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I believe in connectivity because connectivity is productivity. Meeting new challenges is my passion, fashion and obsession.<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="202459106">View All (7)</a></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="202459106" href="https://www.academia.edu/Documents/in/IT_in_healthcare"><div id="js-react-on-rails-context" style="display:none" 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data-dom-id="Pill-react-component-f561796f-4027-4cfc-aec7-e10b63ab805e"></div> <div id="Pill-react-component-f561796f-4027-4cfc-aec7-e10b63ab805e"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="202459106" href="https://www.academia.edu/Documents/in/Fertility"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Fertility"]}" data-trace="false" data-dom-id="Pill-react-component-ea0f7216-97b0-4794-a9cc-414bc79f03eb"></div> <div id="Pill-react-component-ea0f7216-97b0-4794-a9cc-414bc79f03eb"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="202459106" href="https://www.academia.edu/Documents/in/Honey"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Honey"]}" data-trace="false" 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role="tablist"><li class="nav-chip active" role="presentation"><a data-section-name="" data-toggle="tab" href="#all" role="tab">all</a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Papers" data-toggle="tab" href="#papers" role="tab" title="Papers"><span>8</span> <span class="ds2-5-body-sm-bold">Papers</span></a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Conference-Presentations" data-toggle="tab" href="#conferencepresentations" role="tab" title="Conference Presentations"><span>1</span> <span class="ds2-5-body-sm-bold">Conference Presentations</span></a></li></ul></div><div class="divider ds-divider-16" style="margin: 0px;"></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div 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necessary topic in today's world and security in the transaction system is not out of it. Various Blockchain and smart contract methods have been created and put to use in health care applications, banking industry, real property transactions, smart home appliances, cloud-based internet of things (IoT), medical transcript, data access and permission, E-Voting recording, online education, virtual power plant etc. Securing E-commerce trading is one of the basic needs in the present world but there are few proposed Blockchain and Smart Contract findings in this sector and lack of a properly implemented model. Therefore, in this work, there is an implementation of Blockchain and Smart contract model to secure E-commerce trading. There is a proposed algorithm to secure the trading. Besides that, Blockchain model is shown by the proof of work concept. Blockchain transaction implementation is shown in Angular web framework with JavaScript. Having the features of proof of work consensus algorithm, verify Blockchain to prevent tampering, generate wallet and sign transaction. Smart contract implementation is shown by Ethereum smart contract with solidity. The methods is tested by using JavaScript VM virtual Blockchain. This proposed model shows a better performance on fast and secure transaction in E-commerce.","grobid_abstract_attachment_id":91669872},"translated_abstract":null,"internal_url":"https://www.academia.edu/87470872/_TONMOY_ROY","translated_internal_url":"","created_at":"2022-09-28T01:30:09.989-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":202459106,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":91669872,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/91669872/thumbnails/1.jpg","file_name":"203104_TONMOY_ROY.pdf","download_url":"https://www.academia.edu/attachments/91669872/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"TONMOY_ROY.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/91669872/203104_TONMOY_ROY-libre.pdf?1664354141=\u0026response-content-disposition=attachment%3B+filename%3DTONMOY_ROY.pdf\u0026Expires=1733055419\u0026Signature=TIohv1djtv56O7gJFSH0WcH7hG~7TfXm~W6UAwZACCeP-fyV742B6vw71pAYtLPowX672AJwUk-~rAXM3S0fzGmusFbIeNLUF-F7wYQmXLOVogDn41JcA2qjs9tNoQ803XW~GxiO8kHqPK7gYBgKSIR5tGwDxFkELmFZDQm6SPTEDko5TKn7jxmFXvAC8WjDVAVSDif3QE~yPvMNb~r6UXLmHRmDzIUcYK7mAYHCpUlICKXXq-2iPxyMfdidkJl1OZhHRSnmQ2FgswVRpTkf3Xwj~Te1mZnsDXLvs21GWLTnoQ8PnF4F2orexTA5iQkIcV~YDgWLUh9mgghflgvPOA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"_TONMOY_ROY","translated_slug":"","page_count":62,"language":"en","content_type":"Work","owner":{"id":202459106,"first_name":"TONMOY","middle_initials":null,"last_name":"ROY","page_name":"TONMOYROY","domain_name":"northsouth","created_at":"2021-09-06T21:07:30.857-07:00","display_name":"TONMOY ROY","url":"https://northsouth.academia.edu/TONMOYROY"},"attachments":[{"id":91669872,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/91669872/thumbnails/1.jpg","file_name":"203104_TONMOY_ROY.pdf","download_url":"https://www.academia.edu/attachments/91669872/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"TONMOY_ROY.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/91669872/203104_TONMOY_ROY-libre.pdf?1664354141=\u0026response-content-disposition=attachment%3B+filename%3DTONMOY_ROY.pdf\u0026Expires=1733055419\u0026Signature=TIohv1djtv56O7gJFSH0WcH7hG~7TfXm~W6UAwZACCeP-fyV742B6vw71pAYtLPowX672AJwUk-~rAXM3S0fzGmusFbIeNLUF-F7wYQmXLOVogDn41JcA2qjs9tNoQ803XW~GxiO8kHqPK7gYBgKSIR5tGwDxFkELmFZDQm6SPTEDko5TKn7jxmFXvAC8WjDVAVSDif3QE~yPvMNb~r6UXLmHRmDzIUcYK7mAYHCpUlICKXXq-2iPxyMfdidkJl1OZhHRSnmQ2FgswVRpTkf3Xwj~Te1mZnsDXLvs21GWLTnoQ8PnF4F2orexTA5iQkIcV~YDgWLUh9mgghflgvPOA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); 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There are several Blockchain and Smart contract methods have been created and put to use in health care applications, banking industry, real property transactions, smart home appliances, cloud-based internet of things (IoT), medical transcript, data access and permission, E-Voting Recording, Online Education, virtual power plant etc. E-commerce trading is one of the basic needs in the present world, but there are few Blockchain and Smart Contract findings in this area and a lack of properly implemented models. Therefore, in this work, there is an implementation of Blockchain and Smart contract model to secure E-commerce trading. There is a proposed algorithm to secure the trading. Furthermore, the proof of work concept is used to demonstrate Blockchain transaction implementation. The features of the protocol include a proof of work consensus algorithm, a verification of the Blockchain to prevent tampering, the generation of wallets and the signing of transactions. Smart contract implementation is shown by Ethereum smart contract with solidity. The methods are tested by using JavaScript VM virtual Blockchain. In E-commerce, this proposed model shows a better performance in terms of fast and secure transactions.","grobid_abstract_attachment_id":91669776},"translated_abstract":null,"internal_url":"https://www.academia.edu/87470753/Secure_E_commerce_Trading_Using_Blockchain_with_Smart_Contract_Based_on_POW","translated_internal_url":"","created_at":"2022-09-28T01:29:22.600-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":202459106,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":91669776,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/91669776/thumbnails/1.jpg","file_name":"IEEE_TONMOY.pdf","download_url":"https://www.academia.edu/attachments/91669776/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Secure_E_commerce_Trading_Using_Blockcha.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/91669776/IEEE_TONMOY-libre.pdf?1664354120=\u0026response-content-disposition=attachment%3B+filename%3DSecure_E_commerce_Trading_Using_Blockcha.pdf\u0026Expires=1733055419\u0026Signature=A040Q0-hfv72QCPFU9FgsqgIsz88dFNu-B9LMUCMNWf~DAsHq04Cf1vxsYKrVlLFr8~-JNjpuP8~QMlKcnbTH7yUCNui-ppXy~uIM2SIFJ8sktUsSBk0QK8pypnLGsOFA87eaf1qAQkt5g9dC2Wu-OovaVksVBWK-s-iPbnxXmpniEvSgQQRjR2DII279l2tMyG5JHv4TeL~RzMZy3NfUAxFoQHACUEYlt0-u4p0OrMkbaWo1Sk115A6O9caxu0nkDKHi4xZQW-qLE3c1GCt-mfRloDTGld2ys4VtjTsnMTtw75oS9WCvY-E4SeNdyqyHBRdCs6dVKiLpFHgntObsQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Secure_E_commerce_Trading_Using_Blockchain_with_Smart_Contract_Based_on_POW","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":202459106,"first_name":"TONMOY","middle_initials":null,"last_name":"ROY","page_name":"TONMOYROY","domain_name":"northsouth","created_at":"2021-09-06T21:07:30.857-07:00","display_name":"TONMOY ROY","url":"https://northsouth.academia.edu/TONMOYROY"},"attachments":[{"id":91669776,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/91669776/thumbnails/1.jpg","file_name":"IEEE_TONMOY.pdf","download_url":"https://www.academia.edu/attachments/91669776/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Secure_E_commerce_Trading_Using_Blockcha.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/91669776/IEEE_TONMOY-libre.pdf?1664354120=\u0026response-content-disposition=attachment%3B+filename%3DSecure_E_commerce_Trading_Using_Blockcha.pdf\u0026Expires=1733055419\u0026Signature=A040Q0-hfv72QCPFU9FgsqgIsz88dFNu-B9LMUCMNWf~DAsHq04Cf1vxsYKrVlLFr8~-JNjpuP8~QMlKcnbTH7yUCNui-ppXy~uIM2SIFJ8sktUsSBk0QK8pypnLGsOFA87eaf1qAQkt5g9dC2Wu-OovaVksVBWK-s-iPbnxXmpniEvSgQQRjR2DII279l2tMyG5JHv4TeL~RzMZy3NfUAxFoQHACUEYlt0-u4p0OrMkbaWo1Sk115A6O9caxu0nkDKHi4xZQW-qLE3c1GCt-mfRloDTGld2ys4VtjTsnMTtw75oS9WCvY-E4SeNdyqyHBRdCs6dVKiLpFHgntObsQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"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="62748435"><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/62748435/Crowd_Sourced_Bicycle_Sharing_System"><img alt="Research paper thumbnail of Crowd Sourced Bicycle Sharing System" class="work-thumbnail" src="https://attachments.academia-assets.com/75418386/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/62748435/Crowd_Sourced_Bicycle_Sharing_System">Crowd Sourced Bicycle Sharing System</a></div><div class="wp-workCard_item"><span>North South University</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">These last years with the growing population in the smart city demands efficient transportation s...</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">These last years with the growing population in the smart city demands efficient transportation sharing which is a bicycle sharing system for developing the smart city. Bicycle sharing as we know is an affordable, easily accessible and reliable mode of transportation. But an efficient bicycle-sharing capable of not only sharing bikes also provides information regarding the availability of bicycle per station, route business, time/day-wise bicycle schedule. The embedded sensors are able to opportunistically communicate through wireless communication with stations when available, providing real-time data about tours/minutes, speed, effort, rhythm, etc. We have been based on our study analysis data to predict regarding the bicycle’s available at stations, bicycle schedule, a location of the nearest hub where a bike is available etc., reducing the user time and effort.