created_at":"2019-10-01T00:54:29.909-07:00","display_name":"Tangina Sultana","url":"https://khu.academia.edu/TanginaSultana"},"attachments":[{"id":89326500,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326500/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/89326500/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Dynamic_Task_Offloading_for_Cloud_Assist.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326500/pdf-libre.pdf?1659800640=\u0026response-content-disposition=attachment%3B+filename%3DDynamic_Task_Offloading_for_Cloud_Assist.pdf\u0026Expires=1732825483\u0026Signature=a33Zye2C9n~l4NBQE26KfWZu-RhpIE8ouTtoDMW-3PLaEusTYPlpvZclXUJXtx4UTaKhjumK-W~lHlHVEt21ZUXXfyfr2Dm5vcGLF-FmDYg8picRDG7HyS1B8yRL~en0xVVnLoEwdK4DVFsfNzzWSaq8uWCbv3Tgiqqa2fN-Ap62njSWBy25Gvci1qmPebsSNWlkCl1i3me8JXAOf-Vysuii7UTKPw9BwPGlRsCP4vH69Q41gjkSLrFL7m287xqPfXbrva8YExoBfhsTRLvms5q9AUPfoRvsKecVEjX4JjyvRiFJH~SoJfkwOUqJ~Rrb3rCEfUwnjz9vwfTyL08ezQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":524,"name":"Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Analytical_Chemistry"},{"id":55405,"name":"Sensors","url":"https://www.academia.edu/Documents/in/Sensors"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering"}],"urls":[{"id":22675101,"url":"https://www.mdpi.com/1424-8220/22/10/3678/pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84231052"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/84231052/Game_Theory_Based_Dynamic_Computation_Offloading_in_MEC_Enabled_Vehicular_Networks"><img alt="Research paper thumbnail of Game Theory Based Dynamic Computation Offloading in MEC-Enabled Vehicular Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84231052/Game_Theory_Based_Dynamic_Computation_Offloading_in_MEC_Enabled_Vehicular_Networks">Game Theory Based Dynamic Computation Offloading in MEC-Enabled Vehicular Networks</a></div><div class="wp-workCard_item"><span>KIISE Transactions on Computing Practices</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="84231052"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231052"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231052; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231052]").text(description); $(".js-view-count[data-work-id=84231052]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231052; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231052']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231052, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=84231052]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231052,"title":"Game Theory Based Dynamic Computation Offloading in MEC-Enabled Vehicular Networks","translated_title":"","metadata":{"publisher":"Korean Institute of Information Scientists and Engineers","publication_name":"KIISE Transactions on Computing Practices"},"translated_abstract":null,"internal_url":"https://www.academia.edu/84231052/Game_Theory_Based_Dynamic_Computation_Offloading_in_MEC_Enabled_Vehicular_Networks","translated_internal_url":"","created_at":"2022-08-06T08:22:46.625-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":128920438,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Game_Theory_Based_Dynamic_Computation_Offloading_in_MEC_Enabled_Vehicular_Networks","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":128920438,"first_name":"Tangina","middle_initials":null,"last_name":"Sultana","page_name":"TanginaSultana","domain_name":"khu","created_at":"2019-10-01T00:54:29.909-07:00","display_name":"Tangina Sultana","url":"https://khu.academia.edu/TanginaSultana"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":59487,"name":"Computation","url":"https://www.academia.edu/Documents/in/Computation"},{"id":3731186,"name":"Computation offloading ","url":"https://www.academia.edu/Documents/in/Computation_offloading"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84231051"><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/84231051/gRDF_An_Efficient_Compressor_with_Reduced_Structural_Regularities_That_Utilizes_gRePair"><img alt="Research paper thumbnail of gRDF: An Efficient Compressor with Reduced Structural Regularities That Utilizes gRePair" class="work-thumbnail" src="https://attachments.academia-assets.com/89326507/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/84231051/gRDF_An_Efficient_Compressor_with_Reduced_Structural_Regularities_That_Utilizes_gRePair">gRDF: An Efficient Compressor with Reduced Structural Regularities That Utilizes gRePair</a></div><div class="wp-workCard_item"><span>Sensors</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The explosive volume of semantic data published in the Resource Description Framework (RDF) data ...</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 explosive volume of semantic data published in the Resource Description Framework (RDF) data model demands efficient management and compression with better compression ratio and runtime. Although extensive work has been carried out for compressing the RDF datasets, they do not perform well in all dimensions. However, these compressors rarely exploit the graph patterns and structural regularities of real-world datasets. Moreover, there are a variety of existing approaches that reduce the size of a graph by using a grammar-based graph compression algorithm. In this study, we introduce a novel approach named gRDF (graph repair for RDF) that uses gRePair, one of the most efficient grammar-based graph compression schemes, to compress the RDF dataset. In addition to that, we have improved the performance of HDT (header-dictionary-triple), an efficient approach for compressing the RDF datasets based on structural properties, by introducing modified HDT (M-HDT). 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However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and ut...