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Deshpande<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span><a class="ri-more-link js-profile-ri-list-card" data-click-track="profile-user-info-primary-research-interest" data-has-card-for-ri-list="47246275">View All (16)</a></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="47246275" href="https://www.academia.edu/Documents/in/User_Profiling"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://maharashtra.academia.edu/DrMangeshBedekar","location":"/DrMangeshBedekar","scheme":"https","host":"maharashtra.academia.edu","port":null,"pathname":"/DrMangeshBedekar","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div 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class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Dr. Mangesh Bedekar</h3></div><div class="js-work-strip profile--work_container" data-work-id="95066479"><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/95066479/Analysis_of_Research_Paper_Titles_Containing_Covid_19_Keyword_Using_Various_Visualization_Techniques"><img alt="Research paper thumbnail of Analysis of Research Paper Titles Containing Covid-19 Keyword Using Various Visualization 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" rel="nofollow" href="https://www.academia.edu/95066479/Analysis_of_Research_Paper_Titles_Containing_Covid_19_Keyword_Using_Various_Visualization_Techniques">Analysis of Research Paper Titles Containing Covid-19 Keyword Using Various Visualization Techniques</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/SharmishtaDesai1">Sharmishta Desai</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://maharashtra.academia.edu/DrMangeshBedekar">Dr. Mangesh Bedekar</a></span></div><div class="wp-workCard_item"><span>Smart innovation, systems and technologies</span><span>, 2022</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" 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Affective Computing originates from 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">Human emotions are one of the ways to express our feelings. Affective Computing originates from the study of human emotions. Over the years, psychologists have developed various emotional models to explain the emotional or affective states of humans. Affective Computing uses various models of emotion and machine learning algorithms to classify emotions. Machine Learning enables computers to learn from the training datasets and classify new input, thus it can be effectively used to teach computers to understand human emotions. 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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="89920764"><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/89920764/Evaluation_of_metrics_in_hybrid_multichannel_multiradio_wireless_mesh_networks_for_multiple_dynamic_channel_interfaces"><img alt="Research paper thumbnail of Evaluation of metrics in hybrid multichannel multiradio wireless mesh networks for multiple dynamic channel interfaces" 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/89920764/Evaluation_of_metrics_in_hybrid_multichannel_multiradio_wireless_mesh_networks_for_multiple_dynamic_channel_interfaces">Evaluation of metrics in hybrid multichannel multiradio wireless mesh networks for multiple dynamic channel interfaces</a></div><div class="wp-workCard_item"><span>2015 Twelfth International Conference on Wireless and Optical Communications Networks (WOCN)</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A major problem in Wireless Mesh Network is capacity decrease due to wireless interference. A wir...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A major problem in Wireless Mesh Network is capacity decrease due to wireless interference. A wireless mesh router with multiple routers and channels is capable of reducing network interference. Static channel allocation and dynamic channel allocation are types of channel allocation. Adaptive Dynamic Channel Allocation protocol (ADCA) is dynamic channel allocation protocol which decreases the packet delay without degrading the network throughput. The hybrid architecture shows much preferred adaptivity to changing traffic over absolutely static scheme without increasing in overhead, and attains to lower delay than existing methodologies for hybrid networks. 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This depicts the collection and management of ontology knowledge and processing it in the right manner to get the desired insights.","publisher":"Soft Computing Research Society","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"SCRS CONFERENCE PROCEEDINGS ON INTELLIGENT SYSTEMS"},"translated_abstract":"Ontologies are largely responsible for the creation of a framework or taxonomy for a particular domain which represents the shared knowledge, concepts and how these concepts are related with each other. This paper shows the usage of ontology for the comparison of a syllabus structure of universities. This is done with the extraction of the syllabus, creation of ontology for the representing syllabus, then parsing the ontology and applying Natural language processing to remove unwanted information. After getting the appropriate ontologies, a comparative study is made on them. Restrictions are made over the extracted syllabus to the subject “Software Engineering” for convenience. This depicts the collection and management of ontology knowledge and processing it in the right manner to get the desired insights.","internal_url":"https://www.academia.edu/89920763/Ontology_Formation_and_Comparison_for_Syllabus_Structure_Using_NLP","translated_internal_url":"","created_at":"2022-11-03T20:29:14.473-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":47246275,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Ontology_Formation_and_Comparison_for_Syllabus_Structure_Using_NLP","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":47246275,"first_name":"Dr. Mangesh","middle_initials":"","last_name":"Bedekar","page_name":"DrMangeshBedekar","domain_name":"maharashtra","created_at":"2016-04-18T01:35:03.282-07:00","display_name":"Dr. Mangesh Bedekar","url":"https://maharashtra.academia.edu/DrMangeshBedekar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":805,"name":"Ontology","url":"https://www.academia.edu/Documents/in/Ontology"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"},{"id":14493,"name":"Parsing","url":"https://www.academia.edu/Documents/in/Parsing"},{"id":97256,"name":"Process Ontology","url":"https://www.academia.edu/Documents/in/Process_Ontology"},{"id":186232,"name":"Syllabus","url":"https://www.academia.edu/Documents/in/Syllabus"}],"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="89920762"><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/89920762/BMI_Application_Accident_Reduction_Using_Drowsiness_Detection"><img alt="Research paper thumbnail of BMI Application: Accident Reduction Using Drowsiness Detection" 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/89920762/BMI_Application_Accident_Reduction_Using_Drowsiness_Detection">BMI Application: Accident Reduction Using Drowsiness Detection</a></div><div class="wp-workCard_item"><span>Advances in Intelligent Systems and Computing</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Among the numerous factors that are responsible for increasing road accidents, the second most co...</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">Among the numerous factors that are responsible for increasing road accidents, the second most common cause is drowsiness. In an attempt to reduce the rate of accidents, we propose a system which would efficiently handle the timely detection of drowsiness and would accordingly curb the speed of the vehicle being driven. As a proof of concept of the proposed method, we have trained the SVM classifier on the EEG (electroencephalogram) waves derived from “Analysis of a sleep-dependent neuronal feedback loop: the slow-wave micro continuity of the EEG” by Kemp et al. [1, 2]. The data is obtained from a wireless EEG headset. The classification results will determine whether the EEG data corresponds to drowsiness or alertness. This level of drowsiness is then used to determine the maximum speed limit. As the work in [3] has stated, there is a strong correlation between the number of accidents and the speed limit. Hence altogether, the proposed system integrates EEG waves for sleep level detection, and speed lock as a preventive measure to reduce the number of plausible accidents.</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="89920762"><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="89920762"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 89920762; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=89920762]").text(description); $(".js-view-count[data-work-id=89920762]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 89920762; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='89920762']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 89920762, 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" } } $('.js-work-strip[data-work-id=89920762]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":89920762,"title":"BMI Application: Accident Reduction Using Drowsiness Detection","translated_title":"","metadata":{"abstract":"Among the numerous factors that are responsible for increasing road accidents, the second most common cause is drowsiness. In an attempt to reduce the rate of accidents, we propose a system which would efficiently handle the timely detection of drowsiness and would accordingly curb the speed of the vehicle being driven. As a proof of concept of the proposed method, we have trained the SVM classifier on the EEG (electroencephalogram) waves derived from “Analysis of a sleep-dependent neuronal feedback loop: the slow-wave micro continuity of the EEG” by Kemp et al. [1, 2]. The data is obtained from a wireless EEG headset. The classification results will determine whether the EEG data corresponds to drowsiness or alertness. This level of drowsiness is then used to determine the maximum speed limit. As the work in [3] has stated, there is a strong correlation between the number of accidents and the speed limit. Hence altogether, the proposed system integrates EEG waves for sleep level detection, and speed lock as a preventive measure to reduce the number of plausible accidents.","publisher":"Springer International Publishing","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Advances in Intelligent Systems and Computing"},"translated_abstract":"Among the numerous factors that are responsible for increasing road accidents, the second most common cause is drowsiness. In an attempt to reduce the rate of accidents, we propose a system which would efficiently handle the timely detection of drowsiness and would accordingly curb the speed of the vehicle being driven. As a proof of concept of the proposed method, we have trained the SVM classifier on the EEG (electroencephalogram) waves derived from “Analysis of a sleep-dependent neuronal feedback loop: the slow-wave micro continuity of the EEG” by Kemp et al. [1, 2]. The data is obtained from a wireless EEG headset. The classification results will determine whether the EEG data corresponds to drowsiness or alertness. This level of drowsiness is then used to determine the maximum speed limit. As the work in [3] has stated, there is a strong correlation between the number of accidents and the speed limit. Hence altogether, the proposed system integrates EEG waves for sleep level detection, and speed lock as a preventive measure to reduce the number of plausible accidents.","internal_url":"https://www.academia.edu/89920762/BMI_Application_Accident_Reduction_Using_Drowsiness_Detection","translated_internal_url":"","created_at":"2022-11-03T20:29:14.269-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":47246275,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"BMI_Application_Accident_Reduction_Using_Drowsiness_Detection","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":47246275,"first_name":"Dr. Mangesh","middle_initials":"","last_name":"Bedekar","page_name":"DrMangeshBedekar","domain_name":"maharashtra","created_at":"2016-04-18T01:35:03.282-07:00","display_name":"Dr. Mangesh Bedekar","url":"https://maharashtra.academia.edu/DrMangeshBedekar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[{"id":25515092,"url":"http://link.springer.com/content/pdf/10.1007/978-3-030-16681-6_7"}]}, 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="89920751"><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/89920751/Cartoon_Films_Made_in_India_How_do_they_Fare"><img alt="Research paper thumbnail of Cartoon Films: Made in India, How do they Fare?" class="work-thumbnail" src="https://attachments.academia-assets.com/93627250/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/89920751/Cartoon_Films_Made_in_India_How_do_they_Fare">Cartoon Films: Made in India, How do they Fare?</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Cartoons, these days have become an integral part of every child’s childhood. They are amongst th...