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="79a5b2a2a622d704db3be88d2a5ecab1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":75418386,"asset_id":62748435,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/75418386/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&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="62748435"><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="62748435"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 62748435; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=62748435]").text(description); $(".js-view-count[data-work-id=62748435]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 62748435; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='62748435']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 62748435, 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: "79a5b2a2a622d704db3be88d2a5ecab1" } } $('.js-work-strip[data-work-id=62748435]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":62748435,"title":"Crowd Sourced Bicycle Sharing System","translated_title":"","metadata":{"abstract":"These last years with the growing population in the smart city demands efficient transportation sharing which is a bicycle sharing system for developing the smart city. Bicycle sharing as we know is an affordable, easily accessible and reliable mode of transportation. But an efficient bicycle-sharing capable of not only sharing bikes also provides information regarding the availability of bicycle per station, route business, time/day-wise bicycle schedule. The embedded sensors are able to opportunistically communicate through wireless communication with stations when available, providing real-time data about tours/minutes, speed, effort, rhythm, etc. We have been based on our study analysis data to predict regarding the bicycle’s available at stations, bicycle schedule, a location of the nearest hub where a bike is available etc., reducing the user time and effort.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"North South University"},"translated_abstract":"These last years with the growing population in the smart city demands efficient transportation sharing which is a bicycle sharing system for developing the smart city. Bicycle sharing as we know is an affordable, easily accessible and reliable mode of transportation. But an efficient bicycle-sharing capable of not only sharing bikes also provides information regarding the availability of bicycle per station, route business, time/day-wise bicycle schedule. 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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="59384116"><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/59384116/Effects_of_Virtual_Human_Animation_on_Emotion_Contagion_in_Simulated_Inter_Personal_Experiences"><img alt="Research paper thumbnail of Effects of Virtual Human Animation on Emotion Contagion in Simulated Inter-Personal Experiences" 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/59384116/Effects_of_Virtual_Human_Animation_on_Emotion_Contagion_in_Simulated_Inter_Personal_Experiences">Effects of Virtual Human Animation on Emotion Contagion in Simulated Inter-Personal Experiences</a></div><div class="wp-workCard_item"><span>IEEE Transactions on Visualization and Computer Graphics</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We empirically examined the impact of virtual human animation on the emotional responses of parti...</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">We empirically examined the impact of virtual human animation on the emotional responses of participants in a medical virtual reality system for education in the signs and symptoms of patient deterioration. Participants were presented with one of two virtual human conditions in a between-subjects experiment, static (non-animated) and dynamic (animated). Our objective measures included the use of psycho-physical Electro Dermal Activity (EDA) sensors, and subjective measures inspired by social psychology research included the Differential Emotions Survey (DES IV) and Positive and Negative Affect Survey (PANAS). We analyzed the quantitative and qualitative measures associated with participants&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146; emotional state at four distinct time-steps in the simulated interpersonal experience as the virtual patient&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146;s medical condition deteriorated. Results suggest that participants in the dynamic condition with animations exhibited a higher sense of co-presence and greater emotional response as compared to participants in the static condition, corresponding to the deterioration in the medical condition of the virtual patient. Negative affect of participants in the dynamic condition increased at a higher rate than for participants in the static condition. The virtual human animations elicited a stronger response in negative emotions such as anguish, fear, and anger as the virtual patient&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146;s medical condition worsened.</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="59384116"><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="59384116"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 59384116; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=59384116]").text(description); $(".js-view-count[data-work-id=59384116]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 59384116; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='59384116']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 59384116, 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" } } $('.js-work-strip[data-work-id=59384116]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":59384116,"title":"Effects of Virtual Human Animation on Emotion Contagion in Simulated Inter-Personal Experiences","translated_title":"","metadata":{"abstract":"We empirically examined the impact of virtual human animation on the emotional responses of participants in a medical virtual reality system for education in the signs and symptoms of patient deterioration. Participants were presented with one of two virtual human conditions in a between-subjects experiment, static (non-animated) and dynamic (animated). Our objective measures included the use of psycho-physical Electro Dermal Activity (EDA) sensors, and subjective measures inspired by social psychology research included the Differential Emotions Survey (DES IV) and Positive and Negative Affect Survey (PANAS). We analyzed the quantitative and qualitative measures associated with participants\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146; emotional state at four distinct time-steps in the simulated interpersonal experience as the virtual patient\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146;s medical condition deteriorated. Results suggest that participants in the dynamic condition with animations exhibited a higher sense of co-presence and greater emotional response as compared to participants in the static condition, corresponding to the deterioration in the medical condition of the virtual patient. Negative affect of participants in the dynamic condition increased at a higher rate than for participants in the static condition. The virtual human animations elicited a stronger response in negative emotions such as anguish, fear, and anger as the virtual patient\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146;s medical condition worsened.","publisher":"Institute of Electrical and Electronics Engineers (IEEE)","publication_date":{"day":null,"month":null,"year":2000,"errors":{}},"publication_name":"IEEE Transactions on Visualization and Computer Graphics"},"translated_abstract":"We empirically examined the impact of virtual human animation on the emotional responses of participants in a medical virtual reality system for education in the signs and symptoms of patient deterioration. Participants were presented with one of two virtual human conditions in a between-subjects experiment, static (non-animated) and dynamic (animated). Our objective measures included the use of psycho-physical Electro Dermal Activity (EDA) sensors, and subjective measures inspired by social psychology research included the Differential Emotions Survey (DES IV) and Positive and Negative Affect Survey (PANAS). We analyzed the quantitative and qualitative measures associated with participants\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146; emotional state at four distinct time-steps in the simulated interpersonal experience as the virtual patient\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146;s medical condition deteriorated. Results suggest that participants in the dynamic condition with animations exhibited a higher sense of co-presence and greater emotional response as compared to participants in the static condition, corresponding to the deterioration in the medical condition of the virtual patient. Negative affect of participants in the dynamic condition increased at a higher rate than for participants in the static condition. The virtual human animations elicited a stronger response in negative emotions such as anguish, fear, and anger as the virtual patient\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146;s medical condition worsened.","internal_url":"https://www.academia.edu/59384116/Effects_of_Virtual_Human_Animation_on_Emotion_Contagion_in_Simulated_Inter_Personal_Experiences","translated_internal_url":"","created_at":"2021-10-21T14:52:58.932-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":202459106,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Effects_of_Virtual_Human_Animation_on_Emotion_Contagion_in_Simulated_Inter_Personal_Experiences","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":202459106,"first_name":"TONMOY","middle_initials":null,"last_name":"ROY","page_name":"TONMOYROY","domain_name":"northsouth","created_at":"2021-09-06T21:07:30.857-07:00","display_name":"TONMOY ROY","url":"https://northsouth.academia.edu/TONMOYROY"},"attachments":[],"research_interests":[{"id":445,"name":"Computer Graphics","url":"https://www.academia.edu/Documents/in/Computer_Graphics"},{"id":1575,"name":"Animation","url":"https://www.academia.edu/Documents/in/Animation"},{"id":4122,"name":"Computer Animation","url":"https://www.academia.edu/Documents/in/Computer_Animation"},{"id":21269,"name":"Facial expression","url":"https://www.academia.edu/Documents/in/Facial_expression"},{"id":22506,"name":"Adolescent","url":"https://www.academia.edu/Documents/in/Adolescent"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":50238,"name":"Affect","url":"https://www.academia.edu/Documents/in/Affect"},{"id":50642,"name":"Virtual Reality","url":"https://www.academia.edu/Documents/in/Virtual_Reality"},{"id":55405,"name":"Sensors","url":"https://www.academia.edu/Documents/in/Sensors"},{"id":123287,"name":"Three Dimensional Imaging","url":"https://www.academia.edu/Documents/in/Three_Dimensional_Imaging"},{"id":133057,"name":"Young Adult","url":"https://www.academia.edu/Documents/in/Young_Adult"},{"id":172809,"name":"Interpersonal Relations","url":"https://www.academia.edu/Documents/in/Interpersonal_Relations"},{"id":255094,"name":"Computer User Interface Design","url":"https://www.academia.edu/Documents/in/Computer_User_Interface_Design"},{"id":2849038,"name":"photic stimulation","url":"https://www.academia.edu/Documents/in/photic_stimulation"},{"id":3110726,"name":"Whole Body Imaging","url":"https://www.academia.edu/Documents/in/Whole_Body_Imaging"}],"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="59384088"><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/59384088/One_Shot_Learning_Gesture_Recognition_Using_Motion_History_Based_Gesture_Silhouettes"><img alt="Research paper thumbnail of One-Shot-Learning Gesture Recognition Using Motion History Based Gesture Silhouettes" class="work-thumbnail" src="https://attachments.academia-assets.com/73334842/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/59384088/One_Shot_Learning_Gesture_Recognition_Using_Motion_History_Based_Gesture_Silhouettes">One-Shot-Learning Gesture Recognition Using Motion History Based Gesture Silhouettes</a></div><div class="wp-workCard_item"><span>The Proceedings of the 1st International Conference on Industrial Application Engineering 2013</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f7a57f0706cb059ad051b57ec6e67f7c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":73334842,"asset_id":59384088,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/73334842/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&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="59384088"><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="59384088"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 59384088; 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The challenge here is to perform satisfactory recognition operations with only one training example of each action, while no prior knowledge about actions, foreground/background segmentation, or any motion estimation and tracking are available. In the proposed scheme motion history imaging technique is applied to track the motion flow in consecutive frames. The information of motion flow is later utilized to calculate the percent change of motion flow for an action in different spatial regions of the frame. The space-time descriptor computed this way from the query video is a measure of the likeness of a gesture in a lexicon. Finally, gesture classification is performed based on correlation based and Euclidean distance based classifiers and the results are compared. 