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="532d8e25caeea765f7e7f00ee606c59b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89326508,"asset_id":84231049,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89326508/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&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="84231049"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231049"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231049; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231049]").text(description); $(".js-view-count[data-work-id=84231049]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231049; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231049']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231049, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "532d8e25caeea765f7e7f00ee606c59b" } } $('.js-work-strip[data-work-id=84231049]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231049,"title":"Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks","translated_title":"","metadata":{"abstract":"Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. 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To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and ut...","internal_url":"https://www.academia.edu/84231049/Fuzzy_Decision_Based_Efficient_Task_Offloading_Management_Scheme_in_Multi_Tier_MEC_Enabled_Networks","translated_internal_url":"","created_at":"2022-08-06T08:22:46.185-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":128920438,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":89326508,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326508/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/89326508/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Fuzzy_Decision_Based_Efficient_Task_Offl.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326508/pdf-libre.pdf?1659800621=\u0026response-content-disposition=attachment%3B+filename%3DFuzzy_Decision_Based_Efficient_Task_Offl.pdf\u0026Expires=1732825484\u0026Signature=X8bY2k307UPYkKPYInoVBO9S~MLvkWjUMUBzsqSJEjSb1DzqNgt4tFX6D935BprMIJ54s~bQMHOt8WkdLEaiRARGPbcMZJReZ36QKG8lpEkasz5hP9ZXrvn0h5jsJP2D8dyFOPFl3IexXVSmS4jGclCmDUnaiAe1tQ5VNNPDsrKM~yxRjQ8d8rum2XdAGJrGttb-k0F~uMNlM1YLrZ630BUF3fQb3J52V1wBZA07cDGv2IqprmAOyeuwJdmxBPAm6-RhhxXfOL0b~d5SLNP2rdOIB02deW2j1atUyWEM8jvqMtA4DZEXFiwGSAeZ2XOQ6g9KIdj5jVoK1k3ziMcYQQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Fuzzy_Decision_Based_Efficient_Task_Offloading_Management_Scheme_in_Multi_Tier_MEC_Enabled_Networks","translated_slug":"","page_count":26,"language":"en","content_type":"Work","owner":{"id":128920438,"first_name":"Tangina","middle_initials":null,"last_name":"Sultana","page_name":"TanginaSultana","domain_name":"khu","created_at":"2019-10-01T00:54:29.909-07:00","display_name":"Tangina Sultana","url":"https://khu.academia.edu/TanginaSultana"},"attachments":[{"id":89326508,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326508/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/89326508/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Fuzzy_Decision_Based_Efficient_Task_Offl.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326508/pdf-libre.pdf?1659800621=\u0026response-content-disposition=attachment%3B+filename%3DFuzzy_Decision_Based_Efficient_Task_Offl.pdf\u0026Expires=1732825484\u0026Signature=X8bY2k307UPYkKPYInoVBO9S~MLvkWjUMUBzsqSJEjSb1DzqNgt4tFX6D935BprMIJ54s~bQMHOt8WkdLEaiRARGPbcMZJReZ36QKG8lpEkasz5hP9ZXrvn0h5jsJP2D8dyFOPFl3IexXVSmS4jGclCmDUnaiAe1tQ5VNNPDsrKM~yxRjQ8d8rum2XdAGJrGttb-k0F~uMNlM1YLrZ630BUF3fQb3J52V1wBZA07cDGv2IqprmAOyeuwJdmxBPAm6-RhhxXfOL0b~d5SLNP2rdOIB02deW2j1atUyWEM8jvqMtA4DZEXFiwGSAeZ2XOQ6g9KIdj5jVoK1k3ziMcYQQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":524,"name":"Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Analytical_Chemistry"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":55405,"name":"Sensors","url":"https://www.academia.edu/Documents/in/Sensors"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering"}],"urls":[{"id":22675099,"url":"https://www.mdpi.com/1424-8220/21/4/1484/pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84231048"><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/84231048/Orchestration_Based_Task_Offloading_for_Mobile_Edge_Computing_in_Small_Cell_Networks"><img alt="Research paper thumbnail of Orchestration-Based Task Offloading for Mobile Edge Computing in Small-Cell Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84231048/Orchestration_Based_Task_Offloading_for_Mobile_Edge_Computing_in_Small_Cell_Networks">Orchestration-Based Task Offloading for Mobile Edge Computing in Small-Cell Networks</a></div><div class="wp-workCard_item"><span>Proceedings of International Joint Conference on Computational Intelligence</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">To execute computation-intensive applications and stringent latency-critical tasks at resource co...</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">To execute computation-intensive applications and stringent latency-critical tasks at resource constraints smart mobile devices, mobile edge computing (MEC) in small-cell networks is one of the leading thought, where mobile devices will offload their computation-intensive tasks to the adjacent small-cell network for faster processing. Currently, some research work has been done for combining mobile edge computing and small-cell networks together. Existing researches mostly concentrate on the user to small base station (SBS) offloading and improving the radio access performance using optimization, while the computing capability of SBS-MEC server is ignored. In order to acquire superior performance, an efficient orchestration-based task offloading for mobile edge computing in small-cell networks is proposed in this paper where edge orchestrator collects all the information from the neighboring small-cell SBS-MEC server to decide for forwarding the workloads from overloaded SBS-MEC to nearby SBS-MEC with a light workload. Simulation results affirm that orchestration-based task offloading scheme offers the best results not only by reducing the task failure but also with a smaller task completion time compared to other approaches in small-cell networks.