{"id":5429,"date":"2024-09-30T19:58:03","date_gmt":"2024-09-30T19:58:03","guid":{"rendered":"https:\/\/ienext.eng.chula.ac.th\/?page_id=5429"},"modified":"2025-12-27T18:11:26","modified_gmt":"2025-12-27T18:11:26","slug":"daricha-sutivong","status":"publish","type":"page","link":"https:\/\/ienext.eng.chula.ac.th\/?page_id=5429&lang=en","title":{"rendered":"Daricha Sutivong"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"5429\" class=\"elementor elementor-5429\" data-elementor-post-type=\"page\">\n\t\t\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-5442381 e-flex e-con-boxed e-con e-parent\" data-id=\"5442381\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2df925d elementor-widget elementor-widget-html\" data-id=\"2df925d\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t<!-- Flexy Breadcrumb -->\r\n\t\t\t<div class=\"fbc fbc-page\">\r\n\r\n\t\t\t\t<!-- Breadcrumb wrapper -->\r\n\t\t\t\t<div class=\"fbc-wrap\">\r\n\r\n\t\t\t\t\t<!-- Ordered list-->\r\n\t\t\t\t\t<ol class=\"fbc-items\" itemscope itemtype=\"https:\/\/schema.org\/BreadcrumbList\">\r\n\t\t\t\t\t\t            <li itemprop=\"itemListElement\" itemscope itemtype=\"https:\/\/schema.org\/ListItem\">\r\n                <span itemprop=\"name\">\r\n                    <!-- Home Link -->\r\n                    <a itemprop=\"item\" href=\"https:\/\/ienext.eng.chula.ac.th\">\r\n                    \r\n                                                    <i class=\"fa fa-home\" aria-hidden=\"true\"><\/i>Home                    <\/a>\r\n                <\/span>\r\n                <meta itemprop=\"position\" content=\"1\" \/><!-- Meta Position-->\r\n             <\/li><li><span class=\"fbc-separator\">\/<\/span><\/li><li class=\"active\" itemprop=\"itemListElement\" itemscope itemtype=\"https:\/\/schema.org\/ListItem\"><span itemprop=\"name\" title=\"Daricha Sutivong\">Daricha Sutivong<\/span><meta itemprop=\"position\" content=\"2\" \/><\/li>\t\t\t\t\t<\/ol>\r\n\t\t\t\t\t<div class=\"clearfix\"><\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-76dc323 e-flex e-con-boxed e-con e-parent\" data-id=\"76dc323\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-492e764 e-con-full e-flex e-con e-child\" data-id=\"492e764\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1a880c1 elementor-widget elementor-widget-image\" data-id=\"1a880c1\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.22.0 - 26-06-2024 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"500\" height=\"500\" src=\"https:\/\/ienext.eng.chula.ac.th\/wp-content\/uploads\/2024\/07\/Daricha-e1721070516598.jpg\" class=\"attachment-full size-full wp-image-770\" alt=\"\" srcset=\"https:\/\/ienext.eng.chula.ac.th\/wp-content\/uploads\/2024\/07\/Daricha-e1721070516598.jpg 500w, https:\/\/ienext.eng.chula.ac.th\/wp-content\/uploads\/2024\/07\/Daricha-e1721070516598-300x300.jpg 300w, https:\/\/ienext.eng.chula.ac.th\/wp-content\/uploads\/2024\/07\/Daricha-e1721070516598-150x150.jpg 150w\" sizes=\"(max-width: 500px) 100vw, 500px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-fa5e16e e-con-full e-flex e-con e-child\" data-id=\"fa5e16e\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a7102db elementor-widget elementor-widget-heading\" data-id=\"a7102db\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.22.0 - 26-06-2024 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">\t\t\t<span class=\"ctc-inline-copy \" aria-label=\"Copied\">\n\t\t\t\t<span class=\"ctc-inline-copy-text \">Assoc. Prof. Daricha Sutivong, Ph.D.<\/span>\n\t\t\t\t<textarea style=\"display: none;\" class=\"ctc-inline-copy-textarea\" readonly=\"readonly\">Assoc. Prof. Daricha Sutivong, Ph.D.<\/textarea>\n\t\t\t\t<span class=\"ctc-inline-copy-icon\" role=\"button\" aria-label=\"Copied\">\n\t\t\t\t\t<svg aria-hidden=\"true\" focusable=\"false\" role=\"img\" class=\"copy-icon\" viewBox=\"0 0 16 16\" width=\"16\" height=\"16\" fill=\"currentColor\"><path d=\"M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z\"><\/path><path d=\"M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z\"><\/path><\/svg>\t\t\t\t\t<svg aria-hidden=\"true\" height=\"16\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" data-view-component=\"true\" class=\"check-icon\" fill=\"currentColor\"><path d=\"M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 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data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-921b7f0 e-con-full e-flex elementor-invisible e-con e-parent\" data-id=\"921b7f0\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;animation&quot;:&quot;fadeIn&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1bbfa79 elementor-widget elementor-widget-text-editor\" data-id=\"1bbfa79\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.22.0 - 26-06-2024 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<h4><strong><span class=\"ant-typography StyledText css-15ni9ka\">Education<\/span><\/strong><\/h4>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3e26a24 elementor-widget elementor-widget-text-editor\" data-id=\"3e26a24\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p class=\"ant-typography StyledTitle css-1z0n09m\">Ph.D. in Management Science and Engineering (Ph.D. minor in Computer Science)<br \/>Stanford University, <span class=\"LrzXr\">United States<\/span>, 2003<\/p><p class=\"ant-typography StyledTitle css-1z0n09m\">Master of Science in Engineering-Economic Systems and Operations Research<br \/>Stanford University, <span class=\"LrzXr\">United States<\/span>, 1997<\/p><p class=\"ant-typography StyledTitle css-1z0n09m\">Master of Engineering in Electrical Engineering and Computer Science<br \/>Massachusetts Institute of Technology, <span class=\"LrzXr\">United States<\/span>, 1996<\/p><p class=\"ant-typography StyledTitle css-1z0n09m\">Bachelor of Science in Computer Science and Engineering<br \/>Massachusetts Institute of Technology, <span class=\"LrzXr\">United States<\/span>, 1995<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-b43563c e-flex e-con-boxed e-con e-parent\" data-id=\"b43563c\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" 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class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span class=\"ant-typography StyledText css-1oc51h5\">Economics &amp; Financial Engineering<\/span><\/p><p><span class=\"ant-typography StyledText css-1oc51h5\">Statistics &amp; Data Analysis<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-dd10e04 e-flex e-con-boxed e-con e-parent\" data-id=\"dd10e04\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-f3bff30 e-con-full e-flex elementor-invisible e-con e-parent\" data-id=\"f3bff30\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;animation&quot;:&quot;fadeIn&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-70ddead elementor-widget elementor-widget-text-editor\" data-id=\"70ddead\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h4><strong><span class=\"ant-typography StyledText css-15ni9ka\">Publications<br \/><\/span><\/strong><\/h4><p><div class=\"teachpress_pub_list\"><form name=\"tppublistform\" method=\"get\"><a name=\"tppubs\" id=\"tppubs\"><\/a><\/form><div class=\"tablenav\"><div class=\"tablenav-pages\"><span class=\"displaying-num\">30 entries<\/span> <a class=\"page-numbers button disabled\">&laquo;<\/a> <a class=\"page-numbers button disabled\">&lsaquo;<\/a> 1 of 3 <a href=\"https:\/\/ienext.eng.chula.ac.th\/?page_id=5429&#038;lang=en&amp;limit=2&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"next