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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="51054897"><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/51054897/A_Transfer_Learning_Approach_for_Face_Recognition_using_Average_Pooling_and_MobileNetV2"><img alt="Research paper thumbnail of A Transfer Learning Approach for Face Recognition using Average Pooling and MobileNetV2" class="work-thumbnail" src="https://attachments.academia-assets.com/68916033/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/51054897/A_Transfer_Learning_Approach_for_Face_Recognition_using_Average_Pooling_and_MobileNetV2">A Transfer Learning Approach for Face Recognition using Average Pooling and MobileNetV2</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/KarimAsif">Joyece Jane</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://malaya.academia.edu/FMJavedMehediShamrat">F M Javed Mehedi Shamrat</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://northsouth.academia.edu/TONMOYROY">TONMOY ROY</a></span></div><div class="wp-workCard_item"><span>IEEE </span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Facial recognition is a fundamental method in facial-related science such as face detection, auth...</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">Facial recognition is a fundamental method in facial-related science such as face detection, authentication, monitoring, and a crucial phase in computer vision and pattern recognition. Face recognition technology aids in crime prevention by storing the captured image in a database, which can then be used in various ways, including identifying a person. With just a few faces in the frame, most facial recognition systems function sufficiently when the techniques have been tested under artificial illumination, with accurate facial poses and nonblurry images. in our proposed system, a face recognition system is proposed using Average pooling and MobileNetV2. The classifiers are implemented after a set of preprocessing steps on the retrieved image data. To compare the model is more effective, a performance test on the result is performed. It is observed from the study that MobileNetV2 triumphs over Average pooling with an accuracy rate of 98.89% and 99.01% on training and test data, respectively.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f3ec8d5e52219f0f0d693e1af3a533a7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":68916033,"asset_id":51054897,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/68916033/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&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="51054897"><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="51054897"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 51054897; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=51054897]").text(description); $(".js-view-count[data-work-id=51054897]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 51054897; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='51054897']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 51054897, 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: "f3ec8d5e52219f0f0d693e1af3a533a7" } } $('.js-work-strip[data-work-id=51054897]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":51054897,"title":"A Transfer Learning Approach for Face Recognition using Average Pooling and MobileNetV2","translated_title":"","metadata":{"abstract":"Facial recognition is a fundamental method in facial-related science such as face detection, authentication, monitoring, and a crucial phase in computer vision and pattern recognition. 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It is observed from the study that MobileNetV2 triumphs over Average pooling with an accuracy rate of 98.89% and 99.01% on training and test data, respectively.","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"IEEE "},"translated_abstract":"Facial recognition is a fundamental method in facial-related science such as face detection, authentication, monitoring, and a crucial phase in computer vision and pattern recognition. Face recognition technology aids in crime prevention by storing the captured image in a database, which can then be used in various ways, including identifying a person. With just a few faces in the frame, most facial recognition systems function sufficiently when the techniques have been tested under artificial illumination, with accurate facial poses and nonblurry images. in our proposed system, a face recognition system is proposed using Average pooling and MobileNetV2. The classifiers are implemented after a set of preprocessing steps on the retrieved image data. To compare the model is more effective, a performance test on the result is performed. It is observed from the study that MobileNetV2 triumphs over Average pooling with an accuracy rate of 98.89% and 99.01% on training and test data, respectively.","internal_url":"https://www.academia.edu/51054897/A_Transfer_Learning_Approach_for_Face_Recognition_using_Average_Pooling_and_MobileNetV2","translated_internal_url":"","created_at":"2021-08-28T04:02:03.907-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":27426166,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":36817350,"work_id":51054897,"tagging_user_id":27426166,"tagged_user_id":99384668,"co_author_invite_id":null,"email":"j***m@gmail.com","affiliation":"University of Malaya, Malaysia","display_order":1,"name":"F M Javed Mehedi Shamrat","title":"A Transfer Learning Approach for Face Recognition using Average Pooling and MobileNetV2"},{"id":36817351,"work_id":51054897,"tagging_user_id":27426166,"tagged_user_id":102686327,"co_author_invite_id":null,"email":"s***4@gmail.com","affiliation":"Ahsanullah University of Science and Technology","display_order":2,"name":"Sovon Chakraborty","title":"A Transfer Learning Approach for Face Recognition using Average Pooling and MobileNetV2"},{"id":36817352,"work_id":51054897,"tagging_user_id":27426166,"tagged_user_id":202459106,"co_author_invite_id":7290986,"email":"t***4@gmail.com","affiliation":"North South University","display_order":3,"name":"TONMOY ROY","title":"A Transfer Learning Approach for Face Recognition using Average Pooling and MobileNetV2"}],"downloadable_attachments":[{"id":68916033,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/68916033/thumbnails/1.jpg","file_name":"A_Transfer_Learning_Approach_for_Face_Recognition_using_Average_Pooling_and_MobileNetV2_.pdf","download_url":"https://www.academia.edu/attachments/68916033/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Transfer_Learning_Approach_for_Face_Re.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/68916033/A_Transfer_Learning_Approach_for_Face_Recognition_using_Average_Pooling_and_MobileNetV2_-libre.pdf?1630155015=\u0026response-content-disposition=attachment%3B+filename%3DA_Transfer_Learning_Approach_for_Face_Re.pdf\u0026Expires=1733055419\u0026Signature=azmtZSHyJKhrpOry76Oi06CJYvhVIxXNDmeHR3FLj~8lNjEFF~Up6IdQW-ZCaaPWi~caSxfX~cS~9N5pIRyCWkjPU-I4pTpbuSAZPwuE0okFv4dqwmG9Y~7aHseBC1~nXI5qGEQ9snS99iW0BzhQef1b0yCp1ftYtH5XmtiOEHcCykfEzIkmx10KQ3VFGHIgcVgaRv3KOFUpQbFIEKdkvrdxKwJNgKO9dQGKtQmUgTy5bhuspkNEo06fv0KWwlJMWETX8WS9gtB7qQpRDHQlOiBtEmwgLKu~XvgjRzSzLeN2ymlMDdR7xcC-L1KoOxnLGqmwa8mlBxoGMmooVKjYNA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Transfer_Learning_Approach_for_Face_Recognition_using_Average_Pooling_and_MobileNetV2","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":27426166,"first_name":"Joyece","middle_initials":null,"last_name":"Jane","page_name":"KarimAsif","domain_name":"independent","created_at":"2015-03-08T03:08:08.378-07:00","display_name":"Joyece Jane","url":"https://independent.academia.edu/KarimAsif"},"attachments":[{"id":68916033,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/68916033/thumbnails/1.jpg","file_name":"A_Transfer_Learning_Approach_for_Face_Recognition_using_Average_Pooling_and_MobileNetV2_.pdf","download_url":"https://www.academia.edu/attachments/68916033/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Transfer_Learning_Approach_for_Face_Re.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/68916033/A_Transfer_Learning_Approach_for_Face_Recognition_using_Average_Pooling_and_MobileNetV2_-libre.pdf?1630155015=\u0026response-content-disposition=attachment%3B+filename%3DA_Transfer_Learning_Approach_for_Face_Re.pdf\u0026Expires=1733055419\u0026Signature=azmtZSHyJKhrpOry76Oi06CJYvhVIxXNDmeHR3FLj~8lNjEFF~Up6IdQW-ZCaaPWi~caSxfX~cS~9N5pIRyCWkjPU-I4pTpbuSAZPwuE0okFv4dqwmG9Y~7aHseBC1~nXI5qGEQ9snS99iW0BzhQef1b0yCp1ftYtH5XmtiOEHcCykfEzIkmx10KQ3VFGHIgcVgaRv3KOFUpQbFIEKdkvrdxKwJNgKO9dQGKtQmUgTy5bhuspkNEo06fv0KWwlJMWETX8WS9gtB7qQpRDHQlOiBtEmwgLKu~XvgjRzSzLeN2ymlMDdR7xcC-L1KoOxnLGqmwa8mlBxoGMmooVKjYNA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"}],"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="51054901"><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/51054901/IoT_Based_Smart_Automated_Agriculture_and_Real_Time_Monitoring_System"><img alt="Research paper thumbnail of IoT Based Smart Automated Agriculture and Real Time Monitoring System" class="work-thumbnail" src="https://attachments.academia-assets.com/68916028/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/51054901/IoT_Based_Smart_Automated_Agriculture_and_Real_Time_Monitoring_System">IoT Based Smart Automated Agriculture and Real Time Monitoring System</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/KarimAsif">Joyece Jane</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://malaya.academia.edu/FMJavedMehediShamrat">F M Javed Mehedi Shamrat</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://northsouth.academia.edu/TONMOYROY">TONMOY ROY</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MdTareqRahmanHimu">Md. Tareq Rahman [Himu]</a></span></div><div class="wp-workCard_item"><span>IEEE</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In Bangladesh, agriculture is the primary source of income. It has a significant impact on the 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">In Bangladesh, agriculture