</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">Cartoons, these days have become an integral part of every child’s childhood. They are amongst the most prominent forms of entertainment for children. With the advent of the nuclear family and single child families, with no mate/partner to interact, play or learn from, it is with the help of cartoons that kids are exposed to the various facets of the world around us. Cartoon films screened on most TV sets in Indian homes were majorly dubbed versions of successful cartoon films from USA, Japan, Canada, etc. From around 2003 onwards, some Indian cartoons started to appear on TV especially after the Cable TV came to Indian homes. This paper looks at the cartoons, which are made in India, made for India, and their co-relations with the cartoons which are otherwise screened on Indian TV sets, which are dubbed versions of cartoon films from around the world. These Indian cartoons are analysed and correlated with each other based on various attributes namely, the locale, the age of the pro...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d2b49c1ef9de02551d5c3a338af4dbea" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":93627250,"asset_id":89920751,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/93627250/download_file?st=MTczMjgyNDUyNyw4LjIyMi4yMDguMTQ2&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="89920751"><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="89920751"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 89920751; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=89920751]").text(description); $(".js-view-count[data-work-id=89920751]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 89920751; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='89920751']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 89920751, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d2b49c1ef9de02551d5c3a338af4dbea" } } $('.js-work-strip[data-work-id=89920751]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":89920751,"title":"Cartoon Films: Made in India, How do they Fare?","translated_title":"","metadata":{"abstract":"Cartoons, these days have become an integral part of every child’s childhood. They are amongst the most prominent forms of entertainment for children. With the advent of the nuclear family and single child families, with no mate/partner to interact, play or learn from, it is with the help of cartoons that kids are exposed to the various facets of the world around us. Cartoon films screened on most TV sets in Indian homes were majorly dubbed versions of successful cartoon films from USA, Japan, Canada, etc. From around 2003 onwards, some Indian cartoons started to appear on TV especially after the Cable TV came to Indian homes. This paper looks at the cartoons, which are made in India, made for India, and their co-relations with the cartoons which are otherwise screened on Indian TV sets, which are dubbed versions of cartoon films from around the world. These Indian cartoons are analysed and correlated with each other based on various attributes namely, the locale, the age of the pro...","publication_date":{"day":null,"month":null,"year":2021,"errors":{}}},"translated_abstract":"Cartoons, these days have become an integral part of every child’s childhood. They are amongst the most prominent forms of entertainment for children. With the advent of the nuclear family and single child families, with no mate/partner to interact, play or learn from, it is with the help of cartoons that kids are exposed to the various facets of the world around us. Cartoon films screened on most TV sets in Indian homes were majorly dubbed versions of successful cartoon films from USA, Japan, Canada, etc. From around 2003 onwards, some Indian cartoons started to appear on TV especially after the Cable TV came to Indian homes. This paper looks at the cartoons, which are made in India, made for India, and their co-relations with the cartoons which are otherwise screened on Indian TV sets, which are dubbed versions of cartoon films from around the world. 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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="85183159"><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/85183159/The_New_Dataset_MITWPU_1K_for_Object_Recognition_and_Image_Captioning_Tasks"><img alt="Research paper thumbnail of The New Dataset MITWPU-1K for Object Recognition and Image Captioning Tasks" class="work-thumbnail" src="https://attachments.academia-assets.com/89960848/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/85183159/The_New_Dataset_MITWPU_1K_for_Object_Recognition_and_Image_Captioning_Tasks">The New Dataset MITWPU-1K for Object Recognition and Image Captioning Tasks</a></div><div class="wp-workCard_item"><span>Engineering, Technology &amp; Applied Science Research</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the domain of image captioning, many pre-trained datasets are available. Using these datasets,...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the domain of image captioning, many pre-trained datasets are available. Using these datasets, models can be trained to automatically generate image descriptions regarding the contents of an image. Researchers usually do not spend much time in creating and training the new dataset before using it for a specific application, instead, they simply use existing pre-trained datasets. MS COCO, ImageNet, Flicker, and Pascal VOC, are well-known datasets that are widely used in the task of generating image captions. In most available image captioning datasets, image textual information, which can play a vital role in generating more precise image descriptions, is missing. This paper presents the process of creating a new dataset that consists of images along with text and captions. Images of the nearby vicinity of the campus of MIT World Peace University-MITWPU, India, were taken for the new dataset named MITWPU-1K. This dataset can be used in object detection and caption generation of im...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6344b8c2010dda7a897b69893a486276" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89960848,"asset_id":85183159,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89960848/download_file?st=MTczMjgyNDUyNyw4LjIyMi4yMDguMTQ2&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="85183159"><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="85183159"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 85183159; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=85183159]").text(description); $(".js-view-count[data-work-id=85183159]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 85183159; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='85183159']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 85183159, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6344b8c2010dda7a897b69893a486276" } } $('.js-work-strip[data-work-id=85183159]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":85183159,"title":"The New Dataset MITWPU-1K for Object Recognition and Image Captioning Tasks","translated_title":"","metadata":{"abstract":"In the domain of image captioning, many pre-trained datasets are available. Using these datasets, models can be trained to automatically generate image descriptions regarding the contents of an image. Researchers usually do not spend much time in creating and training the new dataset before using it for a specific application, instead, they simply use existing pre-trained datasets. MS COCO, ImageNet, Flicker, and Pascal VOC, are well-known datasets that are widely used in the task of generating image captions. In most available image captioning datasets, image textual information, which can play a vital role in generating more precise image descriptions, is missing. This paper presents the process of creating a new dataset that consists of images along with text and captions. Images of the nearby vicinity of the campus of MIT World Peace University-MITWPU, India, were taken for the new dataset named MITWPU-1K. This dataset can be used in object detection and caption generation of im...","publisher":"Engineering, Technology \u0026 Applied Science Research","publication_name":"Engineering, Technology \u0026amp; Applied Science Research"},"translated_abstract":"In the domain of image captioning, many pre-trained datasets are available. Using these datasets, models can be trained to automatically generate image descriptions regarding the contents of an image. Researchers usually do not spend much time in creating and training the new dataset before using it for a specific application, instead, they simply use existing pre-trained datasets. MS COCO, ImageNet, Flicker, and Pascal VOC, are well-known datasets that are widely used in the task of generating image captions. In most available image captioning datasets, image textual information, which can play a vital role in generating more precise image descriptions, is missing. This paper presents the process of creating a new dataset that consists of images along with text and captions. Images of the nearby vicinity of the campus of MIT World Peace University-MITWPU, India, were taken for the new dataset named MITWPU-1K. This dataset can be used in object detection and caption generation of im...","internal_url":"https://www.academia.edu/85183159/The_New_Dataset_MITWPU_1K_for_Object_Recognition_and_Image_Captioning_Tasks","translated_internal_url":"","created_at":"2022-08-19T21:54:25.147-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":47246275,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":89960848,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89960848/thumbnails/1.jpg","file_name":"2788.pdf","download_url":"https://www.academia.edu/attachments/89960848/download_file?st=MTczMjgyNDUyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"The_New_Dataset_MITWPU_1K_for_Object_Rec.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89960848/2788-libre.pdf?1660972130=\u0026response-content-disposition=attachment%3B+filename%3DThe_New_Dataset_MITWPU_1K_for_Object_Rec.pdf\u0026Expires=1732800652\u0026Signature=MEFxolDELDBG2joOrr8frsRRzH99psJfbZuzUjLP9ziP4r4nUxrN7hIAKCCZGhujVuvTqSSSf1Eia9Vx5oR0NrKTDTrCWEJUhM2b~PRA0xob-n~-7PyuMCQTvNI792eB-HQVyMQ~fbd7E6k4Rc-~NPBRSOGDNsZIXKpLUAoDAMgcNgN-JKc~jlX-IVaRJf4sxIV5MJt6~tIqkp3WgzKZcHJo96EznnjcZGAhNkxeroKRJQOUoPl4v7obLl01Ne~gMsmCVh4lYEqctpsSahw0IG7T9GzdHwHLidQK8qi6id2txiJOlgimHumQEb6qRgcvZGkCFPvwJodJhQ1T0jUihw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"The_New_Dataset_MITWPU_1K_for_Object_Recognition_and_Image_Captioning_Tasks","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":47246275,"first_name":"Dr. Mangesh","middle_initials":"","last_name":"Bedekar","page_name":"DrMangeshBedekar","domain_name":"maharashtra","created_at":"2016-04-18T01:35:03.282-07:00","display_name":"Dr. Mangesh Bedekar","url":"https://maharashtra.academia.edu/DrMangeshBedekar"},"attachments":[{"id":89960848,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89960848/thumbnails/1.jpg","file_name":"2788.pdf","download_url":"https://www.academia.edu/attachments/89960848/download_file?st=MTczMjgyNDUyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"The_New_Dataset_MITWPU_1K_for_Object_Rec.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89960848/2788-libre.pdf?1660972130=\u0026response-content-disposition=attachment%3B+filename%3DThe_New_Dataset_MITWPU_1K_for_Object_Rec.pdf\u0026Expires=1732800652\u0026Signature=MEFxolDELDBG2joOrr8frsRRzH99psJfbZuzUjLP9ziP4r4nUxrN7hIAKCCZGhujVuvTqSSSf1Eia9Vx5oR0NrKTDTrCWEJUhM2b~PRA0xob-n~-7PyuMCQTvNI792eB-HQVyMQ~fbd7E6k4Rc-~NPBRSOGDNsZIXKpLUAoDAMgcNgN-JKc~jlX-IVaRJf4sxIV5MJt6~tIqkp3WgzKZcHJo96EznnjcZGAhNkxeroKRJQOUoPl4v7obLl01Ne~gMsmCVh4lYEqctpsSahw0IG7T9GzdHwHLidQK8qi6id2txiJOlgimHumQEb6qRgcvZGkCFPvwJodJhQ1T0jUihw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[{"id":23142518,"url":"http://www.etasr.com/index.php/ETASR/article/download/5039/2788"}]}, 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="80419858"><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/80419858/D_CNN_A_New_model_for_Generating_Image_Captions_with_Text_Extraction_Using_Deep_Learning_for_Visually_Challenged_Individuals"><img alt="Research paper thumbnail of D-CNN: A New model for Generating Image Captions with Text Extraction Using Deep Learning for Visually Challenged Individuals" class="work-thumbnail" src="https://attachments.academia-assets.com/86807786/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/80419858/D_CNN_A_New_model_for_Generating_Image_Captions_with_Text_Extraction_Using_Deep_Learning_for_Visually_Challenged_Individuals">D-CNN: A New model for Generating Image Captions with Text Extraction Using Deep Learning for Visually Challenged Individuals</a></div><div class="wp-workCard_item"><span>Engineering, Technology & Applied Science Research</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Automatically describing the information of an image using properly constructed sentences is a tr...