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data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111320237/Efficient_Implementation_of_CDP_Algorithms_for_Optimal_Evaluation_of_Array_Expressions"><img alt="Research paper thumbnail of Efficient Implementation of CDP Algorithms for Optimal Evaluation of Array Expressions" class="work-thumbnail" src="https://attachments.academia-assets.com/108891363/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/111320237/Efficient_Implementation_of_CDP_Algorithms_for_Optimal_Evaluation_of_Array_Expressions">Efficient Implementation of CDP Algorithms for Optimal Evaluation of Array Expressions</a></div><div class="wp-workCard_item"><span>The Egyptian International Journal of Engineering Sciences and Technology (Print)</span><span>, Apr 1, 2015</span></div><div 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src="https://attachments.academia-assets.com/103376311/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/103344521/Automatic_Analysis_of_MRI_Images_for_Early_Prediction_of_Alzheimer_s_Disease_Stages_Based_on_Hybrid_Features_of_CNN_and_Handcrafted_Features">Automatic Analysis of MRI Images for Early Prediction of Alzheimer’s Disease Stages Based on Hybrid Features of CNN and Handcrafted Features</a></div><div class="wp-workCard_item"><span>Diagnostics</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Alzheimer’s disease (AD) is considered one of the challenges facing health care in the modern cen...</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">Alzheimer’s disease (AD) is considered one of the challenges facing health care in the modern century; until now, there has been no effective treatment to cure it, but there are drugs to slow its progression. Therefore, early detection of Alzheimer’s is vital to take needful measures before it develops into brain damage which cannot be treated. Magnetic resonance imaging (MRI) techniques have contributed to the diagnosis and prediction of its progression. MRI images require highly experienced doctors and radiologists, and the analysis of MRI images takes time to analyze each slice. Thus, deep learning techniques play a vital role in analyzing a huge amount of MRI images with high accuracy to detect Alzheimer’s and predict its progression. Because of the similarities in the characteristics of the early stages of Alzheimer’s, this study aimed to extract the features in several methods and integrate the features extracted from more than one method into the same features matrix. This st...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5d2bf014593b3836497dcb0f9cc85d7a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103376311,"asset_id":103344521,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103376311/download_file?st=MTczMjQwMTIwMyw4LjIyMi4yMDguMTQ2&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="103344521"><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="103344521"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 103344521; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=103344521]").text(description); $(".js-view-count[data-work-id=103344521]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 103344521; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='103344521']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 103344521, 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: "5d2bf014593b3836497dcb0f9cc85d7a" } } $('.js-work-strip[data-work-id=103344521]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":103344521,"title":"Automatic Analysis of MRI Images for Early Prediction of Alzheimer’s Disease Stages Based on Hybrid Features of CNN and Handcrafted Features","translated_title":"","metadata":{"abstract":"Alzheimer’s disease (AD) is considered one of the challenges facing health care in the modern century; until now, there has been no effective treatment to cure it, but there are drugs to slow its progression. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="103344517"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/103344517/Development_of_Advanced_Monitoring_System_in_Real_Time_Environment"><img alt="Research paper thumbnail of Development of Advanced Monitoring System in Real Time Environment" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/103344517/Development_of_Advanced_Monitoring_System_in_Real_Time_Environment">Development of Advanced Monitoring System in Real Time Environment</a></div><div class="wp-workCard_item"><span>Thamar University Journal of Natural &amp; Applied Sciences</span><span>, Jan 27, 2023</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="103344517"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="103344517"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 103344517; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=103344517]").text(description); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="103344506"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/103344506/A_Study_of_%CE%B1_%CE%B2_Tracker_with_Some_New_Algorithms_for_Selecting_%CE%B1_and_%CE%B2_Parameters"><img alt="Research paper thumbnail of A Study of α β Tracker with Some New Algorithms for Selecting α and β Parameters" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/103344506/A_Study_of_%CE%B1_%CE%B2_Tracker_with_Some_New_Algorithms_for_Selecting_%CE%B1_and_%CE%B2_Parameters">A Study of α β Tracker with Some New Algorithms for Selecting α and β Parameters</a></div><div class="wp-workCard_item"><span>Thamar University Journal of Natural &amp; Applied Sciences</span><span>, Jan 27, 2023</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="103344506"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="103344506"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 103344506; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=103344506]").text(description); 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$(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="103344493"><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/103344493/Hybrid_Techniques_of_X_ray_Analysis_to_Predict_Knee_Osteoarthritis_Grades_Based_on_Fusion_Features_of_CNN_and_Handcrafted"><img alt="Research paper thumbnail of Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted" class="work-thumbnail" src="https://attachments.academia-assets.com/103376299/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/103344493/Hybrid_Techniques_of_X_ray_Analysis_to_Predict_Knee_Osteoarthritis_Grades_Based_on_Fusion_Features_of_CNN_and_Handcrafted">Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted</a></div><div class="wp-workCard_item"><span>Diagnostics</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Knee osteoarthritis (KOA) is a chronic disease that impedes movement, especially in the elderly, ...