is the primary source of income. It has a significant impact on the country's economy. However, agriculture is being hampered these days due to citizens shifting from rural to urban areas. Monitoring environmental factors is not a natural remedy for increasing crop production. Several causes have a significant impact on efficiency. Consequently, to address these issues, agriculture must incorporate automation. A farmer can save time, cost, resources, and energy by using an automated irrigation device. Traditional agricultural irrigation methods necessitate human interference. Human interference can be reduced with automated irrigation technology. We have designed and developed a reliable smart farming system (IoT) to reduce farmers' time costs and resources. Our proposed system can detect temperature, detect the moisture level and water level of the agricultural land, and remotely monitor the land crops. The proposed model sort out into four modules: Water Level Detection Module (WLDM) always detect the water level to avoid drops destruction; Soil Moisture Detection Module (SMDM) calculate the soil moisture level from the land, if the level goes down its start the water pump; Notification Module (NM) is responsible for generating the message service to notify the user if any parameter (Temperature, Soil Moisture, Water) not in well condition; Temperature Detection Module (TDM) is always counting the temperature and humidity of the air if its high then it will start the fan; Cloud and Notification Module (CNM) is handled the user notification through message and remotely monitoring the data of first three modules to take the necessary steps. The result shows the system successfully performed and it can be noted that our proposed can be implemented with any type of environment and agricultural land.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="66f208a3ab1d1135af65a234ee665eb6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":68916028,"asset_id":51054901,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/68916028/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&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="51054901"><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="51054901"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 51054901; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=51054901]").text(description); $(".js-view-count[data-work-id=51054901]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 51054901; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='51054901']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 51054901, 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: "66f208a3ab1d1135af65a234ee665eb6" } } $('.js-work-strip[data-work-id=51054901]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":51054901,"title":"IoT Based Smart Automated Agriculture and Real Time Monitoring System","translated_title":"","metadata":{"abstract":"In Bangladesh, agriculture is the primary source of income. It has a significant impact on the country's economy. However, agriculture is being hampered these days due to citizens shifting from rural to urban areas. Monitoring environmental factors is not a natural remedy for increasing crop production. Several causes have a significant impact on efficiency. Consequently, to address these issues, agriculture must incorporate automation. A farmer can save time, cost, resources, and energy by using an automated irrigation device. Traditional agricultural irrigation methods necessitate human interference. Human interference can be reduced with automated irrigation technology. We have designed and developed a reliable smart farming system (IoT) to reduce farmers' time costs and resources. Our proposed system can detect temperature, detect the moisture level and water level of the agricultural land, and remotely monitor the land crops. The proposed model sort out into four modules: Water Level Detection Module (WLDM) always detect the water level to avoid drops destruction; Soil Moisture Detection Module (SMDM) calculate the soil moisture level from the land, if the level goes down its start the water pump; Notification Module (NM) is responsible for generating the message service to notify the user if any parameter (Temperature, Soil Moisture, Water) not in well condition; Temperature Detection Module (TDM) is always counting the temperature and humidity of the air if its high then it will start the fan; Cloud and Notification Module (CNM) is handled the user notification through message and remotely monitoring the data of first three modules to take the necessary steps. The result shows the system successfully performed and it can be noted that our proposed can be implemented with any type of environment and agricultural land.","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"IEEE"},"translated_abstract":"In Bangladesh, agriculture is the primary source of income. It has a significant impact on the country's economy. However, agriculture is being hampered these days due to citizens shifting from rural to urban areas. Monitoring environmental factors is not a natural remedy for increasing crop production. Several causes have a significant impact on efficiency. Consequently, to address these issues, agriculture must incorporate automation. A farmer can save time, cost, resources, and energy by using an automated irrigation device. Traditional agricultural irrigation methods necessitate human interference. Human interference can be reduced with automated irrigation technology. We have designed and developed a reliable smart farming system (IoT) to reduce farmers' time costs and resources. Our proposed system can detect temperature, detect the moisture level and water level of the agricultural land, and remotely monitor the land crops. The proposed model sort out into four modules: Water Level Detection Module (WLDM) always detect the water level to avoid drops destruction; Soil Moisture Detection Module (SMDM) calculate the soil moisture level from the land, if the level goes down its start the water pump; Notification Module (NM) is responsible for generating the message service to notify the user if any parameter (Temperature, Soil Moisture, Water) not in well condition; Temperature Detection Module (TDM) is always counting the temperature and humidity of the air if its high then it will start the fan; Cloud and Notification Module (CNM) is handled the user notification through message and remotely monitoring the data of first three modules to take the necessary steps. The result shows the system successfully performed and it can be noted that our proposed can be implemented with any type of environment and agricultural land.","internal_url":"https://www.academia.edu/51054901/IoT_Based_Smart_Automated_Agriculture_and_Real_Time_Monitoring_System","translated_internal_url":"","created_at":"2021-08-28T04:02:04.292-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":27426166,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":36817366,"work_id":51054901,"tagging_user_id":27426166,"tagged_user_id":99384668,"co_author_invite_id":null,"email":"j***m@gmail.com","affiliation":"University of Malaya, Malaysia","display_order":1,"name":"F M Javed Mehedi Shamrat","title":"IoT Based Smart Automated Agriculture and Real Time Monitoring System"},{"id":36817367,"work_id":51054901,"tagging_user_id":27426166,"tagged_user_id":null,"co_author_invite_id":7290991,"email":"a***b@gmail.com","display_order":2,"name":"M. Ahasanul","title":"IoT Based Smart Automated Agriculture and Real Time Monitoring System"},{"id":36817368,"work_id":51054901,"tagging_user_id":27426166,"tagged_user_id":202461584,"co_author_invite_id":7290988,"email":"a***k@gmail.com","display_order":3,"name":"Ankit Khater","title":"IoT Based Smart Automated Agriculture and Real Time Monitoring System"},{"id":36817369,"work_id":51054901,"tagging_user_id":27426166,"tagged_user_id":202459106,"co_author_invite_id":7290986,"email":"t***4@gmail.com","affiliation":"North South University","display_order":4,"name":"TONMOY ROY","title":"IoT Based Smart Automated Agriculture and Real Time Monitoring System"},{"id":37020861,"work_id":51054901,"tagging_user_id":99384668,"tagged_user_id":146691743,"co_author_invite_id":null,"email":"t***6@diu.edu.bd","display_order":4194306,"name":"Md. 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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="51054895"><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/51054895/A_Model_Based_on_Convolutional_Neural_Network_CNN_for_Vehicle_Classification"><img alt="Research paper thumbnail of A Model Based on Convolutional Neural Network (CNN) for Vehicle Classification" class="work-thumbnail" src="https://attachments.academia-assets.com/68916032/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/51054895/A_Model_Based_on_Convolutional_Neural_Network_CNN_for_Vehicle_Classification">A Model Based on Convolutional Neural Network (CNN) for Vehicle Classification</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/KarimAsif">Joyece Jane</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://malaya.academia.edu/FMJavedMehediShamrat">F M Javed Mehedi Shamrat</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MahdiaAmina">Mahdia Amina</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://northsouth.academia.edu/TONMOYROY">TONMOY ROY</a></span></div><div class="wp-workCard_item"><span>IEEE</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The Convolutional Neural Network (CNN) is a form of artificial neural network that has become ver...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The Convolutional Neural Network (CNN) is a form of artificial neural network that has become very popular in computer vision. We proposed a convolutional neural network for classifying common types of vehicles in our country in this paper. Vehicle classification is essential in many applications, including surveillance protection systems and traffic control systems. We raised these concerns and set a goal to find a way to eliminate traffic-related road accidents. The most challenging aspect of computer vision is achieving effective outcomes in order to execute a device due to variations of data shapes and colors. We used three learning methods to identify the vehicle: MobileNetV2, DenseNet, and VGG 19, and demonstrated the methods detection accuracy. Convolutional neural networks are capable of performing all three approaches with grace. The system performs impressively on a real-time standard dataset-the Nepal dataset, which contains 4800 photographs of vehicles. DenseNet has a training accuracy of 94.32 % and a validation accuracy of 95.37%. Furthermore, the VGG 19 has a training accuracy of 91.94 % and a validation accuracy of 92.68 %. The MobileNetV2 architecture has the best accuracy, with a training accuracy of 97.01% and validation accuracy of 98.10%.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0d443ba2ec510a0ff9d8afaa3c9f4fc2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":68916032,"asset_id":51054895,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/68916032/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&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="51054895"><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="51054895"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 51054895; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=51054895]").text(description); $(".js-view-count[data-work-id=51054895]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 51054895; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='51054895']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 51054895, 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: "0d443ba2ec510a0ff9d8afaa3c9f4fc2" } } $('.js-work-strip[data-work-id=51054895]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":51054895,"title":"A Model Based on Convolutional Neural Network (CNN) for Vehicle Classification","translated_title":"","metadata":{"abstract":"The Convolutional Neural Network (CNN) is a form of artificial neural network that has become very popular in computer vision. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="profile--tab_heading_container js-section-heading" data-section="Conference Presentations" id="Conference Presentations"><h3 class="profile--tab_heading_container">Conference Presentations by TONMOY ROY</h3></div><div class="js-work-strip profile--work_container" data-work-id="89080709"><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/89080709/Secure_E_commerce_Trading_Using_Blockchain_with_Smart_Contract_Based_on_POW"><img alt="Research paper thumbnail of Secure E-commerce Trading Using Blockchain with Smart Contract Based on POW" class="work-thumbnail" src="https://attachments.academia-assets.com/92946533/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/89080709/Secure_E_commerce_Trading_Using_Blockchain_with_Smart_Contract_Based_on_POW">Secure E-commerce Trading Using Blockchain with Smart Contract Based on POW</a></div><div class="wp-workCard_item"><span>IEEE</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Progressing proficiency and performance are a necessary topic in today's world and security in 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">Progressing proficiency and performance are a necessary topic in today's world and security in the transaction system is not out of it. There are several Blockchain and Smart contract methods have been created and put to use in health care applications, banking industry, real property transactions, smart home appliances, cloud-based internet of things (IoT), medical transcript, data access and permission, E-Voting Recording, Online Education, virtual power plant etc. E-commerce trading is one of the basic needs in the present world, but there are few Blockchain and Smart Contract findings in this area and a lack of properly implemented models. Therefore, in this work, there is an implementation of Blockchain and Smart contract model to secure E-commerce trading. There is a proposed algorithm to secure the trading. Furthermore, the proof of work concept is used to demonstrate Blockchain transaction implementation. The features of the protocol include a proof of work consensus algorithm, a verification of the Blockchain to prevent tampering, the generation of wallets and the signing of transactions. Smart contract implementation is shown by Ethereum smart contract with solidity. The methods are tested by using JavaScript VM virtual Blockchain. In E-commerce, this proposed model shows a better performance in terms of fast and secure transactions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e71edce19a7fdae17070d3aab36a3cc3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92946533,"asset_id":89080709,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92946533/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&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="89080709"><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="89080709"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 89080709; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=89080709]").text(description); $(".js-view-count[data-work-id=89080709]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 89080709; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='89080709']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 89080709, 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: "e71edce19a7fdae17070d3aab36a3cc3" } } $('.js-work-strip[data-work-id=89080709]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":89080709,"title":"Secure E-commerce Trading Using Blockchain with Smart Contract Based on POW","translated_title":"","metadata":{"abstract":"Progressing proficiency and performance are a necessary topic in today's world and security in the transaction system is not out of it. 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Bicycle sharing as we know is an affordable, easily accessible and reliable mode of transportation. But an efficient bicycle-sharing capable of not only sharing bikes also provides information regarding the availability of bicycle per station, route business, time/day-wise bicycle schedule. The embedded sensors are able to opportunistically communicate through wireless communication with stations when available, providing real-time data about tours/minutes, speed, effort, rhythm, etc. We have been based on our study analysis data to predict regarding the bicycle’s available at stations, bicycle schedule, a location of the nearest hub where a bike is available etc., reducing the user time and effort.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="79a5b2a2a622d704db3be88d2a5ecab1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":75418386,"asset_id":62748435,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/75418386/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&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="62748435"><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="62748435"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 62748435; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=62748435]").text(description); $(".js-view-count[data-work-id=62748435]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 62748435; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='62748435']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 62748435, 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: "79a5b2a2a622d704db3be88d2a5ecab1" } } $('.js-work-strip[data-work-id=62748435]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":62748435,"title":"Crowd Sourced Bicycle Sharing System","translated_title":"","metadata":{"abstract":"These last years with the growing population in the smart city demands efficient transportation sharing which is a bicycle sharing system for developing the smart city. Bicycle sharing as we know is an affordable, easily accessible and reliable mode of transportation. But an efficient bicycle-sharing capable of not only sharing bikes also provides information regarding the availability of bicycle per station, route business, time/day-wise bicycle schedule. The embedded sensors are able to opportunistically communicate through wireless communication with stations when available, providing real-time data about tours/minutes, speed, effort, rhythm, etc. We have been based on our study analysis data to predict regarding the bicycle’s available at stations, bicycle schedule, a location of the nearest hub where a bike is available etc., reducing the user time and effort.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"North South University"},"translated_abstract":"These last years with the growing population in the smart city demands efficient transportation sharing which is a bicycle sharing system for developing the smart city. Bicycle sharing as we know is an affordable, easily accessible and reliable mode of transportation. 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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="59384116"><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/59384116/Effects_of_Virtual_Human_Animation_on_Emotion_Contagion_in_Simulated_Inter_Personal_Experiences"><img alt="Research paper thumbnail of Effects of Virtual Human Animation on Emotion Contagion in Simulated Inter-Personal Experiences" 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/59384116/Effects_of_Virtual_Human_Animation_on_Emotion_Contagion_in_Simulated_Inter_Personal_Experiences">Effects of Virtual Human Animation on Emotion Contagion in Simulated Inter-Personal Experiences</a></div><div class="wp-workCard_item"><span>IEEE Transactions on Visualization and Computer Graphics</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We empirically examined the impact of virtual human animation on the emotional responses of parti...</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">We empirically examined the impact of virtual human animation on the emotional responses of participants in a medical virtual reality system for education in the signs and symptoms of patient deterioration. Participants were presented with one of two virtual human conditions in a between-subjects experiment, static (non-animated) and dynamic (animated). Our objective measures included the use of psycho-physical Electro Dermal Activity (EDA) sensors, and subjective measures inspired by social psychology research included the Differential Emotions Survey (DES IV) and Positive and Negative Affect Survey (PANAS). We analyzed the quantitative and qualitative measures associated with participants&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146; emotional state at four distinct time-steps in the simulated interpersonal experience as the virtual patient&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146;s medical condition deteriorated. Results suggest that participants in the dynamic condition with animations exhibited a higher sense of co-presence and greater emotional response as compared to participants in the static condition, corresponding to the deterioration in the medical condition of the virtual patient. Negative affect of participants in the dynamic condition increased at a higher rate than for participants in the static condition. The virtual human animations elicited a stronger response in negative emotions such as anguish, fear, and anger as the virtual patient&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146;s medical condition worsened.