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href="https://www.academia.edu/78796345/Dynamic_Functional_Bandwidth_Kernel_Based_SVM_An_Efficient_Approach_for_Functional_Data_Analysis"><img alt="Research paper thumbnail of Dynamic Functional Bandwidth Kernel-Based SVM: An Efficient Approach for Functional Data Analysis" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/78796345/Dynamic_Functional_Bandwidth_Kernel_Based_SVM_An_Efficient_Approach_for_Functional_Data_Analysis">Dynamic Functional Bandwidth Kernel-Based SVM: An Efficient Approach for Functional Data Analysis</a></div><div class="wp-workCard_item"><span>Advances in Intelligent Systems and Computing</span></div><div class="wp-workCard_item wp-workCard--actions"><span 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this, workJSON: {"id":78796344,"title":"Predicting and Analysing Pedestrian Injury Severity: A Machine Learning-Based Approach","translated_title":"","metadata":{"publisher":"Springer Singapore","publication_date":{"day":null,"month":null,"year":2022,"errors":{}},"publication_name":"Proceedings of the Seventh International Conference on Mathematics and 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src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796342/Root_Cause_Analysis_of_Incidents_Using_Text_Clustering_and_Classification_Algorithms">Root Cause Analysis of Incidents Using Text Clustering and Classification Algorithms</a></div><div class="wp-workCard_item"><span>Proceedings of ICETIT 2019</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The purpose of this study is to cluster the injury narratives to extract the root causes behind t...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The purpose of this study is to cluster the injury narratives to extract the root causes behind the accidents. Analysis is done on incident data collected from the database of an integrated steel plant. Key terms generated from the clustering of incident scenario help us in finding root causes of that particular incident. This study also proposed specific measures to the management that would improve the safety performance. This study uses text document clustering to discover the hidden factors and causes behind the incidents. Understanding previous accidents is necessary to avoid future accidents. However, for companies, management of large accident databases, and accurately classifying accident narratives are very challenging issues. Therefore, the aim of this study is to accurately classify accident reports using text classification approaches and evaluate their usefulness. The study used two machine learning (ML) algorithms, namely random forest (RF), and support vector machine (SVM) and found that SVM performed best in classifying the accident narratives. Further, SVM was experimented with different tokenization of the preprocessed narratives to get more reliable results.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796342"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796342"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796342; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796342]").text(description); $(".js-view-count[data-work-id=78796342]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796342; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796342']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796342, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796342]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796342,"title":"Root Cause Analysis of Incidents Using Text Clustering and Classification Algorithms","translated_title":"","metadata":{"abstract":"The purpose of this study is to cluster the injury narratives to extract the root causes behind the accidents. Analysis is done on incident data collected from the database of an integrated steel plant. Key terms generated from the clustering of incident scenario help us in finding root causes of that particular incident. This study also proposed specific measures to the management that would improve the safety performance. This study uses text document clustering to discover the hidden factors and causes behind the incidents. Understanding previous accidents is necessary to avoid future accidents. However, for companies, management of large accident databases, and accurately classifying accident narratives are very challenging issues. Therefore, the aim of this study is to accurately classify accident reports using text classification approaches and evaluate their usefulness. The study used two machine learning (ML) algorithms, namely random forest (RF), and support vector machine (SVM) and found that SVM performed best in classifying the accident narratives. Further, SVM was experimented with different tokenization of the preprocessed narratives to get more reliable results.","publisher":"Springer International Publishing","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Proceedings of ICETIT 2019"},"translated_abstract":"The purpose of this study is to cluster the injury narratives to extract the root causes behind the accidents. Analysis is done on incident data collected from the database of an integrated steel plant. Key terms generated from the clustering of incident scenario help us in finding root causes of that particular incident. This study also proposed specific measures to the management that would improve the safety performance. This study uses text document clustering to discover the hidden factors and causes behind the incidents. Understanding previous accidents is necessary to avoid future accidents. However, for companies, management of large accident databases, and accurately classifying accident narratives are very challenging issues. Therefore, the aim of this study is to accurately classify accident reports using text classification approaches and evaluate their usefulness. The study used two machine learning (ML) algorithms, namely random forest (RF), and support vector machine (SVM) and found that SVM performed best in classifying the accident narratives. Further, SVM was experimented with different tokenization of the preprocessed narratives to get more reliable results.","internal_url":"https://www.academia.edu/78796342/Root_Cause_Analysis_of_Incidents_Using_Text_Clustering_and_Classification_Algorithms","translated_internal_url":"","created_at":"2022-05-08T19:18:24.717-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Root_Cause_Analysis_of_Incidents_Using_Text_Clustering_and_Classification_Algorithms","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":131237,"name":"Cluster Analysis","url":"https://www.academia.edu/Documents/in/Cluster_Analysis"}],"urls":[{"id":20339069,"url":"http://link.springer.com/content/pdf/10.1007/978-3-030-30577-2_63"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796341"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796341/Prediction_of_Occupational_Incidents_Using_Proactive_and_Reactive_Data_A_Data_Mining_Approach"><img alt="Research paper thumbnail of Prediction of Occupational Incidents Using Proactive and Reactive Data: A Data Mining Approach" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796341/Prediction_of_Occupational_Incidents_Using_Proactive_and_Reactive_Data_A_Data_Mining_Approach">Prediction of Occupational Incidents Using Proactive and Reactive Data: A Data Mining Approach</a></div><div class="wp-workCard_item"><span>Industrial Safety Management</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Prediction of occupational incidents is an important task for any industry. To do this, reactive ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Prediction of occupational incidents is an important task for any industry. To do this, reactive data has been used by most of the previous studies in this domain. As an extension of the existing works, the present study has used the underused proactive data coupled with reactive data to establish the predictive models so that the information inherent in both data sets could be better utilized. The main aim of the study is to predict the incident outcomes using mixed data set comprising reactive and proactive data together. Two decision tree classifiers, i.e. classification and regression tree (CART) and C5.0, have been implemented with tenfold cross validation. Furthermore, the ensemble technique, namely adaptive boosting has been implemented to increase the classification accuracy. Results show that boosted C5.0 produces higher accuracy than others for the prediction task. Furthermore, the rules obtained produce the insight of the incidents. The limitation of the present study includes the use of less amount of data and the requirement of experts’ domain knowledge for a large span of time. Future scope of the study includes the proper feature selection for preparation of the mixed data set and building the better classification algorithm for better prediction of occurrence of accidents. The present work sets out the potential use of both types of data sources together.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796341"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796341"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796341; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796341]").text(description); $(".js-view-count[data-work-id=78796341]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796341; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796341']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796341, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796341]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796341,"title":"Prediction of Occupational Incidents Using Proactive and Reactive Data: A Data Mining Approach","translated_title":"","metadata":{"abstract":"Prediction of occupational incidents is an important task for any industry. To do this, reactive data has been used by most of the previous studies in this domain. As an extension of the existing works, the present study has used the underused proactive data coupled with reactive data to establish the predictive models so that the information inherent in both data sets could be better utilized. The main aim of the study is to predict the incident outcomes using mixed data set comprising reactive and proactive data together. Two decision tree classifiers, i.e. classification and regression tree (CART) and C5.0, have been implemented with tenfold cross validation. Furthermore, the ensemble technique, namely adaptive boosting has been implemented to increase the classification accuracy. Results show that boosted C5.0 produces higher accuracy than others for the prediction task. Furthermore, the rules obtained produce the insight of the incidents. The limitation of the present study includes the use of less amount of data and the requirement of experts’ domain knowledge for a large span of time. Future scope of the study includes the proper feature selection for preparation of the mixed data set and building the better classification algorithm for better prediction of occurrence of accidents. The present work sets out the potential use of both types of data sources together.","publisher":"Springer Singapore","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Industrial Safety Management"},"translated_abstract":"Prediction of occupational incidents is an important task for any industry. To do this, reactive data has been used by most of the previous studies in this domain. As an extension of the existing works, the present study has used the underused proactive data coupled with reactive data to establish the predictive models so that the information inherent in both data sets could be better utilized. The main aim of the study is to predict the incident outcomes using mixed data set comprising reactive and proactive data together. Two decision tree classifiers, i.e. classification and regression tree (CART) and C5.0, have been implemented with tenfold cross validation. Furthermore, the ensemble technique, namely adaptive boosting has been implemented to increase the classification accuracy. Results show that boosted C5.0 produces higher accuracy than others for the prediction task. Furthermore, the rules obtained produce the insight of the incidents. The limitation of the present study includes the use of less amount of data and the requirement of experts’ domain knowledge for a large span of time. Future scope of the study includes the proper feature selection for preparation of the mixed data set and building the better classification algorithm for better prediction of occurrence of accidents. The present work sets out the potential use of both types of data sources together.","internal_url":"https://www.academia.edu/78796341/Prediction_of_Occupational_Incidents_Using_Proactive_and_Reactive_Data_A_Data_Mining_Approach","translated_internal_url":"","created_at":"2022-05-08T19:18:24.596-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Prediction_of_Occupational_Incidents_Using_Proactive_and_Reactive_Data_A_Data_Mining_Approach","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1262914,"name":"Industrial Safety Management","url":"https://www.academia.edu/Documents/in/Industrial_Safety_Management"}],"urls":[{"id":20339068,"url":"http://link.springer.com/content/pdf/10.1007/978-981-10-6328-2_6"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796340"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796340/Text_clustering_based_deep_neural_network_for_prediction_of_occupational_accident_risk_A_case_study"><img alt="Research paper thumbnail of Text-clustering based deep neural network for prediction of occupational accident risk: A case study" class="work-thumbnail" src="https://attachments.academia-assets.com/85714990/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796340/Text_clustering_based_deep_neural_network_for_prediction_of_occupational_accident_risk_A_case_study">Text-clustering based deep neural network for prediction of occupational accident risk: A case study</a></div><div class="wp-workCard_item"><span>2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5abd34df3a7ec87ed788559ffd68299a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85714990,"asset_id":78796340,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85714990/download_file?st=MTczMjgzMTk3Nyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796340"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796340"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796340; 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Unstructured texts, i.e., incident narratives often remain unutilized or under-utilized. Besides the explicit attributes present in the dataset, there exist a large number of hidden attributes in different forms, which are hardly explored by the traditional machine learning algorithms. Therefore, we propose a methodology that utilizes both text-based clustering, namely Expectation Maximization (EM) algorithm for unstructured text analysis and deep neural network (DNN) for prediction of accident risk using the accident data collected from a steel plant in India. EM-based DNN shows the maximum accuracy equal to 83.59% in the prediction of risk while compared to other algorithms, namely single DNN, support vector machine, and random forest. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796339"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796339/COVID_19_Outbreak_A_Data_driven_Optimization_Model_for_Allocation_of_Patients"><img alt="Research paper thumbnail of COVID-19 Outbreak: A Data-driven Optimization Model for Allocation of Patients" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796339/COVID_19_Outbreak_A_Data_driven_Optimization_Model_for_Allocation_of_Patients">COVID-19 Outbreak: A Data-driven Optimization Model for Allocation of Patients</a></div><div class="wp-workCard_item"><span>Computers & Industrial Engineering</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">COVID-19 is an unprecedented pandemic that puts the entire world at stake and the healthcare syst...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">COVID-19 is an unprecedented pandemic that puts the entire world at stake and the healthcare systems across the globe have faced pressing challenges. The number of COVID-19 patients increases rapidly every day. The hospitals across many countries are starving to provide adequate service to the patients due to the shortage of resources and as a consequence, patients do not get admitted to hospitals on time, which in turn creates panic and might contribute to the spread of the pandemic. Under this resource constraint situation, this study proposes a data-driven optimization model for patient allocation in hospitals. First, a compartmental model is developed for characterizing the spread of the COVID-19 virus. Then, Pareto analysis is carried out to identify the most COVID-affected cities. An optimization model is then developed for optimal patient allocation in hospitals in different cities. Finally, a sensitivity analysis is also conducted to investigate the robustness of our decision model. Using published data for Indian cities, obtained from different websites, the proposed methodology has been validated. Experimental results reveal that the proposed model offers some efficient strategies for optimal allocation of patients. A total of ten cities are identified as the most affected. Besides, four factors, namely cooperation, distances between cities, number of patients, and bed capacity per city emerge as important determinants.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796339"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796339"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796339; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796339]").text(description); $(".js-view-count[data-work-id=78796339]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796339; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796339']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796339, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796339]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796339,"title":"COVID-19 Outbreak: A Data-driven Optimization Model for Allocation of Patients","translated_title":"","metadata":{"abstract":"COVID-19 is an unprecedented pandemic that puts the entire world at stake and the healthcare systems across the globe have faced pressing challenges. The number of COVID-19 patients increases rapidly every day. The hospitals across many countries are starving to provide adequate service to the patients due to the shortage of resources and as a consequence, patients do not get admitted to hospitals on time, which in turn creates panic and might contribute to the spread of the pandemic. Under this resource constraint situation, this study proposes a data-driven optimization model for patient allocation in hospitals. First, a compartmental model is developed for characterizing the spread of the COVID-19 virus. Then, Pareto analysis is carried out to identify the most COVID-affected cities. An optimization model is then developed for optimal patient allocation in hospitals in different cities. Finally, a sensitivity analysis is also conducted to investigate the robustness of our decision model. Using published data for Indian cities, obtained from different websites, the proposed methodology has been validated. Experimental results reveal that the proposed model offers some efficient strategies for optimal allocation of patients. A total of ten cities are identified as the most affected. Besides, four factors, namely cooperation, distances between cities, number of patients, and bed capacity per city emerge as important determinants.