</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">Knee osteoarthritis (KOA) is a chronic disease that impedes movement, especially in the elderly, affecting more than 5% of people worldwide. KOA goes through many stages, from the mild grade that can be treated to the severe grade in which the knee must be replaced. Therefore, early diagnosis of KOA is essential to avoid its development to the advanced stages. X-rays are one of the vital techniques for the early detection of knee infections, which requires highly experienced doctors and radiologists to distinguish Kellgren-Lawrence (KL) grading. Thus, artificial intelligence techniques solve the shortcomings of manual diagnosis. This study developed three methodologies for the X-ray analysis of both the Osteoporosis Initiative (OAI) and Rani Channamma University (RCU) datasets for diagnosing KOA and discrimination between KL grades. In all methodologies, the Principal Component Analysis (PCA) algorithm was applied after the CNN models to delete the unimportant and redundant features...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b8aa3bd2eb0b09a0dd292cb8a907c200" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103376299,"asset_id":103344493,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103376299/download_file?st=MTczMjQwMTIwMyw4LjIyMi4yMDguMTQ2&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="103344493"><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="103344493"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 103344493; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=103344493]").text(description); $(".js-view-count[data-work-id=103344493]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 103344493; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='103344493']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 103344493, 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: "b8aa3bd2eb0b09a0dd292cb8a907c200" } } $('.js-work-strip[data-work-id=103344493]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":103344493,"title":"Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted","translated_title":"","metadata":{"abstract":"Knee osteoarthritis (KOA) is a chronic disease that impedes movement, especially in the elderly, affecting more than 5% of people worldwide. 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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="85974728"><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/85974728/Quality_of_Service_Routing_Protocol_for_Mobile_Ad_hoc_Network"><img alt="Research paper thumbnail of Quality of Service Routing Protocol for Mobile Ad hoc Network" class="work-thumbnail" src="https://attachments.academia-assets.com/90529134/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/85974728/Quality_of_Service_Routing_Protocol_for_Mobile_Ad_hoc_Network">Quality of Service Routing Protocol for Mobile Ad hoc Network</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The wireless Ad-Hoc network is infrastruc t re less with nodes in the network area are random ly ...</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 wireless Ad-Hoc network is infrastruc t re less with nodes in the network area are random ly moving and communicating between nodes while roamin g. In this paper we propose the TDMA Based Energy Efficient Quality of Service Routing Protoco l (EEQOSRP). The network scenario is established by considering 1000 X 1000 area and deploying randomly moving nodes using Too l Command Language (TCL). The resource reservation is used to decompos e the total simulation time of network into smaller time slots depending upon number of nodes in the network using TDMA technique. The route is established between Source and Destination node using AODV with QOS and Multi hop routing technique. The data packets are scheduled at Source node by assigning p riority and the path is established between nodes u sing shortest path and implemented using C++. It is obse rved that the values of Energy Consumption, Packet Delivery Ratio, End to End Delay and Throughput are improved Compared to Existing algorit...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="20b39a9362e88a3d7ef623f1e7760ce0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90529134,"asset_id":85974728,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90529134/download_file?st=MTczMjQwMTIwMyw4LjIyMi4yMDguMTQ2&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="85974728"><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="85974728"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 85974728; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=85974728]").text(description); $(".js-view-count[data-work-id=85974728]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 85974728; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='85974728']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 85974728, 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: "20b39a9362e88a3d7ef623f1e7760ce0" } } $('.js-work-strip[data-work-id=85974728]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":85974728,"title":"Quality of Service Routing Protocol for Mobile Ad hoc Network","translated_title":"","metadata":{"abstract":"The wireless Ad-Hoc network is infrastruc t re less with nodes in the network area are random ly moving and communicating between nodes while roamin g. 