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overflow: hidden; text-overflow: ellipsis; -webkit-line-clamp: 3; -webkit-box-orient: vertical; }</style><div class="col-xs-12 clearfix"><div class="u-floatLeft"><h1 class="PageHeader-title u-m0x u-fs30">Data mining (Data Analysis)</h1><div class="u-tcGrayDark">18,741 Followers</div><div class="u-tcGrayDark u-mt2x">Recent papers in <b>Data mining (Data Analysis)</b></div></div></div></div></div></div><div class="TabbedNavigation"><div class="container"><div class="row"><div class="col-xs-12 clearfix"><ul class="nav u-m0x u-p0x list-inline u-displayFlex"><li class="active"><a href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Top Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_/MostCited">Most Cited Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_/MostDownloaded">Most Downloaded Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_/MostRecent">Newest Papers</a></li><li><a class="" href="https://www.academia.edu/People/Data_mining_Data_Analysis_">People</a></li></ul></div><style type="text/css">ul.nav{flex-direction:row}@media(max-width: 567px){ul.nav{flex-direction:column}.TabbedNavigation li{max-width:100%}.TabbedNavigation li.active{background-color:var(--background-grey, #dddde2)}.TabbedNavigation li.active:before,.TabbedNavigation li.active:after{display:none}}</style></div></div></div><div class="container"><div class="row"><div class="col-xs-12"><div class="u-displayFlex"><div class="u-flexGrow1"><div class="works"><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_36015809" data-work_id="36015809" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/36015809/A_SURVEY_ON_DATA_MINING_IN_STEEL_INDUSTRIES">A SURVEY ON DATA MINING IN STEEL INDUSTRIES</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In Industrial environments, huge amount of data is being generated which in turn collected indatabase anddata warehouses from all involved areas such as planning, process design, materials, assembly, production, quality, process control,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36015809" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In Industrial environments, huge amount of data is being generated which in turn collected indatabase anddata warehouses from all involved areas such as planning, process design, materials, assembly, production, quality, process control, scheduling, fault detection,shutdown, customer relation management, and so on. Data Mining has become auseful tool for knowledge acquisition for industrial process of Iron and steel making. Due to the rapid growth in Data Mining, various industries started using data mining technology to search the hidden patterns, which might further be used to the system with the new knowledge which might design new models to enhance the production quality, productivity optimum cost and maintenance etc. The continuous improvement of all steel production process regarding the avoidance of quality deficiencies and the related improvement of production yield is an essential task of steel producer. Therefore, zero defect strategy is popular today and to maintain it several quality assurance techniques areused. The present report explains the methods of data mining and describes its application in the industrial environment and especially, in the steel industry.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36015809" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="1b4e7a124ab68f9b973096cc8c770279" rel="nofollow" data-download="{"attachment_id":55902558,"asset_id":36015809,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/55902558/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="26099471" href="https://independent.academia.edu/JournalIJCSES">International Journal of Computer Science and Engineering Survey (IJCSES)</a><script data-card-contents-for-user="26099471" type="text/json">{"id":26099471,"first_name":"International Journal of Computer Science and Engineering Survey","last_name":"(IJCSES)","domain_name":"independent","page_name":"JournalIJCSES","display_name":"International Journal of Computer Science and Engineering Survey (IJCSES)","profile_url":"https://independent.academia.edu/JournalIJCSES?f_ri=34344","photo":"https://0.academia-photos.com/26099471/8320289/35522477/s65_international_journal_of_computer_science_and_engineering_survey._ijcses_.png"}</script></span></span></li><li class="js-paper-rank-work_36015809 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36015809"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36015809, container: ".js-paper-rank-work_36015809", }); 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Data Mining has become auseful tool for knowledge acquisition for industrial process of Iron and steel making. Due to the rapid growth in Data Mining, various industries started using data mining technology to search the hidden patterns, which might further be used to the system with the new knowledge which might design new models to enhance the production quality, productivity optimum cost and maintenance etc. The continuous improvement of all steel production process regarding the avoidance of quality deficiencies and the related improvement of production yield is an essential task of steel producer. Therefore, zero defect strategy is popular today and to maintain it several quality assurance techniques areused. 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href="https://www.academia.edu/37706240/Scriptural_Justification_for_Data_Mining_Name_Institution_head_SCRIPTURAL_JUSTIFICATION_FOR_DATA_MINING">Scriptural Justification for Data Mining Name Institution head: SCRIPTURAL JUSTIFICATION FOR DATA MINING</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">Data mining is important in many areas of work. This paper explores data mining in relation to the Biblical scripture.</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/37706240" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="16a92116472b8d3e64409d19741c36e0" rel="nofollow" data-download="{"attachment_id":57697736,"asset_id":37706240,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/57697736/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="94854628" href="https://independent.academia.edu/ChozenRhymes">Chozen Rhymes</a><script data-card-contents-for-user="94854628" type="text/json">{"id":94854628,"first_name":"Chozen","last_name":"Rhymes","domain_name":"independent","page_name":"ChozenRhymes","display_name":"Chozen Rhymes","profile_url":"https://independent.academia.edu/ChozenRhymes?f_ri=34344","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_37706240 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="37706240"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 37706240, container: ".js-paper-rank-work_37706240", }); 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$(".js-view-count[data-work-id=37706240]").text(description); $(".js-view-count-work_37706240").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_37706240").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="37706240"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="5332" href="https://www.academia.edu/Documents/in/Theological_Interpretation_of_Christian_Scripture">Theological Interpretation of Christian Scripture</a>, <script data-card-contents-for-ri="5332" type="text/json">{"id":5332,"name":"Theological Interpretation of Christian Scripture","url":"https://www.academia.edu/Documents/in/Theological_Interpretation_of_Christian_Scripture?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="34344" href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Data mining (Data Analysis)</a>, <script data-card-contents-for-ri="34344" type="text/json">{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="49870" href="https://www.academia.edu/Documents/in/Comparative_Religious_Ethics">Comparative Religious Ethics</a><script data-card-contents-for-ri="49870" type="text/json">{"id":49870,"name":"Comparative Religious Ethics","url":"https://www.academia.edu/Documents/in/Comparative_Religious_Ethics?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=37706240]'), work: {"id":37706240,"title":"Scriptural Justification for Data Mining Name Institution head: SCRIPTURAL JUSTIFICATION FOR DATA MINING","created_at":"2018-11-05T09:36:57.237-08:00","url":"https://www.academia.edu/37706240/Scriptural_Justification_for_Data_Mining_Name_Institution_head_SCRIPTURAL_JUSTIFICATION_FOR_DATA_MINING?f_ri=34344","dom_id":"work_37706240","summary":"Data mining is important in many areas of work. This paper explores data mining in relation to the Biblical scripture. ","downloadable_attachments":[{"id":57697736,"asset_id":37706240,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":94854628,"first_name":"Chozen","last_name":"Rhymes","domain_name":"independent","page_name":"ChozenRhymes","display_name":"Chozen Rhymes","profile_url":"https://independent.academia.edu/ChozenRhymes?f_ri=34344","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false},{"id":5332,"name":"Theological Interpretation of Christian Scripture","url":"https://www.academia.edu/Documents/in/Theological_Interpretation_of_Christian_Scripture?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false},{"id":49870,"name":"Comparative Religious Ethics","url":"https://www.academia.edu/Documents/in/Comparative_Religious_Ethics?f_ri=34344","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_73186033" data-work_id="73186033" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/73186033/Towards_Food_Security_the_Prediction_of_Climatic_Factors_in_Nigeria_using_Random_Forest_Approach">Towards Food Security: the Prediction of Climatic Factors in Nigeria using Random Forest Approach</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">With the explosive growth in the world’s population which has little or no corresponding rise in the food production, food insecurity has become eminent, and hence, the need to seek for opportunities to increase food production in order... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_73186033" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">With the explosive growth in the world’s population which has little or no corresponding rise in the food production, food insecurity has become eminent, and hence, the need to seek for opportunities to increase food production in order to cater for this population is paramount. The second goal of the Sustainable Development Goals (SDGs) (i.e., ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture) set by the United Nations (UN) for the year 2030 clearly acknowledged this fact. Improving food production cannot be achieved using the obsolete conventional methods of agriculture by our farmers; hence, this study focuses on developing a model for predicting climatic conditions with a view to reducing their negative impact, and boosting the yield of crop. Temperature, wind, humidity and rainfall were considered as the effect of these factors is more devastating in Nigeria as compared to sun light which is always in abundance. We implemented ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/73186033" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="75f799215a2109c0eec5855ac6c786c4" rel="nofollow" data-download="{"attachment_id":81803887,"asset_id":73186033,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/81803887/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="111154967" href="https://independent.academia.edu/habibatiyemuhammed">habibat iye muhammed</a><script data-card-contents-for-user="111154967" type="text/json">{"id":111154967,"first_name":"habibat iye","last_name":"muhammed","domain_name":"independent","page_name":"habibatiyemuhammed","display_name":"habibat iye muhammed","profile_url":"https://independent.academia.edu/habibatiyemuhammed?f_ri=34344","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_73186033 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="73186033"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 73186033, container: ".js-paper-rank-work_73186033", }); 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$(".js-view-count[data-work-id=73186033]").text(description); $(".js-view-count-work_73186033").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_73186033").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="73186033"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">15</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="422" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="965" href="https://www.academia.edu/Documents/in/Formal_Concept_Analysis_Data_Mining_">Formal Concept Analysis (Data Mining)</a>, <script data-card-contents-for-ri="965" type="text/json">{"id":965,"name":"Formal Concept Analysis (Data Mining)","url":"https://www.academia.edu/Documents/in/Formal_Concept_Analysis_Data_Mining_?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="3541" href="https://www.academia.edu/Documents/in/Food_Security_and_Insecurity">Food Security and Insecurity</a>, <script data-card-contents-for-ri="3541" type="text/json">{"id":3541,"name":"Food Security and Insecurity","url":"https://www.academia.edu/Documents/in/Food_Security_and_Insecurity?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="16722" href="https://www.academia.edu/Documents/in/Impacts_Of_Climatic_Change_On_Agriculture">Impacts Of Climatic Change On Agriculture</a><script data-card-contents-for-ri="16722" type="text/json">{"id":16722,"name":"Impacts Of Climatic Change On Agriculture","url":"https://www.academia.edu/Documents/in/Impacts_Of_Climatic_Change_On_Agriculture?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=73186033]'), work: {"id":73186033,"title":"Towards Food Security: the Prediction of Climatic Factors in Nigeria using Random Forest Approach","created_at":"2022-03-06T08:13:34.385-08:00","url":"https://www.academia.edu/73186033/Towards_Food_Security_the_Prediction_of_Climatic_Factors_in_Nigeria_using_Random_Forest_Approach?f_ri=34344","dom_id":"work_73186033","summary":"With the explosive growth in the world’s population which has little or no corresponding rise in the food production, food insecurity has become eminent, and hence, the need to seek for opportunities to increase food production in order to cater for this population is paramount. The second goal of the Sustainable Development Goals (SDGs) (i.e., ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture) set by the United Nations (UN) for the year 2030 clearly acknowledged this fact. Improving food production cannot be achieved using the obsolete conventional methods of agriculture by our farmers; hence, this study focuses on developing a model for predicting climatic conditions with a view to reducing their negative impact, and boosting the yield of crop. Temperature, wind, humidity and rainfall were considered as the effect of these factors is more devastating in Nigeria as compared to sun light which is always in abundance. We implemented ...","downloadable_attachments":[{"id":81803887,"asset_id":73186033,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":111154967,"first_name":"habibat iye","last_name":"muhammed","domain_name":"independent","page_name":"habibatiyemuhammed","display_name":"habibat iye muhammed","profile_url":"https://independent.academia.edu/habibatiyemuhammed?f_ri=34344","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=34344","nofollow":false},{"id":965,"name":"Formal Concept Analysis (Data Mining)","url":"https://www.academia.edu/Documents/in/Formal_Concept_Analysis_Data_Mining_?f_ri=34344","nofollow":false},{"id":3541,"name":"Food Security and Insecurity","url":"https://www.academia.edu/Documents/in/Food_Security_and_Insecurity?f_ri=34344","nofollow":false},{"id":16722,"name":"Impacts Of Climatic Change On Agriculture","url":"https://www.academia.edu/Documents/in/Impacts_Of_Climatic_Change_On_Agriculture?f_ri=34344","nofollow":false},{"id":31774,"name":"Data sciences","url":"https://www.academia.edu/Documents/in/Data_sciences?f_ri=34344"},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":35801,"name":"Climate Change Impacts","url":"https://www.academia.edu/Documents/in/Climate_Change_Impacts?f_ri=34344"},{"id":38360,"name":"Food Security","url":"https://www.academia.edu/Documents/in/Food_Security?f_ri=34344"},{"id":69100,"name":"Data Science","url":"https://www.academia.edu/Documents/in/Data_Science?f_ri=34344"},{"id":154282,"name":"Climate Change and Its Impact on Food Security and Agriculture","url":"https://www.academia.edu/Documents/in/Climate_Change_and_Its_Impact_on_Food_Security_and_Agriculture?f_ri=34344"},{"id":170056,"name":"Random Forests","url":"https://www.academia.edu/Documents/in/Random_Forests?f_ri=34344"},{"id":191026,"name":"Climate Change and Food Security","url":"https://www.academia.edu/Documents/in/Climate_Change_and_Food_Security?f_ri=34344"},{"id":1311460,"name":"Computer Science Information Technology","url":"https://www.academia.edu/Documents/in/Computer_Science_Information_Technology?f_ri=34344"},{"id":1742173,"name":"data mining and Big data analytics in science and engineering","url":"https://www.academia.edu/Documents/in/data_mining_and_Big_data_analytics_in_science_and_engineering?f_ri=34344"},{"id":3134152,"name":"Random Forest Algorithm","url":"https://www.academia.edu/Documents/in/Random_Forest_Algorithm?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_72953376" data-work_id="72953376" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/72953376/Prediction_of_Agro_Products_Sales_Using_Regression_Algorithm">Prediction of Agro Products Sales Using Regression Algorithm</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This study aimed at developing a system using support vector machine (SVM) that will forecast sales of farm products for an agricultural farm so that managers can take strategic decisions timely to better market the excess farm products... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_72953376" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This study aimed at developing a system using support vector machine (SVM) that will forecast sales of farm products for an agricultural farm so that managers can take strategic decisions timely to better market the excess farm products which some by nature are perishable. The sales prediction model used SVMs and Fuzzy Theory. The implementation was done using Python Programming Language. The system comprised of three (3) modules: web interface, flask and the SVM Framework. To evaluate the result of the SVM model, the RBF neural network was used as a benchmark. Data of previous sales records from University of Agriculture Makurdi (UAM) farm was used to train and test the system. After training the network with data which covered the time period</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/72953376" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="a69acef451a24d3ad6b9a8036c1ebabc" rel="nofollow" data-download="{"attachment_id":81669880,"asset_id":72953376,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/81669880/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="40772826" href="https://umak.academia.edu/TerungwaSimonYange">Terungwa Simon Yange</a><script data-card-contents-for-user="40772826" type="text/json">{"id":40772826,"first_name":"Terungwa Simon","last_name":"Yange","domain_name":"umak","page_name":"TerungwaSimonYange","display_name":"Terungwa Simon Yange","profile_url":"https://umak.academia.edu/TerungwaSimonYange?f_ri=34344","photo":"https://0.academia-photos.com/40772826/14776107/36569527/s65_terungwa_simon.yange.jpg"}</script></span></span></li><li class="js-paper-rank-work_72953376 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="72953376"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 72953376, container: ".js-paper-rank-work_72953376", }); 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$(".js-view-count[data-work-id=72953376]").text(description); $(".js-view-count-work_72953376").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_72953376").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="72953376"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="422" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="965" href="https://www.academia.edu/Documents/in/Formal_Concept_Analysis_Data_Mining_">Formal Concept Analysis (Data Mining)</a>, <script data-card-contents-for-ri="965" type="text/json">{"id":965,"name":"Formal Concept Analysis (Data Mining)","url":"https://www.academia.edu/Documents/in/Formal_Concept_Analysis_Data_Mining_?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4205" href="https://www.academia.edu/Documents/in/Data_Analysis">Data Analysis</a><script data-card-contents-for-ri="4205" type="text/json">{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=72953376]'), work: {"id":72953376,"title":"Prediction of Agro Products Sales Using Regression Algorithm","created_at":"2022-03-03T12:56:49.280-08:00","url":"https://www.academia.edu/72953376/Prediction_of_Agro_Products_Sales_Using_Regression_Algorithm?f_ri=34344","dom_id":"work_72953376","summary":"This study aimed at developing a system using support vector machine (SVM) that will forecast sales of farm products for an agricultural farm so that managers can take strategic decisions timely to better market the excess farm products which some by nature are perishable. The sales prediction model used SVMs and Fuzzy Theory. The implementation was done using Python Programming Language. The system comprised of three (3) modules: web interface, flask and the SVM Framework. To evaluate the result of the SVM model, the RBF neural network was used as a benchmark. Data of previous sales records from University of Agriculture Makurdi (UAM) farm was used to train and test the system. After training the network with data which covered the time period","downloadable_attachments":[{"id":81669880,"asset_id":72953376,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":40772826,"first_name":"Terungwa Simon","last_name":"Yange","domain_name":"umak","page_name":"TerungwaSimonYange","display_name":"Terungwa Simon Yange","profile_url":"https://umak.academia.edu/TerungwaSimonYange?f_ri=34344","photo":"https://0.academia-photos.com/40772826/14776107/36569527/s65_terungwa_simon.yange.jpg"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=34344","nofollow":false},{"id":965,"name":"Formal Concept Analysis (Data Mining)","url":"https://www.academia.edu/Documents/in/Formal_Concept_Analysis_Data_Mining_?f_ri=34344","nofollow":false},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false},{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_43695084" data-work_id="43695084" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/43695084/International_Conference_on_Data_Science_and_Applications_DSA_2020_">International Conference on Data Science and Applications (DSA 2020)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">International Conference on Data Science and Applications (DSA 2020) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Data Science and Applications. It... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_43695084" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">International Conference on Data Science and Applications (DSA 2020) will act as a major<br />forum for the presentation of innovative ideas, approaches, developments, and research<br />projects in the areas of Data Science and Applications. It will also serve to facilitate the<br />exchange of information between researchers and industry professionals to discuss the latest<br />issues and advancement in the area of Data Science & Applications</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/43695084" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="418dba5e23247360c882da721354a6ae" rel="nofollow" data-download="{"attachment_id":64778458,"asset_id":43695084,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/64778458/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="16715850" href="https://independent.academia.edu/IJDKPJOURNAL">International Journal of Data Mining & Knowledge Management Process ( IJDKP )</a><script data-card-contents-for-user="16715850" type="text/json">{"id":16715850,"first_name":"International Journal of Data Mining \u0026 Knowledge Management Process","last_name":"( IJDKP )","domain_name":"independent","page_name":"IJDKPJOURNAL","display_name":"International Journal of Data Mining \u0026 Knowledge Management Process ( IJDKP )","profile_url":"https://independent.academia.edu/IJDKPJOURNAL?f_ri=34344","photo":"https://0.academia-photos.com/16715850/4568261/39147186/s65_international_journal_of_data_mining_knowledge_management_process._ijdkp_.png"}</script></span></span></li><li class="js-paper-rank-work_43695084 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="43695084"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 43695084, container: ".js-paper-rank-work_43695084", }); });</script></li><li class="js-percentile-work_43695084 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 43695084; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_43695084"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_43695084 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="43695084"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43695084; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=43695084]").text(description); $(".js-view-count-work_43695084").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_43695084").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="43695084"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">20</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="3419" href="https://www.academia.edu/Documents/in/Multimedia">Multimedia</a>, <script data-card-contents-for-ri="3419" type="text/json">{"id":3419,"name":"Multimedia","url":"https://www.academia.edu/Documents/in/Multimedia?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="9351" href="https://www.academia.edu/Documents/in/Image_Analysis">Image Analysis</a>, <script data-card-contents-for-ri="9351" type="text/json">{"id":9351,"name":"Image Analysis","url":"https://www.academia.edu/Documents/in/Image_Analysis?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="10601" href="https://www.academia.edu/Documents/in/Data_Warehousing">Data Warehousing</a><script data-card-contents-for-ri="10601" type="text/json">{"id":10601,"name":"Data Warehousing","url":"https://www.academia.edu/Documents/in/Data_Warehousing?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=43695084]'), work: {"id":43695084,"title":"International Conference on Data Science and Applications (DSA 2020)","created_at":"2020-07-23T04:13:28.802-07:00","url":"https://www.academia.edu/43695084/International_Conference_on_Data_Science_and_Applications_DSA_2020_?f_ri=34344","dom_id":"work_43695084","summary":"International Conference on Data Science and Applications (DSA 2020) will act as a major\nforum for the presentation of innovative ideas, approaches, developments, and research\nprojects in the areas of Data Science and Applications. It will also serve to facilitate the\nexchange of information between researchers and industry professionals to discuss the latest\nissues and advancement in the area of Data Science \u0026 Applications","downloadable_attachments":[{"id":64778458,"asset_id":43695084,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":16715850,"first_name":"International Journal of Data Mining \u0026 Knowledge Management Process","last_name":"( IJDKP )","domain_name":"independent","page_name":"IJDKPJOURNAL","display_name":"International Journal of Data Mining \u0026 Knowledge Management Process ( IJDKP )","profile_url":"https://independent.academia.edu/IJDKPJOURNAL?f_ri=34344","photo":"https://0.academia-photos.com/16715850/4568261/39147186/s65_international_journal_of_data_mining_knowledge_management_process._ijdkp_.png"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false},{"id":3419,"name":"Multimedia","url":"https://www.academia.edu/Documents/in/Multimedia?f_ri=34344","nofollow":false},{"id":9351,"name":"Image Analysis","url":"https://www.academia.edu/Documents/in/Image_Analysis?f_ri=34344","nofollow":false},{"id":10601,"name":"Data Warehousing","url":"https://www.academia.edu/Documents/in/Data_Warehousing?f_ri=34344","nofollow":false},{"id":12512,"name":"Data Quality (Computer Science)","url":"https://www.academia.edu/Documents/in/Data_Quality_Computer_Science_?f_ri=34344"},{"id":23995,"name":"Educational Data Mining","url":"https://www.academia.edu/Documents/in/Educational_Data_Mining?f_ri=34344"},{"id":31774,"name":"Data sciences","url":"https://www.academia.edu/Documents/in/Data_sciences?f_ri=34344"},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":39682,"name":"Graph Data Mining","url":"https://www.academia.edu/Documents/in/Graph_Data_Mining?f_ri=34344"},{"id":39693,"name":"Distributed Data Mining","url":"https://www.academia.edu/Documents/in/Distributed_Data_Mining?f_ri=34344"},{"id":60650,"name":"Data Warehousing and Data Mining","url":"https://www.academia.edu/Documents/in/Data_Warehousing_and_Data_Mining?f_ri=34344"},{"id":69100,"name":"Data Science","url":"https://www.academia.edu/Documents/in/Data_Science?f_ri=34344"},{"id":86157,"name":"Graph Mining","url":"https://www.academia.edu/Documents/in/Graph_Mining?f_ri=34344"},{"id":99804,"name":"Privacy Preserving Data Mining","url":"https://www.academia.edu/Documents/in/Privacy_Preserving_Data_Mining?f_ri=34344"},{"id":106145,"name":"Classification","url":"https://www.academia.edu/Documents/in/Classification?f_ri=34344"},{"id":108488,"name":"Data Mining – Concepts and Techniques","url":"https://www.academia.edu/Documents/in/Data_Mining_Concepts_and_Techniques?f_ri=34344"},{"id":295755,"name":"Data Sciences for Complex Systems","url":"https://www.academia.edu/Documents/in/Data_Sciences_for_Complex_Systems?f_ri=34344"},{"id":319636,"name":"Data mining and Warehousing","url":"https://www.academia.edu/Documents/in/Data_mining_and_Warehousing?f_ri=34344"},{"id":413148,"name":"Big Data / Analytics / Data Mining","url":"https://www.academia.edu/Documents/in/Big_Data_Analytics_Data_Mining?f_ri=34344"},{"id":3050442,"name":"Data Processing and Computer Science","url":"https://www.academia.edu/Documents/in/Data_Processing_and_Computer_Science?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_36577765" data-work_id="36577765" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/36577765/A_Conceptual_Framework_for_the_Mining_and_Analysis_of_the_Social_Media_Data">A Conceptual Framework for the Mining and Analysis of the Social Media Data</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Social media data possess the characteristics of Big Data such as volume, veracity, velocity, variability and value. These characteristics make its analysis a bit more challenging than conventional data. Manual analysis approaches are... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36577765" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Social media data possess the characteristics of Big Data such as volume, veracity, velocity, variability and value. These characteristics make its analysis a bit more challenging than conventional data. Manual analysis approaches are unable to cope with the fast pace at which data is being generated. Processing data manually is also time consuming and requires a lot of effort as compared to using computational methods. However, computational analysis methods usually cannot capture in-depth meanings (semantics) within data. On their individual capacity, each approach is insufficient. As a solution, we propose a Conceptual Framework, which integrates both the traditional approaches and computational approaches to the mining and analysis of social media data. This allows us to leverage the strengths of traditional content analysis, with its regular meticulousness and relative understanding, whilst exploiting the extensive capacity of Big Data analytics and accuracy of computational methods. The proposed Conceptual Framework was evaluated in two stages using an example case of the political landscape of Botswana data collected from Facebook and Twitter platforms. Firstly, a user study was carried through the Inductive Content Analysis (ICA) process using the collected data. Additionally, a questionnaire was conducted to evaluate the usability of ICA as perceived by the participants. Secondly, an experimental study was conducted to evaluate the performance of data mining algorithms on the data from the ICA process. The results, from the user study, showed that the ICA process is flexible and systematic in terms of allowing the users to analyse social media data, hence reducing the time and effort required to manually analyse data. The users’ perception in terms of ease of use and usefulness of the ICA on analysing social media data is positive. The results from the experimental study show that data mining algorithms produced higher accurate results in classifying data when supplied with data from the ICA process. That is, when data mining algorithms are integrated with the ICA process, they are able to overcome the difficulty they face to capture semantics within data. Overall, the results of this study, including the Proposed Conceptual Framework are useful to scholars and practitioners who wish to do some researches on social media data mining and analysis. The Framework serves as a guide to the mining and analysis of the social media data in a systematic manner.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36577765" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6233d96f0e4227f742ec829b96d21b6a" rel="nofollow" data-download="{"attachment_id":56504317,"asset_id":36577765,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/56504317/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="7835005" href="https://sethunya.academia.edu/SethunyaJoseph">Sethunya R Joseph</a><script data-card-contents-for-user="7835005" type="text/json">{"id":7835005,"first_name":"Sethunya","last_name":"Joseph","domain_name":"sethunya","page_name":"SethunyaJoseph","display_name":"Sethunya R Joseph","profile_url":"https://sethunya.academia.edu/SethunyaJoseph?f_ri=34344","photo":"https://0.academia-photos.com/7835005/2837757/20049590/s65_sethunya.joseph.jpg"}</script></span></span></li><li class="js-paper-rank-work_36577765 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36577765"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36577765, container: ".js-paper-rank-work_36577765", }); });</script></li><li class="js-percentile-work_36577765 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 36577765; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_36577765"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_36577765 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="36577765"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 36577765; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=36577765]").text(description); $(".js-view-count-work_36577765").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_36577765").