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Computers \u0026 Industrial Engineering"},"translated_abstract":"COVID-19 is an unprecedented pandemic that puts the entire world at stake and the healthcare systems across the globe have faced pressing challenges. The number of COVID-19 patients increases rapidly every day. The hospitals across many countries are starving to provide adequate service to the patients due to the shortage of resources and as a consequence, patients do not get admitted to hospitals on time, which in turn creates panic and might contribute to the spread of the pandemic. Under this resource constraint situation, this study proposes a data-driven optimization model for patient allocation in hospitals. First, a compartmental model is developed for characterizing the spread of the COVID-19 virus. Then, Pareto analysis is carried out to identify the most COVID-affected cities. An optimization model is then developed for optimal patient allocation in hospitals in different cities. Finally, a sensitivity analysis is also conducted to investigate the robustness of our decision model. Using published data for Indian cities, obtained from different websites, the proposed methodology has been validated. Experimental results reveal that the proposed model offers some efficient strategies for optimal allocation of patients. A total of ten cities are identified as the most affected. Besides, four factors, namely cooperation, distances between cities, number of patients, and bed capacity per city emerge as important determinants.","internal_url":"https://www.academia.edu/78796339/COVID_19_Outbreak_A_Data_driven_Optimization_Model_for_Allocation_of_Patients","translated_internal_url":"","created_at":"2022-05-08T19:18:24.350-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"COVID_19_Outbreak_A_Data_driven_Optimization_Model_for_Allocation_of_Patients","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences"}],"urls":[{"id":20339066,"url":"https://api.elsevier.com/content/article/PII:S0360835221005799?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796338"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/78796338/Machine_learning_in_occupational_accident_analysis_A_review_using_science_mapping_approach_with_citation_network_analysis"><img alt="Research paper thumbnail of Machine learning in occupational accident analysis: A review using science mapping approach with citation network analysis" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/78796338/Machine_learning_in_occupational_accident_analysis_A_review_using_science_mapping_approach_with_citation_network_analysis">Machine learning in occupational accident analysis: A review using science mapping approach with citation network analysis</a></div><div class="wp-workCard_item"><span>Safety Science</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract The present study reviews the publications that examine the application of machine learn...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract The present study reviews the publications that examine the application of machine learning (ML) approaches in occupational accident analysis. The review process includes four phases of analysis, namely bibliometric search, descriptive analysis, scientometric analysis, and citation network analysis (CNA). In the bibliometric search, a total of 232 articles are systematically screened out from 1995 to 2019 (up to May). Then, descriptive analysis and scientometric analysis are carried out to find the influences of journals, authors, authors’ keywords, articles/documents, and countries/regions in developing the domain. Thereafter, CNA is carried out to classify the publications according to the research themes and methods used. From this extensive review, several key findings are obtained in the application of ML approaches in occupational accident analysis. USA, China, and Taiwan are the leading countries/regions in publishing articles. The four major research domains are (i) prediction of incident outcomes, (ii) extraction of rule based patterns, (iii) prediction of injury risk, and (iv) prediction of injury severity. Then, a taxonomy of the ML algorithms used is developed. 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Then, a taxonomy of the ML algorithms used is developed. Finally, research gaps and safety issues are highlighted and the scope for future is discussed.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Safety Science"},"translated_abstract":"Abstract The present study reviews the publications that examine the application of machine learning (ML) approaches in occupational accident analysis. The review process includes four phases of analysis, namely bibliometric search, descriptive analysis, scientometric analysis, and citation network analysis (CNA). In the bibliometric search, a total of 232 articles are systematically screened out from 1995 to 2019 (up to May). Then, descriptive analysis and scientometric analysis are carried out to find the influences of journals, authors, authors’ keywords, articles/documents, and countries/regions in developing the domain. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796337"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/78796337/A_comprehensive_methodology_for_quantification_of_Bow_tie_under_type_II_fuzzy_data"><img alt="Research paper thumbnail of A comprehensive methodology for quantification of Bow-tie under type II fuzzy data" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/78796337/A_comprehensive_methodology_for_quantification_of_Bow_tie_under_type_II_fuzzy_data">A comprehensive methodology for quantification of Bow-tie under type II fuzzy data</a></div><div class="wp-workCard_item"><span>Applied Soft Computing</span><span>, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796337"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796337"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796337; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796336"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796336/An_innovative_integrated_modelling_of_safety_data_using_multiple_correspondence_analysis_and_fuzzy_discretization_techniques"><img alt="Research paper thumbnail of An innovative integrated modelling of safety data using multiple correspondence analysis and fuzzy discretization techniques" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796336/An_innovative_integrated_modelling_of_safety_data_using_multiple_correspondence_analysis_and_fuzzy_discretization_techniques">An innovative integrated modelling of safety data using multiple correspondence analysis and fuzzy discretization techniques</a></div><div class="wp-workCard_item"><span>Safety Science</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract In this study, we have proposed an innovative integrated methodology to handle a mix of ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract In this study, we have proposed an innovative integrated methodology to handle a mix of categorical and numeric safety data. We have augmented the traditional multiple correspondence analysis (MCA) through the use of fuzzy discretization approach, t-SNE technique and fuzzy c-means clustering. The fuzzy discretization approach transforms the continuous variables to categorical variables to make them analyzable using MCA. R2-profile is adopted to obtain the best number of hidden dimensions representing the maximum categorical information. Then, t-SNE technique is used to represent the high dimensional categorical information in a 2D map to visualize the significant categorical associations. Then, fuzzy c-means clustering (FCM) is used to group the categories in different clusters based on their membership degree. To determine the optimal number of clusters, cluster validity indices are used. Davies-Bouldin (DB) Index, Dunn’s (DU) Index and Silhouette (SW) coefficients are used to determine the quality of clustering solutions. The proposed methodology is tested using electric overhead traveling (EOT) crane related near-miss incidents and found that our approach is effective. From managerial implication point of view, several safety rules are generated and subsequent safety countermeasures are proposed. Further, the results obtained through FCM is compared with K-means (KM) algorithm and unsupervised fuzzy c-means clustering (UPFCM). FCM outperforms KM and UPFCM on the basis of quality of solutions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796336"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796336"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796336; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796336]").text(description); $(".js-view-count[data-work-id=78796336]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796336; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796336']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796336, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796336]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796336,"title":"An innovative integrated modelling of safety data using multiple correspondence analysis and fuzzy discretization techniques","translated_title":"","metadata":{"abstract":"Abstract In this study, we have proposed an innovative integrated methodology to handle a mix of categorical and numeric safety data. We have augmented the traditional multiple correspondence analysis (MCA) through the use of fuzzy discretization approach, t-SNE technique and fuzzy c-means clustering. The fuzzy discretization approach transforms the continuous variables to categorical variables to make them analyzable using MCA. R2-profile is adopted to obtain the best number of hidden dimensions representing the maximum categorical information. Then, t-SNE technique is used to represent the high dimensional categorical information in a 2D map to visualize the significant categorical associations. Then, fuzzy c-means clustering (FCM) is used to group the categories in different clusters based on their membership degree. To determine the optimal number of clusters, cluster validity indices are used. Davies-Bouldin (DB) Index, Dunn’s (DU) Index and Silhouette (SW) coefficients are used to determine the quality of clustering solutions. The proposed methodology is tested using electric overhead traveling (EOT) crane related near-miss incidents and found that our approach is effective. From managerial implication point of view, several safety rules are generated and subsequent safety countermeasures are proposed. Further, the results obtained through FCM is compared with K-means (KM) algorithm and unsupervised fuzzy c-means clustering (UPFCM). FCM outperforms KM and UPFCM on the basis of quality of solutions.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Safety Science"},"translated_abstract":"Abstract In this study, we have proposed an innovative integrated methodology to handle a mix of categorical and numeric safety data. We have augmented the traditional multiple correspondence analysis (MCA) through the use of fuzzy discretization approach, t-SNE technique and fuzzy c-means clustering. 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From managerial implication point of view, several safety rules are generated and subsequent safety countermeasures are proposed. Further, the results obtained through FCM is compared with K-means (KM) algorithm and unsupervised fuzzy c-means clustering (UPFCM). FCM outperforms KM and UPFCM on the basis of quality of solutions.","internal_url":"https://www.academia.edu/78796336/An_innovative_integrated_modelling_of_safety_data_using_multiple_correspondence_analysis_and_fuzzy_discretization_techniques","translated_internal_url":"","created_at":"2022-05-08T19:18:23.951-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"An_innovative_integrated_modelling_of_safety_data_using_multiple_correspondence_analysis_and_fuzzy_discretization_techniques","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":603594,"name":"Safety Science","url":"https://www.academia.edu/Documents/in/Safety_Science"},{"id":897823,"name":"Elsevier","url":"https://www.academia.edu/Documents/in/Elsevier"},{"id":2922956,"name":"Psychology and Cognitive Sciences","url":"https://www.academia.edu/Documents/in/Psychology_and_Cognitive_Sciences"},{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences"}],"urls":[{"id":20339063,"url":"https://api.elsevier.com/content/article/PII:S0925753520302253?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796335"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796335/A_novel_data_mining_approach_for_analysis_of_accident_paths_and_performance_assessment_of_risk_control_systems"><img alt="Research paper thumbnail of A novel data mining approach for analysis of accident paths and performance assessment of risk control systems" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796335/A_novel_data_mining_approach_for_analysis_of_accident_paths_and_performance_assessment_of_risk_control_systems">A novel data mining approach for analysis of accident paths and performance assessment of risk control systems</a></div><div class="wp-workCard_item"><span>Reliability Engineering & System Safety</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract The data mining researches to facilitate the process of safety management is fairly new,...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract The data mining researches to facilitate the process of safety management is fairly new, compared to other industrial management domains. The implementation of appropriate, effective, and safe risk control systems (RCSs) is vital to ensure zero-accident and zero-harm vision of industrial work-systems. In this work, we propose a data mining based tool to analyze accident paths from incident data and assess the performance of RCSs. Our work upgrades the existing pattern analysis methods through three new types of analyses (i) temporal frequent itemset generation (T-FIG) for studying the time effect on patterns, (ii) elevated severity itemset generation (ESIG) for examining the risk reduction due to RCSs, and (iii) High impact itemset generation (High_impact_IG) to identify accident paths with high risk. T-FIG and ESIG assist in performance assessment of preventive and mitigating RCSs, respectively. The results from each of the analyses are compared and eight types of inferences regarding the performance of RCSs are drawn. The proposed methodology is applied to 612 incident records reported during steel making process in a steel manufacturing plant. It was found that there are four accident paths which have ineffective preventive and mitigating RCSs, have high risk and are probable to recur in future. Two among four of these paths include hot metal/steel/slag as the hazardous element and three of them are due to damaged/degraded/poorly maintained equipment. Moreover, the case study also demonstrates that proposed data mining approach is an effective and easy to use tool for performance assessment of RCSs and accident path analysis.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796335"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796335"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796335; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796335]").text(description); $(".js-view-count[data-work-id=78796335]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796335; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796335']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796335, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796335]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796335,"title":"A novel data mining approach for analysis of accident paths and performance assessment of risk control systems","translated_title":"","metadata":{"abstract":"Abstract The data mining researches to facilitate the process of safety management is fairly new, compared to other industrial management domains. The implementation of appropriate, effective, and safe risk control systems (RCSs) is vital to ensure zero-accident and zero-harm vision of industrial work-systems. In this work, we propose a data mining based tool to analyze accident paths from incident data and assess the performance of RCSs. Our work upgrades the existing pattern analysis methods through three new types of analyses (i) temporal frequent itemset generation (T-FIG) for studying the time effect on patterns, (ii) elevated severity itemset generation (ESIG) for examining the risk reduction due to RCSs, and (iii) High impact itemset generation (High_impact_IG) to identify accident paths with high risk. T-FIG and ESIG assist in performance assessment of preventive and mitigating RCSs, respectively. The results from each of the analyses are compared and eight types of inferences regarding the performance of RCSs are drawn. The proposed methodology is applied to 612 incident records reported during steel making process in a steel manufacturing plant. It was found that there are four accident paths which have ineffective preventive and mitigating RCSs, have high risk and are probable to recur in future. Two among four of these paths include hot metal/steel/slag as the hazardous element and three of them are due to damaged/degraded/poorly maintained equipment. Moreover, the case study also demonstrates that proposed data mining approach is an effective and easy to use tool for performance assessment of RCSs and accident path analysis.