</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="84231048"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231048"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231048; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231048]").text(description); $(".js-view-count[data-work-id=84231048]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231048; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231048']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231048, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=84231048]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231048,"title":"Orchestration-Based Task Offloading for Mobile Edge Computing in Small-Cell Networks","translated_title":"","metadata":{"abstract":"To execute computation-intensive applications and stringent latency-critical tasks at resource constraints smart mobile devices, mobile edge computing (MEC) in small-cell networks is one of the leading thought, where mobile devices will offload their computation-intensive tasks to the adjacent small-cell network for faster processing. Currently, some research work has been done for combining mobile edge computing and small-cell networks together. Existing researches mostly concentrate on the user to small base station (SBS) offloading and improving the radio access performance using optimization, while the computing capability of SBS-MEC server is ignored. In order to acquire superior performance, an efficient orchestration-based task offloading for mobile edge computing in small-cell networks is proposed in this paper where edge orchestrator collects all the information from the neighboring small-cell SBS-MEC server to decide for forwarding the workloads from overloaded SBS-MEC to nearby SBS-MEC with a light workload. Simulation results affirm that orchestration-based task offloading scheme offers the best results not only by reducing the task failure but also with a smaller task completion time compared to other approaches in small-cell networks.","publisher":"Springer Singapore","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Proceedings of International Joint Conference on Computational Intelligence"},"translated_abstract":"To execute computation-intensive applications and stringent latency-critical tasks at resource constraints smart mobile devices, mobile edge computing (MEC) in small-cell networks is one of the leading thought, where mobile devices will offload their computation-intensive tasks to the adjacent small-cell network for faster processing. Currently, some research work has been done for combining mobile edge computing and small-cell networks together. Existing researches mostly concentrate on the user to small base station (SBS) offloading and improving the radio access performance using optimization, while the computing capability of SBS-MEC server is ignored. In order to acquire superior performance, an efficient orchestration-based task offloading for mobile edge computing in small-cell networks is proposed in this paper where edge orchestrator collects all the information from the neighboring small-cell SBS-MEC server to decide for forwarding the workloads from overloaded SBS-MEC to nearby SBS-MEC with a light workload. Simulation results affirm that orchestration-based task offloading scheme offers the best results not only by reducing the task failure but also with a smaller task completion time compared to other approaches in small-cell networks.","internal_url":"https://www.academia.edu/84231048/Orchestration_Based_Task_Offloading_for_Mobile_Edge_Computing_in_Small_Cell_Networks","translated_internal_url":"","created_at":"2022-08-06T08:22:46.006-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":128920438,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Orchestration_Based_Task_Offloading_for_Mobile_Edge_Computing_in_Small_Cell_Networks","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":128920438,"first_name":"Tangina","middle_initials":null,"last_name":"Sultana","page_name":"TanginaSultana","domain_name":"khu","created_at":"2019-10-01T00:54:29.909-07:00","display_name":"Tangina Sultana","url":"https://khu.academia.edu/TanginaSultana"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2236574,"name":"Mobile Edge Computing","url":"https://www.academia.edu/Documents/in/Mobile_Edge_Computing"}],"urls":[{"id":22675098,"url":"http://link.springer.com/content/pdf/10.1007/978-981-15-3607-6_50"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84231047"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/84231047/Peak_to_average_power_ratio_reduction_method_for_orthogonal_frequency_division_multiplexing_signals_in_wireless_communication_systems"><img alt="Research paper thumbnail of Peak-to- average power ratio reduction method for orthogonal frequency division multiplexing signals in wireless communication systems" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84231047/Peak_to_average_power_ratio_reduction_method_for_orthogonal_frequency_division_multiplexing_signals_in_wireless_communication_systems">Peak-to- average power ratio reduction method for orthogonal frequency division multiplexing signals in wireless communication systems</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="84231047"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231047"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231047; 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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="84231046"><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/84231046/Lane_Departure_Warning_through_Message_and_Alarm_and_Vehicle_Position_Tracking_using_Video_Camera_based_Driver_Assist_System"><img alt="Research paper thumbnail of Lane Departure Warning through Message and Alarm & Vehicle Position Tracking using Video Camera based Driver Assist System" class="work-thumbnail" src="https://attachments.academia-assets.com/89326503/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/84231046/Lane_Departure_Warning_through_Message_and_Alarm_and_Vehicle_Position_Tracking_using_Video_Camera_based_Driver_Assist_System">Lane Departure Warning through Message and Alarm & Vehicle Position Tracking using Video Camera based Driver Assist System</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In modern competitive era, people are so much desperate to reach their goal in a short time. They...