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="36577765"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="34344" href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Data mining (Data Analysis)</a>, <script data-card-contents-for-ri="34344" type="text/json">{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="256465" href="https://www.academia.edu/Documents/in/Social_Networking_and_Social_Media">Social Networking & Social Media</a><script data-card-contents-for-ri="256465" type="text/json">{"id":256465,"name":"Social Networking \u0026 Social Media","url":"https://www.academia.edu/Documents/in/Social_Networking_and_Social_Media?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=36577765]'), work: {"id":36577765,"title":"A Conceptual Framework for the Mining and Analysis of the Social Media Data","created_at":"2018-05-06T22:28:44.616-07:00","url":"https://www.academia.edu/36577765/A_Conceptual_Framework_for_the_Mining_and_Analysis_of_the_Social_Media_Data?f_ri=34344","dom_id":"work_36577765","summary":"Social media data possess the characteristics of Big Data such as volume, veracity, velocity, variability and value. These characteristics make its analysis a bit more challenging than conventional data. Manual analysis approaches are unable to cope with the fast pace at which data is being generated. Processing data manually is also time consuming and requires a lot of effort as compared to using computational methods. However, computational analysis methods usually cannot capture in-depth meanings (semantics) within data. On their individual capacity, each approach is insufficient. As a solution, we propose a Conceptual Framework, which integrates both the traditional approaches and computational approaches to the mining and analysis of social media data. This allows us to leverage the strengths of traditional content analysis, with its regular meticulousness and relative understanding, whilst exploiting the extensive capacity of Big Data analytics and accuracy of computational methods. The proposed Conceptual Framework was evaluated in two stages using an example case of the political landscape of Botswana data collected from Facebook and Twitter platforms. Firstly, a user study was carried through the Inductive Content Analysis (ICA) process using the collected data. Additionally, a questionnaire was conducted to evaluate the usability of ICA as perceived by the participants. Secondly, an experimental study was conducted to evaluate the performance of data mining algorithms on the data from the ICA process. The results, from the user study, showed that the ICA process is flexible and systematic in terms of allowing the users to analyse social media data, hence reducing the time and effort required to manually analyse data. The users’ perception in terms of ease of use and usefulness of the ICA on analysing social media data is positive. The results from the experimental study show that data mining algorithms produced higher accurate results in classifying data when supplied with data from the ICA process. That is, when data mining algorithms are integrated with the ICA process, they are able to overcome the difficulty they face to capture semantics within data. Overall, the results of this study, including the Proposed Conceptual Framework are useful to scholars and practitioners who wish to do some researches on social media data mining and analysis. The Framework serves as a guide to the mining and analysis of the social media data in a systematic manner.","downloadable_attachments":[{"id":56504317,"asset_id":36577765,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":7835005,"first_name":"Sethunya","last_name":"Joseph","domain_name":"sethunya","page_name":"SethunyaJoseph","display_name":"Sethunya R Joseph","profile_url":"https://sethunya.academia.edu/SethunyaJoseph?f_ri=34344","photo":"https://0.academia-photos.com/7835005/2837757/20049590/s65_sethunya.joseph.jpg"}],"research_interests":[{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false},{"id":256465,"name":"Social Networking \u0026 Social Media","url":"https://www.academia.edu/Documents/in/Social_Networking_and_Social_Media?f_ri=34344","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_76065353" data-work_id="76065353" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/76065353/Modeling_of_functional_support_of_sports_activities_of_biathletes_of_different_qualifications">Modeling of functional support of sports activities of biathletes of different qualifications</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/76065353" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="a6d34d6821be3b2a9e8841cf4971b215" rel="nofollow" data-download="{"attachment_id":83749783,"asset_id":76065353,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/83749783/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="99975710" href="https://independent.academia.edu/%D0%A1%D0%B5%D1%80%D0%B3%D0%B5%D0%B9%D0%A2%D1%80%D0%B0%D1%87%D1%83%D0%BA">Сергей Трачук</a><script data-card-contents-for-user="99975710" type="text/json">{"id":99975710,"first_name":"Сергей","last_name":"Трачук","domain_name":"independent","page_name":"СергейТрачук","display_name":"Сергей Трачук","profile_url":"https://independent.academia.edu/%D0%A1%D0%B5%D1%80%D0%B3%D0%B5%D0%B9%D0%A2%D1%80%D0%B0%D1%87%D1%83%D0%BA?f_ri=34344","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_76065353 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="76065353"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 76065353, container: ".js-paper-rank-work_76065353", }); });</script></li><li class="js-percentile-work_76065353 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76065353; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_76065353"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_76065353 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="76065353"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76065353; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76065353]").text(description); $(".js-view-count-work_76065353").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_76065353").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="76065353"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="221" href="https://www.academia.edu/Documents/in/Psychology">Psychology</a>, <script data-card-contents-for-ri="221" type="text/json">{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="465" href="https://www.academia.edu/Documents/in/Artificial_Intelligence">Artificial Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a><script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=76065353]'), work: {"id":76065353,"title":"Modeling of functional support of sports activities of biathletes of different qualifications","created_at":"2022-04-10T22:48:46.173-07:00","url":"https://www.academia.edu/76065353/Modeling_of_functional_support_of_sports_activities_of_biathletes_of_different_qualifications?f_ri=34344","dom_id":"work_76065353","summary":null,"downloadable_attachments":[{"id":83749783,"asset_id":76065353,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":99975710,"first_name":"Сергей","last_name":"Трачук","domain_name":"independent","page_name":"СергейТрачук","display_name":"Сергей Трачук","profile_url":"https://independent.academia.edu/%D0%A1%D0%B5%D1%80%D0%B3%D0%B5%D0%B9%D0%A2%D1%80%D0%B0%D1%87%D1%83%D0%BA?f_ri=34344","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=34344","nofollow":false},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=34344","nofollow":false},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=34344","nofollow":false},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":62007,"name":"Artificial Intelligence in Education","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence_in_Education?f_ri=34344"},{"id":526921,"name":"Biathlon","url":"https://www.academia.edu/Documents/in/Biathlon?f_ri=34344"},{"id":3802137,"name":"Physical working capacity","url":"https://www.academia.edu/Documents/in/Physical_working_capacity?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_40775011" data-work_id="40775011" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/40775011/Artificial_gravitational_cuckoo_search_algorithm_along_with_particle_bee_optimized_associative_memory_neural_network_for_feature_selection_in_heart_disease_classification">Artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network for feature selection in heart disease classification</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Now-a-days heart disease is one of the serious disease because most of the people affected by this disease that leads to create death. Due to the serious risk of this heart disease, it has been identified in the beginning stage for... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_40775011" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Now-a-days heart disease is one of the serious disease because most of the people affected by this disease that leads to create death. Due to the serious risk of this heart disease, it has been identified in the beginning stage for avoiding the risk factors. Then the earlier detection system has been created by utilizing optimized and hybridized techniques to recognize the heart disease in earlier stage. So, artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network is introduced to manage the features present in the earlier heart disease classification system. Initially , heart disease related information is collected from Heart Disease Data Set-UCI repository. The collected information is huge in dimension which is difficult to process, that reduces the efficiency of heart disease identification system. So, the dimensionality of the features are reduces according to the behavior of gravitational cuckoo search algorithm. The selected features are processed by above defined associative memory classifier. Then the efficiency of the system is evaluated with the help of MATLAB based experimental results. Keywords Heart disease · Artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network · Heart disease data set-UCI repository</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/40775011" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ed014f94d37cdb3a24f874e6d5715169" rel="nofollow" data-download="{"attachment_id":61054958,"asset_id":40775011,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/61054958/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="133051421" href="https://vit-in.academia.edu/ParveenSultlana">H.Parveen Sultana</a><script data-card-contents-for-user="133051421" type="text/json">{"id":133051421,"first_name":"H.Parveen","last_name":"Sultana","domain_name":"vit-in","page_name":"ParveenSultlana","display_name":"H.Parveen Sultana","profile_url":"https://vit-in.academia.edu/ParveenSultlana?f_ri=34344","photo":"https://0.academia-photos.com/133051421/34854864/30462330/s65_parveen.sultlana.jpg"}</script></span></span></li><li class="js-paper-rank-work_40775011 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="40775011"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 40775011, container: ".js-paper-rank-work_40775011", }); });</script></li><li class="js-percentile-work_40775011 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40775011; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_40775011"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_40775011 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="40775011"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40775011; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40775011]").text(description); $(".js-view-count-work_40775011").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_40775011").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="40775011"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="34344" href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Data mining (Data Analysis)</a>, <script data-card-contents-for-ri="34344" type="text/json">{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="34737" href="https://www.academia.edu/Documents/in/Gravitational_Search_Algorithm">Gravitational Search Algorithm</a>, <script data-card-contents-for-ri="34737" type="text/json">{"id":34737,"name":"Gravitational Search Algorithm","url":"https://www.academia.edu/Documents/in/Gravitational_Search_Algorithm?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="228832" href="https://www.academia.edu/Documents/in/Heart_Disease">Heart Disease</a><script data-card-contents-for-ri="228832" type="text/json">{"id":228832,"name":"Heart Disease","url":"https://www.academia.edu/Documents/in/Heart_Disease?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=40775011]'), work: {"id":40775011,"title":"Artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network for feature selection in heart disease classification","created_at":"2019-10-29T02:48:22.291-07:00","url":"https://www.academia.edu/40775011/Artificial_gravitational_cuckoo_search_algorithm_along_with_particle_bee_optimized_associative_memory_neural_network_for_feature_selection_in_heart_disease_classification?f_ri=34344","dom_id":"work_40775011","summary":"Now-a-days heart disease is one of the serious disease because most of the people affected by this disease that leads to create death. Due to the serious risk of this heart disease, it has been identified in the beginning stage for avoiding the risk factors. Then the earlier detection system has been created by utilizing optimized and hybridized techniques to recognize the heart disease in earlier stage. So, artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network is introduced to manage the features present in the earlier heart disease classification system. Initially , heart disease related information is collected from Heart Disease Data Set-UCI repository. The collected information is huge in dimension which is difficult to process, that reduces the efficiency of heart disease identification system. So, the dimensionality of the features are reduces according to the behavior of gravitational cuckoo search algorithm. The selected features are processed by above defined associative memory classifier. Then the efficiency of the system is evaluated with the help of MATLAB based experimental results. Keywords Heart disease · Artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network · Heart disease data set-UCI repository","downloadable_attachments":[{"id":61054958,"asset_id":40775011,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":133051421,"first_name":"H.Parveen","last_name":"Sultana","domain_name":"vit-in","page_name":"ParveenSultlana","display_name":"H.Parveen Sultana","profile_url":"https://vit-in.academia.edu/ParveenSultlana?f_ri=34344","photo":"https://0.academia-photos.com/133051421/34854864/30462330/s65_parveen.sultlana.jpg"}],"research_interests":[{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false},{"id":34737,"name":"Gravitational Search Algorithm","url":"https://www.academia.edu/Documents/in/Gravitational_Search_Algorithm?f_ri=34344","nofollow":false},{"id":228832,"name":"Heart Disease","url":"https://www.academia.edu/Documents/in/Heart_Disease?f_ri=34344","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_40597774" data-work_id="40597774" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/40597774/%CE%A3%CF%85%CE%BB%CE%BB%CE%AD%CE%B3%CE%BF%CE%BD%CF%84%CE%B1%CF%82_%CE%A0%CE%BB%CE%B7%CF%81%CE%BF%CF%86%CE%BF%CF%81%CE%AF%CE%B5%CF%82_%CF%83%CF%84%CE%B7%CE%BD_%CE%95%CF%80%CE%BF%CF%87%CE%AE_%CF%84%CE%B7%CF%82_%CE%95%CF%80%CE%AF%CE%BC%CE%BF%CE%BD%CE%B7%CF%82_%CE%95%CF%80%CE%B9%CF%84%CE%AE%CF%81%CE%B7%CF%83%CE%B7%CF%82_">Συλλέγοντας Πληροφορίες στην Εποχή της "Επίμονης Επιτήρησης".</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Intelligence Collection in the Era of "Persistent Surveillance" . H Παγκόσμια Στρατηγική Ασφάλειας της Ε.Ε. (EUGS , 2016), που υπογράφεται από την Ύπατη Εκπρόσωπο της Ένωσης για Θέματα Εξωτερικής Πολιτικής και Πολιτικής Ασφαλείας και... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_40597774" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Intelligence Collection in the Era of "Persistent Surveillance" . H Παγκόσμια Στρατηγική Ασφάλειας της Ε.Ε. (EUGS , 2016), που υπογράφεται από την Ύπατη Εκπρόσωπο της Ένωσης για Θέματα Εξωτερικής Πολιτικής και Πολιτικής Ασφαλείας και Αντιπρόεδρος της Ευρωπαϊκής Επιτροπής, F. Mogherini,  περιλαμβάνει ανάμεσα σε άλλες, και την επιχειρησιακή απαίτηση, της «μόνιμης παρατήρησης Γης», η οποία χρειάζεται για να παρέχει τις απαραίτητες πληροφορίες για την υποστήριξη της εξωτερικής της πολιτικής. <br />Η απαίτηση αυτή, αν και έχει καταγραφεί στην Ευρώπη από τα μέσα της δεκαετίας του 2000, είναι η πρώτη φορά που αναδεικνύεται έντονα σε ένα τέτοιου υψηλού επιπέδου κείμενο. Ο όρος έχει πρωτοπαρουσιαστεί στην άλλη πλευρά του Ατλαντικού στις αρχές της δεκαετίας του 2000, ως η εποχή της «επίμονης επιτήρησης» και σχετιστεί με την επιχειρησιακή λειτουργία των μη επανδρωμένων συστημάτων. <br />Όμως και τα συστήματα αυτά δεν μπορούν να ξεπεράσουν αυτό που η Κοινότητα Πληροφοριών εξακολουθεί να αναφέρει ως «άρνηση της επικράτειας»,  που παραπέμπει στην «πολιτική ευαισθησία» χρήσης εναέριων μέσων που κάνουν προβληματική μια υπέρπτηση πάνω από μια εχθρική χώρα, η οποία παραβιάζει το διεθνές δίκαιο. Όπως έδειξαν οι περιπτώσεις στη Σοβιετική Ένωση με το αμερικανικό φωτοαναγνωριστικό U-2 το 1960 και στη Βόρεια Κορέα με το αεροσκάφος ηλεκτρονικής συλλογής EC-121 το 1969, τα εναέρια μέσα μπορεί να καταρριφθούν. <br />Η χρήση του Εξωατμοσφαιρικού Διαστήματος φαντάζει ως η κύρια εναλλακτική λύση, αφού οι δορυφόροι, σε γενικές γραμμές, είναι, ακόμα, άτρωτοι.. Αν και οι δορυφόροι δεν μπορούν να αιωρούνται πάνω από μία θέση για μεγάλο χρονικό διάστημα, εν τούτοις παρουσιάζονται λύσεις που τείνουν να υποστηρίξουν σε μεγάλο βαθμό την ως άνω επιχειρησιακή απαίτηση. Η παρουσίαση αυτή θα αναφερθεί σε συστήματα που αναφέρονται στην ως άνω προσέγγιση, έχοντας πάντοτε υπόψιν ότι για την υλοποίηση της συλλογής πληροφοριών, χρειάζονται όλα τα συστήματα. Αλλά ακόμα και τότε θα υπάρχουν κάποια άγνωστα στοιχεία.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/40597774" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0354e653b4511e4a8f6f5fcdfc77ebbe" rel="nofollow" data-download="{"attachment_id":61514615,"asset_id":40597774,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/61514615/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="13349243" href="https://haf.academia.edu/AlexandrosKolovos">Alexandros Kolovos</a><script data-card-contents-for-user="13349243" type="text/json">{"id":13349243,"first_name":"Alexandros","last_name":"Kolovos","domain_name":"haf","page_name":"AlexandrosKolovos","display_name":"Alexandros Kolovos","profile_url":"https://haf.academia.edu/AlexandrosKolovos?f_ri=34344","photo":"https://0.academia-photos.com/13349243/14239778/15203700/s65_alexandros.kolovos.jpg"}</script></span></span></li><li class="js-paper-rank-work_40597774 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="40597774"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 40597774, container: ".js-paper-rank-work_40597774", }); });</script></li><li class="js-percentile-work_40597774 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40597774; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_40597774"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_40597774 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="40597774"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40597774; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40597774]").text(description); $(".js-view-count-work_40597774").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_40597774").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="40597774"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2674" href="https://www.academia.edu/Documents/in/Intelligence">Intelligence</a>, <script data-card-contents-for-ri="2674" type="text/json">{"id":2674,"name":"Intelligence","url":"https://www.academia.edu/Documents/in/Intelligence?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="7480" href="https://www.academia.edu/Documents/in/Surveillance_Studies">Surveillance Studies</a>, <script data-card-contents-for-ri="7480" type="text/json">{"id":7480,"name":"Surveillance Studies","url":"https://www.academia.edu/Documents/in/Surveillance_Studies?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="29487" href="https://www.academia.edu/Documents/in/Space">Space</a>, <script data-card-contents-for-ri="29487" type="text/json">{"id":29487,"name":"Space","url":"https://www.academia.edu/Documents/in/Space?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="34344" href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Data mining (Data Analysis)</a><script data-card-contents-for-ri="34344" type="text/json">{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=40597774]'), work: {"id":40597774,"title":"Συλλέγοντας Πληροφορίες στην Εποχή της \"Επίμονης Επιτήρησης\".","created_at":"2019-10-12T00:06:30.649-07:00","url":"https://www.academia.edu/40597774/%CE%A3%CF%85%CE%BB%CE%BB%CE%AD%CE%B3%CE%BF%CE%BD%CF%84%CE%B1%CF%82_%CE%A0%CE%BB%CE%B7%CF%81%CE%BF%CF%86%CE%BF%CF%81%CE%AF%CE%B5%CF%82_%CF%83%CF%84%CE%B7%CE%BD_%CE%95%CF%80%CE%BF%CF%87%CE%AE_%CF%84%CE%B7%CF%82_%CE%95%CF%80%CE%AF%CE%BC%CE%BF%CE%BD%CE%B7%CF%82_%CE%95%CF%80%CE%B9%CF%84%CE%AE%CF%81%CE%B7%CF%83%CE%B7%CF%82_?f_ri=34344","dom_id":"work_40597774","summary":"Intelligence Collection in the Era of \"Persistent Surveillance\" . H Παγκόσμια Στρατηγική Ασφάλειας της Ε.Ε. (EUGS , 2016), που υπογράφεται από την Ύπατη Εκπρόσωπο της Ένωσης για Θέματα Εξωτερικής Πολιτικής και Πολιτικής Ασφαλείας και Αντιπρόεδρος της Ευρωπαϊκής Επιτροπής, F. Mogherini, περιλαμβάνει ανάμεσα σε άλλες, και την επιχειρησιακή απαίτηση, της «μόνιμης παρατήρησης Γης», η οποία χρειάζεται για να παρέχει τις απαραίτητες πληροφορίες για την υποστήριξη της εξωτερικής της πολιτικής. \nΗ απαίτηση αυτή, αν και έχει καταγραφεί στην Ευρώπη από τα μέσα της δεκαετίας του 2000, είναι η πρώτη φορά που αναδεικνύεται έντονα σε ένα τέτοιου υψηλού επιπέδου κείμενο. Ο όρος έχει πρωτοπαρουσιαστεί στην άλλη πλευρά του Ατλαντικού στις αρχές της δεκαετίας του 2000, ως η εποχή της «επίμονης επιτήρησης» και σχετιστεί με την επιχειρησιακή λειτουργία των μη επανδρωμένων συστημάτων. \nΌμως και τα συστήματα αυτά δεν μπορούν να ξεπεράσουν αυτό που η Κοινότητα Πληροφοριών εξακολουθεί να αναφέρει ως «άρνηση της επικράτειας», που παραπέμπει στην «πολιτική ευαισθησία» χρήσης εναέριων μέσων που κάνουν προβληματική μια υπέρπτηση πάνω από μια εχθρική χώρα, η οποία παραβιάζει το διεθνές δίκαιο. Όπως έδειξαν οι περιπτώσεις στη Σοβιετική Ένωση με το αμερικανικό φωτοαναγνωριστικό U-2 το 1960 και στη Βόρεια Κορέα με το αεροσκάφος ηλεκτρονικής συλλογής EC-121 το 1969, τα εναέρια μέσα μπορεί να καταρριφθούν. \nΗ χρήση του Εξωατμοσφαιρικού Διαστήματος φαντάζει ως η κύρια εναλλακτική λύση, αφού οι δορυφόροι, σε γενικές γραμμές, είναι, ακόμα, άτρωτοι.. Αν και οι δορυφόροι δεν μπορούν να αιωρούνται πάνω από μία θέση για μεγάλο χρονικό διάστημα, εν τούτοις παρουσιάζονται λύσεις που τείνουν να υποστηρίξουν σε μεγάλο βαθμό την ως άνω επιχειρησιακή απαίτηση. Η παρουσίαση αυτή θα αναφερθεί σε συστήματα που αναφέρονται στην ως άνω προσέγγιση, έχοντας πάντοτε υπόψιν ότι για την υλοποίηση της συλλογής πληροφοριών, χρειάζονται όλα τα συστήματα. Αλλά ακόμα και τότε θα υπάρχουν κάποια άγνωστα στοιχεία.\n","downloadable_attachments":[{"id":61514615,"asset_id":40597774,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":13349243,"first_name":"Alexandros","last_name":"Kolovos","domain_name":"haf","page_name":"AlexandrosKolovos","display_name":"Alexandros Kolovos","profile_url":"https://haf.academia.edu/AlexandrosKolovos?f_ri=34344","photo":"https://0.academia-photos.com/13349243/14239778/15203700/s65_alexandros.kolovos.jpg"}],"research_interests":[{"id":2674,"name":"Intelligence","url":"https://www.academia.edu/Documents/in/Intelligence?f_ri=34344","nofollow":false},{"id":7480,"name":"Surveillance Studies","url":"https://www.academia.edu/Documents/in/Surveillance_Studies?f_ri=34344","nofollow":false},{"id":29487,"name":"Space","url":"https://www.academia.edu/Documents/in/Space?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false},{"id":123217,"name":"UAVs","url":"https://www.academia.edu/Documents/in/UAVs?f_ri=34344"},{"id":130141,"name":"Satellites","url":"https://www.academia.edu/Documents/in/Satellites?f_ri=34344"},{"id":423582,"name":"Dissemination","url":"https://www.academia.edu/Documents/in/Dissemination?f_ri=34344"},{"id":3405368,"name":"High Altitude Balloons","url":"https://www.academia.edu/Documents/in/High_Altitude_Balloons?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_38561952" data-work_id="38561952" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/38561952/Data_mining_approach_to_predicting_the_performance_of_first_year_student_in_a_university_using_the_admission_requirements">Data mining approach to predicting the performance of first year student in a university using the admission requirements</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The academic performance of a student in a university is determined by a number of factors, both academic and non-academic. Student that previously excelled at the secondary school level may lose focus due to peer pressure and social... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_38561952" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The academic performance of a student in a university is determined by a number of factors, both academic and non-academic. Student that previously excelled at the secondary school level may lose focus due to peer pressure and social lifestyle while those who previously struggled due to family distractions may be able to focus away from home, and as a result excel at the university. University admission in Nigeria is typically based on cognitive entry characteristics of a student which is mostly academic , and may not necessarily translate to excellence once in the university. In this study, the relationship between the cognitive admission entry requirements and the academic performance of students in their first year, using their CGPA and class of degree was examined using six data mining algorithms in KNIME and Orange platforms. Maximum accuracies of 50.23% and 51.9% respectively were observed, and the results were verified using regression models, with R2 values of 0.207 and 0.232 recorded which indicate that students' performance in their first year is not fully explained by cognitive entry requirements.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/38561952" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9afbfebb552d51be19c750eb34455ca0" rel="nofollow" data-download="{"attachment_id":58634046,"asset_id":38561952,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/58634046/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="18681046" href="https://ibadan.academia.edu/AderibigbeAdekitan">Aderibigbe Adekitan</a><script data-card-contents-for-user="18681046" type="text/json">{"id":18681046,"first_name":"Aderibigbe","last_name":"Adekitan","domain_name":"ibadan","page_name":"AderibigbeAdekitan","display_name":"Aderibigbe Adekitan","profile_url":"https://ibadan.academia.edu/AderibigbeAdekitan?f_ri=34344","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_38561952 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="38561952"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 38561952, container: ".js-paper-rank-work_38561952", }); });</script></li><li class="js-percentile-work_38561952 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 38561952; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_38561952"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_38561952 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="38561952"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 38561952; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=38561952]").text(description); $(".js-view-count-work_38561952").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_38561952").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="38561952"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="5109" href="https://www.academia.edu/Documents/in/Pattern_Recognition">Pattern Recognition</a>, <script data-card-contents-for-ri="5109" type="text/json">{"id":5109,"name":"Pattern Recognition","url":"https://www.academia.edu/Documents/in/Pattern_Recognition?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="14008" href="https://www.academia.edu/Documents/in/Knowledge_Discovery_in_Databases">Knowledge Discovery in Databases</a>, <script data-card-contents-for-ri="14008" type="text/json">{"id":14008,"name":"Knowledge Discovery in Databases","url":"https://www.academia.edu/Documents/in/Knowledge_Discovery_in_Databases?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="23995" href="https://www.academia.edu/Documents/in/Educational_Data_Mining">Educational Data Mining</a><script data-card-contents-for-ri="23995" type="text/json">{"id":23995,"name":"Educational Data Mining","url":"https://www.academia.edu/Documents/in/Educational_Data_Mining?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=38561952]'), work: {"id":38561952,"title":"Data mining approach to predicting the performance of first year student in a university using the admission requirements","created_at":"2019-03-16T02:16:14.950-07:00","url":"https://www.academia.edu/38561952/Data_mining_approach_to_predicting_the_performance_of_first_year_student_in_a_university_using_the_admission_requirements?f_ri=34344","dom_id":"work_38561952","summary":"The academic performance of a student in a university is determined by a number of factors, both academic and non-academic. Student that previously excelled at the secondary school level may lose focus due to peer pressure and social lifestyle while those who previously struggled due to family distractions may be able to focus away from home, and as a result excel at the university. University admission in Nigeria is typically based on cognitive entry characteristics of a student which is mostly academic , and may not necessarily translate to excellence once in the university. In this study, the relationship between the cognitive admission entry requirements and the academic performance of students in their first year, using their CGPA and class of degree was examined using six data mining algorithms in KNIME and Orange platforms. Maximum accuracies of 50.23% and 51.9% respectively were observed, and the results were verified using regression models, with R2 values of 0.207 and 0.232 recorded which indicate that students' performance in their first year is not fully explained by cognitive entry requirements.","downloadable_attachments":[{"id":58634046,"asset_id":38561952,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":18681046,"first_name":"Aderibigbe","last_name":"Adekitan","domain_name":"ibadan","page_name":"AderibigbeAdekitan","display_name":"Aderibigbe Adekitan","profile_url":"https://ibadan.academia.edu/AderibigbeAdekitan?f_ri=34344","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false},{"id":5109,"name":"Pattern Recognition","url":"https://www.academia.edu/Documents/in/Pattern_Recognition?f_ri=34344","nofollow":false},{"id":14008,"name":"Knowledge Discovery in Databases","url":"https://www.academia.edu/Documents/in/Knowledge_Discovery_in_Databases?f_ri=34344","nofollow":false},{"id":23995,"name":"Educational Data Mining","url":"https://www.academia.edu/Documents/in/Educational_Data_Mining?f_ri=34344","nofollow":false},{"id":30664,"name":"Pattern Recognition and Classification","url":"https://www.academia.edu/Documents/in/Pattern_Recognition_and_Classification?f_ri=34344"},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":60650,"name":"Data Warehousing and Data Mining","url":"https://www.academia.edu/Documents/in/Data_Warehousing_and_Data_Mining?f_ri=34344"},{"id":64642,"name":"Knowledge Discovery","url":"https://www.academia.edu/Documents/in/Knowledge_Discovery?f_ri=34344"},{"id":76424,"name":"Academic Performance","url":"https://www.academia.edu/Documents/in/Academic_Performance?f_ri=34344"},{"id":143038,"name":"Machine Learning and Pattern Recognition","url":"https://www.academia.edu/Documents/in/Machine_Learning_and_Pattern_Recognition?f_ri=34344"},{"id":254085,"name":"Data Mining and Knowledge Discovery","url":"https://www.academia.edu/Documents/in/Data_Mining_and_Knowledge_Discovery?f_ri=34344"},{"id":2047149,"name":"Nigerian university","url":"https://www.academia.edu/Documents/in/Nigerian_university?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_44089957" data-work_id="44089957" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/44089957/Gender_based_comparison_of_students_academic_performance_using_regression_models">Gender-based comparison of students' academic performance using regression models</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The process required to gain admission into tertiary institutions is challenging for Nigerian students. This is due to the various examinations and requirements that must be met to be qualified for admission among innumerable applicants.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_44089957" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The process required to gain admission into tertiary institutions is challenging for Nigerian students. This is due to the various examinations and requirements that must be met to be qualified for admission among innumerable applicants. It is therefore imperative that after admission, students must pursue academic excellence to justify the opportunity given to them, and this will also improve their chances of success after graduation. In a university, some first-year students struggle because of cultural disadvantages, as well as their economic and social backgrounds. This has resulted in poor performance by some students and inevitably led to bad grades at graduation and some drop out of the university without graduating. Female students are often said to perform a bit poorer in terms of academic performance. This study is a comparative performance analysis of male and female students in Science, Technology, Engineering and Mathematics (STEM), conducted using regression models. Trend analysis shows that in this case study, female students have a tendency to improve on their academic performance from their first to their final year. The highest R-squared value of 0.7069 was achieved based on a regression analysis of the performance of 1,093 female students.