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class="label">Following</p><p class="data">3</p></div></a><span><div class="stat-container"><p class="label"><span class="js-profile-total-view-text">Public Views</span></p><p class="data"><span class="js-profile-view-count"></span></p></div></span></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="171427082" href="https://www.academia.edu/Documents/in/International_Mangement"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://independent.academia.edu/MiaVillarVillarica","location":"/MiaVillarVillarica","scheme":"https","host":"independent.academia.edu","port":null,"pathname":"/MiaVillarVillarica","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["International Mangement"]}" data-trace="false" data-dom-id="Pill-react-component-82946a20-5261-45d0-990f-1170d27f1555"></div> <div id="Pill-react-component-82946a20-5261-45d0-990f-1170d27f1555"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="171427082" href="https://www.academia.edu/Documents/in/Publisher"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Publisher"]}" data-trace="false" data-dom-id="Pill-react-component-c472e3d0-26c4-481d-b667-81b49aca0e52"></div> <div id="Pill-react-component-c472e3d0-26c4-481d-b667-81b49aca0e52"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="171427082" href="https://www.academia.edu/Documents/in/Electronics_and_Telecommunication_Engineering"><div class="js-react-on-rails-component" 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data-work-id="90145146"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/90145146/Correlation_Analysis_between_Sensors_for_Sensing_Coffee_Variations"><img alt="Research paper thumbnail of Correlation Analysis between Sensors for Sensing Coffee Variations" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/90145146/Correlation_Analysis_between_Sensors_for_Sensing_Coffee_Variations">Correlation Analysis between Sensors for Sensing Coffee Variations</a></div><div class="wp-workCard_item"><span>2022 IEEE 18th International Colloquium on Signal Processing &amp; Applications (CSPA)</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="90145146"><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="90145146"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 90145146; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=90145146]").text(description); 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})(["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" } } $('.js-work-strip[data-work-id=90145146]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":90145146,"title":"Correlation Analysis between Sensors for Sensing Coffee Variations","translated_title":"","metadata":{"publisher":"IEEE","publication_name":"2022 IEEE 18th International Colloquium on Signal Processing \u0026amp; Applications 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class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/90134431/Classification_of_Coffee_Variety_using_Electronic_Nose">Classification of Coffee Variety using Electronic Nose</a></div><div class="wp-workCard_item"><span>2022 IEEE 18th International Colloquium on Signal Processing &amp; Applications (CSPA)</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="90134431"><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 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href="https://www.academia.edu/83430229/Building_Model_for_Crime_Pattern_Analysis_Through_Machine_Learning_Using_Predictive_Analytics"><img alt="Research paper thumbnail of Building Model for Crime Pattern Analysis Through Machine Learning Using Predictive Analytics" 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/83430229/Building_Model_for_Crime_Pattern_Analysis_Through_Machine_Learning_Using_Predictive_Analytics">Building Model for Crime Pattern Analysis Through Machine Learning Using Predictive Analytics</a></div><div class="wp-workCard_item"><span>International Journal of Science, Technology, Engineering and Mathematics</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Crime has a big impact in both the human lives and the society鈥檚 growth, which needs to be addres...</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">Crime has a big impact in both the human lives and the society鈥檚 growth, which needs to be addressed and controlled. Machine learning algorithms as the fanciest technology to assist decision makers in policy making has proven its reliability in showing unseen patterns in crime. This research aims to examine the capability of trees and ensemble trees in classifying crime through model development. Experiments were done to enhance the capability of the ensembles in both classification and regression. Feature extraction like synthetic minority oversampling technique was applied in order to address the problem in the imbalanced data. Different metrics relevant to classification and regression were considered in evaluating the performance of each model used. With the use of different metrics, Gradient boosted tree was found to have better classification capability in crime dataset after outperforming decision tree and random forest in both classification and regression problem. Furthermo...</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="83430229"><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="83430229"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83430229; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83430229]").text(description); $(".js-view-count[data-work-id=83430229]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83430229; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83430229']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83430229, 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" } } $('.js-work-strip[data-work-id=83430229]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83430229,"title":"Building Model for Crime Pattern Analysis Through Machine Learning Using Predictive Analytics","translated_title":"","metadata":{"abstract":"Crime has a big impact in both the human lives and the society鈥檚 growth, which needs to be addressed and controlled. 