</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">Automatically describing the information of an image using properly constructed sentences is a tricky task in any language. However, it has the potential to have a significant effect by enabling visually challenged individuals to better understand their surroundings. This paper proposes an image captioning system that generates detailed captions and extracts text from an image, if any, and uses it as a part of the caption to provide a more precise description of the image. To extract the image features, the proposed model uses Convolutional Neural Networks (CNNs) followed by Long Short-Term Memory (LSTM) that generates corresponding sentences based on the learned image features. Further, using the text extraction module, the extracted text (if any) is included in the image description and the captions are presented in audio form. Publicly available benchmark datasets for image captioning like MS COCO, Flickr-8k, Flickr-30k have a variety of images, but they hardly have images that c...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="06972caf1466cb0d0fb6cc8d23a130bc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86807786,"asset_id":80419858,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86807786/download_file?st=MTczMjgyNDUyNyw4LjIyMi4yMDguMTQ2&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="80419858"><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="80419858"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80419858; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80419858]").text(description); $(".js-view-count[data-work-id=80419858]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80419858; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80419858']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80419858, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "06972caf1466cb0d0fb6cc8d23a130bc" } } $('.js-work-strip[data-work-id=80419858]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80419858,"title":"D-CNN: A New model for Generating Image Captions with Text Extraction Using Deep Learning for Visually Challenged Individuals","translated_title":"","metadata":{"abstract":"Automatically describing the information of an image using properly constructed sentences is a tricky task in any language. However, it has the potential to have a significant effect by enabling visually challenged individuals to better understand their surroundings. This paper proposes an image captioning system that generates detailed captions and extracts text from an image, if any, and uses it as a part of the caption to provide a more precise description of the image. To extract the image features, the proposed model uses Convolutional Neural Networks (CNNs) followed by Long Short-Term Memory (LSTM) that generates corresponding sentences based on the learned image features. Further, using the text extraction module, the extracted text (if any) is included in the image description and the captions are presented in audio form. Publicly available benchmark datasets for image captioning like MS COCO, Flickr-8k, Flickr-30k have a variety of images, but they hardly have images that c...","publisher":"Engineering, Technology \u0026 Applied Science Research","publication_name":"Engineering, Technology \u0026 Applied Science Research"},"translated_abstract":"Automatically describing the information of an image using properly constructed sentences is a tricky task in any language. However, it has the potential to have a significant effect by enabling visually challenged individuals to better understand their surroundings. This paper proposes an image captioning system that generates detailed captions and extracts text from an image, if any, and uses it as a part of the caption to provide a more precise description of the image. To extract the image features, the proposed model uses Convolutional Neural Networks (CNNs) followed by Long Short-Term Memory (LSTM) that generates corresponding sentences based on the learned image features. Further, using the text extraction module, the extracted text (if any) is included in the image description and the captions are presented in audio form. Publicly available benchmark datasets for image captioning like MS COCO, Flickr-8k, Flickr-30k have a variety of images, but they hardly have images that c...","internal_url":"https://www.academia.edu/80419858/D_CNN_A_New_model_for_Generating_Image_Captions_with_Text_Extraction_Using_Deep_Learning_for_Visually_Challenged_Individuals","translated_internal_url":"","created_at":"2022-05-31T22:09:58.802-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":47246275,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":86807786,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86807786/thumbnails/1.jpg","file_name":"2710.pdf","download_url":"https://www.academia.edu/attachments/86807786/download_file?st=MTczMjgyNDUyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"D_CNN_A_New_model_for_Generating_Image_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86807786/2710-libre.pdf?1654060832=\u0026response-content-disposition=attachment%3B+filename%3DD_CNN_A_New_model_for_Generating_Image_C.pdf\u0026Expires=1732800652\u0026Signature=Z1JI-X68BnbRqDICYV3fukIGCFcFBsm7BdFJmMNr91EG9-cb9oSIgARGlCkDtk6zmlDT4aw~TgUnCCyqfN2GlgsL88uLmcA8RqK17PmY4POKGAPGKota9njFWnXZWLny74~5U4leJoKWX5U26scJEWFVbyxELcHwaHOjmBTbh~WkU1BYncGC7B1HvlePGQY-nM~LvLpcKg~GxAalAcBkfxSH7DUwOU~jTpjPebeI-JsOsmU5sjFmDBUlwTary-W83vJCDJPiVodYv8YRxmIhf1b~Yz0L0X9Tl~ysSIS0UQJjsU471hKoVjPAQWXonEj3qCe264H-jfJCqlhww~m98g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"D_CNN_A_New_model_for_Generating_Image_Captions_with_Text_Extraction_Using_Deep_Learning_for_Visually_Challenged_Individuals","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":47246275,"first_name":"Dr. Mangesh","middle_initials":"","last_name":"Bedekar","page_name":"DrMangeshBedekar","domain_name":"maharashtra","created_at":"2016-04-18T01:35:03.282-07:00","display_name":"Dr. Mangesh Bedekar","url":"https://maharashtra.academia.edu/DrMangeshBedekar"},"attachments":[{"id":86807786,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86807786/thumbnails/1.jpg","file_name":"2710.pdf","download_url":"https://www.academia.edu/attachments/86807786/download_file?st=MTczMjgyNDUyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"D_CNN_A_New_model_for_Generating_Image_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86807786/2710-libre.pdf?1654060832=\u0026response-content-disposition=attachment%3B+filename%3DD_CNN_A_New_model_for_Generating_Image_C.pdf\u0026Expires=1732800652\u0026Signature=Z1JI-X68BnbRqDICYV3fukIGCFcFBsm7BdFJmMNr91EG9-cb9oSIgARGlCkDtk6zmlDT4aw~TgUnCCyqfN2GlgsL88uLmcA8RqK17PmY4POKGAPGKota9njFWnXZWLny74~5U4leJoKWX5U26scJEWFVbyxELcHwaHOjmBTbh~WkU1BYncGC7B1HvlePGQY-nM~LvLpcKg~GxAalAcBkfxSH7DUwOU~jTpjPebeI-JsOsmU5sjFmDBUlwTary-W83vJCDJPiVodYv8YRxmIhf1b~Yz0L0X9Tl~ysSIS0UQJjsU471hKoVjPAQWXonEj3qCe264H-jfJCqlhww~m98g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":86807785,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86807785/thumbnails/1.jpg","file_name":"2710.pdf","download_url":"https://www.academia.edu/attachments/86807785/download_file","bulk_download_file_name":"D_CNN_A_New_model_for_Generating_Image_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86807785/2710-libre.pdf?1654060832=\u0026response-content-disposition=attachment%3B+filename%3DD_CNN_A_New_model_for_Generating_Image_C.pdf\u0026Expires=1732800652\u0026Signature=B9P1MH3UqATlWSHE4ndWfcevbzAK97UjdkuUxMxSiGaTSdqie7X3Ii3eGSP3baGMXQ8rJ1N3GF3sv9ihTWK0OlcCIOJB6M5O2I4VdIpt6ailkd4UiftchzsiYuRfYHb-vxJRjUEmyKtr0MMthSbn85RXnLkIAIGaPkzcW~mFnxygb~qSYBVh3iEjUR5c8IFjwVT6xPVPwx~FizpJRWtrg42ET1SrpU~TQ-gc4xcrXin8kkqcFAIEmG0LrNdRAHAJ0ulayCSSCXKtJooIuKRtesf2VgAQn9WNev2zs2WzkkMb-9dq4Xb~Yjgciw63V4qsHNtSGkpUjckJoyOm5VqTWw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":30193,"name":"Image Analysis (Mathematics)","url":"https://www.academia.edu/Documents/in/Image_Analysis_Mathematics_"},{"id":283974,"name":"Closed Captioning","url":"https://www.academia.edu/Documents/in/Closed_Captioning"},{"id":1568111,"name":"Convolutional Neural Network","url":"https://www.academia.edu/Documents/in/Convolutional_Neural_Network"}],"urls":[{"id":20980895,"url":"https://www.etasr.com/index.php/ETASR/article/download/4772/2710"}]}, 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="80419853"><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/80419853/Intelligent_Twitter_Spam_Detection_A_Hybrid_Approach"><img alt="Research paper thumbnail of Intelligent Twitter Spam Detection: A Hybrid 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" rel="nofollow" href="https://www.academia.edu/80419853/Intelligent_Twitter_Spam_Detection_A_Hybrid_Approach">Intelligent Twitter Spam Detection: A Hybrid Approach</a></div><div class="wp-workCard_item"><span>Lecture Notes in Networks and Systems</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Over the years there has been a large upheaval in the social networking arena. Twitter being one ...</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">Over the years there has been a large upheaval in the social networking arena. Twitter being one of the most widely-used social networks in the world has always been a key target for intruders. Privacy concerns, stealing of important information and leakage of key credentials to spammers has been on the rise. In this paper, we have developed an Intelligent Twitter Spam Detection System which gives the precise details about spam profiles by identifying and detecting twitter spam. The system is a Hybrid approach as opposed to single-tier, single-classifier approaches which takes into account some unique feature sets before analyzing the tweets and also checks the links with Google Safe Browsing API for added security. This in turn leads to better tweet classification and improved as well as intelligent twitter spam detection.</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="80419853"><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="80419853"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80419853; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80419853]").text(description); $(".js-view-count[data-work-id=80419853]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80419853; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80419853']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80419853, 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" } } $('.js-work-strip[data-work-id=80419853]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80419853,"title":"Intelligent Twitter Spam Detection: A Hybrid Approach","translated_title":"","metadata":{"abstract":"Over the years there has been a large upheaval in the social networking arena. Twitter being one of the most widely-used social networks in the world has always been a key target for intruders. Privacy concerns, stealing of important information and leakage of key credentials to spammers has been on the rise. In this paper, we have developed an Intelligent Twitter Spam Detection System which gives the precise details about spam profiles by identifying and detecting twitter spam. The system is a Hybrid approach as opposed to single-tier, single-classifier approaches which takes into account some unique feature sets before analyzing the tweets and also checks the links with Google Safe Browsing API for added security. This in turn leads to better tweet classification and improved as well as intelligent twitter spam detection.","publisher":"Springer Singapore","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Lecture Notes in Networks and Systems"},"translated_abstract":"Over the years there has been a large upheaval in the social networking arena. Twitter being one of the most widely-used social networks in the world has always been a key target for intruders. Privacy concerns, stealing of important information and leakage of key credentials to spammers has been on the rise. In this paper, we have developed an Intelligent Twitter Spam Detection System which gives the precise details about spam profiles by identifying and detecting twitter spam. The system is a Hybrid approach as opposed to single-tier, single-classifier approaches which takes into account some unique feature sets before analyzing the tweets and also checks the links with Google Safe Browsing API for added security. 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These parameters can be user keyboard typing style, mouse movements, and some physiological sensors are used. This field of retrieving emotions from machines comes under the field of affective computing.