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/44089957" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7f07f58521313f8d978a569dd98bafff" rel="nofollow" data-download="{"attachment_id":64436230,"asset_id":44089957,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/64436230/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="18681046" href="https://ibadan.academia.edu/AderibigbeAdekitan">Aderibigbe Adekitan</a><script data-card-contents-for-user="18681046" type="text/json">{"id":18681046,"first_name":"Aderibigbe","last_name":"Adekitan","domain_name":"ibadan","page_name":"AderibigbeAdekitan","display_name":"Aderibigbe Adekitan","profile_url":"https://ibadan.academia.edu/AderibigbeAdekitan?f_ri=34344","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_44089957 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="44089957"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 44089957, container: ".js-paper-rank-work_44089957", }); });</script></li><li class="js-percentile-work_44089957 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 44089957; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_44089957"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_44089957 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="44089957"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44089957; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=44089957]").text(description); $(".js-view-count-work_44089957").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_44089957").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="44089957"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4205" href="https://www.academia.edu/Documents/in/Data_Analysis">Data Analysis</a>, <script data-card-contents-for-ri="4205" type="text/json">{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="14008" href="https://www.academia.edu/Documents/in/Knowledge_Discovery_in_Databases">Knowledge Discovery in Databases</a>, <script data-card-contents-for-ri="14008" type="text/json">{"id":14008,"name":"Knowledge Discovery in Databases","url":"https://www.academia.edu/Documents/in/Knowledge_Discovery_in_Databases?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="23995" href="https://www.academia.edu/Documents/in/Educational_Data_Mining">Educational Data Mining</a><script data-card-contents-for-ri="23995" type="text/json">{"id":23995,"name":"Educational Data Mining","url":"https://www.academia.edu/Documents/in/Educational_Data_Mining?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=44089957]'), work: {"id":44089957,"title":"Gender-based comparison of students' academic performance using regression models","created_at":"2020-09-15T00:40:23.230-07:00","url":"https://www.academia.edu/44089957/Gender_based_comparison_of_students_academic_performance_using_regression_models?f_ri=34344","dom_id":"work_44089957","summary":"The process required to gain admission into tertiary institutions is challenging for Nigerian students. This is due to the various examinations and requirements that must be met to be qualified for admission among innumerable applicants. It is therefore imperative that after admission, students must pursue academic excellence to justify the opportunity given to them, and this will also improve their chances of success after graduation. In a university, some first-year students struggle because of cultural disadvantages, as well as their economic and social backgrounds. This has resulted in poor performance by some students and inevitably led to bad grades at graduation and some drop out of the university without graduating. Female students are often said to perform a bit poorer in terms of academic performance. This study is a comparative performance analysis of male and female students in Science, Technology, Engineering and Mathematics (STEM), conducted using regression models. Trend analysis shows that in this case study, female students have a tendency to improve on their academic performance from their first to their final year. The highest R-squared value of 0.7069 was achieved based on a regression analysis of the performance of 1,093 female students.","downloadable_attachments":[{"id":64436230,"asset_id":44089957,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":18681046,"first_name":"Aderibigbe","last_name":"Adekitan","domain_name":"ibadan","page_name":"AderibigbeAdekitan","display_name":"Aderibigbe Adekitan","profile_url":"https://ibadan.academia.edu/AderibigbeAdekitan?f_ri=34344","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false},{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=34344","nofollow":false},{"id":14008,"name":"Knowledge Discovery in Databases","url":"https://www.academia.edu/Documents/in/Knowledge_Discovery_in_Databases?f_ri=34344","nofollow":false},{"id":23995,"name":"Educational Data Mining","url":"https://www.academia.edu/Documents/in/Educational_Data_Mining?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":107672,"name":"Regression","url":"https://www.academia.edu/Documents/in/Regression?f_ri=34344"},{"id":123230,"name":"Regression Analysis","url":"https://www.academia.edu/Documents/in/Regression_Analysis?f_ri=34344"},{"id":199316,"name":"Multiple Linear Regression","url":"https://www.academia.edu/Documents/in/Multiple_Linear_Regression?f_ri=34344"},{"id":254085,"name":"Data Mining and Knowledge Discovery","url":"https://www.academia.edu/Documents/in/Data_Mining_and_Knowledge_Discovery?f_ri=34344"},{"id":403949,"name":"Teaching and Learning Strategies","url":"https://www.academia.edu/Documents/in/Teaching_and_Learning_Strategies?f_ri=34344"},{"id":2564997,"name":"Student Performance Prediction","url":"https://www.academia.edu/Documents/in/Student_Performance_Prediction?f_ri=34344"},{"id":3763258,"name":"Performance evaluation methodologies","url":"https://www.academia.edu/Documents/in/Performance_evaluation_methodologies?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_81556993" data-work_id="81556993" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/81556993/Forged_Character_Detection_Datasets_Passports_Driving_Licences_and_Visa_Stickers">Forged Character Detection Datasets: Passports, Driving Licences and Visa Stickers</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Forged documents specifically passport, driving licence and VISA stickers are used for fraud purposes including robbery, theft and many more. So detecting forged characters from documents is a significantly important and challenging task... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_81556993" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Forged documents specifically passport, driving licence and VISA stickers are used for fraud purposes including robbery, theft and many more. So detecting forged characters from documents is a significantly important and challenging task in digital forensic imaging. Forged characters detection has two big challenges. First challenge is, data for forged characters detection is extremely difficult to get due to several reasons including limited access of data, unlabeled data or work is done on private data. Second challenge is, deep learning (DL) algorithms require labeled data, which poses a further challenge as getting labeled is tedious, time-consuming, expensive and requires domain expertise. To end these issues, in this paper we propose a novel algorithm, which generates the three datasets namely forged characters detection for passport (FCD-P), forged characters detection for driving licence (FCD-D) and forged characters detection for VISA stickers (FCD-V). To the best of our kn...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/81556993" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="19065df7ad25f3079c8537597f7ceefe" rel="nofollow" data-download="{"attachment_id":87559700,"asset_id":81556993,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/87559700/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="35801746" href="https://ucd.academia.edu/RobBrennan">Rob Brennan</a><script data-card-contents-for-user="35801746" type="text/json">{"id":35801746,"first_name":"Rob","last_name":"Brennan","domain_name":"ucd","page_name":"RobBrennan","display_name":"Rob Brennan","profile_url":"https://ucd.academia.edu/RobBrennan?f_ri=34344","photo":"https://0.academia-photos.com/35801746/10599836/11831310/s65_rob.brennan.jpg"}</script></span></span></li><li class="js-paper-rank-work_81556993 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="81556993"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 81556993, container: ".js-paper-rank-work_81556993", }); });</script></li><li class="js-percentile-work_81556993 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 81556993; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_81556993"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_81556993 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="81556993"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 81556993; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=81556993]").text(description); $(".js-view-count-work_81556993").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_81556993").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="81556993"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">19</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2482" href="https://www.academia.edu/Documents/in/Database_Systems">Database Systems</a>, <script data-card-contents-for-ri="2482" type="text/json">{"id":2482,"name":"Database Systems","url":"https://www.academia.edu/Documents/in/Database_Systems?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="14008" href="https://www.academia.edu/Documents/in/Knowledge_Discovery_in_Databases">Knowledge Discovery in Databases</a>, <script data-card-contents-for-ri="14008" type="text/json">{"id":14008,"name":"Knowledge Discovery in Databases","url":"https://www.academia.edu/Documents/in/Knowledge_Discovery_in_Databases?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="27360" href="https://www.academia.edu/Documents/in/Databases">Databases</a><script data-card-contents-for-ri="27360" type="text/json">{"id":27360,"name":"Databases","url":"https://www.academia.edu/Documents/in/Databases?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=81556993]'), work: {"id":81556993,"title":"Forged Character Detection Datasets: Passports, Driving Licences and Visa Stickers","created_at":"2022-06-15T08:18:31.054-07:00","url":"https://www.academia.edu/81556993/Forged_Character_Detection_Datasets_Passports_Driving_Licences_and_Visa_Stickers?f_ri=34344","dom_id":"work_81556993","summary":"Forged documents specifically passport, driving licence and VISA stickers are used for fraud purposes including robbery, theft and many more. So detecting forged characters from documents is a significantly important and challenging task in digital forensic imaging. Forged characters detection has two big challenges. First challenge is, data for forged characters detection is extremely difficult to get due to several reasons including limited access of data, unlabeled data or work is done on private data. Second challenge is, deep learning (DL) algorithms require labeled data, which poses a further challenge as getting labeled is tedious, time-consuming, expensive and requires domain expertise. To end these issues, in this paper we propose a novel algorithm, which generates the three datasets namely forged characters detection for passport (FCD-P), forged characters detection for driving licence (FCD-D) and forged characters detection for VISA stickers (FCD-V). To the best of our kn...","downloadable_attachments":[{"id":87559700,"asset_id":81556993,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":35801746,"first_name":"Rob","last_name":"Brennan","domain_name":"ucd","page_name":"RobBrennan","display_name":"Rob Brennan","profile_url":"https://ucd.academia.edu/RobBrennan?f_ri=34344","photo":"https://0.academia-photos.com/35801746/10599836/11831310/s65_rob.brennan.jpg"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false},{"id":2482,"name":"Database Systems","url":"https://www.academia.edu/Documents/in/Database_Systems?f_ri=34344","nofollow":false},{"id":14008,"name":"Knowledge Discovery in Databases","url":"https://www.academia.edu/Documents/in/Knowledge_Discovery_in_Databases?f_ri=34344","nofollow":false},{"id":27360,"name":"Databases","url":"https://www.academia.edu/Documents/in/Databases?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":43971,"name":"Face Detection","url":"https://www.academia.edu/Documents/in/Face_Detection?f_ri=34344"},{"id":45090,"name":"Database Management Systems","url":"https://www.academia.edu/Documents/in/Database_Management_Systems?f_ri=34344"},{"id":45871,"name":"Data Warehouse","url":"https://www.academia.edu/Documents/in/Data_Warehouse?f_ri=34344"},{"id":47980,"name":"Data Visualization","url":"https://www.academia.edu/Documents/in/Data_Visualization?f_ri=34344"},{"id":55071,"name":"Dataware House","url":"https://www.academia.edu/Documents/in/Dataware_House?f_ri=34344"},{"id":81182,"name":"Deep Learning","url":"https://www.academia.edu/Documents/in/Deep_Learning?f_ri=34344"},{"id":81219,"name":"Data Analytics","url":"https://www.academia.edu/Documents/in/Data_Analytics?f_ri=34344"},{"id":84224,"name":"Data Communication and Computer Networks","url":"https://www.academia.edu/Documents/in/Data_Communication_and_Computer_Networks?f_ri=34344"},{"id":406250,"name":"Data Handling","url":"https://www.academia.edu/Documents/in/Data_Handling?f_ri=34344"},{"id":413148,"name":"Big Data / Analytics / Data Mining","url":"https://www.academia.edu/Documents/in/Big_Data_Analytics_Data_Mining?f_ri=34344"},{"id":872990,"name":"Laptops Dataset","url":"https://www.academia.edu/Documents/in/Laptops_Dataset?f_ri=34344"},{"id":1029902,"name":"Dataset","url":"https://www.academia.edu/Documents/in/Dataset?f_ri=34344"},{"id":1802155,"name":"Deep learning algorithms","url":"https://www.academia.edu/Documents/in/Deep_learning_algorithms?f_ri=34344"},{"id":2532663,"name":"Deep Reinforcement Learning","url":"https://www.academia.edu/Documents/in/Deep_Reinforcement_Learning?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_33269763" data-work_id="33269763" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/33269763/PREDIKSI_CHURN_DAN_SEGMENTASI_PELANGGAN_TV_BERLANGGANAN_STUDI_KASUS_TRANSVISION_JAWA_BARAT">PREDIKSI CHURN DAN SEGMENTASI PELANGGAN TV BERLANGGANAN (STUDI KASUS TRANSVISION JAWA BARAT</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Bisnis TV berlangganan merupakan salah satu bisnis masa depan bagi sebuah perusahaan yang memiliki bisnis inti telekomunikasi seperti Telkomvision. Pada akhir tahun 2011 jumlah pelanggan Telkomvision mencapai lebih dari satu juta... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_33269763" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Bisnis TV berlangganan merupakan salah satu bisnis masa depan bagi sebuah perusahaan yang memiliki bisnis inti telekomunikasi seperti Telkomvision. Pada akhir tahun 2011 jumlah pelanggan Telkomvision mencapai lebih dari satu juta pelanggan, namun demikian pada tahun 2012 menurunnya pendapatan per pelanggan TV berlangganan atau average revenue per user (ARPU) menjadi penyebab perusahaan ini mengalami kerugian. <br />Tingkat churn pelanggan yang tinggi harus diprediksi secara akurat, karena hasil prediksi yang akurat dapat menentukan strategi apa dan promosi bagaimana yang tepat untuk retensi pelanggan. Kemudian dilakukan segmentasi pelanggan untuk merumuskan program retensi yang tepat sesuai dengan kebutuhan pelanggan yang berpotensi churn tinggi.<br />Metode data mining digunakan untuk memprediksi potensi churn dan segementasinya pada pelanggan PT Indonusa Telemedia, dan dapat hal ini dapat mendukung proses monitoring, pengendalian, serta penyusunan strategi pada management. Metode Decision Tree dan Clustering merupakan metode data mining yang populer karena sangat mudah dipahami dan diinterpretasikan, sehingga dapat dengan mudah untuk digunakan sebagai teknik untuk melakukan prediksi churn dan segmentasinya.<br />Tujuan utama dari prediksi pelanggan churn adalah untuk memperoleh informasi dari pelanggan yang mempunyai potensi churn tinggi sehingga dapat disusun rencana strategis dan promo perusahaan yang tepat sesuai dengan segmentasinya.<br />Metode Decision Tree dengan pemodelan menggunakan algoritma C4.5 menghasilkan tingkat akurasi 90,89%. Kemudian pelanggan yang mempunyai potensi churn tinggi dilakukan clustering menggunakan algoritma K-Means dan dari 5956 total pelanggan dapat merekomendasikan 5792 pelanggan yang mendapatkan penawaran program retensi.<br /><br />Kata kunci : churn, segmentasi , data mining, decision tree, clustering, k-means</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/33269763" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="1175992e5a68ac583e2f7d812391504b" rel="nofollow" data-download="{"attachment_id":53338466,"asset_id":33269763,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/53338466/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="38072488" href="https://independent.academia.edu/NanaSuryana59">Nana Suryana</a><script data-card-contents-for-user="38072488" type="text/json">{"id":38072488,"first_name":"Nana","last_name":"Suryana","domain_name":"independent","page_name":"NanaSuryana59","display_name":"Nana Suryana","profile_url":"https://independent.academia.edu/NanaSuryana59?f_ri=34344","photo":"https://0.academia-photos.com/38072488/10681889/11924940/s65_nana.suryana.jpg"}</script></span></span></li><li class="js-paper-rank-work_33269763 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="33269763"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 33269763, container: ".js-paper-rank-work_33269763", }); });</script></li><li class="js-percentile-work_33269763 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 33269763; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_33269763"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_33269763 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="33269763"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 33269763; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=33269763]").text(description); $(".js-view-count-work_33269763").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_33269763").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="33269763"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="34344" href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Data mining (Data Analysis)</a>, <script data-card-contents-for-ri="34344" type="text/json">{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="60650" href="https://www.academia.edu/Documents/in/Data_Warehousing_and_Data_Mining">Data Warehousing and Data Mining</a><script data-card-contents-for-ri="60650" type="text/json">{"id":60650,"name":"Data Warehousing and Data Mining","url":"https://www.academia.edu/Documents/in/Data_Warehousing_and_Data_Mining?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=33269763]'), work: {"id":33269763,"title":"PREDIKSI CHURN DAN SEGMENTASI PELANGGAN TV BERLANGGANAN (STUDI KASUS TRANSVISION JAWA BARAT","created_at":"2017-05-30T21:56:53.006-07:00","url":"https://www.academia.edu/33269763/PREDIKSI_CHURN_DAN_SEGMENTASI_PELANGGAN_TV_BERLANGGANAN_STUDI_KASUS_TRANSVISION_JAWA_BARAT?f_ri=34344","dom_id":"work_33269763","summary":"Bisnis TV berlangganan merupakan salah satu bisnis masa depan bagi sebuah perusahaan yang memiliki bisnis inti telekomunikasi seperti Telkomvision. Pada akhir tahun 2011 jumlah pelanggan Telkomvision mencapai lebih dari satu juta pelanggan, namun demikian pada tahun 2012 menurunnya pendapatan per pelanggan TV berlangganan atau average revenue per user (ARPU) menjadi penyebab perusahaan ini mengalami kerugian. \nTingkat churn pelanggan yang tinggi harus diprediksi secara akurat, karena hasil prediksi yang akurat dapat menentukan strategi apa dan promosi bagaimana yang tepat untuk retensi pelanggan. Kemudian dilakukan segmentasi pelanggan untuk merumuskan program retensi yang tepat sesuai dengan kebutuhan pelanggan yang berpotensi churn tinggi.\nMetode data mining digunakan untuk memprediksi potensi churn dan segementasinya pada pelanggan PT Indonusa Telemedia, dan dapat hal ini dapat mendukung proses monitoring, pengendalian, serta penyusunan strategi pada management. Metode Decision Tree dan Clustering merupakan metode data mining yang populer karena sangat mudah dipahami dan diinterpretasikan, sehingga dapat dengan mudah untuk digunakan sebagai teknik untuk melakukan prediksi churn dan segmentasinya.\nTujuan utama dari prediksi pelanggan churn adalah untuk memperoleh informasi dari pelanggan yang mempunyai potensi churn tinggi sehingga dapat disusun rencana strategis dan promo perusahaan yang tepat sesuai dengan segmentasinya.\nMetode Decision Tree dengan pemodelan menggunakan algoritma C4.5 menghasilkan tingkat akurasi 90,89%. Kemudian pelanggan yang mempunyai potensi churn tinggi dilakukan clustering menggunakan algoritma K-Means dan dari 5956 total pelanggan dapat merekomendasikan 5792 pelanggan yang mendapatkan penawaran program retensi.\n\nKata kunci : churn, segmentasi , data mining, decision tree, clustering, k-means","downloadable_attachments":[{"id":53338466,"asset_id":33269763,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":38072488,"first_name":"Nana","last_name":"Suryana","domain_name":"independent","page_name":"NanaSuryana59","display_name":"Nana Suryana","profile_url":"https://independent.academia.edu/NanaSuryana59?f_ri=34344","photo":"https://0.academia-photos.com/38072488/10681889/11924940/s65_nana.suryana.jpg"}],"research_interests":[{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false},{"id":60650,"name":"Data Warehousing and Data Mining","url":"https://www.academia.edu/Documents/in/Data_Warehousing_and_Data_Mining?f_ri=34344","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_22969093" data-work_id="22969093" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/22969093/Detection_and_Avoidance_of_Sensitive_Data_in_Host_assisted_Mechanism_using_Fuzzy_Fingerprint_Technique">Detection and Avoidance of Sensitive Data in Host-assisted Mechanism using Fuzzy Fingerprint Technique</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">— The Data-leak cases, human mistakes are one of the causes of data loss. Deliberately planned attacks, inadvertent and human mistakes lead to most of the data-leak incidents. The detecting solutions of inadvertent sensitive data leaks... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_22969093" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">— The Data-leak cases, human mistakes are one of the causes of data loss. Deliberately planned attacks, inadvertent and human mistakes lead to most of the data-leak incidents. The detecting solutions of inadvertent sensitive data leaks caused by human mistakes and provide alerts for organizations. A common approach is to screen content in the storage and transmission for exposed sensitive information. Such an approach requires the detection operation to be conducted in secrecy. The data-leak detection (DLD) privacy-preserving solution to solve the special set of sensitive data digests is used in detection. The advantage of data owner is safely delegate the detection operation to a semihonest provider without revealing sensitive data to the provider. Internet service providers can offer their customers DLD as an add-on service with strong privacy guarantees. Evaluation results support accurate detection with very small number of false alarms under various data-leak scenarios. Host-assisted mechanism for the complete data-leak detection for large-scale organizations. To design the Host-assisted mechanism for DLD, using data signature and fuzzy fingerprint.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/22969093" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="bdf6e958d23b57793a6159a93c943538" rel="nofollow" data-download="{"attachment_id":43491435,"asset_id":22969093,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43491435/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="14545732" href="https://cvr.academia.edu/Ijcertpublications">IJCERT Publications</a><script data-card-contents-for-user="14545732" type="text/json">{"id":14545732,"first_name":"IJCERT","last_name":"Publications","domain_name":"cvr","page_name":"Ijcertpublications","display_name":"IJCERT Publications","profile_url":"https://cvr.academia.edu/Ijcertpublications?f_ri=34344","photo":"https://0.academia-photos.com/14545732/3964209/13239444/s65_ijcert.journal.jpg"}</script></span></span></li><li class="js-paper-rank-work_22969093 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="22969093"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 22969093, container: ".js-paper-rank-work_22969093", }); 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$(".js-view-count[data-work-id=22969093]").text(description); $(".js-view-count-work_22969093").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_22969093").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="22969093"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="34344" href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Data mining (Data Analysis)</a>, <script data-card-contents-for-ri="34344" type="text/json">{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="126300" href="https://www.academia.edu/Documents/in/Big_Data">Big Data</a>, <script data-card-contents-for-ri="126300" type="text/json">{"id":126300,"name":"Big Data","url":"https://www.academia.edu/Documents/in/Big_Data?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="413148" href="https://www.academia.edu/Documents/in/Big_Data_Analytics_Data_Mining">Big Data / Analytics / Data Mining</a><script data-card-contents-for-ri="413148" type="text/json">{"id":413148,"name":"Big Data / Analytics / Data Mining","url":"https://www.academia.edu/Documents/in/Big_Data_Analytics_Data_Mining?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=22969093]'), work: {"id":22969093,"title":"Detection and Avoidance of Sensitive Data in Host-assisted Mechanism using Fuzzy Fingerprint Technique","created_at":"2016-03-07T23:49:11.657-08:00","url":"https://www.academia.edu/22969093/Detection_and_Avoidance_of_Sensitive_Data_in_Host_assisted_Mechanism_using_Fuzzy_Fingerprint_Technique?f_ri=34344","dom_id":"work_22969093","summary":"— The Data-leak cases, human mistakes are one of the causes of data loss. Deliberately planned attacks, inadvertent and human mistakes lead to most of the data-leak incidents. The detecting solutions of inadvertent sensitive data leaks caused by human mistakes and provide alerts for organizations. A common approach is to screen content in the storage and transmission for exposed sensitive information. Such an approach requires the detection operation to be conducted in secrecy. The data-leak detection (DLD) privacy-preserving solution to solve the special set of sensitive data digests is used in detection. The advantage of data owner is safely delegate the detection operation to a semihonest provider without revealing sensitive data to the provider. Internet service providers can offer their customers DLD as an add-on service with strong privacy guarantees. Evaluation results support accurate detection with very small number of false alarms under various data-leak scenarios. Host-assisted mechanism for the complete data-leak detection for large-scale organizations. To design the Host-assisted mechanism for DLD, using data signature and fuzzy fingerprint.","downloadable_attachments":[{"id":43491435,"asset_id":22969093,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":14545732,"first_name":"IJCERT","last_name":"Publications","domain_name":"cvr","page_name":"Ijcertpublications","display_name":"IJCERT Publications","profile_url":"https://cvr.academia.edu/Ijcertpublications?f_ri=34344","photo":"https://0.academia-photos.com/14545732/3964209/13239444/s65_ijcert.journal.jpg"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false},{"id":126300,"name":"Big Data","url":"https://www.academia.edu/Documents/in/Big_Data?f_ri=34344","nofollow":false},{"id":413148,"name":"Big Data / Analytics / Data Mining","url":"https://www.academia.edu/Documents/in/Big_Data_Analytics_Data_Mining?f_ri=34344","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_41733525" data-work_id="41733525" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/41733525/Customers_Churn_Prediction_Model_Comprising_of_Clustering_and_Classification_An_Application_of_Improvised_Kmeans_Clustering_Algorithm_and_Non_Linear_Support_Vector_Machine_">Customers Churn Prediction Model Comprising of Clustering and Classification: An Application of Improvised Kmeans Clustering Algorithm and Non Linear Support Vector Machine</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Customer churn is a significant issue that is regularly related with the existence cycle of the business. At the point when the business is in a development period of its life cycle, deals are expanding exponentially and the quantity of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_41733525" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Customer churn is a significant issue that is regularly related with the existence cycle of the business. At the point when the business is in a development period of its life cycle, deals are expanding exponentially and the quantity of new clients to a great extent dwarfs the quantity of churners. On the other side, organizations in a develop period of in their life cycle, set their attention on lessening the rate of customer churn. This research work proposes an efficient computational intelligence model comprising of clustering achieved through improvised K-Means algorithm and classification achieved through Non Linear Support Vector Machine.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/41733525" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e7216701f2728b4efc01f0806c0a375d" rel="nofollow" data-download="{"attachment_id":61897852,"asset_id":41733525,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/61897852/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="2902340" href="https://ijcsmc.academia.edu/IJCSMCJournal">IJCSMC Journal</a><script data-card-contents-for-user="2902340" type="text/json">{"id":2902340,"first_name":"IJCSMC","last_name":"Journal","domain_name":"ijcsmc","page_name":"IJCSMCJournal","display_name":"IJCSMC Journal","profile_url":"https://ijcsmc.academia.edu/IJCSMCJournal?f_ri=34344","photo":"https://0.academia-photos.com/2902340/955759/1197349/s65_ijcsmc.journal.jpg"}</script></span></span></li><li class="js-paper-rank-work_41733525 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="41733525"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 41733525, container: ".js-paper-rank-work_41733525", }); 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$(".js-view-count[data-work-id=41733525]").text(description); $(".js-view-count-work_41733525").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_41733525").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="41733525"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">20</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="300" href="https://www.academia.edu/Documents/in/Mathematics">Mathematics</a>, <script data-card-contents-for-ri="300" type="text/json">{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="422" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="428" href="https://www.academia.edu/Documents/in/Algorithms">Algorithms</a>, <script data-card-contents-for-ri="428" type="text/json">{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="491" href="https://www.academia.edu/Documents/in/Information_Technology">Information Technology</a><script data-card-contents-for-ri="491" type="text/json">{"id":491,"name":"Information Technology","url":"https://www.academia.edu/Documents/in/Information_Technology?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=41733525]'), work: {"id":41733525,"title":"Customers Churn Prediction Model Comprising of Clustering and Classification: An Application of Improvised Kmeans Clustering Algorithm and Non Linear Support Vector Machine","created_at":"2020-01-25T05:15:02.442-08:00","url":"https://www.academia.edu/41733525/Customers_Churn_Prediction_Model_Comprising_of_Clustering_and_Classification_An_Application_of_Improvised_Kmeans_Clustering_Algorithm_and_Non_Linear_Support_Vector_Machine_?f_ri=34344","dom_id":"work_41733525","summary":"Customer churn is a significant issue that is regularly related with the existence cycle of the business. At the point when the business is in a development period of its life cycle, deals are expanding exponentially and the quantity of new clients to a great extent dwarfs the quantity of churners. On the other side, organizations in a develop period of in their life cycle, set their attention on lessening the rate of customer churn. This research work proposes an efficient computational intelligence model comprising of clustering achieved through improvised K-Means algorithm and classification achieved through Non Linear Support Vector Machine.","downloadable_attachments":[{"id":61897852,"asset_id":41733525,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":2902340,"first_name":"IJCSMC","last_name":"Journal","domain_name":"ijcsmc","page_name":"IJCSMCJournal","display_name":"IJCSMC Journal","profile_url":"https://ijcsmc.academia.edu/IJCSMCJournal?f_ri=34344","photo":"https://0.academia-photos.com/2902340/955759/1197349/s65_ijcsmc.journal.jpg"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=34344","nofollow":false},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=34344","nofollow":false},{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=34344","nofollow":false},{"id":491,"name":"Information Technology","url":"https://www.academia.edu/Documents/in/Information_Technology?f_ri=34344","nofollow":false},{"id":923,"name":"Technology","url":"https://www.academia.edu/Documents/in/Technology?f_ri=34344"},{"id":1380,"name":"Computer Engineering","url":"https://www.academia.edu/Documents/in/Computer_Engineering?f_ri=34344"},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344"},{"id":5486,"name":"Clustering and Classification Methods","url":"https://www.academia.edu/Documents/in/Clustering_and_Classification_Methods?f_ri=34344"},{"id":13143,"name":"Clustering Algorithms","url":"https://www.academia.edu/Documents/in/Clustering_Algorithms?f_ri=34344"},{"id":27305,"name":"Computational Mathematics","url":"https://www.academia.edu/Documents/in/Computational_Mathematics?f_ri=34344"},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":51449,"name":"K-means","url":"https://www.academia.edu/Documents/in/K-means?f_ri=34344"},{"id":60650,"name":"Data Warehousing and Data Mining","url":"https://www.academia.edu/Documents/in/Data_Warehousing_and_Data_Mining?f_ri=34344"},{"id":84990,"name":"Clustering","url":"https://www.academia.edu/Documents/in/Clustering?f_ri=34344"},{"id":328538,"name":"Computer Science And Engineering","url":"https://www.academia.edu/Documents/in/Computer_Science_And_Engineering?f_ri=34344"},{"id":413148,"name":"Big Data / Analytics / Data Mining","url":"https://www.academia.edu/Documents/in/Big_Data_Analytics_Data_Mining?f_ri=34344"},{"id":798200,"name":"K means algorithm","url":"https://www.academia.edu/Documents/in/K_means_algorithm?f_ri=34344"},{"id":1032324,"name":"K means Clustering","url":"https://www.academia.edu/Documents/in/K_means_Clustering?f_ri=34344"},{"id":1226904,"name":"Computer Science and Engineering","url":"https://www.academia.edu/Documents/in/Computer_Science_and_Engineering-1?f_ri=34344"},{"id":1556799,"name":"Customer Churn Preditcion","url":"https://www.academia.edu/Documents/in/Customer_Churn_Preditcion?