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Reliability Engineering \u0026 System Safety"},"translated_abstract":"Abstract The data mining researches to facilitate the process of safety management is fairly new, compared to other industrial management domains. The implementation of appropriate, effective, and safe risk control systems (RCSs) is vital to ensure zero-accident and zero-harm vision of industrial work-systems. In this work, we propose a data mining based tool to analyze accident paths from incident data and assess the performance of RCSs. Our work upgrades the existing pattern analysis methods through three new types of analyses (i) temporal frequent itemset generation (T-FIG) for studying the time effect on patterns, (ii) elevated severity itemset generation (ESIG) for examining the risk reduction due to RCSs, and (iii) High impact itemset generation (High_impact_IG) to identify accident paths with high risk. T-FIG and ESIG assist in performance assessment of preventive and mitigating RCSs, respectively. The results from each of the analyses are compared and eight types of inferences regarding the performance of RCSs are drawn. The proposed methodology is applied to 612 incident records reported during steel making process in a steel manufacturing plant. It was found that there are four accident paths which have ineffective preventive and mitigating RCSs, have high risk and are probable to recur in future. Two among four of these paths include hot metal/steel/slag as the hazardous element and three of them are due to damaged/degraded/poorly maintained equipment. Moreover, the case study also demonstrates that proposed data mining approach is an effective and easy to use tool for performance assessment of RCSs and accident path analysis.","internal_url":"https://www.academia.edu/78796335/A_novel_data_mining_approach_for_analysis_of_accident_paths_and_performance_assessment_of_risk_control_systems","translated_internal_url":"","created_at":"2022-05-08T19:18:23.826-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_novel_data_mining_approach_for_analysis_of_accident_paths_and_performance_assessment_of_risk_control_systems","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences"},{"id":897823,"name":"Elsevier","url":"https://www.academia.edu/Documents/in/Elsevier"}],"urls":[{"id":20339062,"url":"https://api.elsevier.com/content/article/PII:S0951832020305421?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796334"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796334/Application_of_optimized_machine_learning_techniques_for_prediction_of_occupational_accidents"><img alt="Research paper thumbnail of Application of optimized machine learning techniques for prediction of occupational accidents" class="work-thumbnail" src="https://attachments.academia-assets.com/85714992/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796334/Application_of_optimized_machine_learning_techniques_for_prediction_of_occupational_accidents">Application of optimized machine learning techniques for prediction of occupational accidents</a></div><div class="wp-workCard_item"><span>Computers & Operations Research</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5b7d996a1a855f5dfc271ca92cc51397" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85714992,"asset_id":78796334,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85714992/download_file?st=MTczMjgzMTk3Nyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796334"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796334"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796334; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796334]").text(description); $(".js-view-count[data-work-id=78796334]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796334; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796334']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796334, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  Both categorical data and unstructured text data have been analysed.  Incident outcomes are predicted using GA and PSO optimized SVM and ANN approaches.  PSO-SVM outperforms other algorithms in terms of accuracy (i.e., 90.67%).  Nine useful rules are extracted using PSO-SVM based C5.0 algorithm.  Root causes of incidents have been identified.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Computers \u0026 Operations Research","grobid_abstract_attachment_id":85714992},"translated_abstract":null,"internal_url":"https://www.academia.edu/78796334/Application_of_optimized_machine_learning_techniques_for_prediction_of_occupational_accidents","translated_internal_url":"","created_at":"2022-05-08T19:18:23.703-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":85714992,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85714992/thumbnails/1.jpg","file_name":"j.cor.2018.02.02120220509-1-ni2wic.pdf","download_url":"https://www.academia.edu/attachments/85714992/download_file?st=MTczMjgzMTk3Nyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Application_of_optimized_machine_learnin.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85714992/j.cor.2018.02.02120220509-1-ni2wic-libre.pdf?1652063457=\u0026response-content-disposition=attachment%3B+filename%3DApplication_of_optimized_machine_learnin.pdf\u0026Expires=1732801874\u0026Signature=LG2WolrjkZRW0QeW0vATdUyxg0xaIUvivqiQ708X1rcAAwszjHLs9YzILHej27zxZEXaSAuGkhj8~omZPtxT4psrXOnEwSE70ZglWZubHP-dqDGZYR6yHQWD0KI7PrCxrbpc0-ftRTIsn9-xNDCuRmexmcjscmt9CfrpnafsP95Jx3Wx5mwPD8g7stu1t2AfPGIQRsQMXu9nya-vuRNNuq3W8jvIySmg0nSsB-X1MzrX4q2AM71VsB9BU-eoGgqNpgDuMNLpc1RFfWmUMbC9tGryDEpu~MhqDYvKKcBAxZygNaRB~qYgDQA3nj75XLxtSLCRG0n1amakvBzmgnM8Fg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Application_of_optimized_machine_learning_techniques_for_prediction_of_occupational_accidents","translated_slug":"","page_count":38,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[{"id":85714992,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85714992/thumbnails/1.jpg","file_name":"j.cor.2018.02.02120220509-1-ni2wic.pdf","download_url":"https://www.academia.edu/attachments/85714992/download_file?st=MTczMjgzMTk3Nyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Application_of_optimized_machine_learnin.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85714992/j.cor.2018.02.02120220509-1-ni2wic-libre.pdf?1652063457=\u0026response-content-disposition=attachment%3B+filename%3DApplication_of_optimized_machine_learnin.pdf\u0026Expires=1732801874\u0026Signature=LG2WolrjkZRW0QeW0vATdUyxg0xaIUvivqiQ708X1rcAAwszjHLs9YzILHej27zxZEXaSAuGkhj8~omZPtxT4psrXOnEwSE70ZglWZubHP-dqDGZYR6yHQWD0KI7PrCxrbpc0-ftRTIsn9-xNDCuRmexmcjscmt9CfrpnafsP95Jx3Wx5mwPD8g7stu1t2AfPGIQRsQMXu9nya-vuRNNuq3W8jvIySmg0nSsB-X1MzrX4q2AM71VsB9BU-eoGgqNpgDuMNLpc1RFfWmUMbC9tGryDEpu~MhqDYvKKcBAxZygNaRB~qYgDQA3nj75XLxtSLCRG0n1amakvBzmgnM8Fg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":4593,"name":"Occupational Health \u0026 Safety","url":"https://www.academia.edu/Documents/in/Occupational_Health_and_Safety"},{"id":43981,"name":"Optimization","url":"https://www.academia.edu/Documents/in/Optimization"},{"id":51531,"name":"Pergamon","url":"https://www.academia.edu/Documents/in/Pergamon"},{"id":63857,"name":"Categorical data analysis","url":"https://www.academia.edu/Documents/in/Categorical_data_analysis"},{"id":162271,"name":"Decision Tree","url":"https://www.academia.edu/Documents/in/Decision_Tree"},{"id":556845,"name":"Numerical Analysis and Computational Mathematics","url":"https://www.academia.edu/Documents/in/Numerical_Analysis_and_Computational_Mathematics"},{"id":3657206,"name":"Rule generation","url":"https://www.academia.edu/Documents/in/Rule_generation"}],"urls":[{"id":20339061,"url":"https://api.elsevier.com/content/article/PII:S0305054818300601?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796333"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796333/Reduction_of_life_cycle_costs_for_a_contemporary_helicopter_through_improvement_of_reliability_and_maintainability_parameters"><img alt="Research paper thumbnail of Reduction of life cycle costs for a contemporary helicopter through improvement of reliability and maintainability parameters" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796333/Reduction_of_life_cycle_costs_for_a_contemporary_helicopter_through_improvement_of_reliability_and_maintainability_parameters">Reduction of life cycle costs for a contemporary helicopter through improvement of reliability and maintainability parameters</a></div><div class="wp-workCard_item"><span>International Journal of Quality & Reliability Management</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose The modern helicopters are designed with maximum serviceability and long life expectancy ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose The modern helicopters are designed with maximum serviceability and long life expectancy to ensure minimum life cycle cost. The purpose of this paper is to present a framework to incorporate the customer requirements on reliability and maintainability (R&M) parameters into the design and development phase of a contemporary helicopter, and to discuss the way to capture operational data to establish and improve the R&M parameters to reduce life cycle cost. Design/methodology/approach From the analysis, it is established that the reliability and maintainability cost is the major contributor to the life cost. The significant reliability and maintainability parameters which influence R&M cost are identified from analysis. The operational and design data of a contemporary helicopter are collected, compiled and analyzed to establish and improve the reliability and maintainability parameters. Findings The process depicted in the paper is followed for a contemporary helicopter and su...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796333"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796333"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796333; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796333]").text(description); $(".js-view-count[data-work-id=78796333]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796333; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796333']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796333, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796333]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796333,"title":"Reduction of life cycle costs for a contemporary helicopter through improvement of reliability and maintainability parameters","translated_title":"","metadata":{"abstract":"Purpose The modern helicopters are designed with maximum serviceability and long life expectancy to ensure minimum life cycle cost. The purpose of this paper is to present a framework to incorporate the customer requirements on reliability and maintainability (R\u0026M) parameters into the design and development phase of a contemporary helicopter, and to discuss the way to capture operational data to establish and improve the R\u0026M parameters to reduce life cycle cost. Design/methodology/approach From the analysis, it is established that the reliability and maintainability cost is the major contributor to the life cost. The significant reliability and maintainability parameters which influence R\u0026M cost are identified from analysis. The operational and design data of a contemporary helicopter are collected, compiled and analyzed to establish and improve the reliability and maintainability parameters. Findings The process depicted in the paper is followed for a contemporary helicopter and su...","publisher":"Emerald","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"International Journal of Quality \u0026 Reliability Management"},"translated_abstract":"Purpose The modern helicopters are designed with maximum serviceability and long life expectancy to ensure minimum life cycle cost. The purpose of this paper is to present a framework to incorporate the customer requirements on reliability and maintainability (R\u0026M) parameters into the design and development phase of a contemporary helicopter, and to discuss the way to capture operational data to establish and improve the R\u0026M parameters to reduce life cycle cost. Design/methodology/approach From the analysis, it is established that the reliability and maintainability cost is the major contributor to the life cost. The significant reliability and maintainability parameters which influence R\u0026M cost are identified from analysis. The operational and design data of a contemporary helicopter are collected, compiled and analyzed to establish and improve the reliability and maintainability parameters. Findings The process depicted in the paper is followed for a contemporary helicopter and su...","internal_url":"https://www.academia.edu/78796333/Reduction_of_life_cycle_costs_for_a_contemporary_helicopter_through_improvement_of_reliability_and_maintainability_parameters","translated_internal_url":"","created_at":"2022-05-08T19:18:23.571-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Reduction_of_life_cycle_costs_for_a_contemporary_helicopter_through_improvement_of_reliability_and_maintainability_parameters","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":73149,"name":"Business and Management","url":"https://www.academia.edu/Documents/in/Business_and_Management"}],"urls":[{"id":20339060,"url":"https://www.emeraldinsight.com/doi/full-xml/10.1108/IJQRM-11-2016-0199"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796332"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796332/Decision_support_system_for_safety_improvement_An_approach_using_multiple_correspondence_analysis_t_SNE_algorithm_and_K_means_clustering"><img alt="Research paper thumbnail of Decision support system for safety improvement: An approach using multiple correspondence analysis, t-SNE algorithm and K-means clustering" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796332/Decision_support_system_for_safety_improvement_An_approach_using_multiple_correspondence_analysis_t_SNE_algorithm_and_K_means_clustering">Decision support system for safety improvement: An approach using multiple correspondence analysis, t-SNE algorithm and K-means clustering</a></div><div class="wp-workCard_item"><span>Computers & Industrial Engineering</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract An attempt has been made to develop a decision support system (DSS) for safety improveme...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract An attempt has been made to develop a decision support system (DSS) for safety improvement using a multi-step knowledge discovery process involving multiple correspondence analysis (MCA), t-SNE algorithm and K-means clustering. MCA is used for dimension reduction and perceptual mapping from categorical data. Usually, the first two dimensions are used for perceptual mapping if these two dimensions explain a significant percentage of variance. Otherwise, the traditional method of two dimensional mapping, leads to loss of important categorical information involved with other dimensions. Considering the above, a novel R2-profile approach, as an alternate to inertia based approach, is adopted to obtain the desired number of dimensions to be retained without loss of significant amount of information. t-SNE technique reduces the high dimensional data into two dimensional (2D) map, which provides the associations amongst different categories. K-means clustering grouped the 2D categories in homogenous clusters as per the similarities of the categories. A novel kernel category based chi-square distance method is proposed to identify sub-clusters within a cluster which subsequently provides useful rules for safety improvement. The methodology also provides a logical approach of dimension reduction in a form called ‘funnel diagram’. Finally, the DSS is applied to analysing near miss incidents occurred in electric overhead traveling (EOT) crane operations in a steel plant. Several safety rules are identified and safety interventions are proposed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796332"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796332"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796332; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796332]").text(description); $(".js-view-count[data-work-id=78796332]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796332; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796332']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796332, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796332]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796332,"title":"Decision support system for safety improvement: An approach using multiple correspondence analysis, t-SNE algorithm and K-means clustering","translated_title":"","metadata":{"abstract":"Abstract An attempt has been made to develop a decision support system (DSS) for safety improvement using a multi-step knowledge discovery process involving multiple correspondence analysis (MCA), t-SNE algorithm and K-means clustering. MCA is used for dimension reduction and perceptual mapping from categorical data. Usually, the first two dimensions are used for perceptual mapping if these two dimensions explain a significant percentage of variance. Otherwise, the traditional method of two dimensional mapping, leads to loss of important categorical information involved with other dimensions. Considering the above, a novel R2-profile approach, as an alternate to inertia based approach, is adopted to obtain the desired number of dimensions to be retained without loss of significant amount of information. t-SNE technique reduces the high dimensional data into two dimensional (2D) map, which provides the associations amongst different categories. K-means clustering grouped the 2D categories in homogenous clusters as per the similarities of the categories. A novel kernel category based chi-square distance method is proposed to identify sub-clusters within a cluster which subsequently provides useful rules for safety improvement. The methodology also provides a logical approach of dimension reduction in a form called ‘funnel diagram’. Finally, the DSS is applied to analysing near miss incidents occurred in electric overhead traveling (EOT) crane operations in a steel plant. Several safety rules are identified and safety interventions are proposed.