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The IBACH satisfies the question of parallel calculating numerical integration in engineering and those segmentation points that are adaptive. Several numerical simulation results show that the algorithm offers an effective way to calculate numerical value of definite integrals; it has a high convergence rate, a high accuracy and robustness.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e6f976f951fe8df6967ea6600bbf9bf6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90529124,"asset_id":85974714,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90529124/download_file?st=MTczMjQwMTIwMyw4LjIyMi4yMDguMTQ2&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="85974714"><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="85974714"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 85974714; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=85974714]").text(description); $(".js-view-count[data-work-id=85974714]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 85974714; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='85974714']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 85974714, 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: "e6f976f951fe8df6967ea6600bbf9bf6" } } $('.js-work-strip[data-work-id=85974714]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":85974714,"title":"A Chaotic Bat Algorithm for Solving Definite Integral","translated_title":"","metadata":{"abstract":"In this paper, an Improved Bat Algorithm with Chaos (IBACH) is presented for solving definite integral. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="72796768"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/72796768/An_Effective_Algorithm_for_Mining_Interesting_Maximal_Association_Rules"><img alt="Research paper thumbnail of An Effective Algorithm for Mining Interesting Maximal Association Rules" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/72796768/An_Effective_Algorithm_for_Mining_Interesting_Maximal_Association_Rules">An Effective Algorithm for Mining Interesting Maximal Association Rules</a></div><div class="wp-workCard_item"><span>2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI)</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="72796768"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="72796768"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 72796768; 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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="72796766"><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/72796766/An_Improved_Flower_Pollination_Algorithm_for_Solving_Integer_Programming_Problems"><img alt="Research paper thumbnail of An Improved Flower Pollination Algorithm for Solving Integer Programming Problems" class="work-thumbnail" src="https://attachments.academia-assets.com/81580994/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/72796766/An_Improved_Flower_Pollination_Algorithm_for_Solving_Integer_Programming_Problems">An Improved Flower Pollination Algorithm for Solving Integer Programming Problems</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics 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">Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, a new method is developed based on the flower pollination algorithm combined with chaos theory (IFPCH) to solve integer programming problems. IFPCH rounds the parameter values to the closest integer after producing new solutions. Numerical simulation results show that the algorithm proved to be superior in almost all tested problems.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="74c232d190d86c40651a3b99b219a795" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81580994,"asset_id":72796766,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81580994/download_file?st=MTczMjQwMTIwMyw4LjIyMi4yMDguMTQ2&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="72796766"><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="72796766"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 72796766; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=72796766]").text(description); $(".js-view-count[data-work-id=72796766]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 72796766; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='72796766']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 72796766, 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: "74c232d190d86c40651a3b99b219a795" } } $('.js-work-strip[data-work-id=72796766]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":72796766,"title":"An Improved Flower Pollination Algorithm for Solving Integer Programming Problems","translated_title":"","metadata":{"abstract":"Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. 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For the flexible deployment of Network Functions (NFs) in an NFV environment, NF decompositions and control/user plane separation have been introduced in the literature. That is to map traditional functions into their corresponding Virtual Network Functions (VNFs). This mapping requires the NFV Resource Allocation (NFV-RA) for multi-path service graphs with a high number of virtual nodes and links, which is a complex NP-hard problem that inherited its complexity from the Virtual Network Embedding (VNE). This paper proposes a new path mapping approach to solving the NFV-RA problem for decomposed Network Service Chains (NSCs). The proposed solution has symmetrically considered optimizing an average embedding cost with an enhancement on average execution time. The proposed approach has been compared to two other existing schemes using 6 and 16 scenarios of short a...