</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="80419849"><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="80419849"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80419849; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80419849]").text(description); $(".js-view-count[data-work-id=80419849]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80419849; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80419849']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80419849, 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" } } $('.js-work-strip[data-work-id=80419849]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80419849,"title":"Inferring User Emotions from Keyboard and Mouse","translated_title":"","metadata":{"abstract":"This chapter emphasizes on retrieving user emotions from keyboard and mouse using different parameters. 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We attempt to identify drivers based on this characteristic driving profile. Users were made to playa realistic 3D driving simulation, StuntRally. Their keypress events were logged. These logs were used to train and test different classifiers such as Support Vector Machine(SVM), K Nearest Neighbour(KNN) and Naive Bayes(NB). The SVM performed best with an average testing accuracy of 80 %.</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="80419847"><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="80419847"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80419847; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80419847]").text(description); $(".js-view-count[data-work-id=80419847]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80419847; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80419847']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80419847, 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" } } $('.js-work-strip[data-work-id=80419847]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80419847,"title":"Driver Profiling Using Realistic Racing Games","translated_title":"","metadata":{"abstract":"All humans operating vehicles with several in-puts(steering wheel, pedals, gears etc.) do so in a unique way. We attempt to identify drivers based on this characteristic driving profile. Users were made to playa realistic 3D driving simulation, StuntRally. Their keypress events were logged. These logs were used to train and test different classifiers such as Support Vector Machine(SVM), K Nearest Neighbour(KNN) and Naive Bayes(NB). The SVM performed best with an average testing accuracy of 80 %.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT)"},"translated_abstract":"All humans operating vehicles with several in-puts(steering wheel, pedals, gears etc.) do so in a unique way. We attempt to identify drivers based on this characteristic driving profile. Users were made to playa realistic 3D driving simulation, StuntRally. Their keypress events were logged. These logs were used to train and test different classifiers such as Support Vector Machine(SVM), K Nearest Neighbour(KNN) and Naive Bayes(NB). The SVM performed best with an average testing accuracy of 80 %.","internal_url":"https://www.academia.edu/80419847/Driver_Profiling_Using_Realistic_Racing_Games","translated_internal_url":"","created_at":"2022-05-31T22:09:42.965-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":47246275,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Driver_Profiling_Using_Realistic_Racing_Games","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":47246275,"first_name":"Dr. Mangesh","middle_initials":"","last_name":"Bedekar","page_name":"DrMangeshBedekar","domain_name":"maharashtra","created_at":"2016-04-18T01:35:03.282-07:00","display_name":"Dr. Mangesh Bedekar","url":"https://maharashtra.academia.edu/DrMangeshBedekar"},"attachments":[],"research_interests":[{"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":9173,"name":"Biometrics","url":"https://www.academia.edu/Documents/in/Biometrics"},{"id":55284,"name":"Gaming","url":"https://www.academia.edu/Documents/in/Gaming"},{"id":141109,"name":"User Profiling","url":"https://www.academia.edu/Documents/in/User_Profiling"},{"id":319462,"name":"Keystroke Dynamic","url":"https://www.academia.edu/Documents/in/Keystroke_Dynamic"},{"id":558449,"name":"Keystroke logging","url":"https://www.academia.edu/Documents/in/Keystroke_logging"}],"urls":[{"id":20980890,"url":"http://xplorestaging.ieee.org/ielx7/8466130/8472948/08473154.pdf?arnumber=8473154"}]}, 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="80419844"><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/80419844/Inferring_emotional_state_of_a_user_by_user_profiling"><img alt="Research paper thumbnail of Inferring emotional state of a user by user profiling" 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/80419844/Inferring_emotional_state_of_a_user_by_user_profiling">Inferring emotional state of a user by user profiling</a></div><div class="wp-workCard_item"><span>2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">User profiles are important in many areas in which it is essential to obtain knowledge about user...</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">User profiles are important in many areas in which it is essential to obtain knowledge about users of software applications. Knowledge about a user includes his likes, dislikes, even his emotional state can be determined by user profiling. In this paper we examine what information constitutes a user profile; and how the profile information is used to get the emotional state of a user. We also study the main issues regarding user profiles from the perspectives of these research fields.</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="80419844"><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="80419844"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80419844; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80419844]").text(description); $(".js-view-count[data-work-id=80419844]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80419844; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80419844']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80419844, 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" } } $('.js-work-strip[data-work-id=80419844]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80419844,"title":"Inferring emotional state of a user by user profiling","translated_title":"","metadata":{"abstract":"User profiles are important in many areas in which it is essential to obtain knowledge about users of software applications. Knowledge about a user includes his likes, dislikes, even his emotional state can be determined by user profiling. In this paper we examine what information constitutes a user profile; and how the profile information is used to get the emotional state of a user. We also study the main issues regarding user profiles from the perspectives of these research fields.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)"},"translated_abstract":"User profiles are important in many areas in which it is essential to obtain knowledge about users of software applications. Knowledge about a user includes his likes, dislikes, even his emotional state can be determined by user profiling. In this paper we examine what information constitutes a user profile; and how the profile information is used to get the emotional state of a user. We also study the main issues regarding user profiles from the perspectives of these research fields.","internal_url":"https://www.academia.edu/80419844/Inferring_emotional_state_of_a_user_by_user_profiling","translated_internal_url":"","created_at":"2022-05-31T22:09:36.668-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":47246275,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Inferring_emotional_state_of_a_user_by_user_profiling","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":47246275,"first_name":"Dr. Mangesh","middle_initials":"","last_name":"Bedekar","page_name":"DrMangeshBedekar","domain_name":"maharashtra","created_at":"2016-04-18T01:35:03.282-07:00","display_name":"Dr. Mangesh Bedekar","url":"https://maharashtra.academia.edu/DrMangeshBedekar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[{"id":20980888,"url":"http://xplorestaging.ieee.org/ielx7/7911113/7917923/07918021.pdf?arnumber=7918021"}]}, 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="80419840"><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/80419840/Inferring_user_emotions_using_physiological_signals_from_mouse_and_keyboard"><img alt="Research paper thumbnail of Inferring user emotions using physiological signals from mouse and keyboard" 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/80419840/Inferring_user_emotions_using_physiological_signals_from_mouse_and_keyboard">Inferring user emotions using physiological signals from mouse and keyboard</a></div><div class="wp-workCard_item"><span>2017 International Conference on Intelligent Computing and Control Systems (ICICCS)</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, we have developed a mouse and keyboard which contains heart beat sensor, temperatu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, we have developed a mouse and keyboard which contains heart beat sensor, temperature sensor and force sensor. These sensors will generate the physiological signals. The signals from these devices will provide us the result for the current user. These results will be processed by the microcontroller and transmitted to the android device through Bluetooth module. The results will help us to determine the user&#39;s current emotional state and if found negative it can be altered.</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="80419840"><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="80419840"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80419840; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80419840]").text(description); $(".js-view-count[data-work-id=80419840]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80419840; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80419840']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80419840, 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" } } $('.js-work-strip[data-work-id=80419840]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80419840,"title":"Inferring user emotions using physiological signals from mouse and keyboard","translated_title":"","metadata":{"abstract":"In this paper, we have developed a mouse and keyboard which contains heart beat sensor, temperature sensor and force sensor. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="5523409" id="papers"><div class="js-work-strip profile--work_container" data-work-id="95066479"><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/95066479/Analysis_of_Research_Paper_Titles_Containing_Covid_19_Keyword_Using_Various_Visualization_Techniques"><img alt="Research paper thumbnail of Analysis of Research Paper Titles Containing Covid-19 Keyword Using Various Visualization 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" rel="nofollow" href="https://www.academia.edu/95066479/Analysis_of_Research_Paper_Titles_Containing_Covid_19_Keyword_Using_Various_Visualization_Techniques">Analysis of Research Paper Titles Containing Covid-19 Keyword Using Various Visualization Techniques</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/SharmishtaDesai1">Sharmishta Desai</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://maharashtra.academia.edu/DrMangeshBedekar">Dr. Mangesh Bedekar</a></span></div><div class="wp-workCard_item"><span>Smart innovation, systems and technologies</span><span>, 2022</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="95066479"><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="95066479"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95066479; 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Affective Computing originates from 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">Human emotions are one of the ways to express our feelings. Affective Computing originates from the study of human emotions. Over the years, psychologists have developed various emotional models to explain the emotional or affective states of humans. Affective Computing uses various models of emotion and machine learning algorithms to classify emotions. Machine Learning enables computers to learn from the training datasets and classify new input, thus it can be effectively used to teach computers to understand human emotions. 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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="89920764"><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/89920764/Evaluation_of_metrics_in_hybrid_multichannel_multiradio_wireless_mesh_networks_for_multiple_dynamic_channel_interfaces"><img alt="Research paper thumbnail of Evaluation of metrics in hybrid multichannel multiradio wireless mesh networks for multiple dynamic channel interfaces" 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/89920764/Evaluation_of_metrics_in_hybrid_multichannel_multiradio_wireless_mesh_networks_for_multiple_dynamic_channel_interfaces">Evaluation of metrics in hybrid multichannel multiradio wireless mesh networks for multiple dynamic channel interfaces</a></div><div class="wp-workCard_item"><span>2015 Twelfth International Conference on Wireless and Optical Communications Networks (WOCN)</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A major problem in Wireless Mesh Network is capacity decrease due to wireless interference. A wir...