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Computers \u0026 Industrial Engineering"},"translated_abstract":"Abstract An attempt has been made to develop a decision support system (DSS) for safety improvement using a multi-step knowledge discovery process involving multiple correspondence analysis (MCA), t-SNE algorithm and K-means clustering. MCA is used for dimension reduction and perceptual mapping from categorical data. Usually, the first two dimensions are used for perceptual mapping if these two dimensions explain a significant percentage of variance. Otherwise, the traditional method of two dimensional mapping, leads to loss of important categorical information involved with other dimensions. Considering the above, a novel R2-profile approach, as an alternate to inertia based approach, is adopted to obtain the desired number of dimensions to be retained without loss of significant amount of information. t-SNE technique reduces the high dimensional data into two dimensional (2D) map, which provides the associations amongst different categories. K-means clustering grouped the 2D categories in homogenous clusters as per the similarities of the categories. A novel kernel category based chi-square distance method is proposed to identify sub-clusters within a cluster which subsequently provides useful rules for safety improvement. The methodology also provides a logical approach of dimension reduction in a form called ‘funnel diagram’. Finally, the DSS is applied to analysing near miss incidents occurred in electric overhead traveling (EOT) crane operations in a steel plant. Several safety rules are identified and safety interventions are proposed.","internal_url":"https://www.academia.edu/78796332/Decision_support_system_for_safety_improvement_An_approach_using_multiple_correspondence_analysis_t_SNE_algorithm_and_K_means_clustering","translated_internal_url":"","created_at":"2022-05-08T19:18:23.446-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Decision_support_system_for_safety_improvement_An_approach_using_multiple_correspondence_analysis_t_SNE_algorithm_and_K_means_clustering","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences"},{"id":131237,"name":"Cluster Analysis","url":"https://www.academia.edu/Documents/in/Cluster_Analysis"}],"urls":[{"id":20339059,"url":"https://api.elsevier.com/content/article/PII:S0360835218306478?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796331"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796331/Text_document_clustering_based_cause_and_effect_analysis_methodology_for_steel_plant_incident_data"><img alt="Research paper thumbnail of Text-document clustering-based cause and effect analysis methodology for steel plant incident data" class="work-thumbnail" src="https://attachments.academia-assets.com/85714991/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796331/Text_document_clustering_based_cause_and_effect_analysis_methodology_for_steel_plant_incident_data">Text-document clustering-based cause and effect analysis methodology for steel plant incident data</a></div><div class="wp-workCard_item"><span>International Journal of Injury Control and Safety Promotion</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="63ec93110bc1b7b979fe00d318fa1b3e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85714991,"asset_id":78796331,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85714991/download_file?st=MTczMjgzMTk3Nyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796331"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796331"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796331; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796331]").text(description); $(".js-view-count[data-work-id=78796331]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796331; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796331']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796331, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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A cause-effect diagram is usually prepared by using experts' knowledge which may fail to capture all the causes present at a workplace. On the other hand, the description of incidents provided by the workers in the form of incident reports is typically a rich data source and can be utilized to explore the causes and sub-causes of incidents. In this study, data were collected from an integrated steel plant. The text data were analysed using singular value decomposition (SVD) and expectation-maximization (EM) algorithm. Results suggest that text-document clustering can be used as a feasible method for exploring the hidden factors and trends from the description of incidents occurred at workplaces. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796330"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/78796330/An_Application_of_Logit_Model_to_Injury_Experience_Data"><img alt="Research paper thumbnail of An Application of Logit Model to Injury Experience Data" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/78796330/An_Application_of_Logit_Model_to_Injury_Experience_Data">An Application of Logit Model to Injury Experience Data</a></div><div class="wp-workCard_item"><span>Mineral Resources Engineering</span><span>, 1999</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The influence of various casual factors on the severity of injury was examined using the 4-year i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The influence of various casual factors on the severity of injury was examined using the 4-year injury data from a group of underground coal mines. Although there are many factors that could affect the injury severity: miners&#39; age, experience, occupation, injury-location, shift-time and specific-mine were chosen as the affecting variables. The logit model was applied for this analysis. The bivariate analysis was conducted to separately study the effect of each of the factors on the severity of injury and to estimate the odds of severe injuries. For the case study mines, it was concluded that the injury severity varies by age, occupation, injury-location, shift-time and specific-mine. However, experience in general does not have any significant effect on it.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796330"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796330"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796330; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796330]").text(description); $(".js-view-count[data-work-id=78796330]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796330; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796330']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796330, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796330]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796330,"title":"An Application of Logit Model to Injury Experience Data","translated_title":"","metadata":{"abstract":"The influence of various casual factors on the severity of injury was examined using the 4-year injury data from a group of underground coal mines. Although there are many factors that could affect the injury severity: miners\u0026#39; age, experience, occupation, injury-location, shift-time and specific-mine were chosen as the affecting variables. The logit model was applied for this analysis. The bivariate analysis was conducted to separately study the effect of each of the factors on the severity of injury and to estimate the odds of severe injuries. For the case study mines, it was concluded that the injury severity varies by age, occupation, injury-location, shift-time and specific-mine. However, experience in general does not have any significant effect on it.","publisher":"World Scientific Pub Co Pte Lt","publication_date":{"day":null,"month":null,"year":1999,"errors":{}},"publication_name":"Mineral Resources Engineering"},"translated_abstract":"The influence of various casual factors on the severity of injury was examined using the 4-year injury data from a group of underground coal mines. Although there are many factors that could affect the injury severity: miners\u0026#39; age, experience, occupation, injury-location, shift-time and specific-mine were chosen as the affecting variables. The logit model was applied for this analysis. The bivariate analysis was conducted to separately study the effect of each of the factors on the severity of injury and to estimate the odds of severe injuries. For the case study mines, it was concluded that the injury severity varies by age, occupation, injury-location, shift-time and specific-mine. However, experience in general does not have any significant effect on it.","internal_url":"https://www.academia.edu/78796330/An_Application_of_Logit_Model_to_Injury_Experience_Data","translated_internal_url":"","created_at":"2022-05-08T19:18:23.210-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"An_Application_of_Logit_Model_to_Injury_Experience_Data","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":146671,"name":"Mineral Resources","url":"https://www.academia.edu/Documents/in/Mineral_Resources"}],"urls":[{"id":20339057,"url":"http://www.worldscientific.com/doi/pdf/10.1142/S0950609899000244"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796329"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796329/Segmented_point_process_models_for_work_system_safety_analysis"><img alt="Research paper thumbnail of Segmented point process models for work system safety analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/85714988/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796329/Segmented_point_process_models_for_work_system_safety_analysis">Segmented point process models for work system safety analysis</a></div><div class="wp-workCard_item"><span>Safety Science</span><span>, 2017</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e1befc2d40b59a11d64a6f76400894ec" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85714988,"asset_id":78796329,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85714988/download_file?st=MTczMjgzMTk3Nyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796329"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796329"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796329; 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Data, collected from an underground coal mine, were analyzed using homogeneous (HPP) as well as non-homogeneous (NHPP) point process models. Time between occurrences (TBO) and number of occurrences (NOC) were modeled followed by the development of loss functions. The methodology can be used to monitor safety performance and to check safety program effectiveness. The findings of the case study application showed that the injury occurrences data fit the models for (i) 'all incidents' and 'first aid' cases with one change point at 458 days, (ii) 'near-miss' case with one change point at 441 days, and (iii) 'minor' injury case with two-change points at 11 days and 375 days, respectively.","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Safety Science","grobid_abstract_attachment_id":85714988},"translated_abstract":null,"internal_url":"https://www.academia.edu/78796329/Segmented_point_process_models_for_work_system_safety_analysis","translated_internal_url":"","created_at":"2022-05-08T19:18:23.042-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":85714988,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85714988/thumbnails/1.jpg","file_name":"108391.pdf","download_url":"https://www.academia.edu/attachments/85714988/download_file?st=MTczMjgzMTk3Nyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Segmented_point_process_models_for_work.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85714988/108391-libre.pdf?1652063443=\u0026response-content-disposition=attachment%3B+filename%3DSegmented_point_process_models_for_work.pdf\u0026Expires=1732835577\u0026Signature=L-A~zYX8bdcVQBQNwly9zfnZ~OAdoOMGvuy0icCczSOpfeAB-Z8k7jqZODGyqL7wyFRWjym2KTagMFlCcxnT6IR17JIPwqJOVWSZpJkOBZD0yAuXxs74vJOZcp~siiCc4y8KwzrzTB~x4nSpLNOlsxfu72MpRyR6um1QSzhXy8vNowZ9rNXHDA0Jknml7kWvoTBmRVOfEWtWqneXtiCEyFoCw6~uwHwp9jENl7~lPFH5fWE6WGWFvM6uAxPm~b89B7Q8TX1Wq93Udsh337xEF0EfHcRrmCSisor9uQGWWqRlnj7hnkTNWRxwevkWDuGHSji2XdB~7sGILEKY26H1Mg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Segmented_point_process_models_for_work_system_safety_analysis","translated_slug":"","page_count":3,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[{"id":85714988,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85714988/thumbnails/1.jpg","file_name":"108391.pdf","download_url":"https://www.academia.edu/attachments/85714988/download_file?st=MTczMjgzMTk3Nyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Segmented_point_process_models_for_work.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85714988/108391-libre.pdf?1652063443=\u0026response-content-disposition=attachment%3B+filename%3DSegmented_point_process_models_for_work.pdf\u0026Expires=1732835577\u0026Signature=L-A~zYX8bdcVQBQNwly9zfnZ~OAdoOMGvuy0icCczSOpfeAB-Z8k7jqZODGyqL7wyFRWjym2KTagMFlCcxnT6IR17JIPwqJOVWSZpJkOBZD0yAuXxs74vJOZcp~siiCc4y8KwzrzTB~x4nSpLNOlsxfu72MpRyR6um1QSzhXy8vNowZ9rNXHDA0Jknml7kWvoTBmRVOfEWtWqneXtiCEyFoCw6~uwHwp9jENl7~lPFH5fWE6WGWFvM6uAxPm~b89B7Q8TX1Wq93Udsh337xEF0EfHcRrmCSisor9uQGWWqRlnj7hnkTNWRxwevkWDuGHSji2XdB~7sGILEKY26H1Mg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":472306,"name":"Workplace health and safety","url":"https://www.academia.edu/Documents/in/Workplace_health_and_safety"},{"id":603594,"name":"Safety Science","url":"https://www.academia.edu/Documents/in/Safety_Science"},{"id":2922956,"name":"Psychology and Cognitive Sciences","url":"https://www.academia.edu/Documents/in/Psychology_and_Cognitive_Sciences"},{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796328"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796328/Human_error_identification_and_risk_prioritization_in_overhead_crane_operations_using_HTA_SHERPA_and_fuzzy_VIKOR_method"><img alt="Research paper thumbnail of Human error identification and risk prioritization in overhead crane operations using HTA, SHERPA and fuzzy VIKOR method" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796328/Human_error_identification_and_risk_prioritization_in_overhead_crane_operations_using_HTA_SHERPA_and_fuzzy_VIKOR_method">Human error identification and risk prioritization in overhead crane operations using HTA, SHERPA and fuzzy VIKOR method</a></div><div class="wp-workCard_item"><span>Expert Systems with Applications</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Human error identification and subsequent prioritization are the foremost tasks involved...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT Human error identification and subsequent prioritization are the foremost tasks involved in HRA. In this study a methodology is developed for performing these tasks with an application to overhead crane operations. The application of the present methodology will help to understand how the risk associated with the human errors propagates through different hierarchy levels. The methodology provides a framework for quantifying the risk of different human errors using the experts’ subjective opinions only. The incorporation of fuzzy VIKOR technique enables us develop a ranking mechanism for the failure modes where the individual constituent components are non-commensurable in nature. The developed ranking mechanism helps the decision makers in optimal allocation of safety critical resources, used for risk mitigation purposes.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796328"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796328"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796328; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796328]").text(description); $(".js-view-count[data-work-id=78796328]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796328; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796328']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796328, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796328]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796328,"title":"Human error identification and risk prioritization in overhead crane operations using HTA, SHERPA and fuzzy VIKOR method","translated_title":"","metadata":{"abstract":"ABSTRACT Human error identification and subsequent prioritization are the foremost tasks involved in HRA. 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The main reasons for roof and side fall accidents in Indian underground coal mines are well d...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">... The main reasons for roof and side fall accidents in Indian underground coal mines are well documented by Kejriwal (2002) based on his survey of accidents in Indian mines for the last 100 years. ... This retrospective study also confirms the findings of Kejriwal (2002). ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796327"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796327"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796327; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796327]").text(description); $(".js-view-count[data-work-id=78796327]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796327; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796327']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796327, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796327]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796327,"title":"Development of a relative risk model for roof and side fall fatal accidents in underground coal mines in India","translated_title":"","metadata":{"abstract":"... 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796342"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796342/Root_Cause_Analysis_of_Incidents_Using_Text_Clustering_and_Classification_Algorithms"><img alt="Research paper thumbnail of Root Cause Analysis of Incidents Using Text Clustering and Classification Algorithms" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796342/Root_Cause_Analysis_of_Incidents_Using_Text_Clustering_and_Classification_Algorithms">Root Cause Analysis of Incidents Using Text Clustering and Classification Algorithms</a></div><div class="wp-workCard_item"><span>Proceedings of ICETIT 2019</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The purpose of this study is to cluster the injury narratives to extract the root causes behind t...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The purpose of this study is to cluster the injury narratives to extract the root causes behind the accidents. Analysis is done on incident data collected from the database of an integrated steel plant. Key terms generated from the clustering of incident scenario help us in finding root causes of that particular incident. This study also proposed specific measures to the management that would improve the safety performance. This study uses text document clustering to discover the hidden factors and causes behind the incidents. Understanding previous accidents is necessary to avoid future accidents. However, for companies, management of large accident databases, and accurately classifying accident narratives are very challenging issues. Therefore, the aim of this study is to accurately classify accident reports using text classification approaches and evaluate their usefulness. The study used two machine learning (ML) algorithms, namely random forest (RF), and support vector machine (SVM) and found that SVM performed best in classifying the accident narratives. Further, SVM was experimented with different tokenization of the preprocessed narratives to get more reliable results.