page\" class=\"page-numbers button\">&rsaquo;<\/a> <a href=\"https:\/\/ienext.eng.chula.ac.th\/?page_id=5429&#038;lang=en&amp;limit=3&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"last page\" class=\"page-numbers button\">&raquo;<\/a> <\/div><\/div><div class=\"teachpress_publication_list\"><h3 class=\"tp_h3\" id=\"tp_h3_2025\">2025<\/h3><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Passiri Bodhidatta, Daricha Sutivong<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('597','tp_links')\" style=\"cursor:pointer;\">Understanding unnecessary stops and police use of force in NYPD Stop, Question, and Frisk with machine learning techniques<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Artificial Intelligence and Law, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 09248463<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 0)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_597\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('597','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_597\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('597','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_597\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('597','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_597\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Bodhidatta2025,<br \/>\r\ntitle = {Understanding unnecessary stops and police use of force in NYPD Stop, Question, and Frisk with machine learning techniques},<br \/>\r\nauthor = {Passiri Bodhidatta and Daricha Sutivong},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-86000624761&doi=10.1007%2fs10506-025-09444-y&partnerID=40&md5=cb643b7adb5d47c6b58bb0166c8c0e0e},<br \/>\r\ndoi = {10.1007\/s10506-025-09444-y},<br \/>\r\nissn = {09248463},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\njournal = {Artificial Intelligence and Law},<br \/>\r\npublisher = {Springer Nature},<br \/>\r\nabstract = {Even though the New York Police Department (NYPD) reform in 2013 led to a substantial reduction in the total number of stops, unnecessary stops and weapon use against innocent citizens remain critical issues. This study analyzes stop-and-frisk records during 2014 \u2013 2019 using tree-based machine learning approaches along with logistic regression and Multi-Layer Perceptron (MLP) models, in order to discover patterns and insights. By developing predictive models for both suspect convictions and the level of force applied by police, this study provides a basis for a discussion whether weapon usage aligns with indicators of guilt or conviction. Findings show that XGBoost outperforms other machine learning techniques in predicting both conviction and the level of force used. Key factors associated with a suspect\u2019s conviction include weapon possession, carrying suspicious objects, and trespassing. However, an excessive number of unnecessary stops appear to be associated with inaccurate assumptions about suspects\u2019 weapon possession, which are also linked to police gunfire against innocent citizens. Refining suspicion criteria for Criminal Possession of Weapon and suspect actions could help reduce unnecessary stops and excessive force. \u00a9 The Author(s), under exclusive licence to Springer Nature B.V. 2025.},<br \/>\r\nnote = {Cited by: 0},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('597','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_597\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Even though the New York Police Department (NYPD) reform in 2013 led to a substantial reduction in the total number of stops, unnecessary stops and weapon use against innocent citizens remain critical issues. This study analyzes stop-and-frisk records during 2014 \u2013 2019 using tree-based machine learning approaches along with logistic regression and Multi-Layer Perceptron (MLP) models, in order to discover patterns and insights. By developing predictive models for both suspect convictions and the level of force applied by police, this study provides a basis for a discussion whether weapon usage aligns with indicators of guilt or conviction. Findings show that XGBoost outperforms other machine learning techniques in predicting both conviction and the level of force used. Key factors associated with a suspect\u2019s conviction include weapon possession, carrying suspicious objects, and trespassing. However, an excessive number of unnecessary stops appear to be associated with inaccurate assumptions about suspects\u2019 weapon possession, which are also linked to police gunfire against innocent citizens. Refining suspicion criteria for Criminal Possession of Weapon and suspect actions could help reduce unnecessary stops and excessive force. \u00a9 The Author(s), under exclusive licence to Springer Nature B.V. 2025.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('597','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_597\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-86000624761&amp;doi=10.1007%2fs10506-025-09444-y&amp;partnerID=40&amp;md5=cb643b7adb5d47c6b58bb0166c8c0e0e\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-86000624761&amp;doi=10.1007%[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-86000624761&amp;doi=10.1007%[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/s10506-025-09444-y\" title=\"Follow DOI:10.1007\/s10506-025-09444-y\" target=\"_blank\">doi:10.1007\/s10506-025-09444-y<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('597','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2024\">2024<\/h3><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Korakot Kanjai, Kolratut Supbungerd, Ekkavich Chareonjirasak, Phattara Sripawatakul, Daricha Sutivong<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('598','tp_links')\" style=\"cursor:pointer;\">Analyzing Effects of Time Series Data Characteristics on LSTM Performance<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Proceedings of the IEEE International Conference on Computer and Communications, ICCC, <\/span><span class=\"tp_pub_additional_number\">no. 2024, <\/span><span class=\"tp_pub_additional_pages\">pp. 12 \u2013 16, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 28377109<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 0)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_598\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('598','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_598\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('598','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_598\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('598','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_598\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Kanjai202412,<br \/>\r\ntitle = {Analyzing Effects of Time Series Data Characteristics on LSTM Performance},<br \/>\r\nauthor = {Korakot Kanjai and Kolratut Supbungerd and Ekkavich Chareonjirasak and Phattara Sripawatakul and Daricha Sutivong},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105006564553&doi=10.1109%2fICCC62609.2024.10941819&partnerID=40&md5=5eafc188aaa4233d172a4051079b2843},<br \/>\r\ndoi = {10.1109\/ICCC62609.2024.10941819},<br \/>\r\nissn = {28377109},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Proceedings of the IEEE International Conference on Computer and Communications, ICCC},<br \/>\r\nnumber = {2024},<br \/>\r\npages = {12 \u2013 16},<br \/>\r\npublisher = {Institute of Electrical and Electronics Engineers Inc.