</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 modern competitive era, people are so much desperate to reach their goal in a short time. They are not even concerned much about their lives. When these people are driving on road, they have more attention about the time to reach the destination. As a result, they are risking their and others lives on r ad due to driver’s inattention or incompetence or drowsine ss. If driver have an assist system in their vehicle which alert him when he drives car out of lane, then he can sav e himself and others from accident. This paper propos es a video based driver assist system which alerts the d river with audio alarm and visual message about lane departure as well as can track the specific vehicle using license plate extraction. The proposed system uses camera sensor to get the real time input data of ro ad environment which is then passes through the 2D FIR filter and thresholding process. After that Hough transform and Hough line is used to detect lane mar ker and line. Kalman filter is ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="abe3bcde5dc88e8d641feda5eb411f1f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89326503,"asset_id":84231046,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89326503/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&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="84231046"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231046"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231046; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231046]").text(description); $(".js-view-count[data-work-id=84231046]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231046; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231046']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231046, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "abe3bcde5dc88e8d641feda5eb411f1f" } } $('.js-work-strip[data-work-id=84231046]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231046,"title":"Lane Departure Warning through Message and Alarm \u0026 Vehicle Position Tracking using Video Camera based Driver Assist System","translated_title":"","metadata":{"abstract":"In modern competitive era, people are so much desperate to reach their goal in a short time. They are not even concerned much about their lives. When these people are driving on road, they have more attention about the time to reach the destination. As a result, they are risking their and others lives on r ad due to driver’s inattention or incompetence or drowsine ss. If driver have an assist system in their vehicle which alert him when he drives car out of lane, then he can sav e himself and others from accident. This paper propos es a video based driver assist system which alerts the d river with audio alarm and visual message about lane departure as well as can track the specific vehicle using license plate extraction. The proposed system uses camera sensor to get the real time input data of ro ad environment which is then passes through the 2D FIR filter and thresholding process. After that Hough transform and Hough line is used to detect lane mar ker and line. Kalman filter is ...","publication_date":{"day":null,"month":null,"year":2016,"errors":{}}},"translated_abstract":"In modern competitive era, people are so much desperate to reach their goal in a short time. They are not even concerned much about their lives. When these people are driving on road, they have more attention about the time to reach the destination. As a result, they are risking their and others lives on r ad due to driver’s inattention or incompetence or drowsine ss. If driver have an assist system in their vehicle which alert him when he drives car out of lane, then he can sav e himself and others from accident. This paper propos es a video based driver assist system which alerts the d river with audio alarm and visual message about lane departure as well as can track the specific vehicle using license plate extraction. The proposed system uses camera sensor to get the real time input data of ro ad environment which is then passes through the 2D FIR filter and thresholding process. After that Hough transform and Hough line is used to detect lane mar ker and line. Kalman filter is ...","internal_url":"https://www.academia.edu/84231046/Lane_Departure_Warning_through_Message_and_Alarm_and_Vehicle_Position_Tracking_using_Video_Camera_based_Driver_Assist_System","translated_internal_url":"","created_at":"2022-08-06T08:22:45.739-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":128920438,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":89326503,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326503/thumbnails/1.jpg","file_name":"28_20IJAEMS-JUN-2016-79-Lane_20Departure_20Warning_20through_20Message_20and_20Alarm_20__20Vehicle_20Position_20Tracking_20using_20Video.pdf","download_url":"https://www.academia.edu/attachments/89326503/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Lane_Departure_Warning_through_Message_a.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326503/28_20IJAEMS-JUN-2016-79-Lane_20Departure_20Warning_20through_20Message_20and_20Alarm_20__20Vehicle_20Position_20Tracking_20using_20Video-libre.pdf?1659800610=\u0026response-content-disposition=attachment%3B+filename%3DLane_Departure_Warning_through_Message_a.pdf\u0026Expires=1732825484\u0026Signature=Nj~l3uHmWvBJJ5XPhJSi74EWSKTa1Hj~AZ9G-g5-IeasSFKasrfDMgI8uQmi275Uf995GfU~ck47amTWPeuCdPp-Ch4yq-3BetU29MLeNVOi1zRxU4Dq5l7Ez3SMtAjYn3dO8yCeSzWIPCHlrWuQYfpSW5NbQOLHsK5HIffKX6aQzfOHWvlJ68i1bSN5W-5Mp2pqhphrI~utFvaHCI0vgjZsY3Kb8EBEc7wZXtEP2oNFP4HY-FFNg8XWOU6TJVcN28X~z5uZ~TBQv5Voyg2bzzf-K3YRQO7R6qlbBY62UjZvFcSIIJSIvQn5~b24dV3OgLzhSc12aZV9XmFpz3ovuw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Lane_Departure_Warning_through_Message_and_Alarm_and_Vehicle_Position_Tracking_using_Video_Camera_based_Driver_Assist_System","translated_slug":"","page_count":5,"language":"en","content_type":"Work","owner":{"id":128920438,"first_name":"Tangina","middle_initials":null,"last_name":"Sultana","page_name":"TanginaSultana","domain_name":"khu","created_at":"2019-10-01T00:54:29.909-07:00","display_name":"Tangina Sultana","url":"https://khu.academia.edu/TanginaSultana"},"attachments":[{"id":89326503,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326503/thumbnails/1.jpg","file_name":"28_20IJAEMS-JUN-2016-79-Lane_20Departure_20Warning_20through_20Message_20and_20Alarm_20__20Vehicle_20Position_20Tracking_20using_20Video.pdf","download_url":"https://www.academia.edu/attachments/89326503/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Lane_Departure_Warning_through_Message_a.