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796342"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796342"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796342; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796342]").text(description); $(".js-view-count[data-work-id=78796342]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796342; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796342']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796342, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796342]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796342,"title":"Root Cause Analysis of Incidents Using Text Clustering and Classification Algorithms","translated_title":"","metadata":{"abstract":"The purpose of this study is to cluster the injury narratives to extract the root causes behind the accidents. 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Further, SVM was experimented with different tokenization of the preprocessed narratives to get more reliable results.","publisher":"Springer International Publishing","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Proceedings of ICETIT 2019"},"translated_abstract":"The purpose of this study is to cluster the injury narratives to extract the root causes behind the accidents. Analysis is done on incident data collected from the database of an integrated steel plant. Key terms generated from the clustering of incident scenario help us in finding root causes of that particular incident. This study also proposed specific measures to the management that would improve the safety performance. This study uses text document clustering to discover the hidden factors and causes behind the incidents. Understanding previous accidents is necessary to avoid future accidents. However, for companies, management of large accident databases, and accurately classifying accident narratives are very challenging issues. Therefore, the aim of this study is to accurately classify accident reports using text classification approaches and evaluate their usefulness. The study used two machine learning (ML) algorithms, namely random forest (RF), and support vector machine (SVM) and found that SVM performed best in classifying the accident narratives. Further, SVM was experimented with different tokenization of the preprocessed narratives to get more reliable results.","internal_url":"https://www.academia.edu/78796342/Root_Cause_Analysis_of_Incidents_Using_Text_Clustering_and_Classification_Algorithms","translated_internal_url":"","created_at":"2022-05-08T19:18:24.717-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Root_Cause_Analysis_of_Incidents_Using_Text_Clustering_and_Classification_Algorithms","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":131237,"name":"Cluster Analysis","url":"https://www.academia.edu/Documents/in/Cluster_Analysis"}],"urls":[{"id":20339069,"url":"http://link.springer.com/content/pdf/10.1007/978-3-030-30577-2_63"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796341"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796341/Prediction_of_Occupational_Incidents_Using_Proactive_and_Reactive_Data_A_Data_Mining_Approach"><img alt="Research paper thumbnail of Prediction of Occupational Incidents Using Proactive and Reactive Data: A Data Mining Approach" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796341/Prediction_of_Occupational_Incidents_Using_Proactive_and_Reactive_Data_A_Data_Mining_Approach">Prediction of Occupational Incidents Using Proactive and Reactive Data: A Data Mining Approach</a></div><div class="wp-workCard_item"><span>Industrial Safety Management</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Prediction of occupational incidents is an important task for any industry. To do this, reactive ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Prediction of occupational incidents is an important task for any industry. To do this, reactive data has been used by most of the previous studies in this domain. As an extension of the existing works, the present study has used the underused proactive data coupled with reactive data to establish the predictive models so that the information inherent in both data sets could be better utilized. The main aim of the study is to predict the incident outcomes using mixed data set comprising reactive and proactive data together. Two decision tree classifiers, i.e. classification and regression tree (CART) and C5.0, have been implemented with tenfold cross validation. Furthermore, the ensemble technique, namely adaptive boosting has been implemented to increase the classification accuracy. Results show that boosted C5.0 produces higher accuracy than others for the prediction task. Furthermore, the rules obtained produce the insight of the incidents. The limitation of the present study includes the use of less amount of data and the requirement of experts’ domain knowledge for a large span of time. Future scope of the study includes the proper feature selection for preparation of the mixed data set and building the better classification algorithm for better prediction of occurrence of accidents. The present work sets out the potential use of both types of data sources together.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796341"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796341"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796341; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796341]").text(description); $(".js-view-count[data-work-id=78796341]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796341; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796341']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796341, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796341]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796341,"title":"Prediction of Occupational Incidents Using Proactive and Reactive Data: A Data Mining Approach","translated_title":"","metadata":{"abstract":"Prediction of occupational incidents is an important task for any industry. To do this, reactive data has been used by most of the previous studies in this domain. As an extension of the existing works, the present study has used the underused proactive data coupled with reactive data to establish the predictive models so that the information inherent in both data sets could be better utilized. The main aim of the study is to predict the incident outcomes using mixed data set comprising reactive and proactive data together. Two decision tree classifiers, i.e. classification and regression tree (CART) and C5.0, have been implemented with tenfold cross validation. Furthermore, the ensemble technique, namely adaptive boosting has been implemented to increase the classification accuracy. Results show that boosted C5.0 produces higher accuracy than others for the prediction task. Furthermore, the rules obtained produce the insight of the incidents. The limitation of the present study includes the use of less amount of data and the requirement of experts’ domain knowledge for a large span of time. Future scope of the study includes the proper feature selection for preparation of the mixed data set and building the better classification algorithm for better prediction of occurrence of accidents. The present work sets out the potential use of both types of data sources together.","publisher":"Springer Singapore","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Industrial Safety Management"},"translated_abstract":"Prediction of occupational incidents is an important task for any industry. To do this, reactive data has been used by most of the previous studies in this domain. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796340"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796340/Text_clustering_based_deep_neural_network_for_prediction_of_occupational_accident_risk_A_case_study"><img alt="Research paper thumbnail of Text-clustering based deep neural network for prediction of occupational accident risk: A case study" class="work-thumbnail" src="https://attachments.academia-assets.com/85714990/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796340/Text_clustering_based_deep_neural_network_for_prediction_of_occupational_accident_risk_A_case_study">Text-clustering based deep neural network for prediction of occupational accident risk: A case study</a></div><div class="wp-workCard_item"><span>2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5abd34df3a7ec87ed788559ffd68299a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85714990,"asset_id":78796340,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85714990/download_file?st=MTczMjgzMTk3OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796340"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796340"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796340; 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Unstructured texts, i.e., incident narratives often remain unutilized or under-utilized. Besides the explicit attributes present in the dataset, there exist a large number of hidden attributes in different forms, which are hardly explored by the traditional machine learning algorithms. Therefore, we propose a methodology that utilizes both text-based clustering, namely Expectation Maximization (EM) algorithm for unstructured text analysis and deep neural network (DNN) for prediction of accident risk using the accident data collected from a steel plant in India. EM-based DNN shows the maximum accuracy equal to 83.59% in the prediction of risk while compared to other algorithms, namely single DNN, support vector machine, and random forest. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796339"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796339/COVID_19_Outbreak_A_Data_driven_Optimization_Model_for_Allocation_of_Patients"><img alt="Research paper thumbnail of COVID-19 Outbreak: A Data-driven Optimization Model for Allocation of Patients" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796339/COVID_19_Outbreak_A_Data_driven_Optimization_Model_for_Allocation_of_Patients">COVID-19 Outbreak: A Data-driven Optimization Model for Allocation of Patients</a></div><div class="wp-workCard_item"><span>Computers & Industrial Engineering</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">COVID-19 is an unprecedented pandemic that puts the entire world at stake and the healthcare syst...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">COVID-19 is an unprecedented pandemic that puts the entire world at stake and the healthcare systems across the globe have faced pressing challenges. The number of COVID-19 patients increases rapidly every day. The hospitals across many countries are starving to provide adequate service to the patients due to the shortage of resources and as a consequence, patients do not get admitted to hospitals on time, which in turn creates panic and might contribute to the spread of the pandemic. Under this resource constraint situation, this study proposes a data-driven optimization model for patient allocation in hospitals. First, a compartmental model is developed for characterizing the spread of the COVID-19 virus. Then, Pareto analysis is carried out to identify the most COVID-affected cities. An optimization model is then developed for optimal patient allocation in hospitals in different cities. Finally, a sensitivity analysis is also conducted to investigate the robustness of our decision model. Using published data for Indian cities, obtained from different websites, the proposed methodology has been validated. Experimental results reveal that the proposed model offers some efficient strategies for optimal allocation of patients. A total of ten cities are identified as the most affected. Besides, four factors, namely cooperation, distances between cities, number of patients, and bed capacity per city emerge as important determinants.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796339"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796339"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796339; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796339]").text(description); $(".js-view-count[data-work-id=78796339]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796339; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796339']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796339, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796339]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796339,"title":"COVID-19 Outbreak: A Data-driven Optimization Model for Allocation of Patients","translated_title":"","metadata":{"abstract":"COVID-19 is an unprecedented pandemic that puts the entire world at stake and the healthcare systems across the globe have faced pressing challenges. The number of COVID-19 patients increases rapidly every day. The hospitals across many countries are starving to provide adequate service to the patients due to the shortage of resources and as a consequence, patients do not get admitted to hospitals on time, which in turn creates panic and might contribute to the spread of the pandemic. Under this resource constraint situation, this study proposes a data-driven optimization model for patient allocation in hospitals. First, a compartmental model is developed for characterizing the spread of the COVID-19 virus. Then, Pareto analysis is carried out to identify the most COVID-affected cities. An optimization model is then developed for optimal patient allocation in hospitals in different cities. Finally, a sensitivity analysis is also conducted to investigate the robustness of our decision model. Using published data for Indian cities, obtained from different websites, the proposed methodology has been validated. Experimental results reveal that the proposed model offers some efficient strategies for optimal allocation of patients. A total of ten cities are identified as the most affected. Besides, four factors, namely cooperation, distances between cities, number of patients, and bed capacity per city emerge as important determinants.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Computers \u0026 Industrial Engineering"},"translated_abstract":"COVID-19 is an unprecedented pandemic that puts the entire world at stake and the healthcare systems across the globe have faced pressing challenges. The number of COVID-19 patients increases rapidly every day. The hospitals across many countries are starving to provide adequate service to the patients due to the shortage of resources and as a consequence, patients do not get admitted to hospitals on time, which in turn creates panic and might contribute to the spread of the pandemic. Under this resource constraint situation, this study proposes a data-driven optimization model for patient allocation in hospitals. First, a compartmental model is developed for characterizing the spread of the COVID-19 virus. Then, Pareto analysis is carried out to identify the most COVID-affected cities. An optimization model is then developed for optimal patient allocation in hospitals in different cities. Finally, a sensitivity analysis is also conducted to investigate the robustness of our decision model. Using published data for Indian cities, obtained from different websites, the proposed methodology has been validated. Experimental results reveal that the proposed model offers some efficient strategies for optimal allocation of patients. A total of ten cities are identified as the most affected. Besides, four factors, namely cooperation, distances between cities, number of patients, and bed capacity per city emerge as important determinants.","internal_url":"https://www.academia.edu/78796339/COVID_19_Outbreak_A_Data_driven_Optimization_Model_for_Allocation_of_Patients","translated_internal_url":"","created_at":"2022-05-08T19:18:24.350-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"COVID_19_Outbreak_A_Data_driven_Optimization_Model_for_Allocation_of_Patients","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences"}],"urls":[{"id":20339066,"url":"https://api.elsevier.com/content/article/PII:S0360835221005799?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796338"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/78796338/Machine_learning_in_occupational_accident_analysis_A_review_using_science_mapping_approach_with_citation_network_analysis"><img alt="Research paper thumbnail of Machine learning in occupational accident analysis: A review using science mapping approach with citation network analysis" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/78796338/Machine_learning_in_occupational_accident_analysis_A_review_using_science_mapping_approach_with_citation_network_analysis">Machine learning in occupational accident analysis: A review using science mapping approach with citation network analysis</a></div><div class="wp-workCard_item"><span>Safety Science</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract The present study reviews the publications that examine the application of machine learn...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract The present study reviews the publications that examine the application of machine learning (ML) approaches in occupational accident analysis. The review process includes four phases of analysis, namely bibliometric search, descriptive analysis, scientometric analysis, and citation network analysis (CNA). In the bibliometric search, a total of 232 articles are systematically screened out from 1995 to 2019 (up to May). Then, descriptive analysis and scientometric analysis are carried out to find the influences of journals, authors, authors’ keywords, articles/documents, and countries/regions in developing the domain. Thereafter, CNA is carried out to classify the publications according to the research themes and methods used. From this extensive review, several key findings are obtained in the application of ML approaches in occupational accident analysis. USA, China, and Taiwan are the leading countries/regions in publishing articles. The four major research domains are (i) prediction of incident outcomes, (ii) extraction of rule based patterns, (iii) prediction of injury risk, and (iv) prediction of injury severity. Then, a taxonomy of the ML algorithms used is developed. Finally, research gaps and safety issues are highlighted and the scope for future is discussed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796338"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796338"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796338; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796338]").text(description); $(".js-view-count[data-work-id=78796338]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796338; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796338']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796338, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796338]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796338,"title":"Machine learning in occupational accident analysis: A review using science mapping approach with citation network analysis","translated_title":"","metadata":{"abstract":"Abstract The present study reviews the publications that examine the application of machine learning (ML) approaches in occupational accident analysis. 