},<br \/>\r\nabstract = {The use of deep learning techniques for time series forecasting has increased in recent years. Long Short-Term Memory (LSTM) is a deep learning technique that has been shown to be effective for time series forecasting. This research proposes a methodology to study the relationship between LSTM performance and time series data characteristics, including trend, seasonality, skewness, kurtosis, chaotic behavior, and coefficient of variation. The methodology was applied to 48 stock price datasets. Three techniques, namely multiple linear regression, feature importance, and SHAP value, were used to compare LSTM performance across different data characteristics. The results show that coefficient of variation and chaotic behavior are the two most crucial characteristics affecting LSTM performance. \u00a9 2024 IEEE.},<br \/>\r\nnote = {Cited by: 0},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('598','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_598\" style=\"display:none;\"><div class=\"tp_abstract_entry\">The use of deep learning techniques for time series forecasting has increased in recent years. Long Short-Term Memory (LSTM) is a deep learning technique that has been shown to be effective for time series forecasting. This research proposes a methodology to study the relationship between LSTM performance and time series data characteristics, including trend, seasonality, skewness, kurtosis, chaotic behavior, and coefficient of variation. The methodology was applied to 48 stock price datasets. Three techniques, namely multiple linear regression, feature importance, and SHAP value, were used to compare LSTM performance across different data characteristics. The results show that coefficient of variation and chaotic behavior are the two most crucial characteristics affecting LSTM performance. \u00a9 2024 IEEE.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('598','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_598\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105006564553&amp;doi=10.1109%2fICCC62609.2024.10941819&amp;partnerID=40&amp;md5=5eafc188aaa4233d172a4051079b2843\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105006564553&amp;doi=10.1109[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105006564553&amp;doi=10.1109[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/ICCC62609.2024.10941819\" title=\"Follow DOI:10.1109\/ICCC62609.2024.10941819\" target=\"_blank\">doi:10.1109\/ICCC62609.2024.10941819<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('598','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2023\">2023<\/h3><div class=\"tp_publication tp_publication_conference\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Kanticha Wongnongtaey, Krissana Srisomboon, Daricha Sutivong<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('222','tp_links')\" style=\"cursor:pointer;\">Analyzing Suicide and Contributing Factors in Thailand Using Classification by Logistic Regression, Decision Tree and Support Vector Machine<\/a> <span class=\"tp_pub_type tp_  conference\">Conference<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_publisher\">Institute of Electrical and Electronics Engineers Inc., <\/span><span class=\"tp_pub_additional_year\">2023<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 979-835033386-2<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 0)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_222\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('222','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_222\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('222','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_222\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('222','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_222\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@conference{Wongnongtaey2023130,<br \/>\r\ntitle = {Analyzing Suicide and Contributing Factors in Thailand Using Classification by Logistic Regression, Decision Tree and Support Vector Machine},<br \/>\r\nauthor = {Kanticha Wongnongtaey and Krissana Srisomboon and Daricha Sutivong},<br \/>\r\neditor = {Meen T.},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85166378651&doi=10.1109%2fICEIB57887.2023.10170197&partnerID=40&md5=79fd094b5cf5b6d789fd8376d567654c},<br \/>\r\ndoi = {10.1109\/ICEIB57887.2023.10170197},<br \/>\r\nisbn = {979-835033386-2},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-01-01},<br \/>\r\njournal = {2023 IEEE 3rd International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023},<br \/>\r\npages = {130 \u2013 135},<br \/>\r\npublisher = {Institute of Electrical and Electronics Engineers Inc.},<br \/>\r\nabstract = {The World Health Organization (WHO) reports that over 700,000 people die from suicide each year, with the number of attempts far exceeding that. Suicide is also the fourth leading cause of death among teenagers [1]. The suicide rate in Thailand follows a similar trend. This research aims to utilize machine learning techniques to identify cases that lead to suicide deaths and to study the contributing factors, both internal and external. Specifically, this paper proposes using classification methods to create a prediction model from people's demography, behaviors, and other factors in order to analyze features that influence the risk of suicide deaths. Classification techniques employed include Logistic Regression, Decision Tree, and Support Vector Machine. We have analyzed the data gathered by Thailand National Suicide Prevention Department from January 2018 to September 2021, which includes 49,417 suicidal cases, 30% of which resulted in fatalities. Two models are proposed: a general model to identify significant factors leading to suicide deaths and a practical model to predict suicide deaths given only available factors in practice. \u00a9 2023 IEEE.},<br \/>\r\nnote = {Cited by: 0},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {conference}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('222','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_222\" style=\"display:none;\"><div class=\"tp_abstract_entry\">The World Health Organization (WHO) reports that over 700,000 people die from suicide each year, with the number of attempts far exceeding that. Suicide is also the fourth leading cause of death among teenagers [1]. The suicide rate in Thailand follows a similar trend. This research aims to utilize machine learning techniques to identify cases that lead to suicide deaths and to study the contributing factors, both internal and external. Specifically, this paper proposes using classification methods to create a prediction model from people&#8217;s demography, behaviors, and other factors in order to analyze features that influence the risk of suicide deaths. Classification techniques employed include Logistic Regression, Decision Tree, and Support Vector Machine. We have analyzed the data gathered by Thailand National Suicide Prevention Department from January 2018 to September 2021, which includes 49,417 suicidal cases, 30% of which resulted in fatalities. Two models are proposed: a general model to identify significant factors leading to suicide deaths and a practical model to predict suicide deaths given only available factors in practice. \u00a9 2023 IEEE.