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_35838815" data-work_id="35838815" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/35838815/A_SURVEY_OF_BIG_DATA_ANALYTICS">A SURVEY OF BIG DATA ANALYTICS</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Due to the arrival of new technologies, devices, and communication means, the amount of data produced by mankind is growing rapidly every year. This gives rise to the era of big data. The term big data comes with the new challenges to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_35838815" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Due to the arrival of new technologies, devices, and communication means, the amount of data produced by mankind is growing rapidly every year. This gives rise to the era of big data. The term big data comes with the new challenges to input, process and output the data. The paper focuses on limitation of traditional approach to manage the data and the components that are useful in handling big data. One of the approaches used in processing big data is Hadoop framework, the paper presents the major components of the framework and working process within the framework.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/35838815" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d9b23abc696ba9b0e012c82dbb7eaff5" rel="nofollow" data-download="{"attachment_id":55716957,"asset_id":35838815,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/55716957/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="74068792" href="https://independent.academia.edu/Journal_IJIST">International Journal of Information Sciences and Techniques (IJIST)</a><script data-card-contents-for-user="74068792" type="text/json">{"id":74068792,"first_name":"International Journal of Information Sciences and Techniques","last_name":"(IJIST)","domain_name":"independent","page_name":"Journal_IJIST","display_name":"International Journal of Information Sciences and Techniques (IJIST)","profile_url":"https://independent.academia.edu/Journal_IJIST?f_ri=34344","photo":"https://0.academia-photos.com/74068792/18783181/111422471/s65_international_journal_of_information_sciences_and_techniques._ijist_.png"}</script></span></span></li><li class="js-paper-rank-work_35838815 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="35838815"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 35838815, container: ".js-paper-rank-work_35838815", }); 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This gives rise to the era of big data. The term big data comes with the new challenges to input, process and output the data. The paper focuses on limitation of traditional approach to manage the data and the components that are useful in handling big data. One of the approaches used in processing big data is Hadoop framework, the paper presents the major components of the framework and working process within the framework.","downloadable_attachments":[{"id":55716957,"asset_id":35838815,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":74068792,"first_name":"International Journal of Information Sciences and Techniques","last_name":"(IJIST)","domain_name":"independent","page_name":"Journal_IJIST","display_name":"International Journal of Information Sciences and Techniques (IJIST)","profile_url":"https://independent.academia.edu/Journal_IJIST?f_ri=34344","photo":"https://0.academia-photos.com/74068792/18783181/111422471/s65_international_journal_of_information_sciences_and_techniques._ijist_.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=34344","nofollow":false},{"id":965,"name":"Formal Concept Analysis (Data Mining)","url":"https://www.academia.edu/Documents/in/Formal_Concept_Analysis_Data_Mining_?f_ri=34344","nofollow":false},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false},{"id":2482,"name":"Database Systems","url":"https://www.academia.edu/Documents/in/Database_Systems?f_ri=34344","nofollow":false},{"id":7960,"name":"Temporal Data Mining","url":"https://www.academia.edu/Documents/in/Temporal_Data_Mining?f_ri=34344"},{"id":9447,"name":"Stream Mining (Data Mining)","url":"https://www.academia.edu/Documents/in/Stream_Mining_Data_Mining_?f_ri=34344"},{"id":14494,"name":"Opinion Mining (Data Mining)","url":"https://www.academia.edu/Documents/in/Opinion_Mining_Data_Mining_?f_ri=34344"},{"id":23995,"name":"Educational Data Mining","url":"https://www.academia.edu/Documents/in/Educational_Data_Mining?f_ri=34344"},{"id":27360,"name":"Databases","url":"https://www.academia.edu/Documents/in/Databases?f_ri=34344"},{"id":32008,"name":"Data mining in Decision Support Systems","url":"https://www.academia.edu/Documents/in/Data_mining_in_Decision_Support_Systems?f_ri=34344"},{"id":32701,"name":"Data Mining in Bioinformatics","url":"https://www.academia.edu/Documents/in/Data_Mining_in_Bioinformatics?f_ri=34344"},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":39682,"name":"Graph Data Mining","url":"https://www.academia.edu/Documents/in/Graph_Data_Mining?f_ri=34344"},{"id":39693,"name":"Distributed Data Mining","url":"https://www.academia.edu/Documents/in/Distributed_Data_Mining?f_ri=34344"},{"id":99804,"name":"Privacy Preserving Data Mining","url":"https://www.academia.edu/Documents/in/Privacy_Preserving_Data_Mining?f_ri=34344"},{"id":108488,"name":"Data Mining – Concepts and Techniques","url":"https://www.academia.edu/Documents/in/Data_Mining_Concepts_and_Techniques?f_ri=34344"},{"id":167122,"name":"Health Care Informatics, Health Data Analytics","url":"https://www.academia.edu/Documents/in/Health_Care_Informatics_Health_Data_Analytics?f_ri=34344"},{"id":394230,"name":"Current Trends in Data Mining","url":"https://www.academia.edu/Documents/in/Current_Trends_in_Data_Mining?f_ri=34344"},{"id":460675,"name":"Data Mining Techniques","url":"https://www.academia.edu/Documents/in/Data_Mining_Techniques?f_ri=34344"},{"id":633426,"name":"Social Media Data Analytics","url":"https://www.academia.edu/Documents/in/Social_Media_Data_Analytics?f_ri=34344"},{"id":994859,"name":"Data Mining and Business Intelligence","url":"https://www.academia.edu/Documents/in/Data_Mining_and_Business_Intelligence?f_ri=34344"},{"id":1003611,"name":"Business Analytics; 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Perishable and deli food items have short freshness-life and this is why the volume of food wastage is higher in this particular segment of supply chain system. Most of the retail store managers are struggling to predict a relatively precised demand and supply of such products to control food wastage at the minimum levels. The forecasting and decision-making is mostly done using sales reports which is not a completely reliable data-source for decision-ing. It is also observed that an educated guessing by store managers is another common practice for inaccurate demand forecasting that causes stock in-availability, over-storage and sizable wastage of food items among fruits, vegetables, cereals, bakery, dairy and meat products etc. These issues are empirically analysed in this research dissertation using BI concepts and methods.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/38361376" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="3c9d14125afe4f88a25be7ae26ed14a1" rel="nofollow" data-download="{"attachment_id":58414772,"asset_id":38361376,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/58414772/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="102523945" href="https://brunel.academia.edu/KhawajaUsmanSarfraz">Khawaja Usman Sarfraz</a><script data-card-contents-for-user="102523945" type="text/json">{"id":102523945,"first_name":"Khawaja Usman","last_name":"Sarfraz","domain_name":"brunel","page_name":"KhawajaUsmanSarfraz","display_name":"Khawaja Usman Sarfraz","profile_url":"https://brunel.academia.edu/KhawajaUsmanSarfraz?f_ri=34344","photo":"https://0.academia-photos.com/102523945/22660243/21840054/s65_khawaja_usman.sarfraz.jpg"}</script></span></span></li><li class="js-paper-rank-work_38361376 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="38361376"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 38361376, container: ".js-paper-rank-work_38361376", }); 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Perishable and deli food items have short freshness-life and this is why the volume of food wastage is higher in this particular segment of supply chain system. Most of the retail store managers are struggling to predict a relatively precised demand and supply of such products to control food wastage at the minimum levels. The forecasting and decision-making is mostly done using sales reports which is not a completely reliable data-source for decision-ing. It is also observed that an educated guessing by store managers is another common practice for inaccurate demand forecasting that causes stock in-availability, over-storage and sizable wastage of food items among fruits, vegetables, cereals, bakery, dairy and meat products etc. These issues are empirically analysed in this research dissertation using BI concepts and methods.","downloadable_attachments":[{"id":58414772,"asset_id":38361376,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":102523945,"first_name":"Khawaja Usman","last_name":"Sarfraz","domain_name":"brunel","page_name":"KhawajaUsmanSarfraz","display_name":"Khawaja Usman Sarfraz","profile_url":"https://brunel.academia.edu/KhawajaUsmanSarfraz?f_ri=34344","photo":"https://0.academia-photos.com/102523945/22660243/21840054/s65_khawaja_usman.sarfraz.jpg"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false},{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false},{"id":53779,"name":"statistics with SPSS and Excel","url":"https://www.academia.edu/Documents/in/statistics_with_SPSS_and_Excel?f_ri=34344","nofollow":false},{"id":69100,"name":"Data Science","url":"https://www.academia.edu/Documents/in/Data_Science?f_ri=34344"},{"id":103873,"name":"Digital Marketing","url":"https://www.academia.edu/Documents/in/Digital_Marketing?f_ri=34344"},{"id":413148,"name":"Big Data / Analytics / Data Mining","url":"https://www.academia.edu/Documents/in/Big_Data_Analytics_Data_Mining?f_ri=34344"},{"id":462629,"name":"Business Intelligence and Competitive Marketing","url":"https://www.academia.edu/Documents/in/Business_Intelligence_and_Competitive_Marketing?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_38241914" data-work_id="38241914" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/38241914/TOP_DOWNLOADED_PAPERS_International_Journal_of_Computer_Aided_technologies_IJCAx_">TOP DOWNLOADED PAPERS - International Journal of Computer-Aided technologies (IJCAx)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Computer-aided technologies (CAx) mean the use of computer technology to aid in the design, analysis and manufacture various products. International Journal of Computer-Aided technologies (IJCAx) is an open access, peer-reviewed journal... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_38241914" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Computer-aided technologies (CAx) mean the use of computer technology to aid in the design, analysis and manufacture various products. International Journal of Computer-Aided technologies (IJCAx)  is an open access, peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Aided technologies such as CAD, CAM, CAIM,CAR, CARD, CASE etc. The journal focuses on all technical and practical aspects of Computer Aided technologies. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced CAx tools and establishing new collaborations in these areas. Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in all Computer Aided technologies</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/38241914" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5816aaa95eec1e6f88ca65f8cca5230c" rel="nofollow" data-download="{"attachment_id":58283596,"asset_id":38241914,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/58283596/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="86974900" href="https://independent.academia.edu/InternationalJournalofComputerAidedtechnologiesIJCAx">International Journal of Computer-Aided technologies (IJCAx)</a><script data-card-contents-for-user="86974900" type="text/json">{"id":86974900,"first_name":"International Journal of Computer-Aided technologies","last_name":"(IJCAx)","domain_name":"independent","page_name":"InternationalJournalofComputerAidedtechnologiesIJCAx","display_name":"International Journal of Computer-Aided technologies (IJCAx)","profile_url":"https://independent.academia.edu/InternationalJournalofComputerAidedtechnologiesIJCAx?f_ri=34344","photo":"https://0.academia-photos.com/86974900/20109798/74507170/s65_international_journal_of_computer-aided_technologies._ijcax_.jpg"}</script></span></span></li><li class="js-paper-rank-work_38241914 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="38241914"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 38241914, container: ".js-paper-rank-work_38241914", }); 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Blocking access... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_6864795" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The Internet may be free, but service provider’s indispensable to access services are not, to the extent that while the complexity and burden of the sites increases, it is becoming more and more expensive to surf the net. Blocking access under the guise of protecting us from offensive or sexually explicit content, to pages, chat rooms, newsgroups and other Web options is not anymore an excuse, but a lie. Government surveillance in the Internet, uncontrolled practices of data harvesting and restriction of free speech, open discussion of issues and even political activism has spread in the last 20 years to include countries that are considered democracies such as the U.S. Internet has become a battleground of power and therefore become increasingly militarized.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/6864795" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="11d102f6fbd1eead4f32b2e52a0b19f7" rel="nofollow" data-download="{"attachment_id":33553722,"asset_id":6864795,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/33553722/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1181534" href="https://barry.academia.edu/PedroGonz%C3%A1lezJrMJMACPhD">Dr. Pedro A González Jr MJ MAC</a><script data-card-contents-for-user="1181534" type="text/json">{"id":1181534,"first_name":"Dr. Pedro","last_name":"González Jr MJ MAC","domain_name":"barry","page_name":"PedroGonzálezJrMJMACPhD","display_name":"Dr. Pedro A González Jr MJ MAC","profile_url":"https://barry.academia.edu/PedroGonz%C3%A1lezJrMJMACPhD?f_ri=34344","photo":"https://0.academia-photos.com/1181534/421037/26303716/s65_pedro.gonz_lez_jr_mj_mac_phdc.jpg"}</script></span></span></li><li class="js-paper-rank-work_6864795 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="6864795"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 6864795, container: ".js-paper-rank-work_6864795", }); 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Assault","url":"https://www.academia.edu/Documents/in/Free_Speech_Under_Assault?f_ri=34344"},{"id":1191356,"name":"Internet","url":"https://www.academia.edu/Documents/in/Internet?f_ri=34344"},{"id":1228644,"name":"Social Media and Freedom of Speech and Expression","url":"https://www.academia.edu/Documents/in/Social_Media_and_Freedom_of_Speech_and_Expression?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_32112398 coauthored" data-work_id="32112398" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/32112398/Building_Prediction_Model_using_Market_Basket_Analysis">Building Prediction Model using Market Basket Analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In the recent years, analyzing shopping baskets turned out to be very appealing to retailers. Sophisticated technology made it possible for them to collect information of their customers and what they purchase. The introduction of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_32112398" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In the recent years, analyzing shopping baskets turned out to be very appealing to retailers. Sophisticated technology made it possible for them to collect information of their customers and what they purchase. The introduction of electronic point-of-sale expanded the utilization and application of transactional data in Market Basket Analysis (MBA). In retail business, analyzing such information is exceedingly valuable for understanding purchasing behavior. Mining purchasing patterns allows retailers to adjust promotions, store settings and serve consumers better. Predictive analysis is an advanced branch of data engineering which generally predicts some occurrence or probability based on data. Predictive analytics uses data-mining techniques in order to make predictions about future events, and make recommendations based on these predictions. The process involves an analysis of historic data and based on that analysis to predict the future occurrences or events. A model can be created to predict using Predictive Analytics modelling techniques. The form of these predictive models varies depending on the data they are using. Predictive Analytics is composed of various statistical & analytical techniques used to develop models that will predict future occurrence, events or probabilities. Predictive analytics is able to not only deal with continuous changes, but discontinuous changes as well. Classification, prediction, and to some extent, affinity analysis constitute the analytical methods employed in predictive analytics.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/32112398" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2db7a0103198f69eb4fc34fb51b34f9b" rel="nofollow" data-download="{"attachment_id":52359686,"asset_id":32112398,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/52359686/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="5513967" href="https://jspm.academia.edu/DrBinodKumar">Dr Binod Kumar</a><script data-card-contents-for-user="5513967" type="text/json">{"id":5513967,"first_name":"Dr Binod","last_name":"Kumar","domain_name":"jspm","page_name":"DrBinodKumar","display_name":"Dr Binod Kumar","profile_url":"https://jspm.academia.edu/DrBinodKumar?f_ri=34344","photo":"https://0.academia-photos.com/5513967/2417952/33344562/s65_drbinod.kumar.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-32112398">+1</span><div class="hidden js-additional-users-32112398"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://unipune.academia.edu/Gangurde">Roshan Gangurde</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-32112398'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-32112398').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_32112398 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="32112398"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 32112398, container: ".js-paper-rank-work_32112398", }); });</script></li><li class="js-percentile-work_32112398 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32112398; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_32112398"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_32112398 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="32112398"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32112398; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32112398]").text(description); $(".js-view-count-work_32112398").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_32112398").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="32112398"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="34344" href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Data mining (Data Analysis)</a><script data-card-contents-for-ri="34344" type="text/json">{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=32112398]'), work: {"id":32112398,"title":"Building Prediction Model using Market Basket Analysis","created_at":"2017-03-29T00:00:56.689-07:00","url":"https://www.academia.edu/32112398/Building_Prediction_Model_using_Market_Basket_Analysis?f_ri=34344","dom_id":"work_32112398","summary":"In the recent years, analyzing shopping baskets turned out to be very appealing to retailers. Sophisticated technology made it possible for them to collect information of their customers and what they purchase. The introduction of electronic point-of-sale expanded the utilization and application of transactional data in Market Basket Analysis (MBA). In retail business, analyzing such information is exceedingly valuable for understanding purchasing behavior. Mining purchasing patterns allows retailers to adjust promotions, store settings and serve consumers better. Predictive analysis is an advanced branch of data engineering which generally predicts some occurrence or probability based on data. Predictive analytics uses data-mining techniques in order to make predictions about future events, and make recommendations based on these predictions. The process involves an analysis of historic data and based on that analysis to predict the future occurrences or events. A model can be created to predict using Predictive Analytics modelling techniques. The form of these predictive models varies depending on the data they are using. Predictive Analytics is composed of various statistical \u0026 analytical techniques used to develop models that will predict future occurrence, events or probabilities. Predictive analytics is able to not only deal with continuous changes, but discontinuous changes as well. Classification, prediction, and to some extent, affinity analysis constitute the analytical methods employed in predictive analytics.","downloadable_attachments":[{"id":52359686,"asset_id":32112398,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":5513967,"first_name":"Dr Binod","last_name":"Kumar","domain_name":"jspm","page_name":"DrBinodKumar","display_name":"Dr Binod Kumar","profile_url":"https://jspm.academia.edu/DrBinodKumar?f_ri=34344","photo":"https://0.academia-photos.com/5513967/2417952/33344562/s65_drbinod.kumar.jpg"},{"id":13257222,"first_name":"Roshan","last_name":"Gangurde","domain_name":"unipune","page_name":"Gangurde","display_name":"Roshan Gangurde","profile_url":"https://unipune.academia.edu/Gangurde?f_ri=34344","photo":"https://0.academia-photos.com/13257222/3725045/4364296/s65_roshan.gangurde.jpg"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_38956617 coauthored" data-work_id="38956617" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/38956617/Variance_Ranking_Attributes_Selection_Techniques_for_Binary_Classification_Problem_in_Imbalance_Data">Variance Ranking Attributes Selection Techniques for Binary Classification Problem in Imbalance Data</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Data are being generated and used to support all aspects of healthcare provision, from policy formation to the delivery of primary care services. Particularly, with the change of emphasis from curative to preventive medicine, the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_38956617" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Data are being generated and used to support all aspects of healthcare provision, from policy formation to the delivery of primary care services. Particularly, with the change of emphasis from curative to preventive medicine, the importance of data-based research such as data mining and machine learning has emphasized the issues of class distributions in datasets. In typical predictive modeling, the inability to effectively address a class imbalance in a real-life dataset is an important shortcoming of the existing machine learning algorithms. Most algorithms assume a balanced class in their design, resulting in poor performance in predicting the minority target class. Ironically, the minority target class is usually the focus in predicting processes. The misclassification of the minority target class has resulted in serious consequences in detecting chronic diseases and detecting fraud and intrusion where positive cases are erroneously predicted as not positive. This paper presents a new attribute selection technique called variance ranking for handling imbalance class problems in a dataset. The results obtained were compared to two well-known attribute selection techniques: the Pearson correlation and information gain technique. This paper uses a novel similarity measurement technique ranked order similarity-ROS to evaluate the variance ranking attribute selection compared to the Pearson correlations and information gain. Further validation was carried out using three binary classifications: logistic regression, support vector machine, and decision tree. The proposed variance ranking and ranked order similarity techniques showed better results than the benchmarks. The ROS technique provided an excellent means of grading and measuring the similarities where other similarity measurement techniques were inadequate or not applicable.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/38956617" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="dbad83c178b158a5ab27fcc6d833fc24" rel="nofollow" data-download="{"attachment_id":59059393,"asset_id":38956617,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/59059393/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="27108502" href="https://uel.academia.edu/SolomonEbenuwa">Solomon Ebenuwa</a><script data-card-contents-for-user="27108502" type="text/json">{"id":27108502,"first_name":"Solomon","last_name":"Ebenuwa","domain_name":"uel","page_name":"SolomonEbenuwa","display_name":"Solomon Ebenuwa","profile_url":"https://uel.academia.edu/SolomonEbenuwa?f_ri=34344","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-38956617">+1</span><div class="hidden js-additional-users-38956617"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/MamounAlazab">Mamoun Alazab</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-38956617'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-38956617').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_38956617 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="38956617"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 38956617, container: ".js-paper-rank-work_38956617", }); });</script></li><li class="js-percentile-work_38956617 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 38956617; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_38956617"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_38956617 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="38956617"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 38956617; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=38956617]").text(description); $(".js-view-count-work_38956617").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_38956617").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="38956617"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2008" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="34344" href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Data mining (Data Analysis)</a>, <script data-card-contents-for-ri="34344" type="text/json">{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="43619" href="https://www.academia.edu/Documents/in/Feature_Selection">Feature Selection</a>, <script data-card-contents-for-ri="43619" type="text/json">{"id":43619,"name":"Feature Selection","url":"https://www.academia.edu/Documents/in/Feature_Selection?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="1688213" href="https://www.academia.edu/Documents/in/Imbalanced_learning">Imbalanced learning</a><script data-card-contents-for-ri="1688213" type="text/json">{"id":1688213,"name":"Imbalanced learning","url":"https://www.academia.edu/Documents/in/Imbalanced_learning?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=38956617]'), work: {"id":38956617,"title":"Variance Ranking Attributes Selection Techniques for Binary Classification Problem in Imbalance Data","created_at":"2019-04-28T02:13:57.567-07:00","url":"https://www.academia.edu/38956617/Variance_Ranking_Attributes_Selection_Techniques_for_Binary_Classification_Problem_in_Imbalance_Data?f_ri=34344","dom_id":"work_38956617","summary":"Data are being generated and used to support all aspects of healthcare provision, from policy formation to the delivery of primary care services. Particularly, with the change of emphasis from curative to preventive medicine, the importance of data-based research such as data mining and machine learning has emphasized the issues of class distributions in datasets. In typical predictive modeling, the inability to effectively address a class imbalance in a real-life dataset is an important shortcoming of the existing machine learning algorithms. Most algorithms assume a balanced class in their design, resulting in poor performance in predicting the minority target class. Ironically, the minority target class is usually the focus in predicting processes. The misclassification of the minority target class has resulted in serious consequences in detecting chronic diseases and detecting fraud and intrusion where positive cases are erroneously predicted as not positive. This paper presents a new attribute selection technique called variance ranking for handling imbalance class problems in a dataset. The results obtained were compared to two well-known attribute selection techniques: the Pearson correlation and information gain technique. This paper uses a novel similarity measurement technique ranked order similarity-ROS to evaluate the variance ranking attribute selection compared to the Pearson correlations and information gain. Further validation was carried out using three binary classifications: logistic regression, support vector machine, and decision tree. The proposed variance ranking and ranked order similarity techniques showed better results than the benchmarks. The ROS technique provided an excellent means of grading and measuring the similarities where other similarity measurement techniques were inadequate or not applicable.","downloadable_attachments":[{"id":59059393,"asset_id":38956617,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":27108502,"first_name":"Solomon","last_name":"Ebenuwa","domain_name":"uel","page_name":"SolomonEbenuwa","display_name":"Solomon Ebenuwa","profile_url":"https://uel.academia.edu/SolomonEbenuwa?f_ri=34344","photo":"/images/s65_no_pic.png"},{"id":112098963,"first_name":"Mamoun","last_name":"Alazab","domain_name":"independent","page_name":"MamounAlazab","display_name":"Mamoun Alazab","profile_url":"https://independent.academia.edu/MamounAlazab?f_ri=34344","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false},{"id":43619,"name":"Feature Selection","url":"https://www.academia.edu/Documents/in/Feature_Selection?f_ri=34344","nofollow":false},{"id":1688213,"name":"Imbalanced learning","url":"https://www.academia.edu/Documents/in/Imbalanced_learning?f_ri=34344","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_43855091" data-work_id="43855091" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/43855091/Top_Data_Mining_and_Knowledge_Management_Research_articles_of_2019">Top Data Mining & Knowledge Management Research articles of 2019</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_43855091" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/43855091" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="40677e2bb0731a4f7a1664854d2bfb25" rel="nofollow" data-download="{"attachment_id":64178374,"asset_id":43855091,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/64178374/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="16715850" href="https://independent.academia.edu/IJDKPJOURNAL">International Journal of Data Mining & Knowledge Management Process ( IJDKP )</a><script data-card-contents-for-user="16715850" type="text/json">{"id":16715850,"first_name":"International Journal of Data Mining \u0026 Knowledge Management Process","last_name":"( IJDKP )","domain_name":"independent","page_name":"IJDKPJOURNAL","display_name":"International Journal of Data Mining \u0026 Knowledge Management Process ( IJDKP )","profile_url":"https://independent.academia.edu/IJDKPJOURNAL?f_ri=34344","photo":"https://0.academia-photos.com/16715850/4568261/39147186/s65_international_journal_of_data_mining_knowledge_management_process._ijdkp_.png"}</script></span></span></li><li class="js-paper-rank-work_43855091 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="43855091"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 43855091, container: ".js-paper-rank-work_43855091", }); 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$(".js-view-count[data-work-id=43855091]").text(description); $(".js-view-count-work_43855091").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_43855091").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="43855091"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">20</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="146" href="https://www.academia.edu/Documents/in/Bioinformatics">Bioinformatics</a>, <script data-card-contents-for-ri="146" type="text/json">{"id":146,"name":"Bioinformatics","url":"https://www.academia.edu/Documents/in/Bioinformatics?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="965" href="https://www.academia.edu/Documents/in/Formal_Concept_Analysis_Data_Mining_">Formal Concept Analysis (Data Mining)</a>, <script data-card-contents-for-ri="965" type="text/json">{"id":965,"name":"Formal Concept Analysis (Data Mining)","url":"https://www.academia.edu/Documents/in/Formal_Concept_Analysis_Data_Mining_?