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Then, a taxonomy of the ML algorithms used is developed. Finally, research gaps and safety issues are highlighted and the scope for future is discussed.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Safety Science"},"translated_abstract":"Abstract The present study reviews the publications that examine the application of machine learning (ML) approaches in occupational accident analysis. The review process includes four phases of analysis, namely bibliometric search, descriptive analysis, scientometric analysis, and citation network analysis (CNA). In the bibliometric search, a total of 232 articles are systematically screened out from 1995 to 2019 (up to May). Then, descriptive analysis and scientometric analysis are carried out to find the influences of journals, authors, authors’ keywords, articles/documents, and countries/regions in developing the domain. Thereafter, CNA is carried out to classify the publications according to the research themes and methods used. From this extensive review, several key findings are obtained in the application of ML approaches in occupational accident analysis. USA, China, and Taiwan are the leading countries/regions in publishing articles. The four major research domains are (i) prediction of incident outcomes, (ii) extraction of rule based patterns, (iii) prediction of injury risk, and (iv) prediction of injury severity. Then, a taxonomy of the ML algorithms used is developed. Finally, research gaps and safety issues are highlighted and the scope for future is discussed.","internal_url":"https://www.academia.edu/78796338/Machine_learning_in_occupational_accident_analysis_A_review_using_science_mapping_approach_with_citation_network_analysis","translated_internal_url":"","created_at":"2022-05-08T19:18:24.212-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Machine_learning_in_occupational_accident_analysis_A_review_using_science_mapping_approach_with_citation_network_analysis","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":603594,"name":"Safety Science","url":"https://www.academia.edu/Documents/in/Safety_Science"},{"id":2922956,"name":"Psychology and Cognitive Sciences","url":"https://www.academia.edu/Documents/in/Psychology_and_Cognitive_Sciences"},{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences"}],"urls":[{"id":20339065,"url":"https://api.elsevier.com/content/article/PII:S0925753520302976?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796337"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/78796337/A_comprehensive_methodology_for_quantification_of_Bow_tie_under_type_II_fuzzy_data"><img alt="Research paper thumbnail of A comprehensive methodology for quantification of Bow-tie under type II fuzzy data" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/78796337/A_comprehensive_methodology_for_quantification_of_Bow_tie_under_type_II_fuzzy_data">A comprehensive methodology for quantification of Bow-tie under type II fuzzy data</a></div><div class="wp-workCard_item"><span>Applied Soft Computing</span><span>, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796337"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796337"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796337; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796337]").text(description); $(".js-view-count[data-work-id=78796337]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796337; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796337']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796337, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796337]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796337,"title":"A comprehensive methodology for quantification of Bow-tie under type II fuzzy data","translated_title":"","metadata":{"publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Applied Soft Computing"},"translated_abstract":null,"internal_url":"https://www.academia.edu/78796337/A_comprehensive_methodology_for_quantification_of_Bow_tie_under_type_II_fuzzy_data","translated_internal_url":"","created_at":"2022-05-08T19:18:24.076-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_comprehensive_methodology_for_quantification_of_Bow_tie_under_type_II_fuzzy_data","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems"},{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":897823,"name":"Elsevier","url":"https://www.academia.edu/Documents/in/Elsevier"}],"urls":[{"id":20339064,"url":"https://api.elsevier.com/content/article/PII:S1568494621000715?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796336"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796336/An_innovative_integrated_modelling_of_safety_data_using_multiple_correspondence_analysis_and_fuzzy_discretization_techniques"><img alt="Research paper thumbnail of An innovative integrated modelling of safety data using multiple correspondence analysis and fuzzy discretization techniques" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796336/An_innovative_integrated_modelling_of_safety_data_using_multiple_correspondence_analysis_and_fuzzy_discretization_techniques">An innovative integrated modelling of safety data using multiple correspondence analysis and fuzzy discretization techniques</a></div><div class="wp-workCard_item"><span>Safety Science</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract In this study, we have proposed an innovative integrated methodology to handle a mix of ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract In this study, we have proposed an innovative integrated methodology to handle a mix of categorical and numeric safety data. We have augmented the traditional multiple correspondence analysis (MCA) through the use of fuzzy discretization approach, t-SNE technique and fuzzy c-means clustering. The fuzzy discretization approach transforms the continuous variables to categorical variables to make them analyzable using MCA. R2-profile is adopted to obtain the best number of hidden dimensions representing the maximum categorical information. Then, t-SNE technique is used to represent the high dimensional categorical information in a 2D map to visualize the significant categorical associations. Then, fuzzy c-means clustering (FCM) is used to group the categories in different clusters based on their membership degree. To determine the optimal number of clusters, cluster validity indices are used. Davies-Bouldin (DB) Index, Dunn’s (DU) Index and Silhouette (SW) coefficients are used to determine the quality of clustering solutions. The proposed methodology is tested using electric overhead traveling (EOT) crane related near-miss incidents and found that our approach is effective. From managerial implication point of view, several safety rules are generated and subsequent safety countermeasures are proposed. Further, the results obtained through FCM is compared with K-means (KM) algorithm and unsupervised fuzzy c-means clustering (UPFCM). FCM outperforms KM and UPFCM on the basis of quality of solutions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796336"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796336"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796336; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796336]").text(description); $(".js-view-count[data-work-id=78796336]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796336; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796336']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796336, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796336]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796336,"title":"An innovative integrated modelling of safety data using multiple correspondence analysis and fuzzy discretization techniques","translated_title":"","metadata":{"abstract":"Abstract In this study, we have proposed an innovative integrated methodology to handle a mix of categorical and numeric safety data. We have augmented the traditional multiple correspondence analysis (MCA) through the use of fuzzy discretization approach, t-SNE technique and fuzzy c-means clustering. The fuzzy discretization approach transforms the continuous variables to categorical variables to make them analyzable using MCA. R2-profile is adopted to obtain the best number of hidden dimensions representing the maximum categorical information. Then, t-SNE technique is used to represent the high dimensional categorical information in a 2D map to visualize the significant categorical associations. Then, fuzzy c-means clustering (FCM) is used to group the categories in different clusters based on their membership degree. To determine the optimal number of clusters, cluster validity indices are used. Davies-Bouldin (DB) Index, Dunn’s (DU) Index and Silhouette (SW) coefficients are used to determine the quality of clustering solutions. The proposed methodology is tested using electric overhead traveling (EOT) crane related near-miss incidents and found that our approach is effective. From managerial implication point of view, several safety rules are generated and subsequent safety countermeasures are proposed. Further, the results obtained through FCM is compared with K-means (KM) algorithm and unsupervised fuzzy c-means clustering (UPFCM). FCM outperforms KM and UPFCM on the basis of quality of solutions.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Safety Science"},"translated_abstract":"Abstract In this study, we have proposed an innovative integrated methodology to handle a mix of categorical and numeric safety data. We have augmented the traditional multiple correspondence analysis (MCA) through the use of fuzzy discretization approach, t-SNE technique and fuzzy c-means clustering. The fuzzy discretization approach transforms the continuous variables to categorical variables to make them analyzable using MCA. R2-profile is adopted to obtain the best number of hidden dimensions representing the maximum categorical information. Then, t-SNE technique is used to represent the high dimensional categorical information in a 2D map to visualize the significant categorical associations. Then, fuzzy c-means clustering (FCM) is used to group the categories in different clusters based on their membership degree. To determine the optimal number of clusters, cluster validity indices are used. Davies-Bouldin (DB) Index, Dunn’s (DU) Index and Silhouette (SW) coefficients are used to determine the quality of clustering solutions. The proposed methodology is tested using electric overhead traveling (EOT) crane related near-miss incidents and found that our approach is effective. From managerial implication point of view, several safety rules are generated and subsequent safety countermeasures are proposed. Further, the results obtained through FCM is compared with K-means (KM) algorithm and unsupervised fuzzy c-means clustering (UPFCM). FCM outperforms KM and UPFCM on the basis of quality of solutions.","internal_url":"https://www.academia.edu/78796336/An_innovative_integrated_modelling_of_safety_data_using_multiple_correspondence_analysis_and_fuzzy_discretization_techniques","translated_internal_url":"","created_at":"2022-05-08T19:18:23.951-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"An_innovative_integrated_modelling_of_safety_data_using_multiple_correspondence_analysis_and_fuzzy_discretization_techniques","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":603594,"name":"Safety Science","url":"https://www.academia.edu/Documents/in/Safety_Science"},{"id":897823,"name":"Elsevier","url":"https://www.academia.edu/Documents/in/Elsevier"},{"id":2922956,"name":"Psychology and Cognitive Sciences","url":"https://www.academia.edu/Documents/in/Psychology_and_Cognitive_Sciences"},{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences"}],"urls":[{"id":20339063,"url":"https://api.elsevier.com/content/article/PII:S0925753520302253?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796335"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796335/A_novel_data_mining_approach_for_analysis_of_accident_paths_and_performance_assessment_of_risk_control_systems"><img alt="Research paper thumbnail of A novel data mining approach for analysis of accident paths and performance assessment of risk control systems" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796335/A_novel_data_mining_approach_for_analysis_of_accident_paths_and_performance_assessment_of_risk_control_systems">A novel data mining approach for analysis of accident paths and performance assessment of risk control systems</a></div><div class="wp-workCard_item"><span>Reliability Engineering & System Safety</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract The data mining researches to facilitate the process of safety management is fairly new,...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract The data mining researches to facilitate the process of safety management is fairly new, compared to other industrial management domains. The implementation of appropriate, effective, and safe risk control systems (RCSs) is vital to ensure zero-accident and zero-harm vision of industrial work-systems. In this work, we propose a data mining based tool to analyze accident paths from incident data and assess the performance of RCSs. Our work upgrades the existing pattern analysis methods through three new types of analyses (i) temporal frequent itemset generation (T-FIG) for studying the time effect on patterns, (ii) elevated severity itemset generation (ESIG) for examining the risk reduction due to RCSs, and (iii) High impact itemset generation (High_impact_IG) to identify accident paths with high risk. T-FIG and ESIG assist in performance assessment of preventive and mitigating RCSs, respectively. The results from each of the analyses are compared and eight types of inferences regarding the performance of RCSs are drawn. The proposed methodology is applied to 612 incident records reported during steel making process in a steel manufacturing plant. It was found that there are four accident paths which have ineffective preventive and mitigating RCSs, have high risk and are probable to recur in future. Two among four of these paths include hot metal/steel/slag as the hazardous element and three of them are due to damaged/degraded/poorly maintained equipment. Moreover, the case study also demonstrates that proposed data mining approach is an effective and easy to use tool for performance assessment of RCSs and accident path analysis.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796335"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796335"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796335; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796335]").text(description); $(".js-view-count[data-work-id=78796335]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796335; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796335']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796335, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796335]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796335,"title":"A novel data mining approach for analysis of accident paths and performance assessment of risk control systems","translated_title":"","metadata":{"abstract":"Abstract The data mining researches to facilitate the process of safety management is fairly new, compared to other industrial management domains. The implementation of appropriate, effective, and safe risk control systems (RCSs) is vital to ensure zero-accident and zero-harm vision of industrial work-systems. In this work, we propose a data mining based tool to analyze accident paths from incident data and assess the performance of RCSs. Our work upgrades the existing pattern analysis methods through three new types of analyses (i) temporal frequent itemset generation (T-FIG) for studying the time effect on patterns, (ii) elevated severity itemset generation (ESIG) for examining the risk reduction due to RCSs, and (iii) High impact itemset generation (High_impact_IG) to identify accident paths with high risk. T-FIG and ESIG assist in performance assessment of preventive and mitigating RCSs, respectively. The results from each of the analyses are compared and eight types of inferences regarding the performance of RCSs are drawn. The proposed methodology is applied to 612 incident records reported during steel making process in a steel manufacturing plant. It was found that there are four accident paths which have ineffective preventive and mitigating RCSs, have high risk and are probable to recur in future. Two among four of these paths include hot metal/steel/slag as the hazardous element and three of them are due to damaged/degraded/poorly maintained equipment. Moreover, the case study also demonstrates that proposed data mining approach is an effective and easy to use tool for performance assessment of RCSs and accident path analysis.