</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="59384116"><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="59384116"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 59384116; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=59384116]").text(description); $(".js-view-count[data-work-id=59384116]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 59384116; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='59384116']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 59384116, 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" } } $('.js-work-strip[data-work-id=59384116]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":59384116,"title":"Effects of Virtual Human Animation on Emotion Contagion in Simulated Inter-Personal Experiences","translated_title":"","metadata":{"abstract":"We empirically examined the impact of virtual human animation on the emotional responses of participants in a medical virtual reality system for education in the signs and symptoms of patient deterioration. Participants were presented with one of two virtual human conditions in a between-subjects experiment, static (non-animated) and dynamic (animated). Our objective measures included the use of psycho-physical Electro Dermal Activity (EDA) sensors, and subjective measures inspired by social psychology research included the Differential Emotions Survey (DES IV) and Positive and Negative Affect Survey (PANAS). We analyzed the quantitative and qualitative measures associated with participants\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146; emotional state at four distinct time-steps in the simulated interpersonal experience as the virtual patient\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146;s medical condition deteriorated. Results suggest that participants in the dynamic condition with animations exhibited a higher sense of co-presence and greater emotional response as compared to participants in the static condition, corresponding to the deterioration in the medical condition of the virtual patient. Negative affect of participants in the dynamic condition increased at a higher rate than for participants in the static condition. The virtual human animations elicited a stronger response in negative emotions such as anguish, fear, and anger as the virtual patient\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146;s medical condition worsened.","publisher":"Institute of Electrical and Electronics Engineers (IEEE)","publication_date":{"day":null,"month":null,"year":2000,"errors":{}},"publication_name":"IEEE Transactions on Visualization and Computer Graphics"},"translated_abstract":"We empirically examined the impact of virtual human animation on the emotional responses of participants in a medical virtual reality system for education in the signs and symptoms of patient deterioration. Participants were presented with one of two virtual human conditions in a between-subjects experiment, static (non-animated) and dynamic (animated). Our objective measures included the use of psycho-physical Electro Dermal Activity (EDA) sensors, and subjective measures inspired by social psychology research included the Differential Emotions Survey (DES IV) and Positive and Negative Affect Survey (PANAS). We analyzed the quantitative and qualitative measures associated with participants\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146; emotional state at four distinct time-steps in the simulated interpersonal experience as the virtual patient\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146;s medical condition deteriorated. Results suggest that participants in the dynamic condition with animations exhibited a higher sense of co-presence and greater emotional response as compared to participants in the static condition, corresponding to the deterioration in the medical condition of the virtual patient. Negative affect of participants in the dynamic condition increased at a higher rate than for participants in the static condition. The virtual human animations elicited a stronger response in negative emotions such as anguish, fear, and anger as the virtual patient\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#146;s medical condition worsened.","internal_url":"https://www.academia.edu/59384116/Effects_of_Virtual_Human_Animation_on_Emotion_Contagion_in_Simulated_Inter_Personal_Experiences","translated_internal_url":"","created_at":"2021-10-21T14:52:58.932-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":202459106,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Effects_of_Virtual_Human_Animation_on_Emotion_Contagion_in_Simulated_Inter_Personal_Experiences","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":202459106,"first_name":"TONMOY","middle_initials":null,"last_name":"ROY","page_name":"TONMOYROY","domain_name":"northsouth","created_at":"2021-09-06T21:07:30.857-07:00","display_name":"TONMOY ROY","url":"https://northsouth.academia.edu/TONMOYROY"},"attachments":[],"research_interests":[{"id":445,"name":"Computer Graphics","url":"https://www.academia.edu/Documents/in/Computer_Graphics"},{"id":1575,"name":"Animation","url":"https://www.academia.edu/Documents/in/Animation"},{"id":4122,"name":"Computer Animation","url":"https://www.academia.edu/Documents/in/Computer_Animation"},{"id":21269,"name":"Facial expression","url":"https://www.academia.edu/Documents/in/Facial_expression"},{"id":22506,"name":"Adolescent","url":"https://www.academia.edu/Documents/in/Adolescent"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":50238,"name":"Affect","url":"https://www.academia.edu/Documents/in/Affect"},{"id":50642,"name":"Virtual Reality","url":"https://www.academia.edu/Documents/in/Virtual_Reality"},{"id":55405,"name":"Sensors","url":"https://www.academia.edu/Documents/in/Sensors"},{"id":123287,"name":"Three Dimensional Imaging","url":"https://www.academia.edu/Documents/in/Three_Dimensional_Imaging"},{"id":133057,"name":"Young Adult","url":"https://www.academia.edu/Documents/in/Young_Adult"},{"id":172809,"name":"Interpersonal Relations","url":"https://www.academia.edu/Documents/in/Interpersonal_Relations"},{"id":255094,"name":"Computer User Interface Design","url":"https://www.academia.edu/Documents/in/Computer_User_Interface_Design"},{"id":2849038,"name":"photic stimulation","url":"https://www.academia.edu/Documents/in/photic_stimulation"},{"id":3110726,"name":"Whole Body Imaging","url":"https://www.academia.edu/Documents/in/Whole_Body_Imaging"}],"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="59384088"><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/59384088/One_Shot_Learning_Gesture_Recognition_Using_Motion_History_Based_Gesture_Silhouettes"><img alt="Research paper thumbnail of One-Shot-Learning Gesture Recognition Using Motion History Based Gesture Silhouettes" class="work-thumbnail" src="https://attachments.academia-assets.com/73334842/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/59384088/One_Shot_Learning_Gesture_Recognition_Using_Motion_History_Based_Gesture_Silhouettes">One-Shot-Learning Gesture Recognition Using Motion History Based Gesture Silhouettes</a></div><div class="wp-workCard_item"><span>The Proceedings of the 1st International Conference on Industrial Application Engineering 2013</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f7a57f0706cb059ad051b57ec6e67f7c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":73334842,"asset_id":59384088,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/73334842/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&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="59384088"><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="59384088"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 59384088; 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The challenge here is to perform satisfactory recognition operations with only one training example of each action, while no prior knowledge about actions, foreground/background segmentation, or any motion estimation and tracking are available. In the proposed scheme motion history imaging technique is applied to track the motion flow in consecutive frames. The information of motion flow is later utilized to calculate the percent change of motion flow for an action in different spatial regions of the frame. The space-time descriptor computed this way from the query video is a measure of the likeness of a gesture in a lexicon. Finally, gesture classification is performed based on correlation based and Euclidean distance based classifiers and the results are compared. Through extensive experimentations on a much diversified dataset the effectiveness of employing the proposed scheme is established.","publication_date":{"day":null,"month":null,"year":2013,"errors":{}},"publication_name":"The Proceedings of the 1st International Conference on Industrial Application Engineering 2013","grobid_abstract_attachment_id":73334842},"translated_abstract":null,"internal_url":"https://www.academia.edu/59384088/One_Shot_Learning_Gesture_Recognition_Using_Motion_History_Based_Gesture_Silhouettes","translated_internal_url":"","created_at":"2021-10-21T14:52:51.710-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":202459106,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":73334842,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/73334842/thumbnails/1.jpg","file_name":"106.pdf","download_url":"https://www.academia.edu/attachments/73334842/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"One_Shot_Learning_Gesture_Recognition_Us.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/73334842/106-libre.pdf?1634855554=\u0026response-content-disposition=attachment%3B+filename%3DOne_Shot_Learning_Gesture_Recognition_Us.pdf\u0026Expires=1733055419\u0026Signature=KB1IB7l33lwX9JRYSM1ozUh9ccS4hwhYJV7jEOdo3g-h2O~9QqYsh9UQiWxOXZJF5pPOY90rdPFDi0xmkY7jLqHlgF1r1gZBTS1ABK6k6reGKyjKI4tNt0JuS3eryV0zgS6tiIa~6e8yzbIQJaWkdcC5yQkDcpXXJ~6cAnWo6oTH-hJkmIV0ZaMp74Wbfp-GR7ekISWAlfbOIJuR4zvTb22nlGMQJ-Re1oOycUJfS~reRwh~gu0ummsw5ither1azcwcJhHrI2o69QNe9gDoRdQbFb-RIf3iOUQwjVdYq5ZM0WFOCur0kMd-xar-qFdCLe1cxHvt7Jetza92u-W6DQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"One_Shot_Learning_Gesture_Recognition_Using_Motion_History_Based_Gesture_Silhouettes","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":202459106,"first_name":"TONMOY","middle_initials":null,"last_name":"ROY","page_name":"TONMOYROY","domain_name":"northsouth","created_at":"2021-09-06T21:07:30.857-07:00","display_name":"TONMOY ROY","url":"https://northsouth.academia.edu/TONMOYROY"},"attachments":[{"id":73334842,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/73334842/thumbnails/1.jpg","file_name":"106.pdf","download_url":"https://www.academia.edu/attachments/73334842/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"One_Shot_Learning_Gesture_Recognition_Us.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/73334842/106-libre.pdf?1634855554=\u0026response-content-disposition=attachment%3B+filename%3DOne_Shot_Learning_Gesture_Recognition_Us.pdf\u0026Expires=1733055419\u0026Signature=KB1IB7l33lwX9JRYSM1ozUh9ccS4hwhYJV7jEOdo3g-h2O~9QqYsh9UQiWxOXZJF5pPOY90rdPFDi0xmkY7jLqHlgF1r1gZBTS1ABK6k6reGKyjKI4tNt0JuS3eryV0zgS6tiIa~6e8yzbIQJaWkdcC5yQkDcpXXJ~6cAnWo6oTH-hJkmIV0ZaMp74Wbfp-GR7ekISWAlfbOIJuR4zvTb22nlGMQJ-Re1oOycUJfS~reRwh~gu0ummsw5ither1azcwcJhHrI2o69QNe9gDoRdQbFb-RIf3iOUQwjVdYq5ZM0WFOCur0kMd-xar-qFdCLe1cxHvt7Jetza92u-W6DQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"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="51054897"><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/51054897/A_Transfer_Learning_Approach_for_Face_Recognition_using_Average_Pooling_and_MobileNetV2"><img alt="Research paper thumbnail of A Transfer Learning Approach for Face Recognition using Average Pooling and MobileNetV2" class="work-thumbnail" src="https://attachments.academia-assets.com/68916033/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/51054897/A_Transfer_Learning_Approach_for_Face_Recognition_using_Average_Pooling_and_MobileNetV2">A Transfer Learning Approach for Face Recognition using Average Pooling and MobileNetV2</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/KarimAsif">Joyece Jane</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://malaya.academia.edu/FMJavedMehediShamrat">F M Javed Mehedi Shamrat</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://northsouth.academia.edu/TONMOYROY">TONMOY ROY</a></span></div><div class="wp-workCard_item"><span>IEEE </span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Facial recognition is a fundamental method in facial-related science such as face detection, auth...</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">Facial recognition is a fundamental method in facial-related science such as face detection, authentication, monitoring, and a crucial phase in computer vision and pattern recognition. Face recognition technology aids in crime prevention by storing the captured image in a database, which can then be used in various ways, including identifying a person. With just a few faces in the frame, most facial recognition systems function sufficiently when the techniques have been tested under artificial illumination, with accurate facial poses and nonblurry images. in our proposed system, a face recognition system is proposed using Average pooling and MobileNetV2. The classifiers are implemented after a set of preprocessing steps on the retrieved image data. To compare the model is more effective, a performance test on the result is performed. It is observed from the study that MobileNetV2 triumphs over Average pooling with an accuracy rate of 98.89% and 99.01% on training and test data, respectively.