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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="83430191"><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/83430191/Analysis_of_Membership_Function_in_the_Implementation_of_Neuro_Fuzzy_System_for_Prediction_of_Depressive_Lexicons"><img alt="Research paper thumbnail of Analysis of Membership Function in the Implementation of Neuro-Fuzzy System for Prediction of Depressive Lexicons" 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/83430191/Analysis_of_Membership_Function_in_the_Implementation_of_Neuro_Fuzzy_System_for_Prediction_of_Depressive_Lexicons">Analysis of Membership Function in the Implementation of Neuro-Fuzzy System for Prediction of Depressive Lexicons</a></div><div class="wp-workCard_item"><span>2021 4th International Conference on Data Science and Information Technology</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The soft computing environment does not stop making predictions to save lives. Its calculations, ...</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 soft computing environment does not stop making predictions to save lives. Its calculations, though not precise can still provide solutions to the complex scenarios of computational problems. Over the years, robustness and the accuracy of results became the remarkable challenge despite of the huge number of studies relating to it. In this case, the Neuro fuzzy system is one amongst the model proven to be used for prediction and has been applied to various fields of education, business, engineering and even in health. The Neuro fuzzy has its capability to adopt various strategies like the mix and match approach with different parameters involved during the experimentation. However, there is still a lack of support as to how this membership function could be able to produce a precise result that leads to different views towards soft computing. In this work, we gave emphasize on how the membership function works with NFS, which is essential in preparing data towards the prediction of depressive lexicons. Classifiers like Na茂ve Bayes, Simple Logistics, and Neuro fuzzy through the Multilayer Perceptron (MLP) of Weka tool have been used for the experimentation of the study. This study recommends to use the process of the analysis of membership function and carefully validate parameters from the first layer towards the third layer of the NFS since the strength of the membership function actively happens here.</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="83430191"><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="83430191"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83430191; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83430191]").text(description); $(".js-view-count[data-work-id=83430191]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83430191; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83430191']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83430191, 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" } } $('.js-work-strip[data-work-id=83430191]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83430191,"title":"Analysis of Membership Function in the Implementation of Neuro-Fuzzy System for Prediction of Depressive Lexicons","translated_title":"","metadata":{"abstract":"The soft computing environment does not stop making predictions to save lives. Its calculations, though not precise can still provide solutions to the complex scenarios of computational problems. Over the years, robustness and the accuracy of results became the remarkable challenge despite of the huge number of studies relating to it. In this case, the Neuro fuzzy system is one amongst the model proven to be used for prediction and has been applied to various fields of education, business, engineering and even in health. The Neuro fuzzy has its capability to adopt various strategies like the mix and match approach with different parameters involved during the experimentation. However, there is still a lack of support as to how this membership function could be able to produce a precise result that leads to different views towards soft computing. In this work, we gave emphasize on how the membership function works with NFS, which is essential in preparing data towards the prediction of depressive lexicons. Classifiers like Na茂ve Bayes, Simple Logistics, and Neuro fuzzy through the Multilayer Perceptron (MLP) of Weka tool have been used for the experimentation of the study. This study recommends to use the process of the analysis of membership function and carefully validate parameters from the first layer towards the third layer of the NFS since the strength of the membership function actively happens here.","publisher":"ACM","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"2021 4th International Conference on Data Science and Information Technology"},"translated_abstract":"The soft computing environment does not stop making predictions to save lives. Its calculations, though not precise can still provide solutions to the complex scenarios of computational problems. Over the years, robustness and the accuracy of results became the remarkable challenge despite of the huge number of studies relating to it. In this case, the Neuro fuzzy system is one amongst the model proven to be used for prediction and has been applied to various fields of education, business, engineering and even in health. The Neuro fuzzy has its capability to adopt various strategies like the mix and match approach with different parameters involved during the experimentation. However, there is still a lack of support as to how this membership function could be able to produce a precise result that leads to different views towards soft computing. In this work, we gave emphasize on how the membership function works with NFS, which is essential in preparing data towards the prediction of depressive lexicons. Classifiers like Na茂ve Bayes, Simple Logistics, and Neuro fuzzy through the Multilayer Perceptron (MLP) of Weka tool have been used for the experimentation of the study. This study recommends to use the process of the analysis of membership function