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a4e7c900ddd7ab0db338dd6946a4c1dc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81580954,"asset_id":72796765,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81580954/download_file?st=MTczMjQwMTIwNCw4LjIyMi4yMDguMTQ2&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="72796765"><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="72796765"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 72796765; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=72796765]").text(description); $(".js-view-count[data-work-id=72796765]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 72796765; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='72796765']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 72796765, 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: "a4e7c900ddd7ab0db338dd6946a4c1dc" } } $('.js-work-strip[data-work-id=72796765]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":72796765,"title":"Path Mapping Approach for Network Function Virtualization Resource Allocation with Network Function Decomposition Support","translated_title":"","metadata":{"abstract":"Recently, Network Function Virtualization (NFV) and Software Defined Networking (SDN) have attracted many mobile operators. 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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="72796764"><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/72796764/Applications_of_Kalman_and_Extended_Kalman_Filtering_to_Target_Tracking"><img alt="Research paper thumbnail of Applications of Kalman and Extended Kalman Filtering to Target Tracking" 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/72796764/Applications_of_Kalman_and_Extended_Kalman_Filtering_to_Target_Tracking">Applications of Kalman and Extended Kalman Filtering to Target Tracking</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This study deals with the famous trackers named Kalman and extended Kalman filters. This is intro...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This study deals with the famous trackers named Kalman and extended Kalman filters. This is introduced by describing the state space representation approach to model the target system. A modification to the state prediction equation of Kalman and extended Kalman filters is given in order to offer an ability of multi-step ahead prediction of the target future position. The problem of missed measurements, with different percentages of missing, is studied and a method to estimate these missed measurements is then suggested. Some simulation experiments are performed and indicated that Kalman filtering techniques are promising when they deal with target tracking problem.</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="72796764"><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="72796764"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 72796764; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=72796764]").text(description); $(".js-view-count[data-work-id=72796764]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 72796764; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='72796764']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 72796764, 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" } } $('.js-work-strip[data-work-id=72796764]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":72796764,"title":"Applications of Kalman and Extended Kalman Filtering to Target Tracking","translated_title":"","metadata":{"abstract":"This study deals with the famous trackers named Kalman and extended Kalman filters. 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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="103344521"><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/103344521/Automatic_Analysis_of_MRI_Images_for_Early_Prediction_of_Alzheimer_s_Disease_Stages_Based_on_Hybrid_Features_of_CNN_and_Handcrafted_Features"><img alt="Research paper thumbnail of Automatic Analysis of MRI Images for Early Prediction of Alzheimer’s Disease Stages Based on Hybrid Features of CNN and Handcrafted Features" class="work-thumbnail" src="https://attachments.academia-assets.com/103376311/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/103344521/Automatic_Analysis_of_MRI_Images_for_Early_Prediction_of_Alzheimer_s_Disease_Stages_Based_on_Hybrid_Features_of_CNN_and_Handcrafted_Features">Automatic Analysis of MRI Images for Early Prediction of Alzheimer’s Disease Stages Based on Hybrid Features of CNN and Handcrafted Features</a></div><div class="wp-workCard_item"><span>Diagnostics</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Alzheimer’s disease (AD) is considered one of the challenges facing health care in the modern cen...</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">Alzheimer’s disease (AD) is considered one of the challenges facing health care in the modern century; until now, there has been no effective treatment to cure it, but there are drugs to slow its progression. Therefore, early detection of Alzheimer’s is vital to take needful measures before it develops into brain damage which cannot be treated. Magnetic resonance imaging (MRI) techniques have contributed to the diagnosis and prediction of its progression. MRI images require highly experienced doctors and radiologists, and the analysis of MRI images takes time to analyze each slice. Thus, deep learning techniques play a vital role in analyzing a huge amount of MRI images with high accuracy to detect Alzheimer’s and predict its progression. Because of the similarities in the characteristics of the early stages of Alzheimer’s, this study aimed to extract the features in several methods and integrate the features extracted from more than one method into the same features matrix. 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KOA goes through many stages, from the mild grade that can be treated to the severe grade in which the knee must be replaced. Therefore, early diagnosis of KOA is essential to avoid its development to the advanced stages. X-rays are one of the vital techniques for the early detection of knee infections, which requires highly experienced doctors and radiologists to distinguish Kellgren-Lawrence (KL) grading. Thus, artificial intelligence techniques solve the shortcomings of manual diagnosis. This study developed three methodologies for the X-ray analysis of both the Osteoporosis Initiative (OAI) and Rani Channamma University (RCU) datasets for diagnosing KOA and discrimination between KL grades. 