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('222','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_222\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85166378651&amp;doi=10.1109%2fICEIB57887.2023.10170197&amp;partnerID=40&amp;md5=79fd094b5cf5b6d789fd8376d567654c\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85166378651&amp;doi=10.1109%[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85166378651&amp;doi=10.1109%[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/ICEIB57887.2023.10170197\" title=\"Follow DOI:10.1109\/ICEIB57887.2023.10170197\" target=\"_blank\">doi:10.1109\/ICEIB57887.2023.10170197<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('222','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_conference\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Sutiwat Simtharakao, Daricha Sutivong<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('224','tp_links')\" style=\"cursor:pointer;\">Exploring Normalization Techniques in Neural Networks for Bitcoin Candlestick Price Prediction<\/a> <span class=\"tp_pub_type tp_  conference\">Conference<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_publisher\">Institute of Electrical and Electronics Engineers Inc., <\/span><span class=\"tp_pub_additional_year\">2023<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 978-166545645-6<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 3)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_224\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('224','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_224\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('224','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_224\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('224','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_224\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@conference{Simtharakao2023483,<br \/>\r\ntitle = {Exploring Normalization Techniques in Neural Networks for Bitcoin Candlestick Price Prediction},<br \/>\r\nauthor = {Sutiwat Simtharakao and Daricha Sutivong},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85151995101&doi=10.1109%2fICAIIC57133.2023.10067086&partnerID=40&md5=cb23d63d58b547568a6fc720340cd519},<br \/>\r\ndoi = {10.1109\/ICAIIC57133.2023.10067086},<br \/>\r\nisbn = {978-166545645-6},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-01-01},<br \/>\r\njournal = {5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023},<br \/>\r\npages = {483 \u2013 488},<br \/>\r\npublisher = {Institute of Electrical and Electronics Engineers Inc.},<br \/>\r\nabstract = {Bitcoin is a high-risk asset with a potentially high return. Predicting Bitcoin candlestick, i.e., open, high, low, and close (OHLC) prices, can help investors make trading decisions. This paper aims to explore various data normalization techniques in neural networks to enhance candlestick price prediction. In this study, the sliding window normalization techniques were compared with the whole set normalization techniques for forecasting the daily Bitcoin OHLC prices using two neural network algorithms: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The investigated normalization techniques for both the whole set and the sliding window set included z-score normalization, min-max normalization, and relative change normalization. Historical OHLC prices over several days were used to predict the next day's OHLC prices. The results show that the sliding window normalization techniques outperformed the whole set normalization techniques in terms of RMSE and MAPE with the best technique being the GRU algorithm using the sliding window relative change normalization achieving MAPE of 2.25% and RMSE of 870.52. \u00a9 2023 IEEE.},<br \/>\r\nnote = {Cited by: 3},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {conference}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('224','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_224\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Bitcoin is a high-risk asset with a potentially high return. Predicting Bitcoin candlestick, i.e., open, high, low, and close (OHLC) prices, can help investors make trading decisions. This paper aims to explore various data normalization techniques in neural networks to enhance candlestick price prediction. In this study, the sliding window normalization techniques were compared with the whole set normalization techniques for forecasting the daily Bitcoin OHLC prices using two neural network algorithms: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The investigated normalization techniques for both the whole set and the sliding window set included z-score normalization, min-max normalization, and relative change normalization. Historical OHLC prices over several days were used to predict the next day&#8217;s OHLC prices. The results show that the sliding window normalization techniques outperformed the whole set normalization techniques in terms of RMSE and MAPE with the best technique being the GRU algorithm using the sliding window relative change normalization achieving MAPE of 2.25% and RMSE of 870.52. \u00a9 2023 IEEE.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('224','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_224\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85151995101&amp;doi=10.1109%2fICAIIC57133.2023.10067086&amp;partnerID=40&amp;md5=cb23d63d58b547568a6fc720340cd519\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85151995101&amp;doi=10.1109%[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85151995101&amp;doi=10.1109%[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/ICAIIC57133.2023.10067086\" title=\"Follow DOI:10.1109\/ICAIIC57133.2023.10067086\" target=\"_blank\">doi:10.1109\/ICAIIC57133.2023.10067086<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('224','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_conference\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Nuttawan Sangsawai, Daricha Sutivong<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('241','tp_links')\" style=\"cursor:pointer;\">Analyzing Impact of Economic Indicators on Vietnam Stock Market Using Machine Learning Techniques<\/a> <span class=\"tp_pub_type tp_  conference\">Conference<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_volume\">vol. 35, <\/span><span class=\"tp_pub_additional_publisher\">IOS Press BV, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 2352751X<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_241\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('241','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_241\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('241','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_241\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('241','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_241\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@conference{Sangsawai2023279,<br \/>\r\ntitle = {Analyzing Impact of Economic Indicators on Vietnam Stock Market Using Machine Learning Techniques},<br \/>\r\nauthor = {Nuttawan Sangsawai and Daricha Sutivong},<br \/>\r\neditor = {Tang L.-C.