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f3ec8d5e52219f0f0d693e1af3a533a7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":68916033,"asset_id":51054897,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/68916033/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&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="51054897"><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="51054897"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 51054897; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=51054897]").text(description); $(".js-view-count[data-work-id=51054897]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 51054897; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='51054897']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 51054897, 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: "f3ec8d5e52219f0f0d693e1af3a533a7" } } $('.js-work-strip[data-work-id=51054897]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":51054897,"title":"A Transfer Learning Approach for Face Recognition using Average Pooling and MobileNetV2","translated_title":"","metadata":{"abstract":"Facial recognition is a fundamental method in facial-related science such as face detection, authentication, monitoring, and a crucial phase in computer vision and pattern recognition. Face recognition technology aids in crime prevention by storing the captured image in a database, which can then be used in various ways, including identifying a person. With just a few faces in the frame, most facial recognition systems function sufficiently when the techniques have been tested under artificial illumination, with accurate facial poses and nonblurry images. in our proposed system, a face recognition system is proposed using Average pooling and MobileNetV2. The classifiers are implemented after a set of preprocessing steps on the retrieved image data. To compare the model is more effective, a performance test on the result is performed. It is observed from the study that MobileNetV2 triumphs over Average pooling with an accuracy rate of 98.89% and 99.01% on training and test data, respectively.","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"IEEE "},"translated_abstract":"Facial recognition is a fundamental method in facial-related science such as face detection, authentication, monitoring, and a crucial phase in computer vision and pattern recognition. Face recognition technology aids in crime prevention by storing the captured image in a database, which can then be used in various ways, including identifying a person. With just a few faces in the frame, most facial recognition systems function sufficiently when the techniques have been tested under artificial illumination, with accurate facial poses and nonblurry images. in our proposed system, a face recognition system is proposed using Average pooling and MobileNetV2. The classifiers are implemented after a set of preprocessing steps on the retrieved image data. To compare the model is more effective, a performance test on the result is performed. It is observed from the study that MobileNetV2 triumphs over Average pooling with an accuracy rate of 98.89% and 99.01% on training and test data, respectively.","internal_url":"https://www.academia.edu/51054897/A_Transfer_Learning_Approach_for_Face_Recognition_using_Average_Pooling_and_MobileNetV2","translated_internal_url":"","created_at":"2021-08-28T04:02:03.907-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":27426166,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":36817350,"work_id":51054897,"tagging_user_id":27426166,"tagged_user_id":99384668,"co_author_invite_id":null,"email":"j***m@gmail.com","affiliation":"University of Malaya, Malaysia","display_order":1,"name":"F M Javed Mehedi Shamrat","title":"A Transfer Learning Approach for Face Recognition using Average Pooling and MobileNetV2"},{"id":36817351,"work_id":51054897,"tagging_user_id":27426166,"tagged_user_id":102686327,"co_author_invite_id":null,"email":"s***4@gmail.com","affiliation":"Ahsanullah University of Science and Technology","display_order":2,"name":"Sovon Chakraborty","title":"A Transfer Learning Approach for Face Recognition using Average Pooling and MobileNetV2"},{"id":36817352,"work_id":51054897,"tagging_user_id":27426166,"tagged_user_id":202459106,"co_author_invite_id":7290986,"email":"t***4@gmail.com","affiliation":"North South University","display_order":3,"name":"TONMOY ROY","title":"A Transfer Learning Approach for Face Recognition using Average Pooling and MobileNetV2"}],"downloadable_attachments":[{"id":68916033,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/68916033/thumbnails/1.jpg","file_name":"A_Transfer_Learning_Approach_for_Face_Recognition_using_Average_Pooling_and_MobileNetV2_.pdf","download_url":"https://www.academia.edu/attachments/68916033/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Transfer_Learning_Approach_for_Face_Re.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/68916033/A_Transfer_Learning_Approach_for_Face_Recognition_using_Average_Pooling_and_MobileNetV2_-libre.pdf?1630155015=\u0026response-content-disposition=attachment%3B+filename%3DA_Transfer_Learning_Approach_for_Face_Re.pdf\u0026Expires=1733055419\u0026Signature=azmtZSHyJKhrpOry76Oi06CJYvhVIxXNDmeHR3FLj~8lNjEFF~Up6IdQW-ZCaaPWi~caSxfX~cS~9N5pIRyCWkjPU-I4pTpbuSAZPwuE0okFv4dqwmG9Y~7aHseBC1~nXI5qGEQ9snS99iW0BzhQef1b0yCp1ftYtH5XmtiOEHcCykfEzIkmx10KQ3VFGHIgcVgaRv3KOFUpQbFIEKdkvrdxKwJNgKO9dQGKtQmUgTy5bhuspkNEo06fv0KWwlJMWETX8WS9gtB7qQpRDHQlOiBtEmwgLKu~XvgjRzSzLeN2ymlMDdR7xcC-L1KoOxnLGqmwa8mlBxoGMmooVKjYNA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Transfer_Learning_Approach_for_Face_Recognition_using_Average_Pooling_and_MobileNetV2","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":27426166,"first_name":"Joyece","middle_initials":null,"last_name":"Jane","page_name":"KarimAsif","domain_name":"independent","created_at":"2015-03-08T03:08:08.378-07:00","display_name":"Joyece Jane","url":"https://independent.academia.edu/KarimAsif"},"attachments":[{"id":68916033,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/68916033/thumbnails/1.jpg","file_name":"A_Transfer_Learning_Approach_for_Face_Recognition_using_Average_Pooling_and_MobileNetV2_.pdf","download_url":"https://www.academia.edu/attachments/68916033/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Transfer_Learning_Approach_for_Face_Re.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/68916033/A_Transfer_Learning_Approach_for_Face_Recognition_using_Average_Pooling_and_MobileNetV2_-libre.pdf?1630155015=\u0026response-content-disposition=attachment%3B+filename%3DA_Transfer_Learning_Approach_for_Face_Re.pdf\u0026Expires=1733055419\u0026Signature=azmtZSHyJKhrpOry76Oi06CJYvhVIxXNDmeHR3FLj~8lNjEFF~Up6IdQW-ZCaaPWi~caSxfX~cS~9N5pIRyCWkjPU-I4pTpbuSAZPwuE0okFv4dqwmG9Y~7aHseBC1~nXI5qGEQ9snS99iW0BzhQef1b0yCp1ftYtH5XmtiOEHcCykfEzIkmx10KQ3VFGHIgcVgaRv3KOFUpQbFIEKdkvrdxKwJNgKO9dQGKtQmUgTy5bhuspkNEo06fv0KWwlJMWETX8WS9gtB7qQpRDHQlOiBtEmwgLKu~XvgjRzSzLeN2ymlMDdR7xcC-L1KoOxnLGqmwa8mlBxoGMmooVKjYNA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"}],"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="51054901"><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/51054901/IoT_Based_Smart_Automated_Agriculture_and_Real_Time_Monitoring_System"><img alt="Research paper thumbnail of IoT Based Smart Automated Agriculture and Real Time Monitoring System" class="work-thumbnail" src="https://attachments.academia-assets.com/68916028/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/51054901/IoT_Based_Smart_Automated_Agriculture_and_Real_Time_Monitoring_System">IoT Based Smart Automated Agriculture and Real Time Monitoring System</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/KarimAsif">Joyece Jane</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://malaya.academia.edu/FMJavedMehediShamrat">F M Javed Mehedi Shamrat</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://northsouth.academia.edu/TONMOYROY">TONMOY ROY</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MdTareqRahmanHimu">Md. Tareq Rahman [Himu]</a></span></div><div class="wp-workCard_item"><span>IEEE</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In Bangladesh, agriculture is the primary source of income. It has a significant impact on the 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">In Bangladesh, agriculture is the primary source of income. It has a significant impact on the country's economy. However, agriculture is being hampered these days due to citizens shifting from rural to urban areas. Monitoring environmental factors is not a natural remedy for increasing crop production. Several causes have a significant impact on efficiency. Consequently, to address these issues, agriculture must incorporate automation. A farmer can save time, cost, resources, and energy by using an automated irrigation device. Traditional agricultural irrigation methods necessitate human interference. Human interference can be reduced with automated irrigation technology. We have designed and developed a reliable smart farming system (IoT) to reduce farmers' time costs and resources. Our proposed system can detect temperature, detect the moisture level and water level of the agricultural land, and remotely monitor the land crops. The proposed model sort out into four modules: Water Level Detection Module (WLDM) always detect the water level to avoid drops destruction; Soil Moisture Detection Module (SMDM) calculate the soil moisture level from the land, if the level goes down its start the water pump; Notification Module (NM) is responsible for generating the message service to notify the user if any parameter (Temperature, Soil Moisture, Water) not in well condition; Temperature Detection Module (TDM) is always counting the temperature and humidity of the air if its high then it will start the fan; Cloud and Notification Module (CNM) is handled the user notification through message and remotely monitoring the data of first three modules to take the necessary steps. The result shows the system successfully performed and it can be noted that our proposed can be implemented with any type of environment and agricultural land.