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In all methodologies, the Principal Component Analysis (PCA) algorithm was applied after the CNN models to delete the unimportant and redundant features...","publisher":"MDPI AG","publication_name":"Diagnostics"},"translated_abstract":"Knee osteoarthritis (KOA) is a chronic disease that impedes movement, especially in the elderly, affecting more than 5% of people worldwide. KOA goes through many stages, from the mild grade that can be treated to the severe grade in which the knee must be replaced. Therefore, early diagnosis of KOA is essential to avoid its development to the advanced stages. X-rays are one of the vital techniques for the early detection of knee infections, which requires highly experienced doctors and radiologists to distinguish Kellgren-Lawrence (KL) grading. Thus, artificial intelligence techniques solve the shortcomings of manual diagnosis. This study developed three methodologies for the X-ray analysis of both the Osteoporosis Initiative (OAI) and Rani Channamma University (RCU) datasets for diagnosing KOA and discrimination between KL grades. 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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="85974728"><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/85974728/Quality_of_Service_Routing_Protocol_for_Mobile_Ad_hoc_Network"><img alt="Research paper thumbnail of Quality of Service Routing Protocol for Mobile Ad hoc Network" class="work-thumbnail" src="https://attachments.academia-assets.com/90529134/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/85974728/Quality_of_Service_Routing_Protocol_for_Mobile_Ad_hoc_Network">Quality of Service Routing Protocol for Mobile Ad hoc Network</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The wireless Ad-Hoc network is infrastruc t re less with nodes in the network area are random ly ...</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 wireless Ad-Hoc network is infrastruc t re less with nodes in the network area are random ly moving and communicating between nodes while roamin g. In this paper we propose the TDMA Based Energy Efficient Quality of Service Routing Protoco l (EEQOSRP). The network scenario is established by considering 1000 X 1000 area and deploying randomly moving nodes using Too l Command Language (TCL). The resource reservation is used to decompos e the total simulation time of network into smaller time slots depending upon number of nodes in the network using TDMA technique. The route is established between Source and Destination node using AODV with QOS and Multi hop routing technique. The data packets are scheduled at Source node by assigning p riority and the path is established between nodes u sing shortest path and implemented using C++. It is obse rved that the values of Energy Consumption, Packet Delivery Ratio, End to End Delay and Throughput are improved Compared to Existing algorit...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="20b39a9362e88a3d7ef623f1e7760ce0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90529134,"asset_id":85974728,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90529134/download_file?st=MTczMjQwMTIwNCw4LjIyMi4yMDguMTQ2&st=MTczMjQwMTIwMyw4LjIyMi4yMDguMTQ2&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="85974728"><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="85974728"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 85974728; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=85974728]").text(description); $(".js-view-count[data-work-id=85974728]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 85974728; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='85974728']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 85974728, 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: "20b39a9362e88a3d7ef623f1e7760ce0" } } $('.js-work-strip[data-work-id=85974728]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":85974728,"title":"Quality of Service Routing Protocol for Mobile Ad hoc Network","translated_title":"","metadata":{"abstract":"The wireless Ad-Hoc network is infrastruc t re less with nodes in the network area are random ly moving and communicating between nodes while roamin g. 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The IBACH satisfies the question of parallel calculating numerical integration in engineering and those segmentation points that are adaptive. Several numerical simulation results show that the algorithm offers an effective way to calculate numerical value of definite integrals; it has a high convergence rate, a high accuracy and robustness.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e6f976f951fe8df6967ea6600bbf9bf6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90529124,"asset_id":85974714,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90529124/download_file?st=MTczMjQwMTIwNCw4LjIyMi4yMDguMTQ2&st=MTczMjQwMTIwMyw4LjIyMi4yMDguMTQ2&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="85974714"><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="85974714"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 85974714; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=85974714]").text(description); $(".js-view-count[data-work-id=85974714]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 85974714; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='85974714']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 85974714, 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: "e6f976f951fe8df6967ea6600bbf9bf6" } } $('.js-work-strip[data-work-id=85974714]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":85974714,"title":"A Chaotic Bat Algorithm for Solving Definite Integral","translated_title":"","metadata":{"abstract":"In this paper, an Improved Bat Algorithm with Chaos (IBACH) is presented for solving definite integral. 