},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85173465376&doi=10.3233%2fATDE230054&partnerID=40&md5=fa1718529d6236f9a2ec28cd477e415a},<br \/>\r\ndoi = {10.3233\/ATDE230054},<br \/>\r\nissn = {2352751X},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-01-01},<br \/>\r\njournal = {Advances in Transdisciplinary Engineering},<br \/>\r\nvolume = {35},<br \/>\r\npages = {279 \u2013 288},<br \/>\r\npublisher = {IOS Press BV},<br \/>\r\nabstract = {Many people find interest in stock markets because of the potential financial gains. Understanding the fundamentals of each stock market is crucial as each has its own unique traits and driving factors. Traditionally, statistical methods are commonly used to find the relationship between various economic indicators and the stock markets. This study aims to utilize a different approach, namely machine learning techniques, a widely used tool for data analytics, to analyze the impact of economic indicators on the Vietnam stock index, which is a rising market during the past decade. The investigated machine learning algorithms include tree-based algorithms such as Decision Tree, Random Forest, and XGBoost. Monthly data, totaling 257 observations from August 2000 to December 2021, were used in this study. The results reveal that the XGBoost algorithm achieves the highest accuracy at 96.67% and the five most influential variables affecting the Vietnam stock market are S&P 500 index, consumer price index, exports, imports, and oil price, respectively, all with a positive relationship, while the relationships of the exchange rate, unemployment rate, and GDP with the Vietnam stock market are unclear. \u00a9 2023 The authors and IOS Press.},<br \/>\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {conference}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('241','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_241\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Many people find interest in stock markets because of the potential financial gains. Understanding the fundamentals of each stock market is crucial as each has its own unique traits and driving factors. Traditionally, statistical methods are commonly used to find the relationship between various economic indicators and the stock markets. This study aims to utilize a different approach, namely machine learning techniques, a widely used tool for data analytics, to analyze the impact of economic indicators on the Vietnam stock index, which is a rising market during the past decade. The investigated machine learning algorithms include tree-based algorithms such as Decision Tree, Random Forest, and XGBoost. Monthly data, totaling 257 observations from August 2000 to December 2021, were used in this study. The results reveal that the XGBoost algorithm achieves the highest accuracy at 96.67% and the five most influential variables affecting the Vietnam stock market are S&amp;P 500 index, consumer price index, exports, imports, and oil price, respectively, all with a positive relationship, while the relationships of the exchange rate, unemployment rate, and GDP with the Vietnam stock market are unclear. \u00a9 2023 The authors and IOS Press.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('241','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_241\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85173465376&amp;doi=10.3233%2fATDE230054&amp;partnerID=40&amp;md5=fa1718529d6236f9a2ec28cd477e415a\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85173465376&amp;doi=10.3233%[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85173465376&amp;doi=10.3233%[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.3233\/ATDE230054\" title=\"Follow DOI:10.3233\/ATDE230054\" target=\"_blank\">doi:10.3233\/ATDE230054<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('241','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2020\">2020<\/h3><div class=\"tp_publication tp_publication_conference\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Ponsuda Prutphongs, Daricha Sutivong<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('233','tp_links')\" style=\"cursor:pointer;\">Decision Support System for Power Plant Improvement Investment Using Life-Cycle Cost<\/a> <span class=\"tp_pub_type tp_  conference\">Conference<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_publisher\">Institute of Electrical and Electronics Engineers Inc., <\/span><span class=\"tp_pub_additional_year\">2020<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 978-172818406-7<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 0)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_233\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('233','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_233\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('233','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_233\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('233','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_233\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@conference{Prutphongs2020588,<br \/>\r\ntitle = {Decision Support System for Power Plant Improvement Investment Using Life-Cycle Cost},<br \/>\r\nauthor = {Ponsuda Prutphongs and Daricha Sutivong},<br \/>\r\neditor = {Wibowo F.W.},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85100018178&doi=10.1109%2fISRITI51436.2020.9315385&partnerID=40&md5=7c41d83838e1abb6a252980e80a2cbd4},<br \/>\r\ndoi = {10.1109\/ISRITI51436.2020.9315385},<br \/>\r\nisbn = {978-172818406-7},<br \/>\r\nyear  = {2020},<br \/>\r\ndate = {2020-01-01},<br \/>\r\njournal = {2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020},<br \/>\r\npages = {588 \u2013 592},<br \/>\r\npublisher = {Institute of Electrical and Electronics Engineers Inc.},<br \/>\r\nabstract = {This research designs and develops a Decision Support System (DSS) for evaluating a power plant improvement investment, given a generation plan, contract accounting and associated technical data. In practice, most owners often decide on an improvement investment by considering forward an immediate short period of revenue and expenses. This decision support system helps the owner take into account a more comprehensive period of cash flows in order to maximize the asset value and make an optimal decision. Specifically, the model calculation is based on the Life Cycle Cost Management (LCCM) under certain business rules. Our proposed evaluation model consists of five steps: 1) Consider the structure of revenue and expenses according to the Power Purchase Agreement (PPA). 2) Analyze accounting and technical data. 3) Estimate demand from the energy plan according to the system operator's yearly report. 4) Incorporate the data according to its business rule and the PPA constraints into the forecasting calculation. 5) Evaluate the investment using economic measures, such as Net Present Value (NPV) and Internal Rate of Return (IRR). \u00a9 2020 IEEE.},<br \/>\r\nnote = {Cited by: 0},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {conference}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('233','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_233\" style=\"display:none;\"><div class=\"tp_abstract_entry\">This research designs and develops a Decision Support System (DSS) for evaluating a power plant improvement investment, given a generation plan, contract accounting and associated technical data. In practice, most owners often decide on an improvement investment by considering forward an immediate short period of revenue and expenses. This decision support system helps the owner take into account a more comprehensive period of cash flows in order to maximize the asset value and make an optimal decision. Specifically, the model calculation is based on the Life Cycle Cost Management (LCCM) under certain business rules. Our proposed evaluation model consists of five steps: 1) Consider the structure of revenue and expenses according to the Power Purchase Agreement (PPA). 2) Analyze accounting and technical data. 3) Estimate demand from the energy plan according to the system operator&#8217;s yearly report. 