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326503/28_20IJAEMS-JUN-2016-79-Lane_20Departure_20Warning_20through_20Message_20and_20Alarm_20__20Vehicle_20Position_20Tracking_20using_20Video-libre.pdf?1659800610=\u0026response-content-disposition=attachment%3B+filename%3DLane_Departure_Warning_through_Message_a.pdf\u0026Expires=1732825484\u0026Signature=Nj~l3uHmWvBJJ5XPhJSi74EWSKTa1Hj~AZ9G-g5-IeasSFKasrfDMgI8uQmi275Uf995GfU~ck47amTWPeuCdPp-Ch4yq-3BetU29MLeNVOi1zRxU4Dq5l7Ez3SMtAjYn3dO8yCeSzWIPCHlrWuQYfpSW5NbQOLHsK5HIffKX6aQzfOHWvlJ68i1bSN5W-5Mp2pqhphrI~utFvaHCI0vgjZsY3Kb8EBEc7wZXtEP2oNFP4HY-FFNg8XWOU6TJVcN28X~z5uZ~TBQv5Voyg2bzzf-K3YRQO7R6qlbBY62UjZvFcSIIJSIvQn5~b24dV3OgLzhSc12aZV9XmFpz3ovuw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[{"id":22675097,"url":"https://ijaems.com/upload_images/issue_files/28%20IJAEMS-JUN-2016-79-Lane%20Departure%20Warning%20through%20Message%20and%20Alarm%20\u0026%20Vehicle%20Position%20Tracking%20using%20Video%20Camera%20based%20Driver%20Assist%20System.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84231045"><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/84231045/Online_Garments_Inventory_Management_System"><img alt="Research paper thumbnail of Online Garments Inventory Management System" class="work-thumbnail" src="https://attachments.academia-assets.com/89326504/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/84231045/Online_Garments_Inventory_Management_System">Online Garments Inventory Management System</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Management Systems are usually designed to enhance the efficiency of handling the information of ...</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">Management Systems are usually designed to enhance the efficiency of handling the information of any system, which is running through an inefficient procedure and is expensive, time consuming, insecure and it needs more manpower. The Online Garments Inventory Management System is a system which makes the working procedure of present manual system of Garments easier and increases its efficiency to a high degree. The main objective of this application is to automate the existing system which is manually maintaining. The whole system is accessed from different terminals of the network. There are three major sectors in this system- employee management, product inventory and raw materials inventory. In the employee management system all information of the employees are stored. In the product inventory system, the invoice and the delivery information is maintained. Raw materials inventory controls the purchase information, shipping information, total materials of stock in the garments. Th...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5e6552e3765578998e84e9e6778697ba" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89326504,"asset_id":84231045,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89326504/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&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="84231045"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231045"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231045; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231045]").text(description); $(".js-view-count[data-work-id=84231045]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231045; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231045']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231045, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5e6552e3765578998e84e9e6778697ba" } } $('.js-work-strip[data-work-id=84231045]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231045,"title":"Online Garments Inventory Management System","translated_title":"","metadata":{"abstract":"Management Systems are usually designed to enhance the efficiency of handling the information of any system, which is running through an inefficient procedure and is expensive, time consuming, insecure and it needs more manpower. The Online Garments Inventory Management System is a system which makes the working procedure of present manual system of Garments easier and increases its efficiency to a high degree. The main objective of this application is to automate the existing system which is manually maintaining. The whole system is accessed from different terminals of the network. There are three major sectors in this system- employee management, product inventory and raw materials inventory. In the employee management system all information of the employees are stored. In the product inventory system, the invoice and the delivery information is maintained. Raw materials inventory controls the purchase information, shipping information, total materials of stock in the garments. Th...","publication_date":{"day":null,"month":null,"year":2016,"errors":{}}},"translated_abstract":"Management Systems are usually designed to enhance the efficiency of handling the information of any system, which is running through an inefficient procedure and is expensive, time consuming, insecure and it needs more manpower. The Online Garments Inventory Management System is a system which makes the working procedure of present manual system of Garments easier and increases its efficiency to a high degree. The main objective of this application is to automate the existing system which is manually maintaining. The whole system is accessed from different terminals of the network. There are three major sectors in this system- employee management, product inventory and raw materials inventory. In the employee management system all information of the employees are stored. In the product inventory system, the invoice and the delivery information is maintained. Raw materials inventory controls the purchase information, shipping information, total materials of stock in the garments. 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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="84231044"><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/84231044/A_New_Approach_for_Efficient_Face_Detection_Using_BPV_Algorithm_Based_on_Mathematical_Modeling"><img alt="Research paper thumbnail of A New Approach for Efficient Face Detection Using BPV Algorithm Based on Mathematical Modeling" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84231044/A_New_Approach_for_Efficient_Face_Detection_Using_BPV_Algorithm_Based_on_Mathematical_Modeling">A New Approach for Efficient Face Detection Using BPV Algorithm Based on Mathematical Modeling</a></div><div class="wp-workCard_item"><span>Proceedings of International Joint Conference on Computational Intelligence</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper several face detection algorithms are compared on the basis of mathematical analysi...