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In this paper, a new method is developed based on the flower pollination algorithm combined with chaos theory (IFPCH) to solve integer programming problems. IFPCH rounds the parameter values to the closest integer after producing new solutions. Numerical simulation results show that the algorithm proved to be superior in almost all tested problems.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="74c232d190d86c40651a3b99b219a795" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81580994,"asset_id":72796766,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81580994/download_file?st=MTczMjQwMTIwNCw4LjIyMi4yMDguMTQ2&st=MTczMjQwMTIwMyw4LjIyMi4yMDguMTQ2&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="72796766"><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="72796766"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 72796766; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=72796766]").text(description); $(".js-view-count[data-work-id=72796766]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 72796766; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='72796766']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 72796766, 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: "74c232d190d86c40651a3b99b219a795" } } $('.js-work-strip[data-work-id=72796766]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":72796766,"title":"An Improved Flower Pollination Algorithm for Solving Integer Programming Problems","translated_title":"","metadata":{"abstract":"Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. 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For the flexible deployment of Network Functions (NFs) in an NFV environment, NF decompositions and control/user plane separation have been introduced in the literature. That is to map traditional functions into their corresponding Virtual Network Functions (VNFs). This mapping requires the NFV Resource Allocation (NFV-RA) for multi-path service graphs with a high number of virtual nodes and links, which is a complex NP-hard problem that inherited its complexity from the Virtual Network Embedding (VNE). This paper proposes a new path mapping approach to solving the NFV-RA problem for decomposed Network Service Chains (NSCs). The proposed solution has symmetrically considered optimizing an average embedding cost with an enhancement on average execution time. The proposed approach has been compared to two other existing schemes using 6 and 16 scenarios of short a...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a4e7c900ddd7ab0db338dd6946a4c1dc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81580954,"asset_id":72796765,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81580954/download_file?st=MTczMjQwMTIwNCw4LjIyMi4yMDguMTQ2&st=MTczMjQwMTIwNCw4LjIyMi4yMDguMTQ2&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="72796765"><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="72796765"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 72796765; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=72796765]").text(description); $(".js-view-count[data-work-id=72796765]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 72796765; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='72796765']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 72796765, 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: "a4e7c900ddd7ab0db338dd6946a4c1dc" } } $('.js-work-strip[data-work-id=72796765]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":72796765,"title":"Path Mapping Approach for Network Function Virtualization Resource Allocation with Network Function Decomposition Support","translated_title":"","metadata":{"abstract":"Recently, Network Function Virtualization (NFV) and Software Defined Networking (SDN) have attracted many mobile operators. 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This is intro...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This study deals with the famous trackers named Kalman and extended Kalman filters. This is introduced by describing the state space representation approach to model the target system. A modification to the state prediction equation of Kalman and extended Kalman filters is given in order to offer an ability of multi-step ahead prediction of the target future position. The problem of missed measurements, with different percentages of missing, is studied and a method to estimate these missed measurements is then suggested. Some simulation experiments are performed and indicated that Kalman filtering techniques are promising when they deal with target tracking problem.</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="72796764"><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="72796764"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 72796764; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=72796764]").text(description); $(".js-view-count[data-work-id=72796764]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 72796764; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='72796764']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 72796764, 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" } } $('.js-work-strip[data-work-id=72796764]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":72796764,"title":"Applications of Kalman and Extended Kalman Filtering to Target Tracking","translated_title":"","metadata":{"abstract":"This study deals with the famous trackers named Kalman and extended Kalman filters. 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A modification to the state prediction equation of Kalman and extended Kalman filters is given in order to offer an ability of multi-step ahead prediction of the target future position. The problem of missed measurements, with different percentages of missing, is studied and a method to estimate these missed measurements is then suggested. 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