4) Incorporate the data according to its business rule and the PPA constraints into the forecasting calculation. 5) Evaluate the investment using economic measures, such as Net Present Value (NPV) and Internal Rate of Return (IRR). \u00a9 2020 IEEE.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('233','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_233\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85100018178&amp;doi=10.1109%2fISRITI51436.2020.9315385&amp;partnerID=40&amp;md5=7c41d83838e1abb6a252980e80a2cbd4\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85100018178&amp;doi=10.1109%[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85100018178&amp;doi=10.1109%[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/ISRITI51436.2020.9315385\" title=\"Follow DOI:10.1109\/ISRITI51436.2020.9315385\" target=\"_blank\">doi:10.1109\/ISRITI51436.2020.9315385<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('233','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2018\">2018<\/h3><div class=\"tp_publication tp_publication_conference\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Auth Pisutaporn, Burit Chonvirachkul, Daricha Sutivong<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('216','tp_links')\" style=\"cursor:pointer;\">Relevant factors and classification of student alcohol consumption<\/a> <span class=\"tp_pub_type tp_  conference\">Conference<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_publisher\">Institute of Electrical and Electronics Engineers Inc., <\/span><span class=\"tp_pub_additional_year\">2018<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 978-153865696-9<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 6)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_216\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('216','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_216\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('216','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_216\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('216','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_216\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@conference{Pisutaporn20181,<br \/>\r\ntitle = {Relevant factors and classification of student alcohol consumption},<br \/>\r\nauthor = {Auth Pisutaporn and Burit Chonvirachkul and Daricha Sutivong},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85049886823&doi=10.1109%2fICIRD.2018.8376297&partnerID=40&md5=b824d8e98341a3e07037b73ba8cc9141},<br \/>\r\ndoi = {10.1109\/ICIRD.2018.8376297},<br \/>\r\nisbn = {978-153865696-9},<br \/>\r\nyear  = {2018},<br \/>\r\ndate = {2018-01-01},<br \/>\r\njournal = {2018 IEEE International Conference on Innovative Research and Development, ICIRD 2018},<br \/>\r\npages = {1 \u2013 6},<br \/>\r\npublisher = {Institute of Electrical and Electronics Engineers Inc.},<br \/>\r\nabstract = {Educational data mining is the process of applying data mining tools and techniques to analyze data for educational purpose. This paper carries out educational data mining to study the student alcohol consumption through a public dataset which includes student attributes and their grades. The decision tree algorithm and the random forest algorithm are applied to perform classification and to analyze the variable importance. The regression model is then employed to illustrate the relationship between alcohol consumption level and the students' final grades. Our analysis provides knowledge on the relationship between student characteristics and alcohol consumption. The study also compares performance of the decision tree algorithm and the random forest algorithm. \u00a9 2018 IEEE.},<br \/>\r\nnote = {Cited by: 6},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {conference}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('216','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_216\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Educational data mining is the process of applying data mining tools and techniques to analyze data for educational purpose. This paper carries out educational data mining to study the student alcohol consumption through a public dataset which includes student attributes and their grades. The decision tree algorithm and the random forest algorithm are applied to perform classification and to analyze the variable importance. The regression model is then employed to illustrate the relationship between alcohol consumption level and the students&#8217; final grades. Our analysis provides knowledge on the relationship between student characteristics and alcohol consumption. The study also compares performance of the decision tree algorithm and the random forest algorithm. \u00a9 2018 IEEE.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('216','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_216\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85049886823&amp;doi=10.1109%2fICIRD.2018.8376297&amp;partnerID=40&amp;md5=b824d8e98341a3e07037b73ba8cc9141\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85049886823&amp;doi=10.1109%[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85049886823&amp;doi=10.1109%[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/ICIRD.2018.8376297\" title=\"Follow DOI:10.1109\/ICIRD.2018.8376297\" target=\"_blank\">doi:10.1109\/ICIRD.2018.8376297<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('216','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Suparerk Lekwijit, Daricha Sutivong<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('219','tp_links')\" style=\"cursor:pointer;\">Optimizing the liquidity parameter of logarithmic market scoring rules prediction markets<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Journal of Modelling in Management, <\/span><span class=\"tp_pub_additional_volume\">vol. 13, <\/span><span class=\"tp_pub_additional_number\">no. 3, <\/span><span class=\"tp_pub_additional_pages\">pp. 736 \u2013 754, <\/span><span class=\"tp_pub_additional_year\">2018<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 17465664<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 4)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_219\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('219','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_219\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('219','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_219\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('219','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_219\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Lekwijit2018736,<br \/>\r\ntitle = {Optimizing the liquidity parameter of logarithmic market scoring rules prediction markets},<br \/>\r\nauthor = {Suparerk Lekwijit and Daricha Sutivong},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85053451144&doi=10.1108%2fJM2-06-2017-0066&partnerID=40&md5=8b00c0f1326db9d17a4ad0c266edccb0},<br \/>\r\ndoi = {10.1108\/JM2-06-2017-0066},<br \/>\r\nissn = {17465664},<br \/>\r\nyear  = {2018},<br \/>\r\ndate = {2018-01-01},<br \/>\r\njournal = {Journal of Modelling in Management},<br \/>\r\nvolume = {13},<br \/>\r\nnumber = {3},<br \/>\r\npages = {736 \u2013 754},<br \/>\r\npublisher = {Emerald Group Holdings Ltd.