and carefully validate parameters from the first layer towards the third layer of the NFS since the strength of the membership function actively happens here.","internal_url":"https://www.academia.edu/83430191/Analysis_of_Membership_Function_in_the_Implementation_of_Neuro_Fuzzy_System_for_Prediction_of_Depressive_Lexicons","translated_internal_url":"","created_at":"2022-07-19T17:30:04.995-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":171427082,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Analysis_of_Membership_Function_in_the_Implementation_of_Neuro_Fuzzy_System_for_Prediction_of_Depressive_Lexicons","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":171427082,"first_name":"Mia","middle_initials":null,"last_name":"Villar Villarica","page_name":"MiaVillarVillarica","domain_name":"independent","created_at":"2020-09-22T20:07:13.929-07:00","display_name":"Mia Villar Villarica","url":"https://independent.academia.edu/MiaVillarVillarica"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"}],"urls":[{"id":22301737,"url":"https://dl.acm.org/doi/pdf/10.1145/3478905.3478929"}]}, 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="83430181"><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/83430181/Technical_Analysis_of_Twitter_Data_in_Preparation_of_Prediction_using_Multilayer_Perceptron_Algorithm"><img alt="Research paper thumbnail of Technical Analysis of Twitter Data in Preparation of Prediction using Multilayer Perceptron Algorithm" 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/83430181/Technical_Analysis_of_Twitter_Data_in_Preparation_of_Prediction_using_Multilayer_Perceptron_Algorithm">Technical Analysis of Twitter Data in Preparation of Prediction using Multilayer Perceptron Algorithm</a></div><div class="wp-workCard_item"><span>2021 4th International Conference on Data Science and Information Technology</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Social networking sites have been the partner of everyone today to express feelings and emotions,...</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">Social networking sites have been the partner of everyone today to express feelings and emotions, especially that physical communication has been a bit prohibited caused by the pandemic. The platforms like Twitter, Facebook, etc., has been the choice to express words relating to stress, anxieties, and depressions to which the World Health Organization states that once not paid with enough attention may lead to mental health issues. A depression today stands out to be the leading problem known as mental health disorders that if not to anticipate earlier may also lead to this so called self-harming. In this regard, this study wants to illustrate the technical analysis of twitter data in preparation of prediction using the Multilayer Perceptron (MLP) in Weka algorithms to help the data mining community to dig knowledge from the stored historical data. The technical analysis has been made through the process of extraction, validation, and preparation of the model ready for interpretation and evaluation of predicted model. The legality of data has been permitted by twitter developer&#39;s account that permits the study to extract 1000 tweets and eventually validated 931 tweets necessary for exploration. Optimization was made by setting the epoch to 200 and by testing the eight attributes to 70:30 split of test. The initial result of 79.9141% was optimized to 82.0789% accuracy rate and found reliable based on the result of kappa statistics. However, the study still suggests to explore on varied parameters to increase the reliability of data.</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="83430181"><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="83430181"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83430181; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83430181]").text(description); $(".js-view-count[data-work-id=83430181]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83430181; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83430181']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83430181, 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" } } $('.js-work-strip[data-work-id=83430181]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83430181,"title":"Technical Analysis of Twitter Data in Preparation of Prediction using Multilayer Perceptron Algorithm","translated_title":"","metadata":{"abstract":"Social networking sites have been the partner of everyone today to express feelings and emotions, especially that physical communication has been a bit prohibited caused by the pandemic. The platforms like Twitter, Facebook, etc., has been the choice to express words relating to stress, anxieties, and depressions to which the World Health Organization states that once not paid with enough attention may lead to mental health issues. A depression today stands out to be the leading problem known as mental health disorders that if not to anticipate earlier may also lead to this so called self-harming. In this regard, this study wants to illustrate the technical analysis of twitter data in preparation of prediction using the Multilayer Perceptron (MLP) in Weka algorithms to help the data mining community to dig knowledge from the stored historical data. The technical analysis has been made through the process of extraction, validation, and preparation of the model ready for interpretation and evaluation of predicted model. 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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="83430165"><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/83430165/Intelligent_Investigation_on_Crime_Incident_Reports_in_the_Province_of_Laguna_through_Predictive_Model_Development"><img alt="Research paper thumbnail of Intelligent Investigation on Crime Incident Reports in the Province of Laguna through Predictive Model Development" 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/83430165/Intelligent_Investigation_on_Crime_Incident_Reports_in_the_Province_of_Laguna_through_Predictive_Model_Development">Intelligent Investigation on Crime Incident Reports in the Province of Laguna through Predictive Model Development</a></div><div class="wp-workCard_item"><span>International Journal of Advanced Trends in Computer Science and Engineering</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the past years, crime becomes one of the main concerns in the Philippines for it affects drast...