</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 several face detection algorithms are compared on the basis of mathematical analysis to find out the most efficient algorithm. At first the mathematical model of different face detection algorithms (Camshift, AdaBoost, LBP and Viola Jones algorithms) are analyzed and compared to find out the most efficient one. Mathematical results show that Viola Jones performs best result to detect the face. But in case of Viola Jones, integral image integrates the non-face region pixels with face region pixels as a result, the pixel value redundancy is occurred which degrades its efficiency. To overcome this problem, a new face detection algorithm is proposed in this paper which is named as Break Point Value (BPV) algorithm. The mathematical model of our proposed method is derived where integral images are compared with Local Binary Pattern (LBP) and the compared value is suggested as test value. If the test value is less than or equal to the BPV then the region is a face region and if it is not, the region is a non-face region. Since there is a comparison between integral image value and LBP value of the same pixel region the redundant values are reduced. Furthermore, the use of BPV helps to find out more relevant frames. Thus the proposed method is more efficient face detection process as compared to the previous processes in the field of face detection 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="84231044"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231044"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231044; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231044]").text(description); $(".js-view-count[data-work-id=84231044]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231044; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231044']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231044, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=84231044]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231044,"title":"A New Approach for Efficient Face Detection Using BPV Algorithm Based on Mathematical Modeling","translated_title":"","metadata":{"abstract":"In this paper several face detection algorithms are compared on the basis of mathematical analysis to find out the most efficient algorithm. At first the mathematical model of different face detection algorithms (Camshift, AdaBoost, LBP and Viola Jones algorithms) are analyzed and compared to find out the most efficient one. Mathematical results show that Viola Jones performs best result to detect the face. But in case of Viola Jones, integral image integrates the non-face region pixels with face region pixels as a result, the pixel value redundancy is occurred which degrades its efficiency. To overcome this problem, a new face detection algorithm is proposed in this paper which is named as Break Point Value (BPV) algorithm. The mathematical model of our proposed method is derived where integral images are compared with Local Binary Pattern (LBP) and the compared value is suggested as test value. If the test value is less than or equal to the BPV then the region is a face region and if it is not, the region is a non-face region. Since there is a comparison between integral image value and LBP value of the same pixel region the redundant values are reduced. Furthermore, the use of BPV helps to find out more relevant frames. Thus the proposed method is more efficient face detection process as compared to the previous processes in the field of face detection system.","publisher":"Springer Singapore","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Proceedings of International Joint Conference on Computational Intelligence"},"translated_abstract":"In this paper several face detection algorithms are compared on the basis of mathematical analysis to find out the most efficient algorithm. At first the mathematical model of different face detection algorithms (Camshift, AdaBoost, LBP and Viola Jones algorithms) are analyzed and compared to find out the most efficient one. Mathematical results show that Viola Jones performs best result to detect the face. But in case of Viola Jones, integral image integrates the non-face region pixels with face region pixels as a result, the pixel value redundancy is occurred which degrades its efficiency. To overcome this problem, a new face detection algorithm is proposed in this paper which is named as Break Point Value (BPV) algorithm. The mathematical model of our proposed method is derived where integral images are compared with Local Binary Pattern (LBP) and the compared value is suggested as test value. If the test value is less than or equal to the BPV then the region is a face region and if it is not, the region is a non-face region. Since there is a comparison between integral image value and LBP value of the same pixel region the redundant values are reduced. Furthermore, the use of BPV helps to find out more relevant frames. Thus the proposed method is more efficient face detection process as compared to the previous processes in the field of face detection system.","internal_url":"https://www.academia.edu/84231044/A_New_Approach_for_Efficient_Face_Detection_Using_BPV_Algorithm_Based_on_Mathematical_Modeling","translated_internal_url":"","created_at":"2022-08-06T08:22:45.402-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":128920438,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_New_Approach_for_Efficient_Face_Detection_Using_BPV_Algorithm_Based_on_Mathematical_Modeling","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":128920438,"first_name":"Tangina","middle_initials":null,"last_name":"Sultana","page_name":"TanginaSultana","domain_name":"khu","created_at":"2019-10-01T00:54:29.909-07:00","display_name":"Tangina Sultana","url":"https://khu.academia.edu/TanginaSultana"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm"}],"urls":[{"id":22675095,"url":"http://link.springer.com/content/pdf/10.1007/978-981-13-7564-4_30"}]}, 