},<br \/>\r\nabstract = {Purpose: Prediction markets are techniques to aggregate dispersed public opinions via market mechanisms to predict uncertain future events\u2019 outcome. Many experiments have shown that prediction markets outperform other traditional forecasting methods in terms of accuracy. Logarithmic market scoring rules (LMSR) is one of the most simple and widely used market mechanisms; however, market makers have to confront crucial design decisions including the setting of the parameter \u201cb\u201d or the \u201cliquidity parameter\u201d in the price functions. As the liquidity parameter has significant effects on the market performance, this paper aims to provide a comprehensive basis for the setting of the parameter. Design\/methodology\/approach: The analyses include the effects of the liquidity parameter on the forecast standard error and the amount of time for the market price to converge to the true value. These experiments use artificial prediction markets, the proposed simulation models that mimic real prediction markets. Findings: The simulation results indicate that prediction market\u2019s forecast standard error decreases as the value of the liquidity parameter increases. Moreover, for any given number of traders in the market, there exists an optimal liquidity parameter value that yields appropriate price adaptability and leads to the fastest price convergence. Originality\/value: Understanding these tradeoffs, the market makers can effectively determine the liquidity parameter value under various objectives on the standard error, the time to convergence and cost. \u00a9 2018, Emerald Publishing Limited.},<br \/>\r\nnote = {Cited by: 4},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('219','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_219\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Purpose: Prediction markets are techniques to aggregate dispersed public opinions via market mechanisms to predict uncertain future events\u2019 outcome. Many experiments have shown that prediction markets outperform other traditional forecasting methods in terms of accuracy. Logarithmic market scoring rules (LMSR) is one of the most simple and widely used market mechanisms; however, market makers have to confront crucial design decisions including the setting of the parameter \u201cb\u201d or the \u201cliquidity parameter\u201d in the price functions. As the liquidity parameter has significant effects on the market performance, this paper aims to provide a comprehensive basis for the setting of the parameter. Design\/methodology\/approach: The analyses include the effects of the liquidity parameter on the forecast standard error and the amount of time for the market price to converge to the true value. These experiments use artificial prediction markets, the proposed simulation models that mimic real prediction markets. Findings: The simulation results indicate that prediction market\u2019s forecast standard error decreases as the value of the liquidity parameter increases. Moreover, for any given number of traders in the market, there exists an optimal liquidity parameter value that yields appropriate price adaptability and leads to the fastest price convergence. Originality\/value: Understanding these tradeoffs, the market makers can effectively determine the liquidity parameter value under various objectives on the standard error, the time to convergence and cost. \u00a9 2018, Emerald Publishing Limited.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('219','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_219\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85053451144&amp;doi=10.1108%2fJM2-06-2017-0066&amp;partnerID=40&amp;md5=8b00c0f1326db9d17a4ad0c266edccb0\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85053451144&amp;doi=10.1108%[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85053451144&amp;doi=10.1108%[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1108\/JM2-06-2017-0066\" title=\"Follow DOI:10.1108\/JM2-06-2017-0066\" target=\"_blank\">doi:10.1108\/JM2-06-2017-0066<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('219','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_conference\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Pakom Walanaraya, Weerapat Puengpipattrakul, Daricha Sutivong<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('220','tp_links')\" style=\"cursor:pointer;\">Movie Revenue Prediction Using Regression and Clustering<\/a> <span class=\"tp_pub_type tp_  conference\">Conference<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_publisher\">Institute of Electrical and Electronics Engineers Inc., <\/span><span class=\"tp_pub_additional_year\">2018<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 978-153866105-5<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 4)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_220\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('220','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_220\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('220','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_220\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('220','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_220\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@conference{Walanaraya201863,<br \/>\r\ntitle = {Movie Revenue Prediction Using Regression and Clustering},<br \/>\r\nauthor = {Pakom Walanaraya and Weerapat Puengpipattrakul and Daricha Sutivong},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85054008064&doi=10.1109%2fICEI18.2018.8448610&partnerID=40&md5=29090e603d4640e185f0152141664dc4},<br \/>\r\ndoi = {10.1109\/ICEI18.2018.8448610},<br \/>\r\nisbn = {978-153866105-5},<br \/>\r\nyear  = {2018},<br \/>\r\ndate = {2018-01-01},<br \/>\r\njournal = {2018 2nd International Conference on Engineering Innovation, ICEI 2018},<br \/>\r\npages = {63 \u2013 68},<br \/>\r\npublisher = {Institute of Electrical and Electronics Engineers Inc.},<br \/>\r\nabstract = {Among many movies that have been released, some generate high profit while the others do not. This paper studies the relationship between movie factors and its revenue and build prediction models. Besides analysis on aggregate data, we also divide data into groups using different methods and compare accuracy across these techniques as well as explore whether clustering techniques could help improve accuracy. Specifically, two major steps were employed. Initially, linear regression, polynomial regression and support vector regression (SVR) were applied on the entire movie data to predict the movie revenue. Then, clustering techniques, such as by genre, using Expectation Maximization (EM) and using K-means were applied to divide data into groups before regression analyses are executed. To compare accuracy among different techniques, R-square and the root-mean-square error (RMSE) were used as a performance indicator. Our study shows that generally linear regression without clustering offers the model with the highest R-square, while linear regression with EM clustering yields the lowest RMSE. \u00a9 2018 IEEE.