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4205" href="https://www.academia.edu/Documents/in/Data_Analysis">Data Analysis</a><script data-card-contents-for-ri="4205" type="text/json">{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=43855091]'), work: {"id":43855091,"title":"Top Data Mining \u0026 Knowledge Management Research articles of 2019","created_at":"2020-08-14T01:12:50.161-07:00","url":"https://www.academia.edu/43855091/Top_Data_Mining_and_Knowledge_Management_Research_articles_of_2019?f_ri=34344","dom_id":"work_43855091","summary":"Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data","downloadable_attachments":[{"id":64178374,"asset_id":43855091,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":16715850,"first_name":"International Journal of Data Mining \u0026 Knowledge Management Process","last_name":"( IJDKP )","domain_name":"independent","page_name":"IJDKPJOURNAL","display_name":"International Journal of Data Mining \u0026 Knowledge Management Process ( IJDKP )","profile_url":"https://independent.academia.edu/IJDKPJOURNAL?f_ri=34344","photo":"https://0.academia-photos.com/16715850/4568261/39147186/s65_international_journal_of_data_mining_knowledge_management_process._ijdkp_.png"}],"research_interests":[{"id":146,"name":"Bioinformatics","url":"https://www.academia.edu/Documents/in/Bioinformatics?f_ri=34344","nofollow":false},{"id":965,"name":"Formal Concept Analysis (Data Mining)","url":"https://www.academia.edu/Documents/in/Formal_Concept_Analysis_Data_Mining_?f_ri=34344","nofollow":false},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false},{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=34344","nofollow":false},{"id":5639,"name":"Text Mining","url":"https://www.academia.edu/Documents/in/Text_Mining?f_ri=34344"},{"id":14494,"name":"Opinion Mining (Data Mining)","url":"https://www.academia.edu/Documents/in/Opinion_Mining_Data_Mining_?f_ri=34344"},{"id":20279,"name":"Seismic data processing","url":"https://www.academia.edu/Documents/in/Seismic_data_processing?f_ri=34344"},{"id":23995,"name":"Educational Data Mining","url":"https://www.academia.edu/Documents/in/Educational_Data_Mining?f_ri=34344"},{"id":32701,"name":"Data Mining in Bioinformatics","url":"https://www.academia.edu/Documents/in/Data_Mining_in_Bioinformatics?f_ri=34344"},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":60159,"name":"Database Management Systems and Querry Processing","url":"https://www.academia.edu/Documents/in/Database_Management_Systems_and_Querry_Processing?f_ri=34344"},{"id":60650,"name":"Data Warehousing and Data Mining","url":"https://www.academia.edu/Documents/in/Data_Warehousing_and_Data_Mining?f_ri=34344"},{"id":106145,"name":"Classification","url":"https://www.academia.edu/Documents/in/Classification?f_ri=34344"},{"id":108488,"name":"Data Mining – Concepts and Techniques","url":"https://www.academia.edu/Documents/in/Data_Mining_Concepts_and_Techniques?f_ri=34344"},{"id":126300,"name":"Big Data","url":"https://www.academia.edu/Documents/in/Big_Data?f_ri=34344"},{"id":413148,"name":"Big Data / Analytics / Data Mining","url":"https://www.academia.edu/Documents/in/Big_Data_Analytics_Data_Mining?f_ri=34344"},{"id":459269,"name":"Data mining and Text mining","url":"https://www.academia.edu/Documents/in/Data_mining_and_Text_mining?f_ri=34344"},{"id":460675,"name":"Data Mining Techniques","url":"https://www.academia.edu/Documents/in/Data_Mining_Techniques?f_ri=34344"},{"id":581652,"name":"Data Processing","url":"https://www.academia.edu/Documents/in/Data_Processing?f_ri=34344"},{"id":994859,"name":"Data Mining and Business Intelligence","url":"https://www.academia.edu/Documents/in/Data_Mining_and_Business_Intelligence?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_75616348" data-work_id="75616348" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/75616348/Call_for_Papers_7th_International_Conference_on_Data_Mining_and_Knowledge_Management_DaKM_2022_">Call for Papers - 7th International Conference on Data Mining & Knowledge Management (DaKM 2022)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">7th International Conference on Data Mining & Knowledge Management (DaKM 2022) provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors are solicited to contribute to the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_75616348" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">7th International Conference on Data Mining & Knowledge Management (DaKM 2022) provides<br />a forum for researchers who address this issue and to present their work in a peer-reviewed forum.<br />Authors are solicited to contribute to the conference by submitting articles that illustrate research<br />results, projects, surveying works and industrial experiences that describe significant advances in the<br />following areas, but are not limited to these topics only.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/75616348" 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class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="21252910" href="https://independent.academia.edu/ACIJJournal">Advanced Computing: An International Journal ( ACIJ )</a><script data-card-contents-for-user="21252910" type="text/json">{"id":21252910,"first_name":"Advanced Computing: An International Journal","last_name":"( ACIJ )","domain_name":"independent","page_name":"ACIJJournal","display_name":"Advanced Computing: An International Journal ( ACIJ )","profile_url":"https://independent.academia.edu/ACIJJournal?f_ri=34344","photo":"https://0.academia-photos.com/21252910/5871420/60189180/s65_advanced_computing_an_international_journal._acij_.jpg"}</script></span></span></li><li class="js-paper-rank-work_75616348 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="75616348"><i class="u-m1x fa 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view-count u-mr2x" data-work-id="75616348"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75616348; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75616348]").text(description); $(".js-view-count-work_75616348").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_75616348").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="75616348"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">15</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="442" href="https://www.academia.edu/Documents/in/Parallel_Computing">Parallel Computing</a>, <script data-card-contents-for-ri="442" type="text/json">{"id":442,"name":"Parallel Computing","url":"https://www.academia.edu/Documents/in/Parallel_Computing?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="9351" href="https://www.academia.edu/Documents/in/Image_Analysis">Image Analysis</a>, <script data-card-contents-for-ri="9351" type="text/json">{"id":9351,"name":"Image Analysis","url":"https://www.academia.edu/Documents/in/Image_Analysis?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="14494" href="https://www.academia.edu/Documents/in/Opinion_Mining_Data_Mining_">Opinion Mining (Data Mining)</a><script data-card-contents-for-ri="14494" type="text/json">{"id":14494,"name":"Opinion Mining (Data Mining)","url":"https://www.academia.edu/Documents/in/Opinion_Mining_Data_Mining_?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=75616348]'), work: {"id":75616348,"title":"Call for Papers - 7th International Conference on Data Mining \u0026 Knowledge Management (DaKM 2022)","created_at":"2022-04-06T03:57:44.592-07:00","url":"https://www.academia.edu/75616348/Call_for_Papers_7th_International_Conference_on_Data_Mining_and_Knowledge_Management_DaKM_2022_?f_ri=34344","dom_id":"work_75616348","summary":"7th International Conference on Data Mining \u0026 Knowledge Management (DaKM 2022) provides\na forum for researchers who address this issue and to present their work in a peer-reviewed forum.\nAuthors are solicited to contribute to the conference by submitting articles that illustrate research\nresults, projects, surveying works and industrial experiences that describe significant advances in the\nfollowing areas, but are not limited to these topics only.","downloadable_attachments":[{"id":88208270,"asset_id":75616348,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":21252910,"first_name":"Advanced Computing: An International Journal","last_name":"( ACIJ )","domain_name":"independent","page_name":"ACIJJournal","display_name":"Advanced Computing: An International Journal ( ACIJ )","profile_url":"https://independent.academia.edu/ACIJJournal?f_ri=34344","photo":"https://0.academia-photos.com/21252910/5871420/60189180/s65_advanced_computing_an_international_journal._acij_.jpg"}],"research_interests":[{"id":442,"name":"Parallel 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Techniques","url":"https://www.academia.edu/Documents/in/Data_Mining_Techniques?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_42818627" data-work_id="42818627" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/42818627/April_2020_Top_Read_Articles_In_Data_Mining_and_Knowledge_Management_Process_Research_Articles">April 2020: Top Read Articles In Data Mining & Knowledge Management Process Research Articles</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_42818627" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/42818627" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="a7938f14522101893f381bc125b443f7" rel="nofollow" data-download="{"attachment_id":63049282,"asset_id":42818627,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/63049282/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="16715850" href="https://independent.academia.edu/IJDKPJOURNAL">International Journal of Data Mining & Knowledge Management Process ( IJDKP )</a><script data-card-contents-for-user="16715850" type="text/json">{"id":16715850,"first_name":"International Journal of Data Mining \u0026 Knowledge Management Process","last_name":"( IJDKP )","domain_name":"independent","page_name":"IJDKPJOURNAL","display_name":"International Journal of Data Mining \u0026 Knowledge Management Process ( IJDKP )","profile_url":"https://independent.academia.edu/IJDKPJOURNAL?f_ri=34344","photo":"https://0.academia-photos.com/16715850/4568261/39147186/s65_international_journal_of_data_mining_knowledge_management_process._ijdkp_.png"}</script></span></span></li><li class="js-paper-rank-work_42818627 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="42818627"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 42818627, container: ".js-paper-rank-work_42818627", }); 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$(".js-view-count[data-work-id=42818627]").text(description); $(".js-view-count-work_42818627").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_42818627").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="42818627"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">17</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="422" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="10472" href="https://www.academia.edu/Documents/in/Web_Applications">Web Applications</a>, <script data-card-contents-for-ri="10472" type="text/json">{"id":10472,"name":"Web Applications","url":"https://www.academia.edu/Documents/in/Web_Applications?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="23284" href="https://www.academia.edu/Documents/in/Wireless_Networks_Computer_Science_">Wireless Networks (Computer Science)</a><script data-card-contents-for-ri="23284" type="text/json">{"id":23284,"name":"Wireless Networks (Computer Science)","url":"https://www.academia.edu/Documents/in/Wireless_Networks_Computer_Science_?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=42818627]'), work: {"id":42818627,"title":"April 2020: Top Read Articles In Data Mining \u0026 Knowledge Management Process Research Articles","created_at":"2020-04-22T03:53:09.049-07:00","url":"https://www.academia.edu/42818627/April_2020_Top_Read_Articles_In_Data_Mining_and_Knowledge_Management_Process_Research_Articles?f_ri=34344","dom_id":"work_42818627","summary":"Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. 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Particularly, we investigate two tasks: 1) given a query image, we retrieve textual descriptions that correspond to the visual... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_24779069" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper, we focus on cross-modal (visual and textual) e-commerce search within the fashion domain. Particularly, we investigate two tasks: 1) given a query image, we retrieve textual descriptions that correspond to the visual attributes in the query; and 2) given a textual query that may express an interest in specific visual product characteristics, we retrieve relevant images that exhibit the required visual attributes. To this end, we introduce a new dataset that consists of 53,689 images coupled with textual descriptions. The images contain fashion garments that display a great variety of visual attributes, such as different shapes, colors and textures in natural language. Unlike previous datasets, the text provides a rough and noisy description of the item in the image. We extensively analyze this dataset in the context of cross-modal e-commerce search. We investigate two state-of-the-art latent variable models to bridge between textual and visual data: bilingual latent Dirichlet allocation and canonical correlation analysis. We use state-of-the-art visual and textual features and report promising results.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/24779069" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="fb5ec7dfc41ff04c12e4682449d755dd" rel="nofollow" data-download="{"attachment_id":45107079,"asset_id":24779069,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/45107079/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="47858999" href="https://independent.academia.edu/SusanaZoghbi">Susana Zoghbi</a><script data-card-contents-for-user="47858999" type="text/json">{"id":47858999,"first_name":"Susana","last_name":"Zoghbi","domain_name":"independent","page_name":"SusanaZoghbi","display_name":"Susana Zoghbi","profile_url":"https://independent.academia.edu/SusanaZoghbi?f_ri=34344","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-24779069">+1</span><div class="hidden js-additional-users-24779069"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://ugto.academia.edu/JuanCarlosGomez">Juan Carlos Gomez</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-24779069'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-24779069').html(); 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container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_24779069 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="24779069"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 24779069; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=24779069]").text(description); $(".js-view-count-work_24779069").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_24779069").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="24779069"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">22</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="464" href="https://www.academia.edu/Documents/in/Information_Retrieval">Information Retrieval</a>, <script data-card-contents-for-ri="464" type="text/json">{"id":464,"name":"Information Retrieval","url":"https://www.academia.edu/Documents/in/Information_Retrieval?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="1185" href="https://www.academia.edu/Documents/in/Image_Processing">Image Processing</a>, <script data-card-contents-for-ri="1185" type="text/json">{"id":1185,"name":"Image Processing","url":"https://www.academia.edu/Documents/in/Image_Processing?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a><script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=24779069]'), work: {"id":24779069,"title":"Fashion Meets Computer Vision and NLP at e-Commerce Search","created_at":"2016-04-26T10:25:56.299-07:00","url":"https://www.academia.edu/24779069/Fashion_Meets_Computer_Vision_and_NLP_at_e_Commerce_Search?f_ri=34344","dom_id":"work_24779069","summary":"In this paper, we focus on cross-modal (visual and textual) e-commerce search within the fashion domain. Particularly, we investigate two tasks: 1) given a query image, we retrieve textual descriptions that correspond to the visual attributes in the query; and 2) given a textual query that may express an interest in specific visual product characteristics, we retrieve relevant images that exhibit the required visual attributes. To this end, we introduce a new dataset that consists of 53,689 images coupled with textual descriptions. The images contain fashion garments that display a great variety of visual attributes, such as different shapes, colors and textures in natural language. Unlike previous datasets, the text provides a rough and noisy description of the item in the image. We extensively analyze this dataset in the context of cross-modal e-commerce search. We investigate two state-of-the-art latent variable models to bridge between textual and visual data: bilingual latent Dirichlet allocation and canonical correlation analysis. We use state-of-the-art visual and textual features and report promising results.","downloadable_attachments":[{"id":45107079,"asset_id":24779069,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":47858999,"first_name":"Susana","last_name":"Zoghbi","domain_name":"independent","page_name":"SusanaZoghbi","display_name":"Susana Zoghbi","profile_url":"https://independent.academia.edu/SusanaZoghbi?f_ri=34344","photo":"/images/s65_no_pic.png"},{"id":45395159,"first_name":"Juan Carlos","last_name":"Gomez","domain_name":"ugto","page_name":"JuanCarlosGomez","display_name":"Juan Carlos Gomez","profile_url":"https://ugto.academia.edu/JuanCarlosGomez?f_ri=34344","photo":"https://0.academia-photos.com/45395159/12055019/13429741/s65_juan_carlos.gomez.jpg"}],"research_interests":[{"id":464,"name":"Information Retrieval","url":"https://www.academia.edu/Documents/in/Information_Retrieval?f_ri=34344","nofollow":false},{"id":1185,"name":"Image Processing","url":"https://www.academia.edu/Documents/in/Image_Processing?f_ri=34344","nofollow":false},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=34344","nofollow":false},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=34344","nofollow":false},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344"},{"id":4098,"name":"Content-Based Information Retrieval (CBIR)","url":"https://www.academia.edu/Documents/in/Content-Based_Information_Retrieval_CBIR_?f_ri=34344"},{"id":5109,"name":"Pattern Recognition","url":"https://www.academia.edu/Documents/in/Pattern_Recognition?f_ri=34344"},{"id":5112,"name":"Object Recognition (Pattern Recognition)","url":"https://www.academia.edu/Documents/in/Object_Recognition_Pattern_Recognition_?f_ri=34344"},{"id":9176,"name":"Content-Based Image Retrieval","url":"https://www.academia.edu/Documents/in/Content-Based_Image_Retrieval?f_ri=34344"},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":35938,"name":"Digital Image Processing","url":"https://www.academia.edu/Documents/in/Digital_Image_Processing?f_ri=34344"},{"id":54980,"name":"Topic Models","url":"https://www.academia.edu/Documents/in/Topic_Models?f_ri=34344"},{"id":71997,"name":"Multimedia information retrieval","url":"https://www.academia.edu/Documents/in/Multimedia_information_retrieval?f_ri=34344"},{"id":73938,"name":"Content based image retrieval","url":"https://www.academia.edu/Documents/in/Content_based_image_retrieval?f_ri=34344"},{"id":88876,"name":"Image Retrieval","url":"https://www.academia.edu/Documents/in/Image_Retrieval?f_ri=34344"},{"id":143038,"name":"Machine Learning and Pattern Recognition","url":"https://www.academia.edu/Documents/in/Machine_Learning_and_Pattern_Recognition?f_ri=34344"},{"id":224113,"name":"Latent Dirichlet Allocation","url":"https://www.academia.edu/Documents/in/Latent_Dirichlet_Allocation?f_ri=34344"},{"id":537605,"name":"Natural Language Processing(NLP)","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing_NLP_?f_ri=34344"},{"id":622592,"name":"E-Commerce","url":"https://www.academia.edu/Documents/in/E-Commerce?f_ri=34344"},{"id":954064,"name":"Content based Retrieval","url":"https://www.academia.edu/Documents/in/Content_based_Retrieval?f_ri=34344"},{"id":959260,"name":"Multi Modality","url":"https://www.academia.edu/Documents/in/Multi_Modality?f_ri=34344"},{"id":1033104,"name":"Electronic Commerce and E ‐business","url":"https://www.academia.edu/Documents/in/Electronic_Commerce_and_E_-business?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_36171948" data-work_id="36171948" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/36171948/SENTIMENT_ANALYSIS_OF_PRODUCT_REVIEWS_FOR_OVERALL_PRODUCT_RATING">SENTIMENT ANALYSIS OF PRODUCT REVIEWS FOR OVERALL PRODUCT RATING</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Every second plethora of reviews on various product lines are being posted in a trending e-commerce website. The objective of a review section in such websites is to analyze customer satisfaction for sales growth and to aid buyers make... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36171948" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Every second plethora of reviews on various product lines are being posted in a trending e-commerce website. The objective of a review section in such websites is to analyze customer satisfaction for sales growth and to aid buyers make right purchase decisions. This becomes a herculean task for the business analyst when the search space for interesting patterns is vast. Buyers could benefit from a trusted recommendation label for the product and an overall product rating purely based on data mining of the customer generated live review dataset. The proposed sentiment analysis system makes use of data mining and natural language processing algorithms. The words in the corpus selected for the sentiment classification is associated with fuzzy scores to indicate degree of polarity strength. Naïve bayes algorithm forms the basis for the project. It is simple yet yields effective results. Sentiment scoring of the review is used for automated five star rating that replaces the conventional manual rating. Context based anomaly detection is done to remove irrelevant portions of each review. Selective-feature analysis is performed to assist vendors as well as buyers. A threshold value is compared against the overall rating for the recommendation label. The polarity detection gave fair results. It was observed that the richness of corpus and grammar rules contributed to the accuracy of the model. Threshold chosen for product recommendation gave mediocre credence when it was solely based on reviews. Therefore Threshold was set using more complex criteria that involves purchase patterns. Results obtained from the proposed model is ideal for e-commerce websites. Model can be improved by addition of sarcasm detection algorithm which is quite challenging.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36171948" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="398e6b9c648e773d7c8187087371104d" rel="nofollow" data-download="{"attachment_id":56070115,"asset_id":36171948,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/56070115/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="66586042" href="https://independent.academia.edu/ANJANAMADHAVC14MSE0016">ANJANA MADHAV C 14MSE0016</a><script data-card-contents-for-user="66586042" type="text/json">{"id":66586042,"first_name":"ANJANA MADHAV C","last_name":"14MSE0016","domain_name":"independent","page_name":"ANJANAMADHAVC14MSE0016","display_name":"ANJANA MADHAV C 14MSE0016","profile_url":"https://independent.academia.edu/ANJANAMADHAVC14MSE0016?f_ri=34344","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_36171948 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36171948"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36171948, container: ".js-paper-rank-work_36171948", }); });</script></li><li class="js-percentile-work_36171948 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 36171948; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_36171948"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_36171948 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="36171948"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 36171948; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=36171948]").text(description); $(".js-view-count-work_36171948").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_36171948").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="36171948"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">18</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="1432" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="5486" href="https://www.academia.edu/Documents/in/Clustering_and_Classification_Methods">Clustering and Classification Methods</a><script data-card-contents-for-ri="5486" type="text/json">{"id":5486,"name":"Clustering and Classification Methods","url":"https://www.academia.edu/Documents/in/Clustering_and_Classification_Methods?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=36171948]'), work: {"id":36171948,"title":"SENTIMENT ANALYSIS OF PRODUCT REVIEWS FOR OVERALL PRODUCT RATING","created_at":"2018-03-15T09:54:51.441-07:00","url":"https://www.academia.edu/36171948/SENTIMENT_ANALYSIS_OF_PRODUCT_REVIEWS_FOR_OVERALL_PRODUCT_RATING?f_ri=34344","dom_id":"work_36171948","summary":"Every second plethora of reviews on various product lines are being posted in a trending e-commerce website. The objective of a review section in such websites is to analyze customer satisfaction for sales growth and to aid buyers make right purchase decisions. This becomes a herculean task for the business analyst when the search space for interesting patterns is vast. Buyers could benefit from a trusted recommendation label for the product and an overall product rating purely based on data mining of the customer generated live review dataset. The proposed sentiment analysis system makes use of data mining and natural language processing algorithms. The words in the corpus selected for the sentiment classification is associated with fuzzy scores to indicate degree of polarity strength. Naïve bayes algorithm forms the basis for the project. It is simple yet yields effective results. Sentiment scoring of the review is used for automated five star rating that replaces the conventional manual rating. Context based anomaly detection is done to remove irrelevant portions of each review. Selective-feature analysis is performed to assist vendors as well as buyers. A threshold value is compared against the overall rating for the recommendation label. The polarity detection gave fair results. It was observed that the richness of corpus and grammar rules contributed to the accuracy of the model. Threshold chosen for product recommendation gave mediocre credence when it was solely based on reviews. Therefore Threshold was set using more complex criteria that involves purchase patterns. Results obtained from the proposed model is ideal for e-commerce websites. Model can be improved by addition of sarcasm detection algorithm which is quite challenging.","downloadable_attachments":[{"id":56070115,"asset_id":36171948,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":66586042,"first_name":"ANJANA MADHAV C","last_name":"14MSE0016","domain_name":"independent","page_name":"ANJANAMADHAVC14MSE0016","display_name":"ANJANA MADHAV C 14MSE0016","profile_url":"https://independent.academia.edu/ANJANAMADHAVC14MSE0016?f_ri=34344","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=34344","nofollow":false},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=34344","nofollow":false},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=34344","nofollow":false},{"id":5486,"name":"Clustering and Classification Methods","url":"https://www.academia.edu/Documents/in/Clustering_and_Classification_Methods?f_ri=34344","nofollow":false},{"id":5639,"name":"Text Mining","url":"https://www.academia.edu/Documents/in/Text_Mining?f_ri=34344"},{"id":14494,"name":"Opinion Mining (Data Mining)","url":"https://www.academia.edu/Documents/in/Opinion_Mining_Data_Mining_?f_ri=34344"},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=34344"},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":47980,"name":"Data Visualization","url":"https://www.academia.edu/Documents/in/Data_Visualization?f_ri=34344"},{"id":108557,"name":"Statistical Inference, Outliers Detection, Statistical Methods","url":"https://www.academia.edu/Documents/in/Statistical_Inference_Outliers_Detection_Statistical_Methods?f_ri=34344"},{"id":131096,"name":"Outlier detection","url":"https://www.academia.edu/Documents/in/Outlier_detection?f_ri=34344"},{"id":139125,"name":"Product Reviews","url":"https://www.academia.edu/Documents/in/Product_Reviews?f_ri=34344"},{"id":224113,"name":"Latent Dirichlet Allocation","url":"https://www.academia.edu/Documents/in/Latent_Dirichlet_Allocation?f_ri=34344"},{"id":345767,"name":"Naive Bayes","url":"https://www.academia.edu/Documents/in/Naive_Bayes?f_ri=34344"},{"id":455746,"name":"OPINION MINING AND SENTIMENT ANALYSIS","url":"https://www.academia.edu/Documents/in/OPINION_MINING_AND_SENTIMENT_ANALYSIS?f_ri=34344"},{"id":521021,"name":"Outlier Analysis","url":"https://www.academia.edu/Documents/in/Outlier_Analysis?f_ri=34344"},{"id":1032324,"name":"K means Clustering","url":"https://www.academia.edu/Documents/in/K_means_Clustering?f_ri=34344"},{"id":1676937,"name":"Python Nltk","url":"https://www.academia.edu/Documents/in/Python_Nltk?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_13534937" data-work_id="13534937" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/13534937/Data_Analysis_Using_WEKA_Issues_in_Customer_Churning">Data Analysis Using WEKA-Issues in Customer Churning</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Data mining is one of the best ways to identify patterns and problems in large amount of data to support problem solving process. In this paper the causes business problems under a company’s will be identified from the data collected from... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_13534937" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Data mining is one of the best ways to identify patterns and problems in large amount of data to support problem solving process. In this paper the causes business problems under a company’s will be identified from the data collected from the day to day activities of the company. iTelecom, the provider of telephone and broadband services, is facing customer management problems. There were high customer churning in company but the company does not know what is exactly causing it.  The company collected vast amount of data related to its customer, the plan customers<br />signed on and the services it provides. iTelecom has placed a high emphasis on churning problem and it is trying to find new ways of reducing it. The directors of iTelecom would like to answer the following questions.<br />-    What is it that makes a customer churn?<br /><br />-    Are some customers more likely to churn than others?<br /><br />-    How can we identify these customers before they churn?<br /><br />In an attempt to answer these questions the company data was organised and analysed using Weka software. The data is composed of 21 attributes with nominal and numeric data types.  There were<br />3333 instances used to analyse the data. The data is pre-processed tested with at different levels for missing data, for inconsistencies. Then, appropriate attributes were selected. The appropriateness<br />an attribute was measure with its ability in predicting churning. Then, the data mining performed using different classifiers and tests. Then the data is divided into sub datasets to find out which attribute or group of attribute can better predict churning. In conclusion the best group was selected and used to spot why these attributes are linked to the risk of churning.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/13534937" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4493f4dd29e2579b95194cc0e39f22df" rel="nofollow" data-download="{"attachment_id":38069671,"asset_id":13534937,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38069671/download_file?st=MTczMjQxNTk4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="16813501" href="https://independent.academia.edu/TesfayeOnsho">Tesfaye Onsho</a><script data-card-contents-for-user="16813501" type="text/json">{"id":16813501,"first_name":"Tesfaye","last_name":"Onsho","domain_name":"independent","page_name":"TesfayeOnsho","display_name":"Tesfaye Onsho","profile_url":"https://independent.academia.edu/TesfayeOnsho?f_ri=34344","photo":"https://0.academia-photos.com/16813501/4608353/5326606/s65_tesfaye.onsho.jpg"}</script></span></span></li><li class="js-paper-rank-work_13534937 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="13534937"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 13534937, container: ".js-paper-rank-work_13534937", }); 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In this paper the causes business problems under a company’s will be identified from the data collected from the day to day activities of the company. iTelecom, the provider of telephone and broadband services, is facing customer management problems. There were high customer churning in company but the company does not know what is exactly causing it. The company collected vast amount of data related to its customer, the plan customers\nsigned on and the services it provides. iTelecom has placed a high emphasis on churning problem and it is trying to find new ways of reducing it. The directors of iTelecom would like to answer the following questions.\n- What is it that makes a customer churn?\n\n- Are some customers more likely to churn than others?\n\n- How can we identify these customers before they churn?\n\nIn an attempt to answer these questions the company data was organised and analysed using Weka software. The data is composed of 21 attributes with nominal and numeric data types. There were\n3333 instances used to analyse the data. The data is pre-processed tested with at different levels for missing data, for inconsistencies. Then, appropriate attributes were selected. The appropriateness\nan attribute was measure with its ability in predicting churning. Then, the data mining performed using different classifiers and tests. Then the data is divided into sub datasets to find out which attribute or group of attribute can better predict churning. In conclusion the best group was selected and used to spot why these attributes are linked to the risk of churning.\n \n","downloadable_attachments":[{"id":38069671,"asset_id":13534937,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":16813501,"first_name":"Tesfaye","last_name":"Onsho","domain_name":"independent","page_name":"TesfayeOnsho","display_name":"Tesfaye Onsho","profile_url":"https://independent.academia.edu/TesfayeOnsho?f_ri=34344","photo":"https://0.academia-photos.com/16813501/4608353/5326606/s65_tesfaye.onsho.jpg"}],"research_interests":[{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false},{"id":123233,"name":"Qualitative data analysis","url":"https://www.academia.edu/Documents/in/Qualitative_data_analysis?f_ri=34344","nofollow":false},{"id":215498,"name":"Genetic Data Analysis Using 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href="https://www.academia.edu/33342882/%D8%A8%D9%87%DB%8C%D9%86%D9%87_%D8%B3%D8%A7%D8%B2%DB%8C_%D8%B2%D9%85%D8%A7%D9%86_%D9%87%D8%B2%DB%8C%D9%86%D9%87_%D9%BE%D8%B1%D9%88%DA%98%D9%87_%D8%A8%D8%A7_%D8%AF%D8%B1_%D9%86%D8%B8%D8%B1_%DA%AF%D8%B1%D9%81%D8%AA%D9%86_%D9%85%D8%AD%D8%AF%D9%88%D8%AF%DB%8C%D8%AA_%D9%85%D9%86%D8%A7%D8%A8%D8%B9_%D8%AA%D9%88%D8%B3%D8%B7_%D8%A7%D9%84%DA%AF%D9%88%D8%B1%DB%8C%D8%AA%D9%85_%DA%98%D9%86%D8%AA%DB%8C%DA%A9">بهینه سازی زمان-هزینه پروژه با در نظر گرفتن محدودیت منابع توسط الگوریتم ژنتیک</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">کاهش هزینه و زمان پروژه توأماً، در شرایط رقابتی حاکم بین شرکتهای پیمانکاری، امروزه به امری حیاتی تبدیل شده است. لازمه این امرمصالحه بین زمان و هزینه است. از اینرو سازمان های پیمانکاری باید به دقت رویکردهای مختلف را جهت رسیدن به یک موازنه... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_33342882" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">کاهش هزینه و زمان پروژه توأماً، در شرایط رقابتی حاکم بین شرکتهای پیمانکاری، امروزه به امری حیاتی تبدیل شده است. لازمه این امرمصالحه بین زمان و هزینه است. از اینرو سازمان های پیمانکاری باید به دقت رویکردهای مختلف را جهت رسیدن به یک موازنه بهینه زمان-هزینه بررسی کنند. اگر چه تا کنون مدلهای مختلفی برای بهینهسازی زمان-هزینهTCO گسترش یافتهاند، ولی اکثراً حالتی را در نظر میگیرند که زمان پروژه بر طبق قرارداد، مشخص و ثابت است. بنابراین هدف از بهینه سازی در این موارد، به یافتن راه حلی برای مینیمم کردن هزینه پروژه محدود میشود. با افزایش رواج سیستم پیشنهاد زمان تحویل پروژه، کارفرما و پیمانکار هر دو در پی افزایش سود و جذب موقعیتهای حاصل از اتمام هر چه سریعتر پروژه هستند. مدل چند هدفه پیشنهاد شده در این مقاله، بر مبنای جستجوی فراکاوشی توسط الگوریتم ژنتیک GAs) و بر پایه تکنیک های NSGA II جهت بهبود جبهه پارتوی دو بعدی زمان- هزینه گسترش داده شده است. مفهوم بهینه سازی چند هدفه مدل TCOبا یک مثال دستی ساده نمایش داده شده و نتایج بیانگر این موضوع هستند که مدل میتواند به تصمیم گیران پروژه جهت رسیدن به زمان و هزینهی بهینه به طور همزمان، کمک کند</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/33342882" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="36ff7fef1519cff894db4b26c953671f" rel="nofollow" data-download="{"attachment_id":53402296,"asset_id":33342882,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" 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Simulation","url":"https://www.academia.edu/Documents/in/Modeling_and_Simulation?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="34344" href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Data mining (Data Analysis)</a><script data-card-contents-for-ri="34344" type="text/json">{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=33342882]'), work: {"id":33342882,"title":"بهینه سازی زمان-هزینه پروژه با در نظر گرفتن محدودیت منابع توسط الگوریتم ژنتیک","created_at":"2017-06-05T22:15:45.054-07:00","url":"https://www.academia.edu/33342882/%D8%A8%D9%87%DB%8C%D9%86%D9%87_%D8%B3%D8%A7%D8%B2%DB%8C_%D8%B2%D9%85%D8%A7%D9%86_%D9%87%D8%B2%DB%8C%D9%86%D9%87_%D9%BE%D8%B1%D9%88%DA%98%D9%87_%D8%A8%D8%A7_%D8%AF%D8%B1_%D9%86%D8%B8%D8%B1_%DA%AF%D8%B1%D9%81%D8%AA%D9%86_%D9%85%D8%AD%D8%AF%D9%88%D8%AF%DB%8C%D8%AA_%D9%85%D9%86%D8%A7%D8%A8%D8%B9_%D8%AA%D9%88%D8%B3%D8%B7_%D8%A7%D9%84%DA%AF%D9%88%D8%B1%DB%8C%D8%AA%D9%85_%DA%98%D9%86%D8%AA%DB%8C%DA%A9?f_ri=34344","dom_id":"work_33342882","summary":"کاهش هزینه و زمان پروژه توأماً، در شرایط رقابتی حاکم بین شرکتهای پیمانکاری، امروزه به امری حیاتی تبدیل شده است. لازمه این امرمصالحه بین زمان و هزینه است. از اینرو سازمان های پیمانکاری باید به دقت رویکردهای مختلف را جهت رسیدن به یک موازنه بهینه زمان-هزینه بررسی کنند. اگر چه تا کنون مدلهای مختلفی برای بهینهسازی زمان-هزینهTCO گسترش یافتهاند، ولی اکثراً حالتی را در نظر میگیرند که زمان پروژه بر طبق قرارداد، مشخص و ثابت است. بنابراین هدف از بهینه سازی در این موارد، به یافتن راه حلی برای مینیمم کردن هزینه پروژه محدود میشود. با افزایش رواج سیستم پیشنهاد زمان تحویل پروژه، کارفرما و پیمانکار هر دو در پی افزایش سود و جذب موقعیتهای حاصل از اتمام هر چه سریعتر پروژه هستند. مدل چند هدفه پیشنهاد شده در این مقاله، بر مبنای جستجوی فراکاوشی توسط الگوریتم ژنتیک GAs) و بر پایه تکنیک های NSGA II جهت بهبود جبهه پارتوی دو بعدی زمان- هزینه گسترش داده شده است. مفهوم بهینه سازی چند هدفه مدل TCOبا یک مثال دستی ساده نمایش داده شده و نتایج بیانگر این موضوع هستند که مدل میتواند به تصمیم گیران پروژه جهت رسیدن به زمان و هزینهی بهینه به طور همزمان، کمک کند","downloadable_attachments":[{"id":53402296,"asset_id":33342882,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":27913301,"first_name":"Ashtad","last_name":"Javanmardi","domain_name":"ncstate","page_name":"AshtadJavanmardi","display_name":"Ashtad Javanmardi","profile_url":"https://ncstate.academia.edu/AshtadJavanmardi?f_ri=34344","photo":"https://0.academia-photos.com/27913301/16964746/17169263/s65_ashtad.javanmardi.jpg"}],"research_interests":[{"id":3105,"name":"Construction Management","url":"https://www.academia.edu/Documents/in/Construction_Management?f_ri=34344","nofollow":false},{"id":11820,"name":"Modeling and Simulation","url":"https://www.academia.edu/Documents/in/Modeling_and_Simulation?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_7715317" data-work_id="7715317" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/7715317/WEB_MINING_TECHNOLOGIES_FOR_THE_E_COMMERCE_SOLUTIONS_IN_THE_SOCIAL_NETWORKS_SYSTEMS_A_THESIS_MASTER_OF_SCIENCE_INFORMATION_SYSTEMS">WEB MINING TECHNOLOGIES FOR THE E-COMMERCE SOLUTIONS IN THE SOCIAL NETWORKS SYSTEMS A THESIS MASTER OF SCIENCE -INFORMATION SYSTEMS</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/7715317" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7c96e077d99bc293502994672fc19c34" rel="nofollow" data-download="{"attachment_id":34241651,"asset_id":7715317,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/34241651/download_file?st=MTczMjQxNTk5MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="4397284" href="https://stevens.academia.edu/WillisPolanco">Willis Polanco</a><script data-card-contents-for-user="4397284" type="text/json">{"id":4397284,"first_name":"Willis","last_name":"Polanco","domain_name":"stevens","page_name":"WillisPolanco","display_name":"Willis Polanco","profile_url":"https://stevens.academia.edu/WillisPolanco?f_ri=34344","photo":"https://gravatar.com/avatar/e88b7b03b94fbb19eb9ab9ec96f79bb2?s=65"}</script></span></span></li><li class="js-paper-rank-work_7715317 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="7715317"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 7715317, container: ".js-paper-rank-work_7715317", }); });</script></li><li class="js-percentile-work_7715317 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7715317; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_7715317"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_7715317 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="7715317"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7715317; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7715317]").text(description); $(".js-view-count-work_7715317").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_7715317").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="7715317"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">21</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="37" href="https://www.academia.edu/Documents/in/Information_Systems">Information Systems</a>, <script data-card-contents-for-ri="37" type="text/json">{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="39" href="https://www.academia.edu/Documents/in/Marketing">Marketing</a>, <script data-card-contents-for-ri="39" type="text/json">{"id":39,"name":"Marketing","url":"https://www.academia.edu/Documents/in/Marketing?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="422" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="449" href="https://www.academia.edu/Documents/in/Software_Engineering">Software Engineering</a><script data-card-contents-for-ri="449" type="text/json">{"id":449,"name":"Software Engineering","url":"https://www.academia.edu/Documents/in/Software_Engineering?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=7715317]'), work: {"id":7715317,"title":"WEB MINING TECHNOLOGIES FOR THE E-COMMERCE SOLUTIONS IN THE SOCIAL NETWORKS SYSTEMS A THESIS MASTER OF SCIENCE -INFORMATION SYSTEMS","created_at":"2014-07-19T08:58:22.432-07:00","url":"https://www.academia.edu/7715317/WEB_MINING_TECHNOLOGIES_FOR_THE_E_COMMERCE_SOLUTIONS_IN_THE_SOCIAL_NETWORKS_SYSTEMS_A_THESIS_MASTER_OF_SCIENCE_INFORMATION_SYSTEMS?f_ri=34344","dom_id":"work_7715317","summary":null,"downloadable_attachments":[{"id":34241651,"asset_id":7715317,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":4397284,"first_name":"Willis","last_name":"Polanco","domain_name":"stevens","page_name":"WillisPolanco","display_name":"Willis Polanco","profile_url":"https://stevens.academia.edu/WillisPolanco?f_ri=34344","photo":"https://gravatar.com/avatar/e88b7b03b94fbb19eb9ab9ec96f79bb2?s=65"}],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems?f_ri=34344","nofollow":false},{"id":39,"name":"Marketing","url":"https://www.academia.edu/Documents/in/Marketing?f_ri=34344","nofollow":false},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=34344","nofollow":false},{"id":449,"name":"Software Engineering","url":"https://www.academia.edu/Documents/in/Software_Engineering?f_ri=34344","nofollow":false},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=34344"},{"id":472,"name":"Human Computer Interaction","url":"https://www.academia.edu/Documents/in/Human_Computer_Interaction?f_ri=34344"},{"id":491,"name":"Information Technology","url":"https://www.academia.edu/Documents/in/Information_Technology?f_ri=34344"},{"id":1233,"name":"Social Networks","url":"https://www.academia.edu/Documents/in/Social_Networks?f_ri=34344"},{"id":1241,"name":"Knowledge Management","url":"https://www.academia.edu/Documents/in/Knowledge_Management?f_ri=34344"},{"id":1283,"name":"Information Security","url":"https://www.academia.edu/Documents/in/Information_Security?f_ri=34344"},{"id":1380,"name":"Computer Engineering","url":"https://www.academia.edu/Documents/in/Computer_Engineering?f_ri=34344"},{"id":1614,"name":"Web Design","url":"https://www.academia.edu/Documents/in/Web_Design?f_ri=34344"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=34344"},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344"},{"id":2482,"name":"Database Systems","url":"https://www.academia.edu/Documents/in/Database_Systems?f_ri=34344"},{"id":4278,"name":"Web Mining","url":"https://www.academia.edu/Documents/in/Web_Mining?f_ri=34344"},{"id":13923,"name":"Computer Security","url":"https://www.academia.edu/Documents/in/Computer_Security?f_ri=34344"},{"id":27360,"name":"Databases","url":"https://www.academia.edu/Documents/in/Databases?f_ri=34344"},{"id":30947,"name":"The Internet","url":"https://www.academia.edu/Documents/in/The_Internet?f_ri=34344"},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":622592,"name":"E-Commerce","url":"https://www.academia.edu/Documents/in/E-Commerce?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_48855634" data-work_id="48855634" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/48855634/RAINFALL_PREDICTION_USING_REGRESSION_AND_MULTIPLE_ALGORITHMS">RAINFALL PREDICTION USING REGRESSION AND MULTIPLE ALGORITHMS</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Rainfall is very important aspect for farmers in day-to-day agriculture and also very important in prospect to Indian economical growth. As now a day we see there is daily climate change in our India as there no stable season any month... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_48855634" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Rainfall is very important aspect for farmers in day-to-day agriculture and also very important in prospect to Indian economical growth. As now a day we see there is daily climate change in our India as there no stable season any month the rainfall occurs in India which also cause damage to the farming industry as the crop seeds gets damaged. In the proposed study the future rainfall is predicted using Linear Regression Technique as we also compared various model such as SVM, Random Forest Regression, Neural Network model so we obtained better results in Linear Regression so we further consider this model for future prediction of rainfall.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/48855634" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6c954e438e25511fbbb109e4d45b68c1" rel="nofollow" data-download="{"attachment_id":67269884,"asset_id":48855634,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/67269884/download_file?st=MTczMjQxNTk5MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="50335348" href="https://iiitb.academia.edu/IRJCSInternationalResearchJournalofComputerScience">IRJCS: : International Research Journal of Computer Science</a><script data-card-contents-for-user="50335348" type="text/json">{"id":50335348,"first_name":"IRJCS:","last_name":"International Research Journal of Computer Science","domain_name":"iiitb","page_name":"IRJCSInternationalResearchJournalofComputerScience","display_name":"IRJCS: : International Research Journal of Computer Science","profile_url":"https://iiitb.academia.edu/IRJCSInternationalResearchJournalofComputerScience?f_ri=34344","photo":"https://0.academia-photos.com/50335348/13242592/35131027/s65_irjcs_.international_research_journal_of_computer_science.jpg"}</script></span></span></li><li class="js-paper-rank-work_48855634 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="48855634"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 48855634, container: ".js-paper-rank-work_48855634", }); 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$(".js-view-count[data-work-id=48855634]").text(description); $(".js-view-count-work_48855634").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_48855634").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="48855634"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">13</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="1565" href="https://www.academia.edu/Documents/in/Data_Analysis_Engineering_">Data Analysis (Engineering)</a>, <script data-card-contents-for-ri="1565" type="text/json">{"id":1565,"name":"Data Analysis (Engineering)","url":"https://www.academia.edu/Documents/in/Data_Analysis_Engineering_?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4205" href="https://www.academia.edu/Documents/in/Data_Analysis">Data Analysis</a>, <script data-card-contents-for-ri="4205" type="text/json">{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="17888" href="https://www.academia.edu/Documents/in/Data_Envelopment_Analysis">Data Envelopment Analysis</a><script data-card-contents-for-ri="17888" type="text/json">{"id":17888,"name":"Data Envelopment Analysis","url":"https://www.academia.edu/Documents/in/Data_Envelopment_Analysis?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=48855634]'), work: {"id":48855634,"title":"RAINFALL PREDICTION USING REGRESSION AND MULTIPLE ALGORITHMS","created_at":"2021-05-09T07:33:28.965-07:00","url":"https://www.academia.edu/48855634/RAINFALL_PREDICTION_USING_REGRESSION_AND_MULTIPLE_ALGORITHMS?f_ri=34344","dom_id":"work_48855634","summary":"Rainfall is very important aspect for farmers in day-to-day agriculture and also very important in prospect to Indian economical growth. As now a day we see there is daily climate change in our India as there no stable season any month the rainfall occurs in India which also cause damage to the farming industry as the crop seeds gets damaged. In the proposed study the future rainfall is predicted using Linear Regression Technique as we also compared various model such as SVM, Random Forest Regression, Neural Network model so we obtained better results in Linear Regression so we further consider this model for future prediction of rainfall.","downloadable_attachments":[{"id":67269884,"asset_id":48855634,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":50335348,"first_name":"IRJCS:","last_name":"International Research Journal of Computer Science","domain_name":"iiitb","page_name":"IRJCSInternationalResearchJournalofComputerScience","display_name":"IRJCS: : International Research Journal of Computer Science","profile_url":"https://iiitb.academia.edu/IRJCSInternationalResearchJournalofComputerScience?f_ri=34344","photo":"https://0.academia-photos.com/50335348/13242592/35131027/s65_irjcs_.international_research_journal_of_computer_science.jpg"}],"research_interests":[{"id":1565,"name":"Data Analysis (Engineering)","url":"https://www.academia.edu/Documents/in/Data_Analysis_Engineering_?f_ri=34344","nofollow":false},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=34344","nofollow":false},{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=34344","nofollow":false},{"id":17888,"name":"Data Envelopment Analysis","url":"https://www.academia.edu/Documents/in/Data_Envelopment_Analysis?f_ri=34344","nofollow":false},{"id":19297,"name":"Microarray Data Analysis","url":"https://www.academia.edu/Documents/in/Microarray_Data_Analysis?f_ri=34344"},{"id":23892,"name":"Multivariate Data Analysis","url":"https://www.academia.edu/Documents/in/Multivariate_Data_Analysis?f_ri=34344"},{"id":29831,"name":"Cluster Analysis (Multivariate Data Analysis)","url":"https://www.academia.edu/Documents/in/Cluster_Analysis_Multivariate_Data_Analysis_?f_ri=34344"},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":123233,"name":"Qualitative data analysis","url":"https://www.academia.edu/Documents/in/Qualitative_data_analysis?f_ri=34344"},{"id":129810,"name":"Experimental Research Design and Statistical Data Analysis","url":"https://www.academia.edu/Documents/in/Experimental_Research_Design_and_Statistical_Data_Analysis?f_ri=34344"},{"id":199316,"name":"Multiple Linear Regression","url":"https://www.academia.edu/Documents/in/Multiple_Linear_Regression?f_ri=34344"},{"id":795003,"name":"Linear Regression","url":"https://www.academia.edu/Documents/in/Linear_Regression?f_ri=34344"},{"id":856942,"name":"Rainfall Prediction","url":"https://www.academia.edu/Documents/in/Rainfall_Prediction?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_44567078 coauthored" data-work_id="44567078" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/44567078/Exploratory_and_Predictive_Analysis_for_Carvana_Auction_Dataset">Exploratory and Predictive Analysis for Carvana Auction Dataset</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Data Science project given by the University of Pisa conducted in a team of 4 people. We try an analysis of the Carvana dataset with Data Mining tools. The goal is to predict whether a vehicle is a good buy or not. In the first section... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_44567078" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Data Science project given by the University of Pisa conducted in a team of 4 people.<br />We try an analysis of the Carvana dataset with Data Mining tools. The goal is to predict whether a vehicle is a good buy or not. In the first section the data have been explored, in order to evaluate and improve the data quality (semantic errors, redundancies, inconsisten-cies and outliers) and make some statistical considerations about the target problem(good buy or bad buy). In the second section, different clustering algorithms such as partition clustering with K-Means, density-based clustering with DBSCAN and hierarchical agglomerative clustering have beenused in order to explore the data and toassess the similarity between the records.In the third section, frequent patterns and association rules have been extracted with Apriori algorithm. In the last section, a model based on Decision Tree and Random Forest has been developed in order to predict the target variable.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/44567078" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="dc6d974da5790937d35583488fb59a96" rel="nofollow" data-download="{"attachment_id":65022000,"asset_id":44567078,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/65022000/download_file?st=MTczMjQxNTk5MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="179558611" href="https://pisa.academia.edu/GianMariaPandolfi">Gian Maria Pandolfi</a><script data-card-contents-for-user="179558611" type="text/json">{"id":179558611,"first_name":"Gian Maria","last_name":"Pandolfi","domain_name":"pisa","page_name":"GianMariaPandolfi","display_name":"Gian Maria Pandolfi","profile_url":"https://pisa.academia.edu/GianMariaPandolfi?f_ri=34344","photo":"https://gravatar.com/avatar/6ca9ef318327a1545b1a1e8b3a3930f2?s=65"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-44567078">+1</span><div class="hidden js-additional-users-44567078"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://pisa.academia.edu/LuftjanSaliaj">Luftjan Saliaj</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-44567078'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-44567078').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_44567078 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="44567078"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 44567078, container: ".js-paper-rank-work_44567078", }); });</script></li><li class="js-percentile-work_44567078 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 44567078; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_44567078"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_44567078 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="44567078"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44567078; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=44567078]").text(description); $(".js-view-count-work_44567078").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_44567078").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="44567078"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="34344" href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Data mining (Data Analysis)</a>, <script data-card-contents-for-ri="34344" type="text/json">{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="69100" href="https://www.academia.edu/Documents/in/Data_Science">Data Science</a><script data-card-contents-for-ri="69100" type="text/json">{"id":69100,"name":"Data Science","url":"https://www.academia.edu/Documents/in/Data_Science?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=44567078]'), work: {"id":44567078,"title":"Exploratory and Predictive Analysis for Carvana Auction Dataset","created_at":"2020-11-24T08:29:52.040-08:00","url":"https://www.academia.edu/44567078/Exploratory_and_Predictive_Analysis_for_Carvana_Auction_Dataset?f_ri=34344","dom_id":"work_44567078","summary":"Data Science project given by the University of Pisa conducted in a team of 4 people.\nWe try an analysis of the Carvana dataset with Data Mining tools. The goal is to predict whether a vehicle is a good buy or not. In the first section the data have been explored, in order to evaluate and improve the data quality (semantic errors, redundancies, inconsisten-cies and outliers) and make some statistical considerations about the target problem(good buy or bad buy). In the second section, different clustering algorithms such as partition clustering with K-Means, density-based clustering with DBSCAN and hierarchical agglomerative clustering have beenused in order to explore the data and toassess the similarity between the records.In the third section, frequent patterns and association rules have been extracted with Apriori algorithm. In the last section, a model based on Decision Tree and Random Forest has been developed in order to predict the target variable.","downloadable_attachments":[{"id":65022000,"asset_id":44567078,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":179558611,"first_name":"Gian Maria","last_name":"Pandolfi","domain_name":"pisa","page_name":"GianMariaPandolfi","display_name":"Gian Maria Pandolfi","profile_url":"https://pisa.academia.edu/GianMariaPandolfi?f_ri=34344","photo":"https://gravatar.com/avatar/6ca9ef318327a1545b1a1e8b3a3930f2?s=65"},{"id":134803376,"first_name":"Luftjan","last_name":"Saliaj","domain_name":"pisa","page_name":"LuftjanSaliaj","display_name":"Luftjan Saliaj","profile_url":"https://pisa.academia.edu/LuftjanSaliaj?f_ri=34344","photo":"https://0.academia-photos.com/134803376/49732833/62230404/s65_luftjan.saliaj.jpg"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false},{"id":69100,"name":"Data Science","url":"https://www.academia.edu/Documents/in/Data_Science?f_ri=34344","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_43559078 coauthored" data-work_id="43559078" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/43559078/Predicting_Customer_Churn_in_Telecommunication_Industry_Using_Convolutional_Neural_Network_Model">Predicting Customer Churn in Telecommunication Industry Using Convolutional Neural Network Model</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this study a Convolutional Neural Network (CNN) model was proposed for the prediction of customer churn in a telecommunication industry. Many supervised machine learning models have been built and used for predicting customer churn in... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_43559078" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this study a Convolutional Neural Network (CNN) model was proposed for the prediction of customer churn in a telecommunication industry. Many supervised machine learning models have been built and used for predicting customer churn in past researches. However, in thebuilding of these models, there is need for human intervention to carry out attributeselectionwhich is very tedious, time-consuming, tailored to specific datasets and often result to attribute selection problems. This study proposed a convolutional neural network model for predicting customer churning behavior and to also get rid of human attribute selectionand its problems. Two datasets were created from the fourteen thousand data instances that were gotten from one of the major cellular companies operating in Nigeria. Python programming language via the anaconda distribution was used for the development and implementation of our model. Jupyter notebook was our IDE choice. In other to achieve a like-for-like comparison, three other models were developed, which were two Multi-layer Perceptron (MLP) models and one other CNN model. The accuracy rates for the MLP models; MLP1 and MLP2, are 80% and 81% respectively while the CNN models, CNN1 and CNN2, are 81% and 89% respectively.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/43559078" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7d7e8000b026430fed1dd7bb28b06711" rel="nofollow" data-download="{"attachment_id":63866811,"asset_id":43559078,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/63866811/download_file?st=MTczMjQxNTk5MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="67267382" href="https://ibadan.academia.edu/AOJO">ADEBOLA OJO</a><script data-card-contents-for-user="67267382" type="text/json">{"id":67267382,"first_name":"ADEBOLA","last_name":"OJO","domain_name":"ibadan","page_name":"AOJO","display_name":"ADEBOLA OJO","profile_url":"https://ibadan.academia.edu/AOJO?f_ri=34344","photo":"https://0.academia-photos.com/67267382/29713742/27617632/s65_adebola.ojo.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-43559078">+1</span><div class="hidden js-additional-users-43559078"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://ibadan.academia.edu/SundayAMATARE">Sunday AMATARE</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-43559078'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-43559078').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_43559078 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="43559078"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 43559078, container: ".js-paper-rank-work_43559078", }); });</script></li><li class="js-percentile-work_43559078 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 43559078; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_43559078"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_43559078 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="43559078"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43559078; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=43559078]").text(description); $(".js-view-count-work_43559078").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_43559078").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="43559078"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="4252" href="https://www.academia.edu/Documents/in/Computer_Networks">Computer Networks</a>, <script data-card-contents-for-ri="4252" type="text/json">{"id":4252,"name":"Computer Networks","url":"https://www.academia.edu/Documents/in/Computer_Networks?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="5639" href="https://www.academia.edu/Documents/in/Text_Mining">Text Mining</a>, <script data-card-contents-for-ri="5639" type="text/json">{"id":5639,"name":"Text Mining","url":"https://www.academia.edu/Documents/in/Text_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="34344" href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Data mining (Data Analysis)</a><script data-card-contents-for-ri="34344" type="text/json">{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=43559078]'), work: {"id":43559078,"title":"Predicting Customer Churn in Telecommunication Industry Using Convolutional Neural Network Model","created_at":"2020-07-08T13:42:53.983-07:00","url":"https://www.academia.edu/43559078/Predicting_Customer_Churn_in_Telecommunication_Industry_Using_Convolutional_Neural_Network_Model?f_ri=34344","dom_id":"work_43559078","summary":"In this study a Convolutional Neural Network (CNN) model was proposed for the prediction of customer churn in a telecommunication industry. Many supervised machine learning models have been built and used for predicting customer churn in past researches. However, in thebuilding of these models, there is need for human intervention to carry out attributeselectionwhich is very tedious, time-consuming, tailored to specific datasets and often result to attribute selection problems. This study proposed a convolutional neural network model for predicting customer churning behavior and to also get rid of human attribute selectionand its problems. Two datasets were created from the fourteen thousand data instances that were gotten from one of the major cellular companies operating in Nigeria. Python programming language via the anaconda distribution was used for the development and implementation of our model. Jupyter notebook was our IDE choice. In other to achieve a like-for-like comparison, three other models were developed, which were two Multi-layer Perceptron (MLP) models and one other CNN model. The accuracy rates for the MLP models; MLP1 and MLP2, are 80% and 81% respectively while the CNN models, CNN1 and CNN2, are 81% and 89% respectively.","downloadable_attachments":[{"id":63866811,"asset_id":43559078,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":67267382,"first_name":"ADEBOLA","last_name":"OJO","domain_name":"ibadan","page_name":"AOJO","display_name":"ADEBOLA OJO","profile_url":"https://ibadan.academia.edu/AOJO?f_ri=34344","photo":"https://0.academia-photos.com/67267382/29713742/27617632/s65_adebola.ojo.jpg"},{"id":159509618,"first_name":"Sunday","last_name":"AMATARE","domain_name":"ibadan","page_name":"SundayAMATARE","display_name":"Sunday AMATARE","profile_url":"https://ibadan.academia.edu/SundayAMATARE?f_ri=34344","photo":"https://0.academia-photos.com/159509618/44687826/35095543/s65_sunday.amatare.jpg"}],"research_interests":[{"id":4252,"name":"Computer Networks","url":"https://www.academia.edu/Documents/in/Computer_Networks?f_ri=34344","nofollow":false},{"id":5639,"name":"Text Mining","url":"https://www.academia.edu/Documents/in/Text_Mining?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_26255967" data-work_id="26255967" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/26255967/A_Review_of_Data_Mining_Based_Breast_Cancer_Detection_and_Risk_Assessment_Techniques">A Review of Data Mining Based Breast Cancer Detection and Risk Assessment Techniques</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The breast cancer is a very rigorous disease mainly found among females throughout the world. This type of cancer emerges from human breast tissue cells, normally from the lobules or the inward covering of the milk pipes that give the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_26255967" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The breast cancer is a very rigorous disease mainly found among females throughout the world. This type of cancer emerges from human breast tissue cells, normally from the lobules or the inward covering of the milk pipes that give the conduits milk. A latest medicinal overview uncovers that throughout the world. Of all the diseases, breast cancer happens in 22.9% females and it finally causes 13.7% of cancer related deaths. Breast cancer, being very dangerous to all women, might be the reason of breast loss or might even cost their life. Detection of breast cancer disease is an essential area of data mining research. In this paper, we have included a detailed study about the cause and symptoms of breast cancer. We have also conducted a review of data mining based approaches which are used to detect the risk of presence of breast cancer using various methods</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/26255967" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b0ac7a4a8091907942f422afc656b7f3" rel="nofollow" data-download="{"attachment_id":46570772,"asset_id":26255967,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/46570772/download_file?st=MTczMjQxNTk5MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="2328357" href="https://independent.academia.edu/JournalofComputerScienceIJCSIS">Journal of Computer Science IJCSIS</a><script data-card-contents-for-user="2328357" type="text/json">{"id":2328357,"first_name":"Journal of Computer Science","last_name":"IJCSIS","domain_name":"independent","page_name":"JournalofComputerScienceIJCSIS","display_name":"Journal of Computer Science IJCSIS","profile_url":"https://independent.academia.edu/JournalofComputerScienceIJCSIS?f_ri=34344","photo":"https://0.academia-photos.com/2328357/8085511/9052483/s65_journal_of_computer_science.ijcsis.jpg"}</script></span></span></li><li class="js-paper-rank-work_26255967 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="26255967"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 26255967, container: ".js-paper-rank-work_26255967", }); });</script></li><li class="js-percentile-work_26255967 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26255967; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_26255967"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_26255967 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="26255967"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26255967; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26255967]").text(description); $(".js-view-count-work_26255967").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_26255967").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="26255967"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="422" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="491" href="https://www.academia.edu/Documents/in/Information_Technology">Information Technology</a>, <script data-card-contents-for-ri="491" type="text/json">{"id":491,"name":"Information Technology","url":"https://www.academia.edu/Documents/in/Information_Technology?