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A major problem in Wireless Mesh Network is capacity decrease due to wireless interference. A wireless mesh router with multiple routers and channels is capable of reducing network interference. Static channel allocation and dynamic channel allocation are types of channel allocation. Adaptive Dynamic Channel Allocation protocol (ADCA) is dynamic channel allocation protocol which decreases the packet delay without degrading the network throughput. The hybrid architecture shows much preferred adaptivity to changing traffic over absolutely static scheme without increasing in overhead, and attains to lower delay than existing methodologies for hybrid networks. In addition, we have used an Interference and Congestion Aware Routing protocol (ICAR) in the hybrid network which achieves load balancing in the channel usage. Our simulation results show that compared to previous works, ADCA reduces the packet delay considerably without degrading the network throughput. Current hybrid implementa...</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="89920764"><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="89920764"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 89920764; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=89920764]").text(description); $(".js-view-count[data-work-id=89920764]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 89920764; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='89920764']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 89920764, 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" } } $('.js-work-strip[data-work-id=89920764]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":89920764,"title":"Evaluation of metrics in hybrid multichannel multiradio wireless mesh networks for multiple dynamic channel interfaces","translated_title":"","metadata":{"abstract":"A major problem in Wireless Mesh Network is capacity decrease due to wireless interference. A wireless mesh router with multiple routers and channels is capable of reducing network interference. Static channel allocation and dynamic channel allocation are types of channel allocation. Adaptive Dynamic Channel Allocation protocol (ADCA) is dynamic channel allocation protocol which decreases the packet delay without degrading the network throughput. The hybrid architecture shows much preferred adaptivity to changing traffic over absolutely static scheme without increasing in overhead, and attains to lower delay than existing methodologies for hybrid networks. In addition, we have used an Interference and Congestion Aware Routing protocol (ICAR) in the hybrid network which achieves load balancing in the channel usage. Our simulation results show that compared to previous works, ADCA reduces the packet delay considerably without degrading the network throughput. Current hybrid implementa...","publisher":"2015 Twelfth International Conference on Wireless and Optical Communications Networks (WOCN)","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"2015 Twelfth International Conference on Wireless and Optical Communications Networks (WOCN)"},"translated_abstract":"A major problem in Wireless Mesh Network is capacity decrease due to wireless interference. A wireless mesh router with multiple routers and channels is capable of reducing network interference. Static channel allocation and dynamic channel allocation are types of channel allocation. Adaptive Dynamic Channel Allocation protocol (ADCA) is dynamic channel allocation protocol which decreases the packet delay without degrading the network throughput. 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Current hybrid implementa...","internal_url":"https://www.academia.edu/89920764/Evaluation_of_metrics_in_hybrid_multichannel_multiradio_wireless_mesh_networks_for_multiple_dynamic_channel_interfaces","translated_internal_url":"","created_at":"2022-11-03T20:29:14.850-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":47246275,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Evaluation_of_metrics_in_hybrid_multichannel_multiradio_wireless_mesh_networks_for_multiple_dynamic_channel_interfaces","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":47246275,"first_name":"Dr. Mangesh","middle_initials":"","last_name":"Bedekar","page_name":"DrMangeshBedekar","domain_name":"maharashtra","created_at":"2016-04-18T01:35:03.282-07:00","display_name":"Dr. Mangesh Bedekar","url":"https://maharashtra.academia.edu/DrMangeshBedekar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":309737,"name":"Wireless Mesh Network","url":"https://www.academia.edu/Documents/in/Wireless_Mesh_Network"}],"urls":[{"id":25515093,"url":"https://doi.org/10.1109/WOCN.2015.8064494"}]}, 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="89920763"><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/89920763/Ontology_Formation_and_Comparison_for_Syllabus_Structure_Using_NLP"><img alt="Research paper thumbnail of Ontology Formation and Comparison for Syllabus Structure Using NLP" 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/89920763/Ontology_Formation_and_Comparison_for_Syllabus_Structure_Using_NLP">Ontology Formation and Comparison for Syllabus Structure Using NLP</a></div><div class="wp-workCard_item"><span>SCRS CONFERENCE PROCEEDINGS ON INTELLIGENT SYSTEMS</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Ontologies are largely responsible for the creation of a framework or taxonomy for a particular 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">Ontologies are largely responsible for the creation of a framework or taxonomy for a particular domain which represents the shared knowledge, concepts and how these concepts are related with each other. This paper shows the usage of ontology for the comparison of a syllabus structure of universities. This is done with the extraction of the syllabus, creation of ontology for the representing syllabus, then parsing the ontology and applying Natural language processing to remove unwanted information. After getting the appropriate ontologies, a comparative study is made on them. Restrictions are made over the extracted syllabus to the subject “Software Engineering” for convenience. This depicts the collection and management of ontology knowledge and processing it in the right manner to get the desired insights.</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="89920763"><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="89920763"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 89920763; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=89920763]").text(description); $(".js-view-count[data-work-id=89920763]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 89920763; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='89920763']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 89920763, 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" } } $('.js-work-strip[data-work-id=89920763]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":89920763,"title":"Ontology Formation and Comparison for Syllabus Structure Using NLP","translated_title":"","metadata":{"abstract":"Ontologies are largely responsible for the creation of a framework or taxonomy for a particular domain which represents the shared knowledge, concepts and how these concepts are related with each other. This paper shows the usage of ontology for the comparison of a syllabus structure of universities. This is done with the extraction of the syllabus, creation of ontology for the representing syllabus, then parsing the ontology and applying Natural language processing to remove unwanted information. After getting the appropriate ontologies, a comparative study is made on them. Restrictions are made over the extracted syllabus to the subject “Software Engineering” for convenience. This depicts the collection and management of ontology knowledge and processing it in the right manner to get the desired insights.","publisher":"Soft Computing Research Society","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"SCRS CONFERENCE PROCEEDINGS ON INTELLIGENT SYSTEMS"},"translated_abstract":"Ontologies are largely responsible for the creation of a framework or taxonomy for a particular domain which represents the shared knowledge, concepts and how these concepts are related with each other. This paper shows the usage of ontology for the comparison of a syllabus structure of universities. This is done with the extraction of the syllabus, creation of ontology for the representing syllabus, then parsing the ontology and applying Natural language processing to remove unwanted information. After getting the appropriate ontologies, a comparative study is made on them. Restrictions are made over the extracted syllabus to the subject “Software Engineering” for convenience. This depicts the collection and management of ontology knowledge and processing it in the right manner to get the desired insights.","internal_url":"https://www.academia.edu/89920763/Ontology_Formation_and_Comparison_for_Syllabus_Structure_Using_NLP","translated_internal_url":"","created_at":"2022-11-03T20:29:14.473-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":47246275,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Ontology_Formation_and_Comparison_for_Syllabus_Structure_Using_NLP","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":47246275,"first_name":"Dr. Mangesh","middle_initials":"","last_name":"Bedekar","page_name":"DrMangeshBedekar","domain_name":"maharashtra","created_at":"2016-04-18T01:35:03.282-07:00","display_name":"Dr. Mangesh Bedekar","url":"https://maharashtra.academia.edu/DrMangeshBedekar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":805,"name":"Ontology","url":"https://www.academia.edu/Documents/in/Ontology"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"},{"id":14493,"name":"Parsing","url":"https://www.academia.edu/Documents/in/Parsing"},{"id":97256,"name":"Process Ontology","url":"https://www.academia.edu/Documents/in/Process_Ontology"},{"id":186232,"name":"Syllabus","url":"https://www.academia.edu/Documents/in/Syllabus"}],"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="89920762"><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/89920762/BMI_Application_Accident_Reduction_Using_Drowsiness_Detection"><img alt="Research paper thumbnail of BMI Application: Accident Reduction Using Drowsiness Detection" 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/89920762/BMI_Application_Accident_Reduction_Using_Drowsiness_Detection">BMI Application: Accident Reduction Using Drowsiness Detection</a></div><div class="wp-workCard_item"><span>Advances in Intelligent Systems and Computing</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Among the numerous factors that are responsible for increasing road accidents, the second most co...</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">Among the numerous factors that are responsible for increasing road accidents, the second most common cause is drowsiness. In an attempt to reduce the rate of accidents, we propose a system which would efficiently handle the timely detection of drowsiness and would accordingly curb the speed of the vehicle being driven. As a proof of concept of the proposed method, we have trained the SVM classifier on the EEG (electroencephalogram) waves derived from “Analysis of a sleep-dependent neuronal feedback loop: the slow-wave micro continuity of the EEG” by Kemp et al. [1, 2]. The data is obtained from a wireless EEG headset. The classification results will determine whether the EEG data corresponds to drowsiness or alertness. This level of drowsiness is then used to determine the maximum speed limit. As the work in [3] has stated, there is a strong correlation between the number of accidents and the speed limit. Hence altogether, the proposed system integrates EEG waves for sleep level detection, and speed lock as a preventive measure to reduce the number of plausible accidents.