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the past years, crime becomes one of the main concerns in the Philippines for it affects drastically in the economic growth of the country. Awareness was one of the key factors that a police officer must possessed to effectively reduced crime in particular location. Many criminologists study on the number or occurrence of a crime to resolve the problem, however, number vagueness and possible source are often encountered that compromises the possible real effects or pattern. Machine learning is well-known to produce new knowledge and discover hidden pattern intelligently in particular database which can be used to produce data-driven reasoning or policy recommendation. The key objective of this research is to develop a predictive model in investigating crime records in the province of Laguna. Following the famous concept of knowledge discovery in databases, the researchers found out that decision tree algorithm is the best machine learning algorithm in classifying crime occurrence. Furthermore, date, time and place have a significant correlation in crime occurrence. Also shown in this paper, that the bigger district in the province of Laguna is more vulnerable in different crime.</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="83430165"><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="83430165"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83430165; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83430165]").text(description); $(".js-view-count[data-work-id=83430165]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83430165; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83430165']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83430165, 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" } } $('.js-work-strip[data-work-id=83430165]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83430165,"title":"Intelligent Investigation on Crime Incident Reports in the Province of Laguna through Predictive Model Development","translated_title":"","metadata":{"abstract":"In the past years, crime becomes one of the main concerns in the Philippines for it affects drastically in the economic growth of the country. 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Machine learning is well-known to produce new knowledge and discover hidden pattern intelligently in particular database which can be used to produce data-driven reasoning or policy recommendation. The key objective of this research is to develop a predictive model in investigating crime records in the province of Laguna. Following the famous concept of knowledge discovery in databases, the researchers found out that decision tree algorithm is the best machine learning algorithm in classifying crime occurrence. Furthermore, date, time and place have a significant correlation in crime occurrence. 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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="73693490"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/73693490/E_Learning_Adapting_to_Rapid_Pedagogical_Changes"><img alt="Research paper thumbnail of E-Learning: Adapting to Rapid Pedagogical Changes" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/73693490/E_Learning_Adapting_to_Rapid_Pedagogical_Changes">E-Learning: Adapting to Rapid Pedagogical Changes</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A model for the mathematical description of diffusion process is presented through this work and ...</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">A model for the mathematical description of diffusion process is presented through this work and an attempt is also made for the applicability of Green?s function method for solving the one dimensional diffusion equation within the desired limits. From this process the required solution to this diffusion equation by considering the initial condition t = 0 will be obtained. This equation describes the rate of change of concentrations of substances to its own lattice or may be in different substances with a constant diffusion coefficient. At last a computational approach will also be used for getting the numerical solutions. 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Educational institution is focused on monitoring and improving the Licensure Examination performance particularly in Teacher Education Institution (TEI). The study intends to offer a possible solution to most TEIs apprehensions regarding LET performance by providing the students of Teacher Education a student support service in the form of a personalized Learning Management System with performance prediction and recommendation capability. This can be developed through drawing data model using several data mining techniques and tools. Previous literature suggested using data mining to classify students, predict student performance, improve student retention, enhanced student achievement and assess complex students? behavior to name a few. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="12355708" id="papers"><div class="js-work-strip profile--work_container" data-work-id="90145146"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/90145146/Correlation_Analysis_between_Sensors_for_Sensing_Coffee_Variations"><img alt="Research paper thumbnail of Correlation Analysis between Sensors for Sensing Coffee Variations" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/90145146/Correlation_Analysis_between_Sensors_for_Sensing_Coffee_Variations">Correlation Analysis between Sensors for Sensing Coffee Variations</a></div><div class="wp-workCard_item"><span>2022 IEEE 18th International Colloquium on Signal Processing &amp; 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$(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="83430191"><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/83430191/Analysis_of_Membership_Function_in_the_Implementation_of_Neuro_Fuzzy_System_for_Prediction_of_Depressive_Lexicons"><img alt="Research paper thumbnail of Analysis of Membership Function in the Implementation of Neuro-Fuzzy System for Prediction of Depressive Lexicons" 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/83430191/Analysis_of_Membership_Function_in_the_Implementation_of_Neuro_Fuzzy_System_for_Prediction_of_Depressive_Lexicons">Analysis of Membership Function in the Implementation of Neuro-Fuzzy System for Prediction of Depressive Lexicons</a></div><div class="wp-workCard_item"><span>2021 4th International Conference on Data Science and Information Technology</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The soft computing environment does not stop making predictions to save lives. Its calculations, ...</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 soft computing environment does not stop making predictions to save lives. Its calculations, though not precise can still provide solutions to the complex scenarios of computational problems. Over the years, robustness and the accuracy of results became the remarkable challenge despite of the huge number of studies relating to it. In this case, the Neuro fuzzy system is one amongst the model proven to be used for prediction and has been applied to various fields of education, business, engineering and even in health. The Neuro fuzzy has its capability to adopt various strategies like the mix and match approach with different parameters involved during the experimentation. However, there is still a lack of support as to how this membership function could be able to produce a precise result that leads to different views towards soft computing. In this work, we gave emphasize on how the membership function works with NFS, which is essential in preparing data towards the prediction of depressive lexicons. Classifiers like Na茂ve Bayes, Simple Logistics, and Neuro fuzzy through the Multilayer Perceptron (MLP) of Weka tool have been used for the experimentation of the study. This study recommends to use the process of the analysis of membership function and carefully validate parameters from the first layer towards the third layer of the NFS since the strength of the membership function actively happens here.</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="83430191"><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="83430191"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83430191; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83430191]").text(description); $(".js-view-count[data-work-id=83430191]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83430191; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83430191']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83430191, 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" } } $('.js-work-strip[data-work-id=83430191]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83430191,"title":"Analysis of Membership Function in the Implementation of Neuro-Fuzzy System for Prediction of Depressive Lexicons","translated_title":"","metadata":{"abstract":"The soft computing environment does not stop making predictions to save lives. 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Classifiers like Na茂ve Bayes, Simple Logistics, and Neuro fuzzy through the Multilayer Perceptron (MLP) of Weka tool have been used for the experimentation of the study. This study recommends to use the process of the analysis of membership function and carefully validate parameters from the first layer towards the third layer of the NFS since the strength of the membership function actively happens here.","publisher":"ACM","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"2021 4th International Conference on Data Science and Information Technology"},"translated_abstract":"The soft computing environment does not stop making predictions to save lives. Its calculations, though not precise can still provide solutions to the complex scenarios of computational problems. Over the years, robustness and the accuracy of results became the remarkable challenge despite of the huge number of studies relating to it. 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The platforms like Twitter, Facebook, etc., has been the choice to express words relating to stress, anxieties, and depressions to which the World Health Organization states that once not paid with enough attention may lead to mental health issues. A depression today stands out to be the leading problem known as mental health disorders that if not to anticipate earlier may also lead to this so called self-harming. In this regard, this study wants to illustrate the technical analysis of twitter data in preparation of prediction using the Multilayer Perceptron (MLP) in Weka algorithms to help the data mining community to dig knowledge from the stored historical data. The technical analysis has been made through the process of extraction, validation, and preparation of the model ready for interpretation and evaluation of predicted model. The legality of data has been permitted by twitter developer&#39;s account that permits the study to extract 1000 tweets and eventually validated 931 tweets necessary for exploration. Optimization was made by setting the epoch to 200 and by testing the eight attributes to 70:30 split of test. The initial result of 79.9141% was optimized to 82.0789% accuracy rate and found reliable based on the result of kappa statistics. However, the study still suggests to explore on varied parameters to increase the reliability of data.</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="83430181"><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="83430181"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83430181; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83430181]").text(description); $(".js-view-count[data-work-id=83430181]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83430181; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83430181']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83430181, 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" } } $('.js-work-strip[data-work-id=83430181]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83430181,"title":"Technical Analysis of Twitter Data in Preparation of Prediction using Multilayer Perceptron Algorithm","translated_title":"","metadata":{"abstract":"Social networking sites have been the partner of everyone today to express feelings and emotions, especially that physical communication has been a bit prohibited caused by the pandemic. 