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="854" href="https://www.academia.edu/Documents/in/Computer_Vision">Computer Vision</a>, <script data-card-contents-for-ri="854" type="text/json">{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="1380" href="https://www.academia.edu/Documents/in/Computer_Engineering">Computer Engineering</a><script data-card-contents-for-ri="1380" type="text/json">{"id":1380,"name":"Computer Engineering","url":"https://www.academia.edu/Documents/in/Computer_Engineering?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=26255967]'), work: {"id":26255967,"title":"A Review of Data Mining Based Breast Cancer Detection and Risk Assessment Techniques","created_at":"2016-06-17T08:24:35.750-07:00","url":"https://www.academia.edu/26255967/A_Review_of_Data_Mining_Based_Breast_Cancer_Detection_and_Risk_Assessment_Techniques?f_ri=34344","dom_id":"work_26255967","summary":"The breast cancer is a very rigorous disease mainly found among females throughout the world. This type of cancer emerges from human breast tissue cells, normally from the lobules or the inward covering of the milk pipes that give the conduits milk. A latest medicinal overview uncovers that throughout the world. Of all the diseases, breast cancer happens in 22.9% females and it finally causes 13.7% of cancer related deaths. Breast cancer, being very dangerous to all women, might be the reason of breast loss or might even cost their life. Detection of breast cancer disease is an essential area of data mining research. In this paper, we have included a detailed study about the cause and symptoms of breast cancer. We have also conducted a review of data mining based approaches which are used to detect the risk of presence of breast cancer using various methods","downloadable_attachments":[{"id":46570772,"asset_id":26255967,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":2328357,"first_name":"Journal of Computer Science","last_name":"IJCSIS","domain_name":"independent","page_name":"JournalofComputerScienceIJCSIS","display_name":"Journal of Computer Science IJCSIS","profile_url":"https://independent.academia.edu/JournalofComputerScienceIJCSIS?f_ri=34344","photo":"https://0.academia-photos.com/2328357/8085511/9052483/s65_journal_of_computer_science.ijcsis.jpg"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=34344","nofollow":false},{"id":491,"name":"Information Technology","url":"https://www.academia.edu/Documents/in/Information_Technology?f_ri=34344","nofollow":false},{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision?f_ri=34344","nofollow":false},{"id":1380,"name":"Computer Engineering","url":"https://www.academia.edu/Documents/in/Computer_Engineering?f_ri=34344","nofollow":false},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344"},{"id":4252,"name":"Computer Networks","url":"https://www.academia.edu/Documents/in/Computer_Networks?f_ri=34344"},{"id":7454,"name":"Information Communication Technology","url":"https://www.academia.edu/Documents/in/Information_Communication_Technology?f_ri=34344"},{"id":13923,"name":"Computer Security","url":"https://www.academia.edu/Documents/in/Computer_Security?f_ri=34344"},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":89502,"name":"Information and Communication Technologies","url":"https://www.academia.edu/Documents/in/Information_and_Communication_Technologies?f_ri=34344"},{"id":413148,"name":"Big Data / Analytics / Data Mining","url":"https://www.academia.edu/Documents/in/Big_Data_Analytics_Data_Mining?f_ri=34344"},{"id":522782,"name":"Information Technology and System Integration","url":"https://www.academia.edu/Documents/in/Information_Technology_and_System_Integration?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_36086763" data-work_id="36086763" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/36086763/An_Algorithm_for_Predictive_Data_Mining_Approach_in_Medical_Diagnosis">An Algorithm for Predictive Data Mining Approach in Medical Diagnosis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36086763" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning<br />techniques. Advanced data mining techniques are used to discover knowledge in database and for medical research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database.<br />The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the<br />experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36086763" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="97133f7503ffde646c9b18bfd842629d" rel="nofollow" data-download="{"attachment_id":55975904,"asset_id":36086763,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/55975904/download_file?st=MTczMjQxNTk5MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="21255813" href="https://independent.academia.edu/IjcsitJournal">International Journal of Computer Science and Information Technology ( IJCSIT ) INSPEC ,WJCI Indexed</a><script data-card-contents-for-user="21255813" type="text/json">{"id":21255813,"first_name":"International Journal of Computer Science and Information Technology","last_name":"( IJCSIT ) INSPEC ,WJCI Indexed","domain_name":"independent","page_name":"IjcsitJournal","display_name":"International Journal of Computer Science and Information Technology ( IJCSIT ) INSPEC ,WJCI Indexed","profile_url":"https://independent.academia.edu/IjcsitJournal?f_ri=34344","photo":"https://0.academia-photos.com/21255813/5872009/139167812/s65_international_journal_of_computer_science_and_information_technology._ijcsit_inspec_wjci_indexed.jpg"}</script></span></span></li><li class="js-paper-rank-work_36086763 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36086763"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36086763, container: ".js-paper-rank-work_36086763", }); });</script></li><li class="js-percentile-work_36086763 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 36086763; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_36086763"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_36086763 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="36086763"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 36086763; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=36086763]").text(description); $(".js-view-count-work_36086763").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_36086763").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="36086763"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">30</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4095" href="https://www.academia.edu/Documents/in/Classification_Machine_Learning_">Classification (Machine Learning)</a>, <script data-card-contents-for-ri="4095" type="text/json">{"id":4095,"name":"Classification (Machine Learning)","url":"https://www.academia.edu/Documents/in/Classification_Machine_Learning_?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="5486" href="https://www.academia.edu/Documents/in/Clustering_and_Classification_Methods">Clustering and Classification Methods</a>, <script data-card-contents-for-ri="5486" type="text/json">{"id":5486,"name":"Clustering and Classification Methods","url":"https://www.academia.edu/Documents/in/Clustering_and_Classification_Methods?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="9447" href="https://www.academia.edu/Documents/in/Stream_Mining_Data_Mining_">Stream Mining (Data Mining)</a><script data-card-contents-for-ri="9447" type="text/json">{"id":9447,"name":"Stream Mining (Data Mining)","url":"https://www.academia.edu/Documents/in/Stream_Mining_Data_Mining_?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=36086763]'), work: {"id":36086763,"title":"An Algorithm for Predictive Data Mining Approach in Medical Diagnosis","created_at":"2018-03-05T22:45:54.885-08:00","url":"https://www.academia.edu/36086763/An_Algorithm_for_Predictive_Data_Mining_Approach_in_Medical_Diagnosis?f_ri=34344","dom_id":"work_36086763","summary":"The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases \u0026 makes effective decisions with the help of different machine learning\ntechniques. Advanced data mining techniques are used to discover knowledge in database and for medical research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database.\nThe performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the\nexperimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively. ","downloadable_attachments":[{"id":55975904,"asset_id":36086763,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":21255813,"first_name":"International Journal of Computer Science and Information Technology","last_name":"( IJCSIT ) INSPEC ,WJCI Indexed","domain_name":"independent","page_name":"IjcsitJournal","display_name":"International Journal of Computer Science and Information Technology ( IJCSIT ) INSPEC ,WJCI Indexed","profile_url":"https://independent.academia.edu/IjcsitJournal?f_ri=34344","photo":"https://0.academia-photos.com/21255813/5872009/139167812/s65_international_journal_of_computer_science_and_information_technology._ijcsit_inspec_wjci_indexed.jpg"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false},{"id":4095,"name":"Classification (Machine Learning)","url":"https://www.academia.edu/Documents/in/Classification_Machine_Learning_?f_ri=34344","nofollow":false},{"id":5486,"name":"Clustering and Classification Methods","url":"https://www.academia.edu/Documents/in/Clustering_and_Classification_Methods?f_ri=34344","nofollow":false},{"id":9447,"name":"Stream Mining (Data Mining)","url":"https://www.academia.edu/Documents/in/Stream_Mining_Data_Mining_?f_ri=34344","nofollow":false},{"id":14494,"name":"Opinion Mining (Data Mining)","url":"https://www.academia.edu/Documents/in/Opinion_Mining_Data_Mining_?f_ri=34344"},{"id":15426,"name":"Spatial Data Mining (Data Mining)","url":"https://www.academia.edu/Documents/in/Spatial_Data_Mining_Data_Mining_?f_ri=34344"},{"id":23995,"name":"Educational Data Mining","url":"https://www.academia.edu/Documents/in/Educational_Data_Mining?f_ri=34344"},{"id":32701,"name":"Data Mining in Bioinformatics","url":"https://www.academia.edu/Documents/in/Data_Mining_in_Bioinformatics?f_ri=34344"},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":39682,"name":"Graph Data Mining","url":"https://www.academia.edu/Documents/in/Graph_Data_Mining?f_ri=34344"},{"id":39693,"name":"Distributed Data Mining","url":"https://www.academia.edu/Documents/in/Distributed_Data_Mining?f_ri=34344"},{"id":56368,"name":"Image Classification","url":"https://www.academia.edu/Documents/in/Image_Classification?f_ri=34344"},{"id":60650,"name":"Data Warehousing and Data Mining","url":"https://www.academia.edu/Documents/in/Data_Warehousing_and_Data_Mining?f_ri=34344"},{"id":84486,"name":"Disease Prediction","url":"https://www.academia.edu/Documents/in/Disease_Prediction?f_ri=34344"},{"id":93179,"name":"Data Stream Mining","url":"https://www.academia.edu/Documents/in/Data_Stream_Mining?f_ri=34344"},{"id":99804,"name":"Privacy Preserving Data Mining","url":"https://www.academia.edu/Documents/in/Privacy_Preserving_Data_Mining?f_ri=34344"},{"id":106145,"name":"Classification","url":"https://www.academia.edu/Documents/in/Classification?f_ri=34344"},{"id":108488,"name":"Data Mining – Concepts and Techniques","url":"https://www.academia.edu/Documents/in/Data_Mining_Concepts_and_Techniques?f_ri=34344"},{"id":193254,"name":"SVM classifier","url":"https://www.academia.edu/Documents/in/SVM_classifier?f_ri=34344"},{"id":235015,"name":"Model Based Clinical Decision Support Systems","url":"https://www.academia.edu/Documents/in/Model_Based_Clinical_Decision_Support_Systems?f_ri=34344"},{"id":394230,"name":"Current Trends in Data Mining","url":"https://www.academia.edu/Documents/in/Current_Trends_in_Data_Mining?f_ri=34344"},{"id":413148,"name":"Big Data / Analytics / Data Mining","url":"https://www.academia.edu/Documents/in/Big_Data_Analytics_Data_Mining?f_ri=34344"},{"id":417807,"name":"Clinical Decision Support System","url":"https://www.academia.edu/Documents/in/Clinical_Decision_Support_System?f_ri=34344"},{"id":459269,"name":"Data mining and Text mining","url":"https://www.academia.edu/Documents/in/Data_mining_and_Text_mining?f_ri=34344"},{"id":460675,"name":"Data Mining Techniques","url":"https://www.academia.edu/Documents/in/Data_Mining_Techniques?f_ri=34344"},{"id":861863,"name":"Clinical Decision Support Systems","url":"https://www.academia.edu/Documents/in/Clinical_Decision_Support_Systems?f_ri=34344"},{"id":994859,"name":"Data Mining and Business Intelligence","url":"https://www.academia.edu/Documents/in/Data_Mining_and_Business_Intelligence?f_ri=34344"},{"id":1120211,"name":"Svm Kernel","url":"https://www.academia.edu/Documents/in/Svm_Kernel?f_ri=34344"},{"id":2021988,"name":"Disease progression prediction","url":"https://www.academia.edu/Documents/in/Disease_progression_prediction?f_ri=34344"},{"id":2424363,"name":"Disease Risk Prediction","url":"https://www.academia.edu/Documents/in/Disease_Risk_Prediction?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_42811466" data-work_id="42811466" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/42811466/Adidas_Organizational_Transformation_casestudy">Adidas-Organizational Transformation casestudy</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This report provides insights into the global sportswear market as well as a comprehensive analysis of Adidas to develop an understanding about the company’s internal situation in line with the external environment concerning surrounding... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_42811466" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This report provides insights into the global sportswear market as well as a comprehensive analysis of Adidas to develop an understanding about the company’s internal situation in line with the external environment concerning surrounding competitors, customers, and suppliers…etc. Accordingly, opportunities and threats can be identified, which helps realize the reason why Adidas implements proactive changes to not only grasp these opportunities but also address those threats.  At the same time, this finding serves as a reference for anyone to evaluate their profession possibility in this field. At the end of the paper, it is concluded weather the sportswear is an attractive industry and Adidas is an attractive company for employees.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/42811466" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="27fb44d0aaa5ec3a4e003a11bd8f0c6e" rel="nofollow" data-download="{"attachment_id":63041389,"asset_id":42811466,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/63041389/download_file?st=MTczMjQxNTk5MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="155033077" href="https://hs-onsabrueck.academia.edu/TramNguyen">Tram Nguyen</a><script data-card-contents-for-user="155033077" type="text/json">{"id":155033077,"first_name":"Tram","last_name":"Nguyen","domain_name":"hs-onsabrueck","page_name":"TramNguyen","display_name":"Tram Nguyen","profile_url":"https://hs-onsabrueck.academia.edu/TramNguyen?f_ri=34344","photo":"https://0.academia-photos.com/155033077/63200546/51503151/s65_tram.nguyen.png"}</script></span></span></li><li class="js-paper-rank-work_42811466 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="42811466"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 42811466, container: ".js-paper-rank-work_42811466", }); });</script></li><li class="js-percentile-work_42811466 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 42811466; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_42811466"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_42811466 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="42811466"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 42811466; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=42811466]").text(description); $(".js-view-count-work_42811466").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_42811466").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="42811466"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="4736" href="https://www.academia.edu/Documents/in/Datamining_Tools">Datamining Tools</a>, <script data-card-contents-for-ri="4736" type="text/json">{"id":4736,"name":"Datamining Tools","url":"https://www.academia.edu/Documents/in/Datamining_Tools?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="34344" href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Data mining (Data Analysis)</a>, <script data-card-contents-for-ri="34344" type="text/json">{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="38533" href="https://www.academia.edu/Documents/in/Business_Intelligence_BI_">Business Intelligence (BI)</a>, <script data-card-contents-for-ri="38533" type="text/json">{"id":38533,"name":"Business Intelligence (BI)","url":"https://www.academia.edu/Documents/in/Business_Intelligence_BI_?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="126300" href="https://www.academia.edu/Documents/in/Big_Data">Big Data</a><script data-card-contents-for-ri="126300" type="text/json">{"id":126300,"name":"Big Data","url":"https://www.academia.edu/Documents/in/Big_Data?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=42811466]'), work: {"id":42811466,"title":"Adidas-Organizational Transformation casestudy","created_at":"2020-04-21T12:05:38.117-07:00","url":"https://www.academia.edu/42811466/Adidas_Organizational_Transformation_casestudy?f_ri=34344","dom_id":"work_42811466","summary":"This report provides insights into the global sportswear market as well as a comprehensive analysis of Adidas to develop an understanding about the company’s internal situation in line with the external environment concerning surrounding competitors, customers, and suppliers…etc. Accordingly, opportunities and threats can be identified, which helps realize the reason why Adidas implements proactive changes to not only grasp these opportunities but also address those threats. At the same time, this finding serves as a reference for anyone to evaluate their profession possibility in this field. At the end of the paper, it is concluded weather the sportswear is an attractive industry and Adidas is an attractive company for employees.","downloadable_attachments":[{"id":63041389,"asset_id":42811466,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":155033077,"first_name":"Tram","last_name":"Nguyen","domain_name":"hs-onsabrueck","page_name":"TramNguyen","display_name":"Tram Nguyen","profile_url":"https://hs-onsabrueck.academia.edu/TramNguyen?f_ri=34344","photo":"https://0.academia-photos.com/155033077/63200546/51503151/s65_tram.nguyen.png"}],"research_interests":[{"id":4736,"name":"Datamining Tools","url":"https://www.academia.edu/Documents/in/Datamining_Tools?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false},{"id":38533,"name":"Business Intelligence (BI)","url":"https://www.academia.edu/Documents/in/Business_Intelligence_BI_?f_ri=34344","nofollow":false},{"id":126300,"name":"Big Data","url":"https://www.academia.edu/Documents/in/Big_Data?f_ri=34344","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_40105707" data-work_id="40105707" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/40105707/DESIGN_AND_IMPLEMENTATION_OF_ONLINE_NEWS_EDITING_SYSTEM">DESIGN AND IMPLEMENTATION OF ONLINE NEWS EDITING SYSTEM</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This project was centered on online news editing system. The current process of news editing is being operated manually and due to this procedure numerous problem are been encountered. A design was taken to computerized the manual process... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_40105707" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This project was centered on online news editing system. The current process of news editing is being operated manually and due to this procedure numerous problem are been encountered. A design was taken to computerized the manual process in order to check this problem. The problems were identified after series of interviews and examination of documents after which analysis was made and a computerized procedure recommended. This project will also suggest how to successfully implement the computerized procedure and to overcome the obstacle that would hinder the successful implementation of the system. The new system will be designed using top-down design methodology and implemented using XAMPP IDE on windows 7,8,8.1 system using PHP, HTML, CSS and MySQL technologies.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/40105707" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="432ec770a5632e5044399cf2891096e3" rel="nofollow" data-download="{"attachment_id":60316994,"asset_id":40105707,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/60316994/download_file?st=MTczMjQxNTk5MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="18972134" href="https://independent.academia.edu/IgweEEbuka">Igwe E. Kelvin</a><script data-card-contents-for-user="18972134" type="text/json">{"id":18972134,"first_name":"Igwe","last_name":"E. Kelvin","domain_name":"independent","page_name":"IgweEEbuka","display_name":"Igwe E. Kelvin","profile_url":"https://independent.academia.edu/IgweEEbuka?f_ri=34344","photo":"https://0.academia-photos.com/18972134/5269314/20620138/s65_igwe.e._kelvin.jpg"}</script></span></span></li><li class="js-paper-rank-work_40105707 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="40105707"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 40105707, container: ".js-paper-rank-work_40105707", }); });</script></li><li class="js-percentile-work_40105707 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40105707; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_40105707"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_40105707 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="40105707"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40105707; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40105707]").text(description); $(".js-view-count-work_40105707").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_40105707").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="40105707"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="422" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="449" href="https://www.academia.edu/Documents/in/Software_Engineering">Software Engineering</a>, <script data-card-contents-for-ri="449" type="text/json">{"id":449,"name":"Software Engineering","url":"https://www.academia.edu/Documents/in/Software_Engineering?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="8129" href="https://www.academia.edu/Documents/in/Software_Development">Software Development</a>, <script data-card-contents-for-ri="8129" type="text/json">{"id":8129,"name":"Software Development","url":"https://www.academia.edu/Documents/in/Software_Development?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="34344" href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Data mining (Data Analysis)</a><script data-card-contents-for-ri="34344" type="text/json">{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=40105707]'), work: {"id":40105707,"title":"DESIGN AND IMPLEMENTATION OF ONLINE NEWS EDITING SYSTEM","created_at":"2019-08-17T05:26:15.424-07:00","url":"https://www.academia.edu/40105707/DESIGN_AND_IMPLEMENTATION_OF_ONLINE_NEWS_EDITING_SYSTEM?f_ri=34344","dom_id":"work_40105707","summary":"This project was centered on online news editing system. The current process of news editing is being operated manually and due to this procedure numerous problem are been encountered. A design was taken to computerized the manual process in order to check this problem. The problems were identified after series of interviews and examination of documents after which analysis was made and a computerized procedure recommended. This project will also suggest how to successfully implement the computerized procedure and to overcome the obstacle that would hinder the successful implementation of the system. The new system will be designed using top-down design methodology and implemented using XAMPP IDE on windows 7,8,8.1 system using PHP, HTML, CSS and MySQL technologies.","downloadable_attachments":[{"id":60316994,"asset_id":40105707,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":18972134,"first_name":"Igwe","last_name":"E. Kelvin","domain_name":"independent","page_name":"IgweEEbuka","display_name":"Igwe E. Kelvin","profile_url":"https://independent.academia.edu/IgweEEbuka?f_ri=34344","photo":"https://0.academia-photos.com/18972134/5269314/20620138/s65_igwe.e._kelvin.jpg"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=34344","nofollow":false},{"id":449,"name":"Software Engineering","url":"https://www.academia.edu/Documents/in/Software_Engineering?f_ri=34344","nofollow":false},{"id":8129,"name":"Software Development","url":"https://www.academia.edu/Documents/in/Software_Development?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false},{"id":36302,"name":"PHP Programming","url":"https://www.academia.edu/Documents/in/PHP_Programming?f_ri=34344"},{"id":173623,"name":"IT Project Management","url":"https://www.academia.edu/Documents/in/IT_Project_Management?f_ri=34344"},{"id":601581,"name":"News Analysis","url":"https://www.academia.edu/Documents/in/News_Analysis?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_43322353" data-work_id="43322353" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/43322353/May_2020_Top_Read_Articles_In_Data_Mining_and_Knowledge_Management_Process_Research_Articles">May 2020 : Top Read Articles In Data Mining & Knowledge Management Process Research Articles</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_43322353" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/43322353" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="482e9bcf8466eb1e896c00a22c8ea359" rel="nofollow" data-download="{"attachment_id":63604745,"asset_id":43322353,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/63604745/download_file?st=MTczMjQxNTk5MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="16715850" href="https://independent.academia.edu/IJDKPJOURNAL">International Journal of Data Mining & Knowledge Management Process ( IJDKP )</a><script data-card-contents-for-user="16715850" type="text/json">{"id":16715850,"first_name":"International Journal of Data Mining \u0026 Knowledge Management Process","last_name":"( IJDKP )","domain_name":"independent","page_name":"IJDKPJOURNAL","display_name":"International Journal of Data Mining \u0026 Knowledge Management Process ( IJDKP )","profile_url":"https://independent.academia.edu/IJDKPJOURNAL?f_ri=34344","photo":"https://0.academia-photos.com/16715850/4568261/39147186/s65_international_journal_of_data_mining_knowledge_management_process._ijdkp_.png"}</script></span></span></li><li class="js-paper-rank-work_43322353 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="43322353"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 43322353, container: ".js-paper-rank-work_43322353", }); });</script></li><li class="js-percentile-work_43322353 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 43322353; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_43322353"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_43322353 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="43322353"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43322353; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=43322353]").text(description); $(".js-view-count-work_43322353").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_43322353").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="43322353"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">17</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4095" href="https://www.academia.edu/Documents/in/Classification_Machine_Learning_">Classification (Machine Learning)</a>, <script data-card-contents-for-ri="4095" type="text/json">{"id":4095,"name":"Classification (Machine Learning)","url":"https://www.academia.edu/Documents/in/Classification_Machine_Learning_?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="9351" href="https://www.academia.edu/Documents/in/Image_Analysis">Image Analysis</a>, <script data-card-contents-for-ri="9351" type="text/json">{"id":9351,"name":"Image Analysis","url":"https://www.academia.edu/Documents/in/Image_Analysis?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="14008" href="https://www.academia.edu/Documents/in/Knowledge_Discovery_in_Databases">Knowledge Discovery in Databases</a><script data-card-contents-for-ri="14008" type="text/json">{"id":14008,"name":"Knowledge Discovery in Databases","url":"https://www.academia.edu/Documents/in/Knowledge_Discovery_in_Databases?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=43322353]'), work: {"id":43322353,"title":"May 2020 : Top Read Articles In Data Mining \u0026 Knowledge Management Process Research Articles","created_at":"2020-06-12T00:11:35.931-07:00","url":"https://www.academia.edu/43322353/May_2020_Top_Read_Articles_In_Data_Mining_and_Knowledge_Management_Process_Research_Articles?f_ri=34344","dom_id":"work_43322353","summary":"Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data","downloadable_attachments":[{"id":63604745,"asset_id":43322353,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":16715850,"first_name":"International Journal of Data Mining \u0026 Knowledge Management Process","last_name":"( IJDKP )","domain_name":"independent","page_name":"IJDKPJOURNAL","display_name":"International Journal of Data Mining \u0026 Knowledge Management Process ( IJDKP )","profile_url":"https://independent.academia.edu/IJDKPJOURNAL?f_ri=34344","photo":"https://0.academia-photos.com/16715850/4568261/39147186/s65_international_journal_of_data_mining_knowledge_management_process._ijdkp_.png"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false},{"id":4095,"name":"Classification (Machine Learning)","url":"https://www.academia.edu/Documents/in/Classification_Machine_Learning_?f_ri=34344","nofollow":false},{"id":9351,"name":"Image Analysis","url":"https://www.academia.edu/Documents/in/Image_Analysis?f_ri=34344","nofollow":false},{"id":14008,"name":"Knowledge Discovery in Databases","url":"https://www.academia.edu/Documents/in/Knowledge_Discovery_in_Databases?f_ri=34344","nofollow":false},{"id":14494,"name":"Opinion Mining (Data Mining)","url":"https://www.academia.edu/Documents/in/Opinion_Mining_Data_Mining_?f_ri=34344"},{"id":16838,"name":"Medical Image Analysis","url":"https://www.academia.edu/Documents/in/Medical_Image_Analysis?f_ri=34344"},{"id":23995,"name":"Educational Data Mining","url":"https://www.academia.edu/Documents/in/Educational_Data_Mining?f_ri=34344"},{"id":27360,"name":"Databases","url":"https://www.academia.edu/Documents/in/Databases?f_ri=34344"},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":36201,"name":"Object-based image analysis","url":"https://www.academia.edu/Documents/in/Object-based_image_analysis?f_ri=34344"},{"id":39693,"name":"Distributed Data Mining","url":"https://www.academia.edu/Documents/in/Distributed_Data_Mining?f_ri=34344"},{"id":46758,"name":"XML Databases","url":"https://www.academia.edu/Documents/in/XML_Databases?f_ri=34344"},{"id":126289,"name":"Digital Image Analysis","url":"https://www.academia.edu/Documents/in/Digital_Image_Analysis?f_ri=34344"},{"id":143780,"name":"Document Image Analysis","url":"https://www.academia.edu/Documents/in/Document_Image_Analysis?f_ri=34344"},{"id":265521,"name":"Advanced Databases","url":"https://www.academia.edu/Documents/in/Advanced_Databases?f_ri=34344"},{"id":495923,"name":"Digital Image Analysis and Processing","url":"https://www.academia.edu/Documents/in/Digital_Image_Analysis_and_Processing?f_ri=34344"},{"id":741119,"name":"KDD (Knowledge Discovery Databases)","url":"https://www.academia.edu/Documents/in/KDD_Knowledge_Discovery_Databases_?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_43425419" data-work_id="43425419" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/43425419/Essential_Monitoring_and_Evaluation_Skills_for_Development_Projects">Essential Monitoring and Evaluation Skills for Development Projects</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Monitoring, Evaluation, Accountability, and Learning (MEAL) are part of everyday programme management and are critical to the success of all programmes. MEAL technical quality is very important across a portfolio of projects. Without an... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_43425419" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Monitoring, Evaluation, Accountability, and Learning (MEAL) are part of everyday programme management and are critical to the success of all programmes. MEAL technical quality is very important across a portfolio of projects. Without an effective MEAL system we would be unable to track progress, make adjustments, discover unplanned effects of programmes, or judge the impact that we have made on the lives of those with whom we are working. A MEAL system also helps individuals and teams to be accountable to stakeholders through information sharing and developing complaints or feedback mechanism which can help to guide programme implementation. <br /><br />The course provides participants with a strong foundation in monitoring and evaluation, programme design and implementation. Participants will explore the variety of tools and techniques used to measure project progress and to report outcomes to the appropriate internal and external stakeholders-including donors, funders, supervisors, or the population being served. Participants will design relevant and effective frameworks, and learn the required methods to conduct effective data collection, statistical analysis, and reporting. Through the interactive sessions and through exposure to real case studies drawn from relevant organizations, participants will gain the skills needed to conduct a monitoring and evaluation programme for development organizations, NGOs, or private organizations in the development, peacebuilding, energy, gender studies, transnational security sectors among other sectors. The course  introduces participants to MEAL concepts and practices. It will stimulate ideas on how to design and implement monitoring and evaluation processes that strengthen accountability and learning, and so promote project, programme and strategy effectiveness.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/43425419" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="cc643d9bbfc5db9c736c525765af1edb" rel="nofollow" data-download="{"attachment_id":63728052,"asset_id":43425419,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/63728052/download_file?st=MTczMjQxNTk5MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="2764373" href="https://uonbi.academia.edu/SutoMasika">Joel Suto</a><script data-card-contents-for-user="2764373" type="text/json">{"id":2764373,"first_name":"Joel","last_name":"Suto","domain_name":"uonbi","page_name":"SutoMasika","display_name":"Joel Suto","profile_url":"https://uonbi.academia.edu/SutoMasika?f_ri=34344","photo":"https://0.academia-photos.com/2764373/899147/18473160/s65_suto.masika.jpg"}</script></span></span></li><li class="js-paper-rank-work_43425419 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="43425419"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 43425419, container: ".js-paper-rank-work_43425419", }); 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$(".js-view-count[data-work-id=43425419]").text(description); $(".js-view-count-work_43425419").