</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="89920762"><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="89920762"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 89920762; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=89920762]").text(description); $(".js-view-count[data-work-id=89920762]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 89920762; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='89920762']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 89920762, 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" } } $('.js-work-strip[data-work-id=89920762]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":89920762,"title":"BMI Application: Accident Reduction Using Drowsiness Detection","translated_title":"","metadata":{"abstract":"Among the numerous factors that are responsible for increasing road accidents, the second most common cause is drowsiness. In an attempt to reduce the rate of accidents, we propose a system which would efficiently handle the timely detection of drowsiness and would accordingly curb the speed of the vehicle being driven. As a proof of concept of the proposed method, we have trained the SVM classifier on the EEG (electroencephalogram) waves derived from “Analysis of a sleep-dependent neuronal feedback loop: the slow-wave micro continuity of the EEG” by Kemp et al. [1, 2]. The data is obtained from a wireless EEG headset. The classification results will determine whether the EEG data corresponds to drowsiness or alertness. This level of drowsiness is then used to determine the maximum speed limit. As the work in [3] has stated, there is a strong correlation between the number of accidents and the speed limit. Hence altogether, the proposed system integrates EEG waves for sleep level detection, and speed lock as a preventive measure to reduce the number of plausible accidents.","publisher":"Springer International Publishing","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Advances in Intelligent Systems and Computing"},"translated_abstract":"Among the numerous factors that are responsible for increasing road accidents, the second most common cause is drowsiness. In an attempt to reduce the rate of accidents, we propose a system which would efficiently handle the timely detection of drowsiness and would accordingly curb the speed of the vehicle being driven. As a proof of concept of the proposed method, we have trained the SVM classifier on the EEG (electroencephalogram) waves derived from “Analysis of a sleep-dependent neuronal feedback loop: the slow-wave micro continuity of the EEG” by Kemp et al. [1, 2]. The data is obtained from a wireless EEG headset. The classification results will determine whether the EEG data corresponds to drowsiness or alertness. This level of drowsiness is then used to determine the maximum speed limit. As the work in [3] has stated, there is a strong correlation between the number of accidents and the speed limit. Hence altogether, the proposed system integrates EEG waves for sleep level detection, and speed lock as a preventive measure to reduce the number of plausible accidents.","internal_url":"https://www.academia.edu/89920762/BMI_Application_Accident_Reduction_Using_Drowsiness_Detection","translated_internal_url":"","created_at":"2022-11-03T20:29:14.269-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":47246275,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"BMI_Application_Accident_Reduction_Using_Drowsiness_Detection","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":47246275,"first_name":"Dr. Mangesh","middle_initials":"","last_name":"Bedekar","page_name":"DrMangeshBedekar","domain_name":"maharashtra","created_at":"2016-04-18T01:35:03.282-07:00","display_name":"Dr. Mangesh Bedekar","url":"https://maharashtra.academia.edu/DrMangeshBedekar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[{"id":25515092,"url":"http://link.springer.com/content/pdf/10.1007/978-3-030-16681-6_7"}]}, 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="89920751"><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/89920751/Cartoon_Films_Made_in_India_How_do_they_Fare"><img alt="Research paper thumbnail of Cartoon Films: Made in India, How do they Fare?" class="work-thumbnail" src="https://attachments.academia-assets.com/93627250/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/89920751/Cartoon_Films_Made_in_India_How_do_they_Fare">Cartoon Films: Made in India, How do they Fare?</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Cartoons, these days have become an integral part of every child’s childhood. They are amongst th...</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">Cartoons, these days have become an integral part of every child’s childhood. They are amongst the most prominent forms of entertainment for children. With the advent of the nuclear family and single child families, with no mate/partner to interact, play or learn from, it is with the help of cartoons that kids are exposed to the various facets of the world around us. Cartoon films screened on most TV sets in Indian homes were majorly dubbed versions of successful cartoon films from USA, Japan, Canada, etc. From around 2003 onwards, some Indian cartoons started to appear on TV especially after the Cable TV came to Indian homes. This paper looks at the cartoons, which are made in India, made for India, and their co-relations with the cartoons which are otherwise screened on Indian TV sets, which are dubbed versions of cartoon films from around the world. These Indian cartoons are analysed and correlated with each other based on various attributes namely, the locale, the age of the pro...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d2b49c1ef9de02551d5c3a338af4dbea" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":93627250,"asset_id":89920751,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/93627250/download_file?st=MTczMjgyNDUyNyw4LjIyMi4yMDguMTQ2&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="89920751"><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="89920751"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 89920751; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=89920751]").text(description); $(".js-view-count[data-work-id=89920751]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 89920751; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='89920751']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 89920751, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d2b49c1ef9de02551d5c3a338af4dbea" } } $('.js-work-strip[data-work-id=89920751]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":89920751,"title":"Cartoon Films: Made in India, How do they Fare?","translated_title":"","metadata":{"abstract":"Cartoons, these days have become an integral part of every child’s childhood. 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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="85183159"><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/85183159/The_New_Dataset_MITWPU_1K_for_Object_Recognition_and_Image_Captioning_Tasks"><img alt="Research paper thumbnail of The New Dataset MITWPU-1K for Object Recognition and Image Captioning Tasks" class="work-thumbnail" src="https://attachments.academia-assets.com/89960848/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/85183159/The_New_Dataset_MITWPU_1K_for_Object_Recognition_and_Image_Captioning_Tasks">The New Dataset MITWPU-1K for Object Recognition and Image Captioning Tasks</a></div><div class="wp-workCard_item"><span>Engineering, Technology &amp; Applied Science Research</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the domain of image captioning, many pre-trained datasets are available. Using these datasets,...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the domain of image captioning, many pre-trained datasets are available. Using these datasets, models can be trained to automatically generate image descriptions regarding the contents of an image. Researchers usually do not spend much time in creating and training the new dataset before using it for a specific application, instead, they simply use existing pre-trained datasets. MS COCO, ImageNet, Flicker, and Pascal VOC, are well-known datasets that are widely used in the task of generating image captions. In most available image captioning datasets, image textual information, which can play a vital role in generating more precise image descriptions, is missing. This paper presents the process of creating a new dataset that consists of images along with text and captions. Images of the nearby vicinity of the campus of MIT World Peace University-MITWPU, India, were taken for the new dataset named MITWPU-1K. This dataset can be used in object detection and caption generation of im...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6344b8c2010dda7a897b69893a486276" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89960848,"asset_id":85183159,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89960848/download_file?st=MTczMjgyNDUyNyw4LjIyMi4yMDguMTQ2&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="85183159"><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="85183159"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 85183159; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=85183159]").text(description); $(".js-view-count[data-work-id=85183159]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 85183159; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='85183159']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 85183159, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6344b8c2010dda7a897b69893a486276" } } $('.js-work-strip[data-work-id=85183159]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":85183159,"title":"The New Dataset MITWPU-1K for Object Recognition and Image Captioning Tasks","translated_title":"","metadata":{"abstract":"In the domain of image captioning, many pre-trained datasets are available. Using these datasets, models can be trained to automatically generate image descriptions regarding the contents of an image. Researchers usually do not spend much time in creating and training the new dataset before using it for a specific application, instead, they simply use existing pre-trained datasets. MS COCO, ImageNet, Flicker, and Pascal VOC, are well-known datasets that are widely used in the task of generating image captions. In most available image captioning datasets, image textual information, which can play a vital role in generating more precise image descriptions, is missing. This paper presents the process of creating a new dataset that consists of images along with text and captions. Images of the nearby vicinity of the campus of MIT World Peace University-MITWPU, India, were taken for the new dataset named MITWPU-1K. This dataset can be used in object detection and caption generation of im...","publisher":"Engineering, Technology \u0026 Applied Science Research","publication_name":"Engineering, Technology \u0026amp; Applied Science Research"},"translated_abstract":"In the domain of image captioning, many pre-trained datasets are available. Using these datasets, models can be trained to automatically generate image descriptions regarding the contents of an image. Researchers usually do not spend much time in creating and training the new dataset before using it for a specific application, instead, they simply use existing pre-trained datasets. MS COCO, ImageNet, Flicker, and Pascal VOC, are well-known datasets that are widely used in the task of generating image captions. In most available image captioning datasets, image textual information, which can play a vital role in generating more precise image descriptions, is missing. This paper presents the process of creating a new dataset that consists of images along with text and captions. Images of the nearby vicinity of the campus of MIT World Peace University-MITWPU, India, were taken for the new dataset named MITWPU-1K. This dataset can be used in object detection and caption generation of im...","internal_url":"https://www.academia.edu/85183159/The_New_Dataset_MITWPU_1K_for_Object_Recognition_and_Image_Captioning_Tasks","translated_internal_url":"","created_at":"2022-08-19T21:54:25.147-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":47246275,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":89960848,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89960848/thumbnails/1.jpg","file_name":"2788.pdf","download_url":"https://www.academia.edu/attachments/89960848/download_file?st=MTczMjgyNDUyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"The_New_Dataset_MITWPU_1K_for_Object_Rec.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89960848/2788-libre.pdf?1660972130=\u0026response-content-disposition=attachment%3B+filename%3DThe_New_Dataset_MITWPU_1K_for_Object_Rec.pdf\u0026Expires=1732800652\u0026Signature=MEFxolDELDBG2joOrr8frsRRzH99psJfbZuzUjLP9ziP4r4nUxrN7hIAKCCZGhujVuvTqSSSf1Eia9Vx5oR0NrKTDTrCWEJUhM2b~PRA0xob-n~-7PyuMCQTvNI792eB-HQVyMQ~fbd7E6k4Rc-~NPBRSOGDNsZIXKpLUAoDAMgcNgN-JKc~jlX-IVaRJf4sxIV5MJt6~tIqkp3WgzKZcHJo96EznnjcZGAhNkxeroKRJQOUoPl4v7obLl01Ne~gMsmCVh4lYEqctpsSahw0IG7T9GzdHwHLidQK8qi6id2txiJOlgimHumQEb6qRgcvZGkCFPvwJodJhQ1T0jUihw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"The_New_Dataset_MITWPU_1K_for_Object_Recognition_and_Image_Captioning_Tasks","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":47246275,"first_name":"Dr. Mangesh","middle_initials":"","last_name":"Bedekar","page_name":"DrMangeshBedekar","domain_name":"maharashtra","created_at":"2016-04-18T01:35:03.282-07:00","display_name":"Dr. Mangesh Bedekar","url":"https://maharashtra.academia.edu/DrMangeshBedekar"},"attachments":[{"id":89960848,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89960848/thumbnails/1.jpg","file_name":"2788.pdf","download_url":"https://www.academia.edu/attachments/89960848/download_file?st=MTczMjgyNDUyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"The_New_Dataset_MITWPU_1K_for_Object_Rec.