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The legality of data has been permitted by twitter developer\u0026#39;s account that permits the study to extract 1000 tweets and eventually validated 931 tweets necessary for exploration. Optimization was made by setting the epoch to 200 and by testing the eight attributes to 70:30 split of test. The initial result of 79.9141% was optimized to 82.0789% accuracy rate and found reliable based on the result of kappa statistics. However, the study still suggests to explore on varied parameters to increase the reliability of data.","publisher":"ACM","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"2021 4th International Conference on Data Science and Information Technology"},"translated_abstract":"Social networking sites have been the partner of everyone today to express feelings and emotions, especially that physical communication has been a bit prohibited caused by the pandemic. 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The legality of data has been permitted by twitter developer\u0026#39;s account that permits the study to extract 1000 tweets and eventually validated 931 tweets necessary for exploration. Optimization was made by setting the epoch to 200 and by testing the eight attributes to 70:30 split of test. The initial result of 79.9141% was optimized to 82.0789% accuracy rate and found reliable based on the result of kappa statistics. 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Awareness was one of the key factors that a police officer must possessed to effectively reduced crime in particular location. Many criminologists study on the number or occurrence of a crime to resolve the problem, however, number vagueness and possible source are often encountered that compromises the possible real effects or pattern. Machine learning is well-known to produce new knowledge and discover hidden pattern intelligently in particular database which can be used to produce data-driven reasoning or policy recommendation. The key objective of this research is to develop a predictive model in investigating crime records in the province of Laguna. Following the famous concept of knowledge discovery in databases, the researchers found out that decision tree algorithm is the best machine learning algorithm in classifying crime occurrence. Furthermore, date, time and place have a significant correlation in crime occurrence. Also shown in this paper, that the bigger district in the province of Laguna is more vulnerable in different crime.</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="83430165"><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="83430165"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83430165; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83430165]").text(description); $(".js-view-count[data-work-id=83430165]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83430165; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83430165']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83430165, 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" } } $('.js-work-strip[data-work-id=83430165]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83430165,"title":"Intelligent Investigation on Crime Incident Reports in the Province of Laguna through Predictive Model Development","translated_title":"","metadata":{"abstract":"In the past years, crime becomes one of the main concerns in the Philippines for it affects drastically in the economic growth of the country. 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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="73693490"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/73693490/E_Learning_Adapting_to_Rapid_Pedagogical_Changes"><img alt="Research paper thumbnail of E-Learning: Adapting to Rapid Pedagogical Changes" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/73693490/E_Learning_Adapting_to_Rapid_Pedagogical_Changes">E-Learning: Adapting to Rapid Pedagogical Changes</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A model for the mathematical description of diffusion process is presented through this work and ...</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">A model for the mathematical description of diffusion process is presented through this work and an attempt is also made for the applicability of Green?s function method for solving the one dimensional diffusion equation within the desired limits. From this process the required solution to this diffusion equation by considering the initial condition t = 0 will be obtained. This equation describes the rate of change of concentrations of substances to its own lattice or may be in different substances with a constant diffusion coefficient. At last a computational approach will also be used for getting the numerical solutions. 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Educational institution is focused on monitoring and improving the Licensure Examination performance particularly in Teacher Education Institution (TEI). The study intends to offer a possible solution to most TEIs apprehensions regarding LET performance by providing the students of Teacher Education a student support service in the form of a personalized Learning Management System with performance prediction and recommendation capability. This can be developed through drawing data model using several data mining techniques and tools. Previous literature suggested using data mining to classify students, predict student performance, improve student retention, enhanced student achievement and assess complex students? behavior to name a few. 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