},<br \/>\r\nnote = {Cited by: 4},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {conference}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('220','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_220\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Among many movies that have been released, some generate high profit while the others do not. This paper studies the relationship between movie factors and its revenue and build prediction models. Besides analysis on aggregate data, we also divide data into groups using different methods and compare accuracy across these techniques as well as explore whether clustering techniques could help improve accuracy. Specifically, two major steps were employed. Initially, linear regression, polynomial regression and support vector regression (SVR) were applied on the entire movie data to predict the movie revenue. Then, clustering techniques, such as by genre, using Expectation Maximization (EM) and using K-means were applied to divide data into groups before regression analyses are executed. To compare accuracy among different techniques, R-square and the root-mean-square error (RMSE) were used as a performance indicator. Our study shows that generally linear regression without clustering offers the model with the highest R-square, while linear regression with EM clustering yields the lowest RMSE. \u00a9 2018 IEEE.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('220','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_220\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85054008064&amp;doi=10.1109%2fICEI18.2018.8448610&amp;partnerID=40&amp;md5=29090e603d4640e185f0152141664dc4\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85054008064&amp;doi=10.1109%[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85054008064&amp;doi=10.1109%[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/ICEI18.2018.8448610\" title=\"Follow DOI:10.1109\/ICEI18.2018.8448610\" target=\"_blank\">doi:10.1109\/ICEI18.2018.8448610<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('220','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Pannate Jongpanichkultorn, Daricha Sutivong, Prabhas Chongstitvatana<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('230','tp_links')\" style=\"cursor:pointer;\">Designing prediction markets to achieve convergence speed<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Engineering Journal, <\/span><span class=\"tp_pub_additional_volume\">vol. 22, <\/span><span class=\"tp_pub_additional_number\">no. 4, <\/span><span class=\"tp_pub_additional_pages\">pp. 177 \u2013 190, <\/span><span class=\"tp_pub_additional_year\">2018<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 01258281<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 0; All Open Access, Gold Open Access, Green Open Access)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_230\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('230','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_230\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('230','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_230\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('230','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_230\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Jongpanichkultorn2018177,<br \/>\r\ntitle = {Designing prediction markets to achieve convergence speed},<br \/>\r\nauthor = {Pannate Jongpanichkultorn and Daricha Sutivong and Prabhas Chongstitvatana},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85051290622&doi=10.4186%2fej.2018.22.4.177&partnerID=40&md5=db95bf5afbffd66eb3501e85aec0cc6c},<br \/>\r\ndoi = {10.4186\/ej.2018.22.4.177},<br \/>\r\nissn = {01258281},<br \/>\r\nyear  = {2018},<br \/>\r\ndate = {2018-01-01},<br \/>\r\njournal = {Engineering Journal},<br \/>\r\nvolume = {22},<br \/>\r\nnumber = {4},<br \/>\r\npages = {177 \u2013 190},<br \/>\r\npublisher = {Chulalongkorn University},<br \/>\r\nabstract = {The aim of this paper is twofold: to propose the model of artificial prediction markets that capture the characteristics of real prediction markets and to study the impact of key parameters on the performance of the proposed markets. In the experiments, the artificial markets are implemented and the market performance in terms of convergence speed is measured. Our experimental results show that the number of traders and the mean value of initial belief have no significant impact on the convergence speed. However, the trader\u2019s memory size impacts negatively on the convergence because of its delay in adjusting to the true value. Finally, the external information transmission rate and the ratio of smart traders have positive impacts on the convergence of the prediction markets. The insights can assist a market maker in designing and constructing more efficient prediction markets. \u00a9 2018, Chulalongkorn University. All rights reserved.},<br \/>\r\nnote = {Cited by: 0; All Open Access, Gold Open Access, Green Open Access},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('230','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_230\" style=\"display:none;\"><div class=\"tp_abstract_entry\">The aim of this paper is twofold: to propose the model of artificial prediction markets that capture the characteristics of real prediction markets and to study the impact of key parameters on the performance of the proposed markets. In the experiments, the artificial markets are implemented and the market performance in terms of convergence speed is measured. Our experimental results show that the number of traders and the mean value of initial belief have no significant impact on the convergence speed. However, the trader\u2019s memory size impacts negatively on the convergence because of its delay in adjusting to the true value. Finally, the external information transmission rate and the ratio of smart traders have positive impacts on the convergence of the prediction markets. The insights can assist a market maker in designing and constructing more efficient prediction markets. \u00a9 2018, Chulalongkorn University. All rights reserved.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('230','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_230\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85051290622&amp;doi=10.4186%2fej.2018.22.4.177&amp;partnerID=40&amp;md5=db95bf5afbffd66eb3501e85aec0cc6c\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85051290622&amp;doi=10.4186%[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85051290622&amp;doi=10.4186%[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.4186\/ej.2018.22.4.177\" title=\"Follow DOI:10.4186\/ej.2018.22.4.177\" target=\"_blank\">doi:10.4186\/ej.2018.22.4.177<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('230','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><\/div><div class=\"tablenav\"><div class=\"tablenav-pages\"><span class=\"displaying-num\">30 entries<\/span> <a class=\"page-numbers button disabled\">&laquo;<\/a> <a class=\"page-numbers button disabled\">&lsaquo;<\/a> 1 of 3 <a href=\"https:\/\/ienext.eng.chula.ac.th\/?page_id=5429&#038;lang=en&amp;limit=2&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"next page\" class=\"page-numbers button\">&rsaquo;<\/a> <a href=\"https:\/\/ienext.eng.chula.ac.th\/?page_id=5429&#038;lang=en&amp;limit=3&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"last page\" class=\"page-numbers button\">&raquo;<\/a> <\/div><\/div><\/div><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-3df2e2b e-flex e-con-boxed e-con e-parent\" data-id=\"3df2e2b\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>6th Floor of Engineering 4 Bldg., Room 605 +66-2218-6830 daricha.s@chula.ac.th Education Ph.D. in Management Science and Engineering (Ph.D. minor in [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"","ast-site-content-layout":"full-width-container","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"default","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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