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="84231043"><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/84231043/Collaborative_Task_Offloading_for_Overloaded_Mobile_Edge_Computing_in_Small_Cell_Networks"><img alt="Research paper thumbnail of Collaborative Task Offloading for Overloaded Mobile Edge Computing in Small-Cell Networks" class="work-thumbnail" src="https://attachments.academia-assets.com/89326515/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/84231043/Collaborative_Task_Offloading_for_Overloaded_Mobile_Edge_Computing_in_Small_Cell_Networks">Collaborative Task Offloading for Overloaded Mobile Edge Computing in Small-Cell Networks</a></div><div class="wp-workCard_item"><span>2020 International Conference on Information Networking (ICOIN)</span><span>, 2020</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c4acfd2e233fd20f9c4a1f056a8bdb29" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89326515,"asset_id":84231043,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89326515/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&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="84231043"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231043"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231043; 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MECenabled small cell network is regarded as the key technology in future 5G networks where for rapid task execution, a user offloads their tasks to the nearest small BS (SBS). The research work regarding MEC-enabled small cell network is still in its infancy. Recently, some researchers are trying to integrate MEC with small cell networks (SCNs) while ignoring the unlimited computation resource in a remote cloud and the computational capability of a single SBS-MEC server, which has the limited capacity for handling huge number of user request. To effectively handle latency-sensitive tasks and resources-hungry mobile applications in small-cell networks, two collaborative task offloading schemes of our proposed model is introduced in this paper. Our proposed collaborative model can make decision dynamically where the SBS-MEC server collaborate with mobile devices or remote cloud for executing the computation tasks. The simulation results confirm that collaborative task offloading between mobile with SBS-MEC scheme will reduce the average number of task failure more efficiently than other schemes and the collaborative task offloading between SBS-MEC with cloud scheme will provide lower task execution latency than others in small-cell networks.","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"2020 International Conference on Information Networking (ICOIN)","grobid_abstract_attachment_id":89326515},"translated_abstract":null,"internal_url":"https://www.academia.edu/84231043/Collaborative_Task_Offloading_for_Overloaded_Mobile_Edge_Computing_in_Small_Cell_Networks","translated_internal_url":"","created_at":"2022-08-06T08:22:45.197-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":128920438,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":89326515,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326515/thumbnails/1.jpg","file_name":"Collaborative_20Task_20Offloading_20for_20Overloaded_20Mobile_20Edge_20Computing_20in_20Small-Cell_20Networks.pdf","download_url":"https://www.academia.edu/attachments/89326515/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Collaborative_Task_Offloading_for_Overlo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326515/Collaborative_20Task_20Offloading_20for_20Overloaded_20Mobile_20Edge_20Computing_20in_20Small-Cell_20Networks-libre.pdf?1659800616=\u0026response-content-disposition=attachment%3B+filename%3DCollaborative_Task_Offloading_for_Overlo.pdf\u0026Expires=1732825484\u0026Signature=Bz9NeAe1ysr5p8WT1qWjNRMTr24axK~iDoiW1e4RKLhY0wOK~OMgGeK9-tjZlLiM~1CUt-OKDp7chYib1QHFV2MFCK7swVVeTyAd3nZlf2A9PGPxle1owO25UA-HfOBVjA678XLiBQUPz31wj9qU9ule~tuUHUx~k1e0nCKohJc5jRsMAKwGm6OIWYkCRUnXDBw8VkQzTtPVyI1lS7pgd2e~oNi8X2RQySdO3bVZODhHQ0hQhg2vrTN0J~ybynfDbI2uK2Mk97foulefBwOR7jtSQtoVB59IY4~RlgHj~NzFilEWOXQaPoZ1vY7Oa4SYlZ5JYhXUnxorpxeW0sKxvw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Collaborative_Task_Offloading_for_Overloaded_Mobile_Edge_Computing_in_Small_Cell_Networks","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":128920438,"first_name":"Tangina","middle_initials":null,"last_name":"Sultana","page_name":"TanginaSultana","domain_name":"khu","created_at":"2019-10-01T00:54:29.909-07:00","display_name":"Tangina Sultana","url":"https://khu.academia.edu/TanginaSultana"},"attachments":[{"id":89326515,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89326515/thumbnails/1.jpg","file_name":"Collaborative_20Task_20Offloading_20for_20Overloaded_20Mobile_20Edge_20Computing_20in_20Small-Cell_20Networks.pdf","download_url":"https://www.academia.edu/attachments/89326515/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Collaborative_Task_Offloading_for_Overlo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89326515/Collaborative_20Task_20Offloading_20for_20Overloaded_20Mobile_20Edge_20Computing_20in_20Small-Cell_20Networks-libre.pdf?1659800616=\u0026response-content-disposition=attachment%3B+filename%3DCollaborative_Task_Offloading_for_Overlo.pdf\u0026Expires=1732825484\u0026Signature=Bz9NeAe1ysr5p8WT1qWjNRMTr24axK~iDoiW1e4RKLhY0wOK~OMgGeK9-tjZlLiM~1CUt-OKDp7chYib1QHFV2MFCK7swVVeTyAd3nZlf2A9PGPxle1owO25UA-HfOBVjA678XLiBQUPz31wj9qU9ule~tuUHUx~k1e0nCKohJc5jRsMAKwGm6OIWYkCRUnXDBw8VkQzTtPVyI1lS7pgd2e~oNi8X2RQySdO3bVZODhHQ0hQhg2vrTN0J~ybynfDbI2uK2Mk97foulefBwOR7jtSQtoVB59IY4~RlgHj~NzFilEWOXQaPoZ1vY7Oa4SYlZ5JYhXUnxorpxeW0sKxvw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2236574,"name":"Mobile Edge Computing","url":"https://www.academia.edu/Documents/in/Mobile_Edge_Computing"}],"urls":[{"id":22675094,"url":"http://xplorestaging.ieee.org/ielx7/8999388/9016415/09016452.pdf?arnumber=9016452"}]}, dispatcherData: dispatcherData }); 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We investigate local (up to the boundary) properties of generalized solutions to the problem in Hilbert distribution spaces that belong to the refined Sobolev scale. These spaces are parametrized with a real number and a function that varies slowly at infinity. The function parameter refines the number order of the space. We prove theorems on local regularity and a local a priori estimate of generalized solutions to the problem under investigation. 