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89960848/2788-libre.pdf?1660972130=\u0026response-content-disposition=attachment%3B+filename%3DThe_New_Dataset_MITWPU_1K_for_Object_Rec.pdf\u0026Expires=1732800652\u0026Signature=MEFxolDELDBG2joOrr8frsRRzH99psJfbZuzUjLP9ziP4r4nUxrN7hIAKCCZGhujVuvTqSSSf1Eia9Vx5oR0NrKTDTrCWEJUhM2b~PRA0xob-n~-7PyuMCQTvNI792eB-HQVyMQ~fbd7E6k4Rc-~NPBRSOGDNsZIXKpLUAoDAMgcNgN-JKc~jlX-IVaRJf4sxIV5MJt6~tIqkp3WgzKZcHJo96EznnjcZGAhNkxeroKRJQOUoPl4v7obLl01Ne~gMsmCVh4lYEqctpsSahw0IG7T9GzdHwHLidQK8qi6id2txiJOlgimHumQEb6qRgcvZGkCFPvwJodJhQ1T0jUihw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[{"id":23142518,"url":"http://www.etasr.com/index.php/ETASR/article/download/5039/2788"}]}, 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="80419858"><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/80419858/D_CNN_A_New_model_for_Generating_Image_Captions_with_Text_Extraction_Using_Deep_Learning_for_Visually_Challenged_Individuals"><img alt="Research paper thumbnail of D-CNN: A New model for Generating Image Captions with Text Extraction Using Deep Learning for Visually Challenged Individuals" class="work-thumbnail" src="https://attachments.academia-assets.com/86807786/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/80419858/D_CNN_A_New_model_for_Generating_Image_Captions_with_Text_Extraction_Using_Deep_Learning_for_Visually_Challenged_Individuals">D-CNN: A New model for Generating Image Captions with Text Extraction Using Deep Learning for Visually Challenged Individuals</a></div><div class="wp-workCard_item"><span>Engineering, Technology & Applied Science Research</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Automatically describing the information of an image using properly constructed sentences is a tr...</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">Automatically describing the information of an image using properly constructed sentences is a tricky task in any language. However, it has the potential to have a significant effect by enabling visually challenged individuals to better understand their surroundings. This paper proposes an image captioning system that generates detailed captions and extracts text from an image, if any, and uses it as a part of the caption to provide a more precise description of the image. To extract the image features, the proposed model uses Convolutional Neural Networks (CNNs) followed by Long Short-Term Memory (LSTM) that generates corresponding sentences based on the learned image features. Further, using the text extraction module, the extracted text (if any) is included in the image description and the captions are presented in audio form. Publicly available benchmark datasets for image captioning like MS COCO, Flickr-8k, Flickr-30k have a variety of images, but they hardly have images that c...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="06972caf1466cb0d0fb6cc8d23a130bc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86807786,"asset_id":80419858,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86807786/download_file?st=MTczMjgyNDUyNyw4LjIyMi4yMDguMTQ2&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="80419858"><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="80419858"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80419858; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80419858]").text(description); $(".js-view-count[data-work-id=80419858]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80419858; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80419858']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80419858, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "06972caf1466cb0d0fb6cc8d23a130bc" } } $('.js-work-strip[data-work-id=80419858]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80419858,"title":"D-CNN: A New model for Generating Image Captions with Text Extraction Using Deep Learning for Visually Challenged Individuals","translated_title":"","metadata":{"abstract":"Automatically describing the information of an image using properly constructed sentences is a tricky task in any language. However, it has the potential to have a significant effect by enabling visually challenged individuals to better understand their surroundings. This paper proposes an image captioning system that generates detailed captions and extracts text from an image, if any, and uses it as a part of the caption to provide a more precise description of the image. To extract the image features, the proposed model uses Convolutional Neural Networks (CNNs) followed by Long Short-Term Memory (LSTM) that generates corresponding sentences based on the learned image features. Further, using the text extraction module, the extracted text (if any) is included in the image description and the captions are presented in audio form. Publicly available benchmark datasets for image captioning like MS COCO, Flickr-8k, Flickr-30k have a variety of images, but they hardly have images that c...","publisher":"Engineering, Technology \u0026 Applied Science Research","publication_name":"Engineering, Technology \u0026 Applied Science Research"},"translated_abstract":"Automatically describing the information of an image using properly constructed sentences is a tricky task in any language. However, it has the potential to have a significant effect by enabling visually challenged individuals to better understand their surroundings. This paper proposes an image captioning system that generates detailed captions and extracts text from an image, if any, and uses it as a part of the caption to provide a more precise description of the image. To extract the image features, the proposed model uses Convolutional Neural Networks (CNNs) followed by Long Short-Term Memory (LSTM) that generates corresponding sentences based on the learned image features. Further, using the text extraction module, the extracted text (if any) is included in the image description and the captions are presented in audio form. Publicly available benchmark datasets for image captioning like MS COCO, Flickr-8k, Flickr-30k have a variety of images, but they hardly have images that c...","internal_url":"https://www.academia.edu/80419858/D_CNN_A_New_model_for_Generating_Image_Captions_with_Text_Extraction_Using_Deep_Learning_for_Visually_Challenged_Individuals","translated_internal_url":"","created_at":"2022-05-31T22:09:58.802-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":47246275,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":86807786,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86807786/thumbnails/1.jpg","file_name":"2710.pdf","download_url":"https://www.academia.edu/attachments/86807786/download_file?st=MTczMjgyNDUyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"D_CNN_A_New_model_for_Generating_Image_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86807786/2710-libre.pdf?1654060832=\u0026response-content-disposition=attachment%3B+filename%3DD_CNN_A_New_model_for_Generating_Image_C.pdf\u0026Expires=1732800652\u0026Signature=Z1JI-X68BnbRqDICYV3fukIGCFcFBsm7BdFJmMNr91EG9-cb9oSIgARGlCkDtk6zmlDT4aw~TgUnCCyqfN2GlgsL88uLmcA8RqK17PmY4POKGAPGKota9njFWnXZWLny74~5U4leJoKWX5U26scJEWFVbyxELcHwaHOjmBTbh~WkU1BYncGC7B1HvlePGQY-nM~LvLpcKg~GxAalAcBkfxSH7DUwOU~jTpjPebeI-JsOsmU5sjFmDBUlwTary-W83vJCDJPiVodYv8YRxmIhf1b~Yz0L0X9Tl~ysSIS0UQJjsU471hKoVjPAQWXonEj3qCe264H-jfJCqlhww~m98g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"D_CNN_A_New_model_for_Generating_Image_Captions_with_Text_Extraction_Using_Deep_Learning_for_Visually_Challenged_Individuals","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":47246275,"first_name":"Dr. Mangesh","middle_initials":"","last_name":"Bedekar","page_name":"DrMangeshBedekar","domain_name":"maharashtra","created_at":"2016-04-18T01:35:03.282-07:00","display_name":"Dr. Mangesh Bedekar","url":"https://maharashtra.academia.edu/DrMangeshBedekar"},"attachments":[{"id":86807786,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86807786/thumbnails/1.jpg","file_name":"2710.pdf","download_url":"https://www.academia.edu/attachments/86807786/download_file?st=MTczMjgyNDUyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"D_CNN_A_New_model_for_Generating_Image_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86807786/2710-libre.pdf?1654060832=\u0026response-content-disposition=attachment%3B+filename%3DD_CNN_A_New_model_for_Generating_Image_C.pdf\u0026Expires=1732800652\u0026Signature=Z1JI-X68BnbRqDICYV3fukIGCFcFBsm7BdFJmMNr91EG9-cb9oSIgARGlCkDtk6zmlDT4aw~TgUnCCyqfN2GlgsL88uLmcA8RqK17PmY4POKGAPGKota9njFWnXZWLny74~5U4leJoKWX5U26scJEWFVbyxELcHwaHOjmBTbh~WkU1BYncGC7B1HvlePGQY-nM~LvLpcKg~GxAalAcBkfxSH7DUwOU~jTpjPebeI-JsOsmU5sjFmDBUlwTary-W83vJCDJPiVodYv8YRxmIhf1b~Yz0L0X9Tl~ysSIS0UQJjsU471hKoVjPAQWXonEj3qCe264H-jfJCqlhww~m98g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":86807785,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86807785/thumbnails/1.jpg","file_name":"2710.pdf","download_url":"https://www.academia.edu/attachments/86807785/download_file","bulk_download_file_name":"D_CNN_A_New_model_for_Generating_Image_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86807785/2710-libre.pdf?1654060832=\u0026response-content-disposition=attachment%3B+filename%3DD_CNN_A_New_model_for_Generating_Image_C.pdf\u0026Expires=1732800652\u0026Signature=B9P1MH3UqATlWSHE4ndWfcevbzAK97UjdkuUxMxSiGaTSdqie7X3Ii3eGSP3baGMXQ8rJ1N3GF3sv9ihTWK0OlcCIOJB6M5O2I4VdIpt6ailkd4UiftchzsiYuRfYHb-vxJRjUEmyKtr0MMthSbn85RXnLkIAIGaPkzcW~mFnxygb~qSYBVh3iEjUR5c8IFjwVT6xPVPwx~FizpJRWtrg42ET1SrpU~TQ-gc4xcrXin8kkqcFAIEmG0LrNdRAHAJ0ulayCSSCXKtJooIuKRtesf2VgAQn9WNev2zs2WzkkMb-9dq4Xb~Yjgciw63V4qsHNtSGkpUjckJoyOm5VqTWw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":30193,"name":"Image Analysis (Mathematics)","url":"https://www.academia.edu/Documents/in/Image_Analysis_Mathematics_"},{"id":283974,"name":"Closed Captioning","url":"https://www.academia.edu/Documents/in/Closed_Captioning"},{"id":1568111,"name":"Convolutional Neural Network","url":"https://www.academia.edu/Documents/in/Convolutional_Neural_Network"}],"urls":[{"id":20980895,"url":"https://www.etasr.com/index.php/ETASR/article/download/4772/2710"}]}, 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="80419853"><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/80419853/Intelligent_Twitter_Spam_Detection_A_Hybrid_Approach"><img alt="Research paper thumbnail of Intelligent Twitter Spam Detection: A Hybrid 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" rel="nofollow" href="https://www.academia.edu/80419853/Intelligent_Twitter_Spam_Detection_A_Hybrid_Approach">Intelligent Twitter Spam Detection: A Hybrid Approach</a></div><div class="wp-workCard_item"><span>Lecture Notes in Networks and Systems</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Over the years there has been a large upheaval in the social networking arena. Twitter being one ...</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">Over the years there has been a large upheaval in the social networking arena. Twitter being one of the most widely-used social networks in the world has always been a key target for intruders. Privacy concerns, stealing of important information and leakage of key credentials to spammers has been on the rise. In this paper, we have developed an Intelligent Twitter Spam Detection System which gives the precise details about spam profiles by identifying and detecting twitter spam. The system is a Hybrid approach as opposed to single-tier, single-classifier approaches which takes into account some unique feature sets before analyzing the tweets and also checks the links with Google Safe Browsing API for added security. This in turn leads to better tweet classification and improved as well as intelligent twitter spam detection.