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_43425419").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="43425419"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2277" href="https://www.academia.edu/Documents/in/Project_Management">Project Management</a>, <script data-card-contents-for-ri="2277" type="text/json">{"id":2277,"name":"Project Management","url":"https://www.academia.edu/Documents/in/Project_Management?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4423" href="https://www.academia.edu/Documents/in/Monitoring_And_Evaluation">Monitoring And Evaluation</a>, <script data-card-contents-for-ri="4423" type="text/json">{"id":4423,"name":"Monitoring And Evaluation","url":"https://www.academia.edu/Documents/in/Monitoring_And_Evaluation?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="6687" href="https://www.academia.edu/Documents/in/Project_Risk_Management">Project Risk Management</a>, <script data-card-contents-for-ri="6687" type="text/json">{"id":6687,"name":"Project Risk Management","url":"https://www.academia.edu/Documents/in/Project_Risk_Management?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="34344" href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Data mining (Data Analysis)</a><script data-card-contents-for-ri="34344" type="text/json">{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=43425419]'), work: {"id":43425419,"title":"Essential Monitoring and Evaluation Skills for Development Projects","created_at":"2020-06-24T06:13:11.966-07:00","url":"https://www.academia.edu/43425419/Essential_Monitoring_and_Evaluation_Skills_for_Development_Projects?f_ri=34344","dom_id":"work_43425419","summary":"Monitoring, Evaluation, Accountability, and Learning (MEAL) are part of everyday programme management and are critical to the success of all programmes. MEAL technical quality is very important across a portfolio of projects. Without an effective MEAL system we would be unable to track progress, make adjustments, discover unplanned effects of programmes, or judge the impact that we have made on the lives of those with whom we are working. A MEAL system also helps individuals and teams to be accountable to stakeholders through information sharing and developing complaints or feedback mechanism which can help to guide programme implementation. \n\nThe course provides participants with a strong foundation in monitoring and evaluation, programme design and implementation. Participants will explore the variety of tools and techniques used to measure project progress and to report outcomes to the appropriate internal and external stakeholders-including donors, funders, supervisors, or the population being served. Participants will design relevant and effective frameworks, and learn the required methods to conduct effective data collection, statistical analysis, and reporting. Through the interactive sessions and through exposure to real case studies drawn from relevant organizations, participants will gain the skills needed to conduct a monitoring and evaluation programme for development organizations, NGOs, or private organizations in the development, peacebuilding, energy, gender studies, transnational security sectors among other sectors. The course introduces participants to MEAL concepts and practices. It will stimulate ideas on how to design and implement monitoring and evaluation processes that strengthen accountability and learning, and so promote project, programme and strategy effectiveness.","downloadable_attachments":[{"id":63728052,"asset_id":43425419,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":2764373,"first_name":"Joel","last_name":"Suto","domain_name":"uonbi","page_name":"SutoMasika","display_name":"Joel Suto","profile_url":"https://uonbi.academia.edu/SutoMasika?f_ri=34344","photo":"https://0.academia-photos.com/2764373/899147/18473160/s65_suto.masika.jpg"}],"research_interests":[{"id":2277,"name":"Project Management","url":"https://www.academia.edu/Documents/in/Project_Management?f_ri=34344","nofollow":false},{"id":4423,"name":"Monitoring And Evaluation","url":"https://www.academia.edu/Documents/in/Monitoring_And_Evaluation?f_ri=34344","nofollow":false},{"id":6687,"name":"Project Risk Management","url":"https://www.academia.edu/Documents/in/Project_Risk_Management?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false},{"id":53779,"name":"statistics with SPSS and Excel","url":"https://www.academia.edu/Documents/in/statistics_with_SPSS_and_Excel?f_ri=34344"},{"id":72236,"name":"Project Managment","url":"https://www.academia.edu/Documents/in/Project_Managment?f_ri=34344"},{"id":417198,"name":"Programme Evaluation","url":"https://www.academia.edu/Documents/in/Programme_Evaluation?f_ri=34344"},{"id":859942,"name":"Microsoft Project","url":"https://www.academia.edu/Documents/in/Microsoft_Project?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_35961450" data-work_id="35961450" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/35961450/DATA_WAREHOUSING_AND_BUSINESS_INTELLIGENCE_COURSEWORK_1">DATA WAREHOUSING AND BUSINESS INTELLIGENCE COURSEWORK 1</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Mash Industries ltd is a Kenyan manufacturing company specializing in the production of hygiene products. The company was established in 1962 in Nairobi, Kenya the company is committed to developing new products for the east African... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_35961450" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Mash Industries ltd is a Kenyan manufacturing company specializing in the production of hygiene products. The company was established in 1962 in Nairobi, Kenya the company is committed to developing new products for the east African market, as well as innovating products to satisfy the ever changing needs & preferences of the fast growing middle-class consumers. The company also manufactures branded products such as soft tissues and sanitary towels for various other supermarket chains, that sell them as own brands. Mash Industries ltd has branches across east Africa, including in Uganda, Tanzania, and Rwanda. They have a catalogue of over 10 brand products. Mash Industries ltd is involved in wholesale of its products through its warehouse outlets, as well as retail sales by way of its chain of five retail shops across the city of Nairobi.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/35961450" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="eae92ffb08b745ba667a02691431f692" rel="nofollow" data-download="{"attachment_id":55844394,"asset_id":35961450,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/55844394/download_file?st=MTczMjQxNTk5MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="60306516" href="https://mdx.academia.edu/SamTomashi">Sam Tomashi</a><script data-card-contents-for-user="60306516" type="text/json">{"id":60306516,"first_name":"Sam","last_name":"Tomashi","domain_name":"mdx","page_name":"SamTomashi","display_name":"Sam Tomashi","profile_url":"https://mdx.academia.edu/SamTomashi?f_ri=34344","photo":"https://0.academia-photos.com/60306516/19197454/19141687/s65_sam.tomashi.jpg"}</script></span></span></li><li class="js-paper-rank-work_35961450 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="35961450"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 35961450, container: ".js-paper-rank-work_35961450", }); 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$(".js-view-count[data-work-id=35961450]").text(description); $(".js-view-count-work_35961450").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_35961450").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="35961450"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="4205" href="https://www.academia.edu/Documents/in/Data_Analysis">Data Analysis</a>, <script data-card-contents-for-ri="4205" type="text/json">{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="7357" href="https://www.academia.edu/Documents/in/Business_Intelligence">Business Intelligence</a>, <script data-card-contents-for-ri="7357" type="text/json">{"id":7357,"name":"Business Intelligence","url":"https://www.academia.edu/Documents/in/Business_Intelligence?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="27360" href="https://www.academia.edu/Documents/in/Databases">Databases</a>, <script data-card-contents-for-ri="27360" type="text/json">{"id":27360,"name":"Databases","url":"https://www.academia.edu/Documents/in/Databases?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="34344" href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Data mining (Data Analysis)</a><script data-card-contents-for-ri="34344" type="text/json">{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=35961450]'), work: {"id":35961450,"title":"DATA WAREHOUSING AND BUSINESS INTELLIGENCE COURSEWORK 1","created_at":"2018-02-19T11:31:55.445-08:00","url":"https://www.academia.edu/35961450/DATA_WAREHOUSING_AND_BUSINESS_INTELLIGENCE_COURSEWORK_1?f_ri=34344","dom_id":"work_35961450","summary":"Mash Industries ltd is a Kenyan manufacturing company specializing in the production of hygiene products. The company was established in 1962 in Nairobi, Kenya the company is committed to developing new products for the east African market, as well as innovating products to satisfy the ever changing needs \u0026 preferences of the fast growing middle-class consumers. The company also manufactures branded products such as soft tissues and sanitary towels for various other supermarket chains, that sell them as own brands. Mash Industries ltd has branches across east Africa, including in Uganda, Tanzania, and Rwanda. They have a catalogue of over 10 brand products. Mash Industries ltd is involved in wholesale of its products through its warehouse outlets, as well as retail sales by way of its chain of five retail shops across the city of Nairobi.","downloadable_attachments":[{"id":55844394,"asset_id":35961450,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":60306516,"first_name":"Sam","last_name":"Tomashi","domain_name":"mdx","page_name":"SamTomashi","display_name":"Sam Tomashi","profile_url":"https://mdx.academia.edu/SamTomashi?f_ri=34344","photo":"https://0.academia-photos.com/60306516/19197454/19141687/s65_sam.tomashi.jpg"}],"research_interests":[{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=34344","nofollow":false},{"id":7357,"name":"Business Intelligence","url":"https://www.academia.edu/Documents/in/Business_Intelligence?f_ri=34344","nofollow":false},{"id":27360,"name":"Databases","url":"https://www.academia.edu/Documents/in/Databases?f_ri=34344","nofollow":false},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false},{"id":60650,"name":"Data Warehousing and Data Mining","url":"https://www.academia.edu/Documents/in/Data_Warehousing_and_Data_Mining?f_ri=34344"},{"id":101787,"name":"OLAP","url":"https://www.academia.edu/Documents/in/OLAP?f_ri=34344"},{"id":211970,"name":"MAchine Learning Algorithms","url":"https://www.academia.edu/Documents/in/MAchine_Learning_Algorithms?f_ri=34344"},{"id":410581,"name":"Data Mart","url":"https://www.academia.edu/Documents/in/Data_Mart?f_ri=34344"},{"id":2340139,"name":"OLAP cube","url":"https://www.academia.edu/Documents/in/OLAP_cube?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_43717773" data-work_id="43717773" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/43717773/Estat%C3%ADstica_Descritiva_e_An%C3%A1lise_Explorat%C3%B3ria_de_Dados_Estat%C3%ADstica_Descritiva_e_An%C3%A1lise_Explorat%C3%B3ria_de_Dados">Estatística Descritiva e Análise Exploratória de Dados Estatística Descritiva e Análise Exploratória de Dados</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Estatística Descritiva e Análise Exploratória de Dados Ciclo PPDAC - Problema, Plano, Dados, Análise e Conclusão (Spiegelhater, 2019) SPIEGELHALTER, D The Art of Statistics : How to learn from data , New York: Basic Books, Problema:... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_43717773" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Estatística Descritiva e Análise Exploratória de Dados<br /><br />Ciclo PPDAC - Problema, Plano, Dados, Análise e Conclusão (Spiegelhater, 2019)<br /><br />SPIEGELHALTER, D The Art of Statistics : How to learn from data , New York: Basic Books,<br /><br />Problema: Traçar o perfil dos funcionários da Secretaria de Turisimo.<br /><br />Plano: Obter dados de remuneração dos servidores da Secretaria de <br /><br />Turismo (Fonte: Portal da Transparência).<br /><br />Dados: 40 servidores ativos, variávaies: Sexo, Carga Horária, Tempo no Cargo, Remuneração.<br /><br />Análise: Cálculo das distribuições de frequencias e estatísticas descritivas.<br /><br />Conclusão: Perfil dos funcionários da Secretaria de Turismo.<br /><br /><a href="https://rpubs.com/PESSANHA/642558" rel="nofollow">https://rpubs.com/PESSANHA/642558</a></div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/43717773" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d3bb37392923ff8782e07c7b479ef479" rel="nofollow" data-download="{"attachment_id":64024780,"asset_id":43717773,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" 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style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="43717773"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">20</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="892" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4060" 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There is an urgent need for a new generation of computational theories and tools to assist... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_41711668" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/41711668" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="862c22ddae3896af16453f87644defca" rel="nofollow" data-download="{"attachment_id":61877631,"asset_id":41711668,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/61877631/download_file?st=MTczMjQxNTk5MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="16715850" href="https://independent.academia.edu/IJDKPJOURNAL">International Journal of Data Mining & Knowledge Management Process ( IJDKP )</a><script data-card-contents-for-user="16715850" type="text/json">{"id":16715850,"first_name":"International Journal of Data Mining \u0026 Knowledge Management Process","last_name":"( IJDKP )","domain_name":"independent","page_name":"IJDKPJOURNAL","display_name":"International Journal of Data Mining \u0026 Knowledge Management Process ( IJDKP )","profile_url":"https://independent.academia.edu/IJDKPJOURNAL?f_ri=34344","photo":"https://0.academia-photos.com/16715850/4568261/39147186/s65_international_journal_of_data_mining_knowledge_management_process._ijdkp_.png"}</script></span></span></li><li class="js-paper-rank-work_41711668 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="41711668"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 41711668, container: ".js-paper-rank-work_41711668", }); 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It is caused through conflicts, creating selections, or otherwise straining skills, or it will exist owing to various social demands on time. it's noted that... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_32858421" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Stress will exist once operating or paying attention to somebody shouts at you. It is caused through conflicts, creating selections, or otherwise straining skills, or it will exist owing to various social demands on time. it's noted that emotional states tends to not be long lasting, whether or not they are unpleasant emotions like worry, or pleasant ones, like joy. Nonetheless infrequently such states might persist for long periods of your time, or they will reach too high levels. Once this happens, the result typically is labeled "stress", and it's psychological as well as physiological terms. Stress and its manifestations, such as anxiety, depression, and burnout, have always have been a common problem among people in different professions and occupations. In the last few decades, alarm has already been provoked by the proliferation of books, research reports, popular articles and the growing number of organized workshops, aiming to teach people how to cope up with it. The purpose of the study is to find out the level of academic stress among higher secondary students. The present study consists of students studying in higher secondary schools situated in Tamil Nadu, India. The sample was selected by using simple random sampling technique. The present study reveals that the higher secondary students are having moderate level of academic stress and irrespective of sub samples of the higher secondary students are having moderate level of academic stress. The male student's academic stress is higher than female students. The urban student's tutorial stress is higher than rural students. The government college student's tutorial stress is a smaller amount than school students. The science subject student's tutorial stress is over arts student. The scholars whose parents are literates, the educational stress is over their counterparts.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/32858421" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0da65cf7012cec8abdd68bb9031f8a1c" rel="nofollow" data-download="{"attachment_id":53005106,"asset_id":32858421,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/53005106/download_file?st=MTczMjQxNTk5MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="13733577" href="https://iiitb.academia.edu/IJIRAEInternationalJournalofInnovativeResearchinAdvancedEngineering">IJIRAE - International Journal of Innovative Research in Advanced Engineering</a><script data-card-contents-for-user="13733577" type="text/json">{"id":13733577,"first_name":"IJIRAE","last_name":"International Journal of Innovative Research in Advanced Engineering","domain_name":"iiitb","page_name":"IJIRAEInternationalJournalofInnovativeResearchinAdvancedEngineering","display_name":"IJIRAE - International Journal of Innovative Research in Advanced Engineering","profile_url":"https://iiitb.academia.edu/IJIRAEInternationalJournalofInnovativeResearchinAdvancedEngineering?f_ri=34344","photo":"https://0.academia-photos.com/13733577/3819053/35118444/s65_ijirae.international_journal_of_innovative_research_in_advanced_engineering.jpg"}</script></span></span></li><li class="js-paper-rank-work_32858421 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="32858421"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 32858421, container: ".js-paper-rank-work_32858421", }); 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$(".js-view-count[data-work-id=32858421]").text(description); $(".js-view-count-work_32858421").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_32858421").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="32858421"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">37</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="965" href="https://www.academia.edu/Documents/in/Formal_Concept_Analysis_Data_Mining_">Formal Concept Analysis (Data Mining)</a>, <script data-card-contents-for-ri="965" type="text/json">{"id":965,"name":"Formal Concept Analysis (Data Mining)","url":"https://www.academia.edu/Documents/in/Formal_Concept_Analysis_Data_Mining_?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="7960" href="https://www.academia.edu/Documents/in/Temporal_Data_Mining">Temporal Data Mining</a>, <script data-card-contents-for-ri="7960" type="text/json">{"id":7960,"name":"Temporal Data Mining","url":"https://www.academia.edu/Documents/in/Temporal_Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="9447" href="https://www.academia.edu/Documents/in/Stream_Mining_Data_Mining_">Stream Mining (Data Mining)</a><script data-card-contents-for-ri="9447" type="text/json">{"id":9447,"name":"Stream Mining (Data Mining)","url":"https://www.academia.edu/Documents/in/Stream_Mining_Data_Mining_?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=32858421]'), work: {"id":32858421,"title":"SURVEY ON STRESS TYPES USING DATA MINING ALGORITHMS","created_at":"2017-05-05T02:12:07.666-07:00","url":"https://www.academia.edu/32858421/SURVEY_ON_STRESS_TYPES_USING_DATA_MINING_ALGORITHMS?f_ri=34344","dom_id":"work_32858421","summary":"Stress will exist once operating or paying attention to somebody shouts at you. 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Indicar una razonable aproximación a la necesidad de determinar esta información mediante los gráficos que ayudarán a las interpretaciones es probablemente la mecánica a adoptar... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_11060130" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">El comportamiento desplegado de cada equipo, es importante. Indicar una razonable aproximación a la necesidad de determinar esta información mediante los gráficos que ayudarán a las interpretaciones es probablemente la mecánica a adoptar en el presente trabajo. La finalidad es proporcionar al ingeniero de minas herramientas que no solo permitan conocer e interpretar condiciones / índices de  operación; sino también facilitar en la toma de decisiones sobre reorientaciones del curso en las operaciones tan dinámicas que son una permanente característica en mina. Por ello se trabaja en el rendimiento de equipos y proyecciones para el aumento de la productividad. La toma de datos fue realizada en operaciones principales de producción tales como perforación,  voladura, movimiento de tierras y extracción. La obtención de índices de productividad óptimos, derivará en mejores condiciones y por consiguiente el nivel de competitividad irá en aumento, un objetivo tan común; pero a la vez tan primordial.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/11060130" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d4991a6be5acb88c1bd05b100b8b32fb" rel="nofollow" data-download="{"attachment_id":36760826,"asset_id":11060130,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/36760826/download_file?st=MTczMjQxNTk5MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="11692837" href="https://independent.academia.edu/HuberHuaman">Huber Huaman</a><script data-card-contents-for-user="11692837" type="text/json">{"id":11692837,"first_name":"Huber","last_name":"Huaman","domain_name":"independent","page_name":"HuberHuaman","display_name":"Huber Huaman","profile_url":"https://independent.academia.edu/HuberHuaman?f_ri=34344","photo":"https://0.academia-photos.com/11692837/3373405/20040871/s65_huber.huaman.jpg"}</script></span></span></li><li class="js-paper-rank-work_11060130 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="11060130"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 11060130, container: ".js-paper-rank-work_11060130", }); 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$(".js-view-count[data-work-id=11060130]").text(description); $(".js-view-count-work_11060130").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_11060130").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="11060130"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">17</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="64" href="https://www.academia.edu/Documents/in/Mining_Engineering">Mining Engineering</a>, <script data-card-contents-for-ri="64" type="text/json">{"id":64,"name":"Mining Engineering","url":"https://www.academia.edu/Documents/in/Mining_Engineering?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2436" href="https://www.academia.edu/Documents/in/Operations_Management">Operations Management</a>, <script data-card-contents-for-ri="2436" type="text/json">{"id":2436,"name":"Operations Management","url":"https://www.academia.edu/Documents/in/Operations_Management?f_ri=34344","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2507" href="https://www.academia.edu/Documents/in/Combinatorial_Optimization">Combinatorial Optimization</a><script data-card-contents-for-ri="2507" type="text/json">{"id":2507,"name":"Combinatorial Optimization","url":"https://www.academia.edu/Documents/in/Combinatorial_Optimization?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=11060130]'), work: {"id":11060130,"title":"Rendimientos y productividad en equipos","created_at":"2015-02-24T15:06:11.981-08:00","url":"https://www.academia.edu/11060130/Rendimientos_y_productividad_en_equipos?f_ri=34344","dom_id":"work_11060130","summary":"El comportamiento desplegado de cada equipo, es importante. Indicar una razonable aproximación a la necesidad de determinar esta información mediante los gráficos que ayudarán a las interpretaciones es probablemente la mecánica a adoptar en el presente trabajo. La finalidad es proporcionar al ingeniero de minas herramientas que no solo permitan conocer e interpretar condiciones / índices de operación; sino también facilitar en la toma de decisiones sobre reorientaciones del curso en las operaciones tan dinámicas que son una permanente característica en mina. Por ello se trabaja en el rendimiento de equipos y proyecciones para el aumento de la productividad. La toma de datos fue realizada en operaciones principales de producción tales como perforación, voladura, movimiento de tierras y extracción. La obtención de índices de productividad óptimos, derivará en mejores condiciones y por consiguiente el nivel de competitividad irá en aumento, un objetivo tan común; pero a la vez tan primordial.","downloadable_attachments":[{"id":36760826,"asset_id":11060130,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":11692837,"first_name":"Huber","last_name":"Huaman","domain_name":"independent","page_name":"HuberHuaman","display_name":"Huber Huaman","profile_url":"https://independent.academia.edu/HuberHuaman?f_ri=34344","photo":"https://0.academia-photos.com/11692837/3373405/20040871/s65_huber.huaman.jpg"}],"research_interests":[{"id":64,"name":"Mining Engineering","url":"https://www.academia.edu/Documents/in/Mining_Engineering?f_ri=34344","nofollow":false},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=34344","nofollow":false},{"id":2436,"name":"Operations Management","url":"https://www.academia.edu/Documents/in/Operations_Management?f_ri=34344","nofollow":false},{"id":2507,"name":"Combinatorial Optimization","url":"https://www.academia.edu/Documents/in/Combinatorial_Optimization?f_ri=34344","nofollow":false},{"id":2535,"name":"Operations Research","url":"https://www.academia.edu/Documents/in/Operations_Research?f_ri=34344"},{"id":3704,"name":"Operation Management","url":"https://www.academia.edu/Documents/in/Operation_Management?f_ri=34344"},{"id":3853,"name":"Optimization (Mathematics)","url":"https://www.academia.edu/Documents/in/Optimization_Mathematics_?f_ri=34344"},{"id":4278,"name":"Web Mining","url":"https://www.academia.edu/Documents/in/Web_Mining?f_ri=34344"},{"id":5639,"name":"Text Mining","url":"https://www.academia.edu/Documents/in/Text_Mining?f_ri=34344"},{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=34344"},{"id":14494,"name":"Opinion Mining (Data Mining)","url":"https://www.academia.edu/Documents/in/Opinion_Mining_Data_Mining_?f_ri=34344"},{"id":18496,"name":"Mining","url":"https://www.academia.edu/Documents/in/Mining?f_ri=34344"},{"id":25773,"name":"Operations research and Optimization","url":"https://www.academia.edu/Documents/in/Operations_research_and_Optimization?f_ri=34344"},{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344"},{"id":43981,"name":"Optimization","url":"https://www.academia.edu/Documents/in/Optimization?f_ri=34344"},{"id":167261,"name":"Production and Operation Management","url":"https://www.academia.edu/Documents/in/Production_and_Operation_Management?f_ri=34344"},{"id":613846,"name":"Optimization Technology","url":"https://www.academia.edu/Documents/in/Optimization_Technology?f_ri=34344"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_44492693" data-work_id="44492693" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/44492693/Usability_Evaluation_of_a_Web_Based_Portal_Mysikap_Using_ISO_9241_11_Model">Usability Evaluation of a Web-Based Portal (Mysikap) Using ISO 9241-11 Model</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">There is growing effort to evaluate the usability of web-based systems and mobile-based systems. With the increase in the development of web-based, applications coupled with the limitations and associated challenges, it becomes imperative... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_44492693" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">There is growing effort to evaluate the usability of web-based systems and mobile-based systems. With the increase in the development of web-based, applications coupled with the limitations and associated challenges, it becomes imperative to evaluate the web-based systems in the context of usability. Furthermore, a number of international standards/models on usability are available, but rarely use for practical usability evaluation. In this study, a survey was conducted on an online web-based system of Malaysian Road Transport Department MySIKAP. It is a new system for accomplishing road transport related transaction via online. The popular ISO 9241-11 standard was used to evaluate MySIKAP portal, a survey of five hundred (500) participants was conducted to measure users' perspective. The results revealed some pointers to usability issues as well as confirmation that the web-based transaction portal is relatively usable within the components defined in the models. Findings show that, contents, organization and readability contribute more to the usability of MySIKAP, while the user design interface of the portal is not. Although the navigation and links; effectiveness and efficiency are usable to the users, however the level of usability calls for greater improvement to provide users with better experience for an improved usability. Usability Evaluation of a Web-Based Portal (Mysikap) Using ISO 9241-11 Model</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/44492693" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e225d4cdf61d0fa8b79f12388c0d0563" rel="nofollow" data-download="{"attachment_id":64926104,"asset_id":44492693,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/64926104/download_file?st=MTczMjQxNTk5MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="178315569" href="https://fudutsinma.academia.edu/ABUBAKARAHMAD">ABUBAKAR AHMAD</a><script data-card-contents-for-user="178315569" type="text/json">{"id":178315569,"first_name":"ABUBAKAR","last_name":"AHMAD","domain_name":"fudutsinma","page_name":"ABUBAKARAHMAD","display_name":"ABUBAKAR AHMAD","profile_url":"https://fudutsinma.academia.edu/ABUBAKARAHMAD?f_ri=34344","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_44492693 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="44492693"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 44492693, container: ".js-paper-rank-work_44492693", }); });</script></li><li class="js-percentile-work_44492693 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 44492693; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_44492693"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_44492693 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="44492693"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44492693; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=44492693]").text(description); $(".js-view-count-work_44492693").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_44492693").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="44492693"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i></div><span class="InlineList-item-text u-textTruncate u-pl6x"><a class="InlineList-item-text" data-has-card-for-ri="34344" href="https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_">Data mining (Data Analysis)</a><script data-card-contents-for-ri="34344" type="text/json">{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}</script></span></li><script>(function(){ if (false) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=44492693]'), work: {"id":44492693,"title":"Usability Evaluation of a Web-Based Portal (Mysikap) Using ISO 9241-11 Model","created_at":"2020-11-13T05:57:49.791-08:00","url":"https://www.academia.edu/44492693/Usability_Evaluation_of_a_Web_Based_Portal_Mysikap_Using_ISO_9241_11_Model?f_ri=34344","dom_id":"work_44492693","summary":"There is growing effort to evaluate the usability of web-based systems and mobile-based systems. With the increase in the development of web-based, applications coupled with the limitations and associated challenges, it becomes imperative to evaluate the web-based systems in the context of usability. Furthermore, a number of international standards/models on usability are available, but rarely use for practical usability evaluation. In this study, a survey was conducted on an online web-based system of Malaysian Road Transport Department MySIKAP. It is a new system for accomplishing road transport related transaction via online. The popular ISO 9241-11 standard was used to evaluate MySIKAP portal, a survey of five hundred (500) participants was conducted to measure users' perspective. The results revealed some pointers to usability issues as well as confirmation that the web-based transaction portal is relatively usable within the components defined in the models. Findings show that, contents, organization and readability contribute more to the usability of MySIKAP, while the user design interface of the portal is not. Although the navigation and links; effectiveness and efficiency are usable to the users, however the level of usability calls for greater improvement to provide users with better experience for an improved usability. Usability Evaluation of a Web-Based Portal (Mysikap) Using ISO 9241-11 Model","downloadable_attachments":[{"id":64926104,"asset_id":44492693,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":178315569,"first_name":"ABUBAKAR","last_name":"AHMAD","domain_name":"fudutsinma","page_name":"ABUBAKARAHMAD","display_name":"ABUBAKAR AHMAD","profile_url":"https://fudutsinma.academia.edu/ABUBAKARAHMAD?f_ri=34344","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":34344,"name":"Data mining (Data Analysis)","url":"https://www.academia.edu/Documents/in/Data_mining_Data_Analysis_?f_ri=34344","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div></div><div class="u-taCenter Pagination"><ul class="pagination"><li class="next_page"><a href="/Documents/in/Data_mining_Data_Analysis_?after=50%2C44492693" rel="next">Next</a></li><li class="last next"><a href="/Documents/in/Data_mining_Data_Analysis_?page=last">Last »</a></li></ul></div></div><div 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