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Reliability Engineering \u0026 System Safety"},"translated_abstract":"Abstract The data mining researches to facilitate the process of safety management is fairly new, compared to other industrial management domains. The implementation of appropriate, effective, and safe risk control systems (RCSs) is vital to ensure zero-accident and zero-harm vision of industrial work-systems. In this work, we propose a data mining based tool to analyze accident paths from incident data and assess the performance of RCSs. Our work upgrades the existing pattern analysis methods through three new types of analyses (i) temporal frequent itemset generation (T-FIG) for studying the time effect on patterns, (ii) elevated severity itemset generation (ESIG) for examining the risk reduction due to RCSs, and (iii) High impact itemset generation (High_impact_IG) to identify accident paths with high risk. T-FIG and ESIG assist in performance assessment of preventive and mitigating RCSs, respectively. The results from each of the analyses are compared and eight types of inferences regarding the performance of RCSs are drawn. The proposed methodology is applied to 612 incident records reported during steel making process in a steel manufacturing plant. It was found that there are four accident paths which have ineffective preventive and mitigating RCSs, have high risk and are probable to recur in future. Two among four of these paths include hot metal/steel/slag as the hazardous element and three of them are due to damaged/degraded/poorly maintained equipment. Moreover, the case study also demonstrates that proposed data mining approach is an effective and easy to use tool for performance assessment of RCSs and accident path analysis.","internal_url":"https://www.academia.edu/78796335/A_novel_data_mining_approach_for_analysis_of_accident_paths_and_performance_assessment_of_risk_control_systems","translated_internal_url":"","created_at":"2022-05-08T19:18:23.826-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_novel_data_mining_approach_for_analysis_of_accident_paths_and_performance_assessment_of_risk_control_systems","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences"},{"id":897823,"name":"Elsevier","url":"https://www.academia.edu/Documents/in/Elsevier"}],"urls":[{"id":20339062,"url":"https://api.elsevier.com/content/article/PII:S0951832020305421?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796334"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796334/Application_of_optimized_machine_learning_techniques_for_prediction_of_occupational_accidents"><img alt="Research paper thumbnail of Application of optimized machine learning techniques for prediction of occupational accidents" class="work-thumbnail" src="https://attachments.academia-assets.com/85714992/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796334/Application_of_optimized_machine_learning_techniques_for_prediction_of_occupational_accidents">Application of optimized machine learning techniques for prediction of occupational accidents</a></div><div class="wp-workCard_item"><span>Computers & Operations Research</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5b7d996a1a855f5dfc271ca92cc51397" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85714992,"asset_id":78796334,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85714992/download_file?st=MTczMjgzMTk3OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796334"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796334"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796334; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796334]").text(description); $(".js-view-count[data-work-id=78796334]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796334; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796334']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796334, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  Both categorical data and unstructured text data have been analysed.  Incident outcomes are predicted using GA and PSO optimized SVM and ANN approaches.  PSO-SVM outperforms other algorithms in terms of accuracy (i.e., 90.67%).  Nine useful rules are extracted using PSO-SVM based C5.0 algorithm.  Root causes of incidents have been identified.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Computers \u0026 Operations Research","grobid_abstract_attachment_id":85714992},"translated_abstract":null,"internal_url":"https://www.academia.edu/78796334/Application_of_optimized_machine_learning_techniques_for_prediction_of_occupational_accidents","translated_internal_url":"","created_at":"2022-05-08T19:18:23.703-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":85714992,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85714992/thumbnails/1.jpg","file_name":"j.cor.2018.02.02120220509-1-ni2wic.pdf","download_url":"https://www.academia.edu/attachments/85714992/download_file?st=MTczMjgzMTk3OCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Application_of_optimized_machine_learnin.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85714992/j.cor.2018.02.02120220509-1-ni2wic-libre.pdf?1652063457=\u0026response-content-disposition=attachment%3B+filename%3DApplication_of_optimized_machine_learnin.pdf\u0026Expires=1732801874\u0026Signature=LG2WolrjkZRW0QeW0vATdUyxg0xaIUvivqiQ708X1rcAAwszjHLs9YzILHej27zxZEXaSAuGkhj8~omZPtxT4psrXOnEwSE70ZglWZubHP-dqDGZYR6yHQWD0KI7PrCxrbpc0-ftRTIsn9-xNDCuRmexmcjscmt9CfrpnafsP95Jx3Wx5mwPD8g7stu1t2AfPGIQRsQMXu9nya-vuRNNuq3W8jvIySmg0nSsB-X1MzrX4q2AM71VsB9BU-eoGgqNpgDuMNLpc1RFfWmUMbC9tGryDEpu~MhqDYvKKcBAxZygNaRB~qYgDQA3nj75XLxtSLCRG0n1amakvBzmgnM8Fg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Application_of_optimized_machine_learning_techniques_for_prediction_of_occupational_accidents","translated_slug":"","page_count":38,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[{"id":85714992,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85714992/thumbnails/1.jpg","file_name":"j.cor.2018.02.02120220509-1-ni2wic.pdf","download_url":"https://www.academia.edu/attachments/85714992/download_file?st=MTczMjgzMTk3OCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Application_of_optimized_machine_learnin.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85714992/j.cor.2018.02.02120220509-1-ni2wic-libre.pdf?1652063457=\u0026response-content-disposition=attachment%3B+filename%3DApplication_of_optimized_machine_learnin.pdf\u0026Expires=1732801874\u0026Signature=LG2WolrjkZRW0QeW0vATdUyxg0xaIUvivqiQ708X1rcAAwszjHLs9YzILHej27zxZEXaSAuGkhj8~omZPtxT4psrXOnEwSE70ZglWZubHP-dqDGZYR6yHQWD0KI7PrCxrbpc0-ftRTIsn9-xNDCuRmexmcjscmt9CfrpnafsP95Jx3Wx5mwPD8g7stu1t2AfPGIQRsQMXu9nya-vuRNNuq3W8jvIySmg0nSsB-X1MzrX4q2AM71VsB9BU-eoGgqNpgDuMNLpc1RFfWmUMbC9tGryDEpu~MhqDYvKKcBAxZygNaRB~qYgDQA3nj75XLxtSLCRG0n1amakvBzmgnM8Fg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":4593,"name":"Occupational Health \u0026 Safety","url":"https://www.academia.edu/Documents/in/Occupational_Health_and_Safety"},{"id":43981,"name":"Optimization","url":"https://www.academia.edu/Documents/in/Optimization"},{"id":51531,"name":"Pergamon","url":"https://www.academia.edu/Documents/in/Pergamon"},{"id":63857,"name":"Categorical data analysis","url":"https://www.academia.edu/Documents/in/Categorical_data_analysis"},{"id":162271,"name":"Decision Tree","url":"https://www.academia.edu/Documents/in/Decision_Tree"},{"id":556845,"name":"Numerical Analysis and Computational Mathematics","url":"https://www.academia.edu/Documents/in/Numerical_Analysis_and_Computational_Mathematics"},{"id":3657206,"name":"Rule generation","url":"https://www.academia.edu/Documents/in/Rule_generation"}],"urls":[{"id":20339061,"url":"https://api.elsevier.com/content/article/PII:S0305054818300601?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796333"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796333/Reduction_of_life_cycle_costs_for_a_contemporary_helicopter_through_improvement_of_reliability_and_maintainability_parameters"><img alt="Research paper thumbnail of Reduction of life cycle costs for a contemporary helicopter through improvement of reliability and maintainability parameters" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796333/Reduction_of_life_cycle_costs_for_a_contemporary_helicopter_through_improvement_of_reliability_and_maintainability_parameters">Reduction of life cycle costs for a contemporary helicopter through improvement of reliability and maintainability parameters</a></div><div class="wp-workCard_item"><span>International Journal of Quality & Reliability Management</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose The modern helicopters are designed with maximum serviceability and long life expectancy ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose The modern helicopters are designed with maximum serviceability and long life expectancy to ensure minimum life cycle cost. The purpose of this paper is to present a framework to incorporate the customer requirements on reliability and maintainability (R&M) parameters into the design and development phase of a contemporary helicopter, and to discuss the way to capture operational data to establish and improve the R&M parameters to reduce life cycle cost. Design/methodology/approach From the analysis, it is established that the reliability and maintainability cost is the major contributor to the life cost. The significant reliability and maintainability parameters which influence R&M cost are identified from analysis. The operational and design data of a contemporary helicopter are collected, compiled and analyzed to establish and improve the reliability and maintainability parameters. Findings The process depicted in the paper is followed for a contemporary helicopter and su...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796333"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796333"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796333; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796333]").text(description); $(".js-view-count[data-work-id=78796333]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796333; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796333']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796333, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796333]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796333,"title":"Reduction of life cycle costs for a contemporary helicopter through improvement of reliability and maintainability parameters","translated_title":"","metadata":{"abstract":"Purpose The modern helicopters are designed with maximum serviceability and long life expectancy to ensure minimum life cycle cost. The purpose of this paper is to present a framework to incorporate the customer requirements on reliability and maintainability (R\u0026M) parameters into the design and development phase of a contemporary helicopter, and to discuss the way to capture operational data to establish and improve the R\u0026M parameters to reduce life cycle cost. Design/methodology/approach From the analysis, it is established that the reliability and maintainability cost is the major contributor to the life cost. The significant reliability and maintainability parameters which influence R\u0026M cost are identified from analysis. The operational and design data of a contemporary helicopter are collected, compiled and analyzed to establish and improve the reliability and maintainability parameters. Findings The process depicted in the paper is followed for a contemporary helicopter and su...","publisher":"Emerald","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"International Journal of Quality \u0026 Reliability Management"},"translated_abstract":"Purpose The modern helicopters are designed with maximum serviceability and long life expectancy to ensure minimum life cycle cost. The purpose of this paper is to present a framework to incorporate the customer requirements on reliability and maintainability (R\u0026M) parameters into the design and development phase of a contemporary helicopter, and to discuss the way to capture operational data to establish and improve the R\u0026M parameters to reduce life cycle cost. Design/methodology/approach From the analysis, it is established that the reliability and maintainability cost is the major contributor to the life cost. The significant reliability and maintainability parameters which influence R\u0026M cost are identified from analysis. The operational and design data of a contemporary helicopter are collected, compiled and analyzed to establish and improve the reliability and maintainability parameters. Findings The process depicted in the paper is followed for a contemporary helicopter and su...","internal_url":"https://www.academia.edu/78796333/Reduction_of_life_cycle_costs_for_a_contemporary_helicopter_through_improvement_of_reliability_and_maintainability_parameters","translated_internal_url":"","created_at":"2022-05-08T19:18:23.571-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Reduction_of_life_cycle_costs_for_a_contemporary_helicopter_through_improvement_of_reliability_and_maintainability_parameters","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":73149,"name":"Business and Management","url":"https://www.academia.edu/Documents/in/Business_and_Management"}],"urls":[{"id":20339060,"url":"https://www.emeraldinsight.com/doi/full-xml/10.1108/IJQRM-11-2016-0199"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796332"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796332/Decision_support_system_for_safety_improvement_An_approach_using_multiple_correspondence_analysis_t_SNE_algorithm_and_K_means_clustering"><img alt="Research paper thumbnail of Decision support system for safety improvement: An approach using multiple correspondence analysis, t-SNE algorithm and K-means clustering" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796332/Decision_support_system_for_safety_improvement_An_approach_using_multiple_correspondence_analysis_t_SNE_algorithm_and_K_means_clustering">Decision support system for safety improvement: An approach using multiple correspondence analysis, t-SNE algorithm and K-means clustering</a></div><div class="wp-workCard_item"><span>Computers & Industrial Engineering</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract An attempt has been made to develop a decision support system (DSS) for safety improveme...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract An attempt has been made to develop a decision support system (DSS) for safety improvement using a multi-step knowledge discovery process involving multiple correspondence analysis (MCA), t-SNE algorithm and K-means clustering. MCA is used for dimension reduction and perceptual mapping from categorical data. Usually, the first two dimensions are used for perceptual mapping if these two dimensions explain a significant percentage of variance. Otherwise, the traditional method of two dimensional mapping, leads to loss of important categorical information involved with other dimensions. Considering the above, a novel R2-profile approach, as an alternate to inertia based approach, is adopted to obtain the desired number of dimensions to be retained without loss of significant amount of information. t-SNE technique reduces the high dimensional data into two dimensional (2D) map, which provides the associations amongst different categories. K-means clustering grouped the 2D categories in homogenous clusters as per the similarities of the categories. A novel kernel category based chi-square distance method is proposed to identify sub-clusters within a cluster which subsequently provides useful rules for safety improvement. The methodology also provides a logical approach of dimension reduction in a form called ‘funnel diagram’. Finally, the DSS is applied to analysing near miss incidents occurred in electric overhead traveling (EOT) crane operations in a steel plant. Several safety rules are identified and safety interventions are proposed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796332"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796332"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796332; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796332]").text(description); $(".js-view-count[data-work-id=78796332]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796332; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796332']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796332, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796332]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796332,"title":"Decision support system for safety improvement: An approach using multiple correspondence analysis, t-SNE algorithm and K-means clustering","translated_title":"","metadata":{"abstract":"Abstract An attempt has been made to develop a decision support system (DSS) for safety improvement using a multi-step knowledge discovery process involving multiple correspondence analysis (MCA), t-SNE algorithm and K-means clustering. MCA is used for dimension reduction and perceptual mapping from categorical data. Usually, the first two dimensions are used for perceptual mapping if these two dimensions explain a significant percentage of variance. Otherwise, the traditional method of two dimensional mapping, leads to loss of important categorical information involved with other dimensions. Considering the above, a novel R2-profile approach, as an alternate to inertia based approach, is adopted to obtain the desired number of dimensions to be retained without loss of significant amount of information. t-SNE technique reduces the high dimensional data into two dimensional (2D) map, which provides the associations amongst different categories. K-means clustering grouped the 2D categories in homogenous clusters as per the similarities of the categories. A novel kernel category based chi-square distance method is proposed to identify sub-clusters within a cluster which subsequently provides useful rules for safety improvement. The methodology also provides a logical approach of dimension reduction in a form called ‘funnel diagram’. Finally, the DSS is applied to analysing near miss incidents occurred in electric overhead traveling (EOT) crane operations in a steel plant. Several safety rules are identified and safety interventions are proposed.