</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="80419853"><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="80419853"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80419853; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80419853]").text(description); $(".js-view-count[data-work-id=80419853]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80419853; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80419853']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80419853, 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" } } $('.js-work-strip[data-work-id=80419853]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80419853,"title":"Intelligent Twitter Spam Detection: A Hybrid Approach","translated_title":"","metadata":{"abstract":"Over the years there has been a large upheaval in the social networking arena. Twitter being one of the most widely-used social networks in the world has always been a key target for intruders. Privacy concerns, stealing of important information and leakage of key credentials to spammers has been on the rise. In this paper, we have developed an Intelligent Twitter Spam Detection System which gives the precise details about spam profiles by identifying and detecting twitter spam. The system is a Hybrid approach as opposed to single-tier, single-classifier approaches which takes into account some unique feature sets before analyzing the tweets and also checks the links with Google Safe Browsing API for added security. This in turn leads to better tweet classification and improved as well as intelligent twitter spam detection.","publisher":"Springer Singapore","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Lecture Notes in Networks and Systems"},"translated_abstract":"Over the years there has been a large upheaval in the social networking arena. Twitter being one of the most widely-used social networks in the world has always been a key target for intruders. Privacy concerns, stealing of important information and leakage of key credentials to spammers has been on the rise. In this paper, we have developed an Intelligent Twitter Spam Detection System which gives the precise details about spam profiles by identifying and detecting twitter spam. The system is a Hybrid approach as opposed to single-tier, single-classifier approaches which takes into account some unique feature sets before analyzing the tweets and also checks the links with Google Safe Browsing API for added security. 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These parameters can be user keyboard typing style, mouse movements, and some physiological sensors are used. This field of retrieving emotions from machines comes under the field of affective computing.</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="80419849"><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="80419849"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80419849; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80419849]").text(description); $(".js-view-count[data-work-id=80419849]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80419849; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80419849']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80419849, 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" } } $('.js-work-strip[data-work-id=80419849]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80419849,"title":"Inferring User Emotions from Keyboard and Mouse","translated_title":"","metadata":{"abstract":"This chapter emphasizes on retrieving user emotions from keyboard and mouse using different parameters. 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We attempt to identify drivers based on this characteristic driving profile. Users were made to playa realistic 3D driving simulation, StuntRally. Their keypress events were logged. These logs were used to train and test different classifiers such as Support Vector Machine(SVM), K Nearest Neighbour(KNN) and Naive Bayes(NB). The SVM performed best with an average testing accuracy of 80 %.</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="80419847"><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="80419847"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80419847; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80419847]").text(description); $(".js-view-count[data-work-id=80419847]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80419847; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80419847']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80419847, 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" } } $('.js-work-strip[data-work-id=80419847]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80419847,"title":"Driver Profiling Using Realistic Racing Games","translated_title":"","metadata":{"abstract":"All humans operating vehicles with several in-puts(steering wheel, pedals, gears etc.) do so in a unique way. We attempt to identify drivers based on this characteristic driving profile. Users were made to playa realistic 3D driving simulation, StuntRally. Their keypress events were logged. These logs were used to train and test different classifiers such as Support Vector Machine(SVM), K Nearest Neighbour(KNN) and Naive Bayes(NB). The SVM performed best with an average testing accuracy of 80 %.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT)"},"translated_abstract":"All humans operating vehicles with several in-puts(steering wheel, pedals, gears etc.) do so in a unique way. We attempt to identify drivers based on this characteristic driving profile. Users were made to playa realistic 3D driving simulation, StuntRally. Their keypress events were logged. These logs were used to train and test different classifiers such as Support Vector Machine(SVM), K Nearest Neighbour(KNN) and Naive Bayes(NB). The SVM performed best with an average testing accuracy of 80 %.","internal_url":"https://www.academia.edu/80419847/Driver_Profiling_Using_Realistic_Racing_Games","translated_internal_url":"","created_at":"2022-05-31T22:09:42.965-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":47246275,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Driver_Profiling_Using_Realistic_Racing_Games","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":47246275,"first_name":"Dr. Mangesh","middle_initials":"","last_name":"Bedekar","page_name":"DrMangeshBedekar","domain_name":"maharashtra","created_at":"2016-04-18T01:35:03.282-07:00","display_name":"Dr. Mangesh Bedekar","url":"https://maharashtra.academia.edu/DrMangeshBedekar"},"attachments":[],"research_interests":[{"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":9173,"name":"Biometrics","url":"https://www.academia.edu/Documents/in/Biometrics"},{"id":55284,"name":"Gaming","url":"https://www.academia.edu/Documents/in/Gaming"},{"id":141109,"name":"User Profiling","url":"https://www.academia.edu/Documents/in/User_Profiling"},{"id":319462,"name":"Keystroke Dynamic","url":"https://www.academia.edu/Documents/in/Keystroke_Dynamic"},{"id":558449,"name":"Keystroke logging","url":"https://www.academia.edu/Documents/in/Keystroke_logging"}],"urls":[{"id":20980890,"url":"http://xplorestaging.ieee.org/ielx7/8466130/8472948/08473154.pdf?arnumber=8473154"}]}, 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="80419844"><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/80419844/Inferring_emotional_state_of_a_user_by_user_profiling"><img alt="Research paper thumbnail of Inferring emotional state of a user by user profiling" 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/80419844/Inferring_emotional_state_of_a_user_by_user_profiling">Inferring emotional state of a user by user profiling</a></div><div class="wp-workCard_item"><span>2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">User profiles are important in many areas in which it is essential to obtain knowledge about user...</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">User profiles are important in many areas in which it is essential to obtain knowledge about users of software applications. Knowledge about a user includes his likes, dislikes, even his emotional state can be determined by user profiling. In this paper we examine what information constitutes a user profile; and how the profile information is used to get the emotional state of a user. We also study the main issues regarding user profiles from the perspectives of these research fields.</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="80419844"><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="80419844"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80419844; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80419844]").text(description); $(".js-view-count[data-work-id=80419844]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80419844; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80419844']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80419844, 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" } } $('.js-work-strip[data-work-id=80419844]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80419844,"title":"Inferring emotional state of a user by user profiling","translated_title":"","metadata":{"abstract":"User profiles are important in many areas in which it is essential to obtain knowledge about users of software applications. Knowledge about a user includes his likes, dislikes, even his emotional state can be determined by user profiling. In this paper we examine what information constitutes a user profile; and how the profile information is used to get the emotional state of a user. We also study the main issues regarding user profiles from the perspectives of these research fields.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)"},"translated_abstract":"User profiles are important in many areas in which it is essential to obtain knowledge about users of software applications. Knowledge about a user includes his likes, dislikes, even his emotional state can be determined by user profiling. In this paper we examine what information constitutes a user profile; and how the profile information is used to get the emotional state of a user. We also study the main issues regarding user profiles from the perspectives of these research fields.","internal_url":"https://www.academia.edu/80419844/Inferring_emotional_state_of_a_user_by_user_profiling","translated_internal_url":"","created_at":"2022-05-31T22:09:36.668-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":47246275,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Inferring_emotional_state_of_a_user_by_user_profiling","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":47246275,"first_name":"Dr. Mangesh","middle_initials":"","last_name":"Bedekar","page_name":"DrMangeshBedekar","domain_name":"maharashtra","created_at":"2016-04-18T01:35:03.282-07:00","display_name":"Dr. Mangesh Bedekar","url":"https://maharashtra.academia.edu/DrMangeshBedekar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[{"id":20980888,"url":"http://xplorestaging.ieee.org/ielx7/7911113/7917923/07918021.pdf?arnumber=7918021"}]}, 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="80419840"><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/80419840/Inferring_user_emotions_using_physiological_signals_from_mouse_and_keyboard"><img alt="Research paper thumbnail of Inferring user emotions using physiological signals from mouse and keyboard" 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/80419840/Inferring_user_emotions_using_physiological_signals_from_mouse_and_keyboard">Inferring user emotions using physiological signals from mouse and keyboard</a></div><div class="wp-workCard_item"><span>2017 International Conference on Intelligent Computing and Control Systems (ICICCS)</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, we have developed a mouse and keyboard which contains heart beat sensor, temperatu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, we have developed a mouse and keyboard which contains heart beat sensor, temperature sensor and force sensor. These sensors will generate the physiological signals. The signals from these devices will provide us the result for the current user. These results will be processed by the microcontroller and transmitted to the android device through Bluetooth module. The results will help us to determine the user&#39;s current emotional state and if found negative it can be altered.</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="80419840"><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="80419840"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80419840; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80419840]").text(description); $(".js-view-count[data-work-id=80419840]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80419840; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80419840']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80419840, 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" } } $('.js-work-strip[data-work-id=80419840]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80419840,"title":"Inferring user emotions using physiological signals from mouse and keyboard","translated_title":"","metadata":{"abstract":"In this paper, we have developed a mouse and keyboard which contains heart beat sensor, temperature sensor and force sensor. These sensors will generate the physiological signals. The signals from these devices will provide us the result for the current user. These results will be processed by the microcontroller and transmitted to the android device through Bluetooth module. The results will help us to determine the user\u0026#39;s current emotional state and if found negative it can be altered.","publisher":"2017 International Conference on Intelligent Computing and Control Systems (ICICCS)","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"2017 International Conference on Intelligent Computing and Control Systems (ICICCS)"},"translated_abstract":"In this paper, we have developed a mouse and keyboard which contains heart beat sensor, temperature sensor and force sensor. These sensors will generate the physiological signals. The signals from these devices will provide us the result for the current user. 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