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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="84231041"><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/84231041/Expressive_Rule_Pattern_Based_Compression_with_Ranking_in_Horn_Rules_on_RDF_Style_KB"><img alt="Research paper thumbnail of Expressive Rule Pattern Based Compression with Ranking in Horn Rules on RDF Style KB" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84231041/Expressive_Rule_Pattern_Based_Compression_with_Ranking_in_Horn_Rules_on_RDF_Style_KB">Expressive Rule Pattern Based Compression with Ranking in Horn Rules on RDF Style KB</a></div><div class="wp-workCard_item"><span>2021 IEEE International Conference on Big Data and Smart Computing (BigComp)</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">With the increasing growth of linked open datasets published in the RDF format, demands for RDF c...</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">With the increasing growth of linked open datasets published in the RDF format, demands for RDF compression has been becoming extremely important. There are a number of structured compression schemes that consider the structural redundancies. A very few researches have done that focuses on compact representation. In this study, we compress the RDF datasets by using different semantic association rules learned from the RDF graphs. From the RDF datasets, our proposed system mines the logically related Horn rules for accomplishing higher compression. The system keeps the triples that match the antecedent part and deletes them while match with the head part of the rules. We have proposed the grammar based pattern system (GBS) to reduce the search space size and faster projection. Moreover, we also have proposed the ranking rules and top-k approach for effectively utilizing all the rules and decreasing the mining time. The experimental result affirms that our proposed system has achieved greater compression compared to the existing RB compression approach.</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="84231041"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231041"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231041; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231041]").text(description); $(".js-view-count[data-work-id=84231041]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231041; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231041']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231041, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=84231041]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231041,"title":"Expressive Rule Pattern Based Compression with Ranking in Horn Rules on RDF Style KB","translated_title":"","metadata":{"abstract":"With the increasing growth of linked open datasets published in the RDF format, demands for RDF compression has been becoming extremely important. There are a number of structured compression schemes that consider the structural redundancies. A very few researches have done that focuses on compact representation. In this study, we compress the RDF datasets by using different semantic association rules learned from the RDF graphs. From the RDF datasets, our proposed system mines the logically related Horn rules for accomplishing higher compression. The system keeps the triples that match the antecedent part and deletes them while match with the head part of the rules. We have proposed the grammar based pattern system (GBS) to reduce the search space size and faster projection. Moreover, we also have proposed the ranking rules and top-k approach for effectively utilizing all the rules and decreasing the mining time. The experimental result affirms that our proposed system has achieved greater compression compared to the existing RB compression approach.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"2021 IEEE International Conference on Big Data and Smart Computing (BigComp)"},"translated_abstract":"With the increasing growth of linked open datasets published in the RDF format, demands for RDF compression has been becoming extremely important. There are a number of structured compression schemes that consider the structural redundancies. A very few researches have done that focuses on compact representation. In this study, we compress the RDF datasets by using different semantic association rules learned from the RDF graphs. From the RDF datasets, our proposed system mines the logically related Horn rules for accomplishing higher compression. The system keeps the triples that match the antecedent part and deletes them while match with the head part of the rules. We have proposed the grammar based pattern system (GBS) to reduce the search space size and faster projection. Moreover, we also have proposed the ranking rules and top-k approach for effectively utilizing all the rules and decreasing the mining time. 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However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="58f06b893e38870e787b34aedf393ef2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89326511,"asset_id":84231040,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89326511/download_file?st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjgyMTg4NCw4LjIyMi4yMDguMTQ2&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="84231040"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84231040"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84231040; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84231040]").text(description); $(".js-view-count[data-work-id=84231040]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84231040; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84231040']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84231040, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "58f06b893e38870e787b34aedf393ef2" } } $('.js-work-strip[data-work-id=84231040]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84231040,"title":"Fuzzy Based Collaborative Task Offloading Scheme in the Densely Deployed Small-Cell Networks with Multi-Access Edge Computing","translated_title":"","metadata":{"abstract":"Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. 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