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Computers \u0026 Industrial Engineering"},"translated_abstract":"Abstract An attempt has been made to develop a decision support system (DSS) for safety improvement using a multi-step knowledge discovery process involving multiple correspondence analysis (MCA), t-SNE algorithm and K-means clustering. MCA is used for dimension reduction and perceptual mapping from categorical data. Usually, the first two dimensions are used for perceptual mapping if these two dimensions explain a significant percentage of variance. Otherwise, the traditional method of two dimensional mapping, leads to loss of important categorical information involved with other dimensions. Considering the above, a novel R2-profile approach, as an alternate to inertia based approach, is adopted to obtain the desired number of dimensions to be retained without loss of significant amount of information. t-SNE technique reduces the high dimensional data into two dimensional (2D) map, which provides the associations amongst different categories. K-means clustering grouped the 2D categories in homogenous clusters as per the similarities of the categories. A novel kernel category based chi-square distance method is proposed to identify sub-clusters within a cluster which subsequently provides useful rules for safety improvement. The methodology also provides a logical approach of dimension reduction in a form called ‘funnel diagram’. Finally, the DSS is applied to analysing near miss incidents occurred in electric overhead traveling (EOT) crane operations in a steel plant. Several safety rules are identified and safety interventions are proposed.","internal_url":"https://www.academia.edu/78796332/Decision_support_system_for_safety_improvement_An_approach_using_multiple_correspondence_analysis_t_SNE_algorithm_and_K_means_clustering","translated_internal_url":"","created_at":"2022-05-08T19:18:23.446-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Decision_support_system_for_safety_improvement_An_approach_using_multiple_correspondence_analysis_t_SNE_algorithm_and_K_means_clustering","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences"},{"id":131237,"name":"Cluster Analysis","url":"https://www.academia.edu/Documents/in/Cluster_Analysis"}],"urls":[{"id":20339059,"url":"https://api.elsevier.com/content/article/PII:S0360835218306478?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796331"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796331/Text_document_clustering_based_cause_and_effect_analysis_methodology_for_steel_plant_incident_data"><img alt="Research paper thumbnail of Text-document clustering-based cause and effect analysis methodology for steel plant incident data" class="work-thumbnail" src="https://attachments.academia-assets.com/85714991/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796331/Text_document_clustering_based_cause_and_effect_analysis_methodology_for_steel_plant_incident_data">Text-document clustering-based cause and effect analysis methodology for steel plant incident data</a></div><div class="wp-workCard_item"><span>International Journal of Injury Control and Safety Promotion</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="63ec93110bc1b7b979fe00d318fa1b3e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85714991,"asset_id":78796331,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85714991/download_file?st=MTczMjgzMTk3OCw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTk3Nyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796331"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796331"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796331; 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A cause-effect diagram is usually prepared by using experts' knowledge which may fail to capture all the causes present at a workplace. On the other hand, the description of incidents provided by the workers in the form of incident reports is typically a rich data source and can be utilized to explore the causes and sub-causes of incidents. In this study, data were collected from an integrated steel plant. The text data were analysed using singular value decomposition (SVD) and expectation-maximization (EM) algorithm. Results suggest that text-document clustering can be used as a feasible method for exploring the hidden factors and trends from the description of incidents occurred at workplaces. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796330"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/78796330/An_Application_of_Logit_Model_to_Injury_Experience_Data"><img alt="Research paper thumbnail of An Application of Logit Model to Injury Experience Data" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/78796330/An_Application_of_Logit_Model_to_Injury_Experience_Data">An Application of Logit Model to Injury Experience Data</a></div><div class="wp-workCard_item"><span>Mineral Resources Engineering</span><span>, 1999</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The influence of various casual factors on the severity of injury was examined using the 4-year i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The influence of various casual factors on the severity of injury was examined using the 4-year injury data from a group of underground coal mines. Although there are many factors that could affect the injury severity: miners&#39; age, experience, occupation, injury-location, shift-time and specific-mine were chosen as the affecting variables. The logit model was applied for this analysis. The bivariate analysis was conducted to separately study the effect of each of the factors on the severity of injury and to estimate the odds of severe injuries. For the case study mines, it was concluded that the injury severity varies by age, occupation, injury-location, shift-time and specific-mine. However, experience in general does not have any significant effect on it.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796330"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796330"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796330; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796330]").text(description); $(".js-view-count[data-work-id=78796330]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796330; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796330']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796330, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796330]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796330,"title":"An Application of Logit Model to Injury Experience Data","translated_title":"","metadata":{"abstract":"The influence of various casual factors on the severity of injury was examined using the 4-year injury data from a group of underground coal mines. Although there are many factors that could affect the injury severity: miners\u0026#39; age, experience, occupation, injury-location, shift-time and specific-mine were chosen as the affecting variables. The logit model was applied for this analysis. The bivariate analysis was conducted to separately study the effect of each of the factors on the severity of injury and to estimate the odds of severe injuries. For the case study mines, it was concluded that the injury severity varies by age, occupation, injury-location, shift-time and specific-mine. However, experience in general does not have any significant effect on it.","publisher":"World Scientific Pub Co Pte Lt","publication_date":{"day":null,"month":null,"year":1999,"errors":{}},"publication_name":"Mineral Resources Engineering"},"translated_abstract":"The influence of various casual factors on the severity of injury was examined using the 4-year injury data from a group of underground coal mines. Although there are many factors that could affect the injury severity: miners\u0026#39; age, experience, occupation, injury-location, shift-time and specific-mine were chosen as the affecting variables. The logit model was applied for this analysis. The bivariate analysis was conducted to separately study the effect of each of the factors on the severity of injury and to estimate the odds of severe injuries. For the case study mines, it was concluded that the injury severity varies by age, occupation, injury-location, shift-time and specific-mine. However, experience in general does not have any significant effect on it.","internal_url":"https://www.academia.edu/78796330/An_Application_of_Logit_Model_to_Injury_Experience_Data","translated_internal_url":"","created_at":"2022-05-08T19:18:23.210-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"An_Application_of_Logit_Model_to_Injury_Experience_Data","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":146671,"name":"Mineral Resources","url":"https://www.academia.edu/Documents/in/Mineral_Resources"}],"urls":[{"id":20339057,"url":"http://www.worldscientific.com/doi/pdf/10.1142/S0950609899000244"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796329"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796329/Segmented_point_process_models_for_work_system_safety_analysis"><img alt="Research paper thumbnail of Segmented point process models for work system safety analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/85714988/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796329/Segmented_point_process_models_for_work_system_safety_analysis">Segmented point process models for work system safety analysis</a></div><div class="wp-workCard_item"><span>Safety Science</span><span>, 2017</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e1befc2d40b59a11d64a6f76400894ec" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85714988,"asset_id":78796329,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85714988/download_file?st=MTczMjgzMTk3OCw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTk3Nyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796329"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796329"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796329; 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The findings of the case study application showed that the injury occurrences data fit the models for (i) 'all incidents' and 'first aid' cases with one change point at 458 days, (ii) 'near-miss' case with one change point at 441 days, and (iii) 'minor' injury case with two-change points at 11 days and 375 days, respectively.","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Safety Science","grobid_abstract_attachment_id":85714988},"translated_abstract":null,"internal_url":"https://www.academia.edu/78796329/Segmented_point_process_models_for_work_system_safety_analysis","translated_internal_url":"","created_at":"2022-05-08T19:18:23.042-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":85714988,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85714988/thumbnails/1.jpg","file_name":"108391.pdf","download_url":"https://www.academia.edu/attachments/85714988/download_file?st=MTczMjgzMTk3OCw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTk3Nyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Segmented_point_process_models_for_work.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85714988/108391-libre.pdf?1652063443=\u0026response-content-disposition=attachment%3B+filename%3DSegmented_point_process_models_for_work.pdf\u0026Expires=1732835577\u0026Signature=L-A~zYX8bdcVQBQNwly9zfnZ~OAdoOMGvuy0icCczSOpfeAB-Z8k7jqZODGyqL7wyFRWjym2KTagMFlCcxnT6IR17JIPwqJOVWSZpJkOBZD0yAuXxs74vJOZcp~siiCc4y8KwzrzTB~x4nSpLNOlsxfu72MpRyR6um1QSzhXy8vNowZ9rNXHDA0Jknml7kWvoTBmRVOfEWtWqneXtiCEyFoCw6~uwHwp9jENl7~lPFH5fWE6WGWFvM6uAxPm~b89B7Q8TX1Wq93Udsh337xEF0EfHcRrmCSisor9uQGWWqRlnj7hnkTNWRxwevkWDuGHSji2XdB~7sGILEKY26H1Mg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Segmented_point_process_models_for_work_system_safety_analysis","translated_slug":"","page_count":3,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[{"id":85714988,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85714988/thumbnails/1.jpg","file_name":"108391.pdf","download_url":"https://www.academia.edu/attachments/85714988/download_file?st=MTczMjgzMTk3OCw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTk3Nyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Segmented_point_process_models_for_work.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85714988/108391-libre.pdf?1652063443=\u0026response-content-disposition=attachment%3B+filename%3DSegmented_point_process_models_for_work.pdf\u0026Expires=1732835577\u0026Signature=L-A~zYX8bdcVQBQNwly9zfnZ~OAdoOMGvuy0icCczSOpfeAB-Z8k7jqZODGyqL7wyFRWjym2KTagMFlCcxnT6IR17JIPwqJOVWSZpJkOBZD0yAuXxs74vJOZcp~siiCc4y8KwzrzTB~x4nSpLNOlsxfu72MpRyR6um1QSzhXy8vNowZ9rNXHDA0Jknml7kWvoTBmRVOfEWtWqneXtiCEyFoCw6~uwHwp9jENl7~lPFH5fWE6WGWFvM6uAxPm~b89B7Q8TX1Wq93Udsh337xEF0EfHcRrmCSisor9uQGWWqRlnj7hnkTNWRxwevkWDuGHSji2XdB~7sGILEKY26H1Mg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":472306,"name":"Workplace health and safety","url":"https://www.academia.edu/Documents/in/Workplace_health_and_safety"},{"id":603594,"name":"Safety Science","url":"https://www.academia.edu/Documents/in/Safety_Science"},{"id":2922956,"name":"Psychology and Cognitive Sciences","url":"https://www.academia.edu/Documents/in/Psychology_and_Cognitive_Sciences"},{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796328"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78796328/Human_error_identification_and_risk_prioritization_in_overhead_crane_operations_using_HTA_SHERPA_and_fuzzy_VIKOR_method"><img alt="Research paper thumbnail of Human error identification and risk prioritization in overhead crane operations using HTA, SHERPA and fuzzy VIKOR method" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78796328/Human_error_identification_and_risk_prioritization_in_overhead_crane_operations_using_HTA_SHERPA_and_fuzzy_VIKOR_method">Human error identification and risk prioritization in overhead crane operations using HTA, SHERPA and fuzzy VIKOR method</a></div><div class="wp-workCard_item"><span>Expert Systems with Applications</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Human error identification and subsequent prioritization are the foremost tasks involved...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT Human error identification and subsequent prioritization are the foremost tasks involved in HRA. In this study a methodology is developed for performing these tasks with an application to overhead crane operations. The application of the present methodology will help to understand how the risk associated with the human errors propagates through different hierarchy levels. The methodology provides a framework for quantifying the risk of different human errors using the experts’ subjective opinions only. The incorporation of fuzzy VIKOR technique enables us develop a ranking mechanism for the failure modes where the individual constituent components are non-commensurable in nature. The developed ranking mechanism helps the decision makers in optimal allocation of safety critical resources, used for risk mitigation purposes.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796328"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796328"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796328; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796328]").text(description); $(".js-view-count[data-work-id=78796328]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796328; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796328']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796328, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796328]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796328,"title":"Human error identification and risk prioritization in overhead crane operations using HTA, SHERPA and fuzzy VIKOR method","translated_title":"","metadata":{"abstract":"ABSTRACT Human error identification and subsequent prioritization are the foremost tasks involved in HRA. In this study a methodology is developed for performing these tasks with an application to overhead crane operations. The application of the present methodology will help to understand how the risk associated with the human errors propagates through different hierarchy levels. The methodology provides a framework for quantifying the risk of different human errors using the experts’ subjective opinions only. The incorporation of fuzzy VIKOR technique enables us develop a ranking mechanism for the failure modes where the individual constituent components are non-commensurable in nature. 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The developed ranking mechanism helps the decision makers in optimal allocation of safety critical resources, used for risk mitigation purposes.","internal_url":"https://www.academia.edu/78796328/Human_error_identification_and_risk_prioritization_in_overhead_crane_operations_using_HTA_SHERPA_and_fuzzy_VIKOR_method","translated_internal_url":"","created_at":"2022-05-08T19:18:22.953-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163294954,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Human_error_identification_and_risk_prioritization_in_overhead_crane_operations_using_HTA_SHERPA_and_fuzzy_VIKOR_method","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":163294954,"first_name":"J","middle_initials":null,"last_name":"Maiti","page_name":"MaitiJ","domain_name":"independent","created_at":"2020-07-10T04:46:00.412-07:00","display_name":"J Maiti","url":"https://independent.academia.edu/MaitiJ"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":51531,"name":"Pergamon","url":"https://www.academia.edu/Documents/in/Pergamon"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78796327"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/78796327/Development_of_a_relative_risk_model_for_roof_and_side_fall_fatal_accidents_in_underground_coal_mines_in_India"><img alt="Research paper thumbnail of Development of a relative risk model for roof and side fall fatal accidents in underground coal mines in India" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/78796327/Development_of_a_relative_risk_model_for_roof_and_side_fall_fatal_accidents_in_underground_coal_mines_in_India">Development of a relative risk model for roof and side fall fatal accidents in underground coal mines in India</a></div><div class="wp-workCard_item"><span>Safety Science</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">... The main reasons for roof and side fall accidents in Indian underground coal mines are well d...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">... The main reasons for roof and side fall accidents in Indian underground coal mines are well documented by Kejriwal (2002) based on his survey of accidents in Indian mines for the last 100 years. ... This retrospective study also confirms the findings of Kejriwal (2002). ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78796327"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78796327"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78796327; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78796327]").text(description); $(".js-view-count[data-work-id=78796327]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78796327; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78796327']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78796327, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78796327]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78796327,"title":"Development of a relative risk model for roof and side fall fatal accidents in underground coal mines in India","translated_title":"","metadata":{"abstract":"... 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