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="66f208a3ab1d1135af65a234ee665eb6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":68916028,"asset_id":51054901,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/68916028/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&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="51054901"><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="51054901"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 51054901; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=51054901]").text(description); $(".js-view-count[data-work-id=51054901]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 51054901; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='51054901']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 51054901, 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: "66f208a3ab1d1135af65a234ee665eb6" } } $('.js-work-strip[data-work-id=51054901]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":51054901,"title":"IoT Based Smart Automated Agriculture and Real Time Monitoring System","translated_title":"","metadata":{"abstract":"In Bangladesh, agriculture is the primary source of income. It has a significant impact on the country's economy. However, agriculture is being hampered these days due to citizens shifting from rural to urban areas. Monitoring environmental factors is not a natural remedy for increasing crop production. Several causes have a significant impact on efficiency. Consequently, to address these issues, agriculture must incorporate automation. A farmer can save time, cost, resources, and energy by using an automated irrigation device. Traditional agricultural irrigation methods necessitate human interference. Human interference can be reduced with automated irrigation technology. We have designed and developed a reliable smart farming system (IoT) to reduce farmers' time costs and resources. Our proposed system can detect temperature, detect the moisture level and water level of the agricultural land, and remotely monitor the land crops. The proposed model sort out into four modules: Water Level Detection Module (WLDM) always detect the water level to avoid drops destruction; Soil Moisture Detection Module (SMDM) calculate the soil moisture level from the land, if the level goes down its start the water pump; Notification Module (NM) is responsible for generating the message service to notify the user if any parameter (Temperature, Soil Moisture, Water) not in well condition; Temperature Detection Module (TDM) is always counting the temperature and humidity of the air if its high then it will start the fan; Cloud and Notification Module (CNM) is handled the user notification through message and remotely monitoring the data of first three modules to take the necessary steps. The result shows the system successfully performed and it can be noted that our proposed can be implemented with any type of environment and agricultural land.","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"IEEE"},"translated_abstract":"In Bangladesh, agriculture is the primary source of income. It has a significant impact on the country's economy. However, agriculture is being hampered these days due to citizens shifting from rural to urban areas. Monitoring environmental factors is not a natural remedy for increasing crop production. Several causes have a significant impact on efficiency. Consequently, to address these issues, agriculture must incorporate automation. A farmer can save time, cost, resources, and energy by using an automated irrigation device. Traditional agricultural irrigation methods necessitate human interference. Human interference can be reduced with automated irrigation technology. We have designed and developed a reliable smart farming system (IoT) to reduce farmers' time costs and resources. Our proposed system can detect temperature, detect the moisture level and water level of the agricultural land, and remotely monitor the land crops. The proposed model sort out into four modules: Water Level Detection Module (WLDM) always detect the water level to avoid drops destruction; Soil Moisture Detection Module (SMDM) calculate the soil moisture level from the land, if the level goes down its start the water pump; Notification Module (NM) is responsible for generating the message service to notify the user if any parameter (Temperature, Soil Moisture, Water) not in well condition; Temperature Detection Module (TDM) is always counting the temperature and humidity of the air if its high then it will start the fan; Cloud and Notification Module (CNM) is handled the user notification through message and remotely monitoring the data of first three modules to take the necessary steps. The result shows the system successfully performed and it can be noted that our proposed can be implemented with any type of environment and agricultural land.","internal_url":"https://www.academia.edu/51054901/IoT_Based_Smart_Automated_Agriculture_and_Real_Time_Monitoring_System","translated_internal_url":"","created_at":"2021-08-28T04:02:04.292-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":27426166,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":36817366,"work_id":51054901,"tagging_user_id":27426166,"tagged_user_id":99384668,"co_author_invite_id":null,"email":"j***m@gmail.com","affiliation":"University of Malaya, Malaysia","display_order":1,"name":"F M Javed Mehedi Shamrat","title":"IoT Based Smart Automated Agriculture and Real Time Monitoring System"},{"id":36817367,"work_id":51054901,"tagging_user_id":27426166,"tagged_user_id":null,"co_author_invite_id":7290991,"email":"a***b@gmail.com","display_order":2,"name":"M. Ahasanul","title":"IoT Based Smart Automated Agriculture and Real Time Monitoring System"},{"id":36817368,"work_id":51054901,"tagging_user_id":27426166,"tagged_user_id":202461584,"co_author_invite_id":7290988,"email":"a***k@gmail.com","display_order":3,"name":"Ankit Khater","title":"IoT Based Smart Automated Agriculture and Real Time Monitoring System"},{"id":36817369,"work_id":51054901,"tagging_user_id":27426166,"tagged_user_id":202459106,"co_author_invite_id":7290986,"email":"t***4@gmail.com","affiliation":"North South University","display_order":4,"name":"TONMOY ROY","title":"IoT Based Smart Automated Agriculture and Real Time Monitoring System"},{"id":37020861,"work_id":51054901,"tagging_user_id":99384668,"tagged_user_id":146691743,"co_author_invite_id":null,"email":"t***6@diu.edu.bd","display_order":4194306,"name":"Md. 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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="51054895"><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/51054895/A_Model_Based_on_Convolutional_Neural_Network_CNN_for_Vehicle_Classification"><img alt="Research paper thumbnail of A Model Based on Convolutional Neural Network (CNN) for Vehicle Classification" class="work-thumbnail" src="https://attachments.academia-assets.com/68916032/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/51054895/A_Model_Based_on_Convolutional_Neural_Network_CNN_for_Vehicle_Classification">A Model Based on Convolutional Neural Network (CNN) for Vehicle Classification</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/KarimAsif">Joyece Jane</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://malaya.academia.edu/FMJavedMehediShamrat">F M Javed Mehedi Shamrat</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MahdiaAmina">Mahdia Amina</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://northsouth.academia.edu/TONMOYROY">TONMOY ROY</a></span></div><div class="wp-workCard_item"><span>IEEE</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The Convolutional Neural Network (CNN) is a form of artificial neural network that has become ver...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The Convolutional Neural Network (CNN) is a form of artificial neural network that has become very popular in computer vision. We proposed a convolutional neural network for classifying common types of vehicles in our country in this paper. Vehicle classification is essential in many applications, including surveillance protection systems and traffic control systems. We raised these concerns and set a goal to find a way to eliminate traffic-related road accidents. The most challenging aspect of computer vision is achieving effective outcomes in order to execute a device due to variations of data shapes and colors. We used three learning methods to identify the vehicle: MobileNetV2, DenseNet, and VGG 19, and demonstrated the methods detection accuracy. Convolutional neural networks are capable of performing all three approaches with grace. The system performs impressively on a real-time standard dataset-the Nepal dataset, which contains 4800 photographs of vehicles. DenseNet has a training accuracy of 94.32 % and a validation accuracy of 95.37%. Furthermore, the VGG 19 has a training accuracy of 91.94 % and a validation accuracy of 92.68 %. 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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="16152748" id="conferencepresentations"><div class="js-work-strip profile--work_container" data-work-id="89080709"><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/89080709/Secure_E_commerce_Trading_Using_Blockchain_with_Smart_Contract_Based_on_POW"><img alt="Research paper thumbnail of Secure E-commerce Trading Using Blockchain with Smart Contract Based on POW" class="work-thumbnail" src="https://attachments.academia-assets.com/92946533/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/89080709/Secure_E_commerce_Trading_Using_Blockchain_with_Smart_Contract_Based_on_POW">Secure E-commerce Trading Using Blockchain with Smart Contract Based on POW</a></div><div class="wp-workCard_item"><span>IEEE</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Progressing proficiency and performance are a necessary topic in today's world and security in 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">Progressing proficiency and performance are a necessary topic in today's world and security in the transaction system is not out of it. There are several Blockchain and Smart contract methods have been created and put to use in health care applications, banking industry, real property transactions, smart home appliances, cloud-based internet of things (IoT), medical transcript, data access and permission, E-Voting Recording, Online Education, virtual power plant etc. E-commerce trading is one of the basic needs in the present world, but there are few Blockchain and Smart Contract findings in this area and a lack of properly implemented models. Therefore, in this work, there is an implementation of Blockchain and Smart contract model to secure E-commerce trading. There is a proposed algorithm to secure the trading. Furthermore, the proof of work concept is used to demonstrate Blockchain transaction implementation. The features of the protocol include a proof of work consensus algorithm, a verification of the Blockchain to prevent tampering, the generation of wallets and the signing of transactions. Smart contract implementation is shown by Ethereum smart contract with solidity. The methods are tested by using JavaScript VM virtual Blockchain. In E-commerce, this proposed model shows a better performance in terms of fast and secure transactions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e71edce19a7fdae17070d3aab36a3cc3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92946533,"asset_id":89080709,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92946533/download_file?st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&st=MTczMzA1MTgxOSw4LjIyMi4yMDguMTQ2&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="89080709"><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="89080709"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 89080709; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=89080709]").text(description); $(".js-view-count[data-work-id=89080709]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 89080709; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='89080709']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 89080709, 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: "e71edce19a7fdae17070d3aab36a3cc3" } } $('.js-work-strip[data-work-id=89080709]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":89080709,"title":"Secure E-commerce Trading Using Blockchain with Smart Contract Based on POW","translated_title":"","metadata":{"abstract":"Progressing proficiency and performance are a necessary topic in today's world and security in the transaction system is not out of it. 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