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Haloossim</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Columbia University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/KatieWalsh24"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/KatieWalsh24">Katie Walsh</a></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://usp-br.academia.edu/WaldirModotti"><img class="profile-avatar u-positionAbsolute" alt="Waldir Modotti" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" 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backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Noreen Khan</h3></div><div class="js-work-strip profile--work_container" data-work-id="56704382"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/56704382/More_on_how_to_remove_a_Chinese_IUD"><img alt="Research paper thumbnail of More on how to remove a Chinese IUD" class="work-thumbnail" src="https://attachments.academia-assets.com/71959068/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" rel="nofollow" href="https://www.academia.edu/56704382/More_on_how_to_remove_a_Chinese_IUD">More on how to remove a Chinese IUD</a></div><div class="wp-workCard_item"><span>Journal of Family Planning and Reproductive Health Care</span><span>, 2003</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9a8da9e0cbe45d3206783d49c361e368" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":71959068,"asset_id":56704382,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/71959068/download_file?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" 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href="https://www.academia.edu/56704372/An_Optimized_Approach_to_Vehicle_Type_Classification_Using_a_Convolutional_Neural_Network">An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network</a></div><div class="wp-workCard_item"><span>Computers, Materials & Continua</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Vehicle type classification is considered a central part of an intelligent traffic system. In rec...</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">Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used to quickly learn the features by recording a small number of training images from vehicle frontal view images. The proposed system employs extensive dataaugmentation techniques for effective training while avoiding the problem of data shortage. In order to capture rich and discriminative information of vehicles, the convolutional neural network is fine-tuned for the classification of vehicle types using the augmented data. The network extracts the feature maps from the entire dataset and generates a label for each object (vehicle) in an image, which can help in vehicle-type detection and classification. Experimental results on a public dataset and our own dataset demonstrated that the proposed method is quite effective in detection and classification of different types of vehicles. The experimental results show that the proposed model achieves 96.04% accuracy on vehicle type classification.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3bd6edcbadd894dae30f7af1fe7ffd33" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":71958995,"asset_id":56704372,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/71958995/download_file?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="56704372"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="56704372"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 56704372; 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$(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="41996230"><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/41996230/Detection_and_classification_of_Vehicle_Type_by_using_Convolution_Neural_Network"><img alt="Research paper thumbnail of Detection and classification of Vehicle-Type by using Convolution Neural Network" class="work-thumbnail" src="https://attachments.academia-assets.com/62120321/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/41996230/Detection_and_classification_of_Vehicle_Type_by_using_Convolution_Neural_Network">Detection and classification of Vehicle-Type by using Convolution Neural Network</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/AltafHussain216">Altaf Hussain</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MohammadKamil26">Mohammad Kamil</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/NoreenKhan16">Noreen Khan</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this era of automation, deep learning has a vital role in computer vision for objects detectio...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this era of automation, deep learning has a vital role in computer vision for objects detection. Deep learning provides powerful tools that are able to learn semantics, and high-level deep features to address the problems that exist in traditional architectures of hand-crafted feature extraction techniques like HOG and SIFT. In this paper we proposed an automatic surveillance and auditing system for detecting eight categories of automobiles i.e. bus, car, truck, bike, horse buggy, rickshaws, and van that can help vehicle tracking systems for commercialized parking areas. A transfer learning technique has been used in this research to quickly learn the features by recording a small number of training images. A convolution neural network is used, to fine-tune the accuracy of classification for a given set of images. The network extracts the feature maps from all the data set and generate a label for each object (vehicle) in the image that can help in vehicle-type detection and classification. The experimental results showed that the proposed system is working accurately and efficiently by giving 91.09% accuracy.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="db21b1bccf12281dbc068388649d2b93" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":62120321,"asset_id":41996230,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/62120321/download_file?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="41996230"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="41996230"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 41996230; 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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="10132580" id="papers"><div class="js-work-strip profile--work_container" data-work-id="56704382"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/56704382/More_on_how_to_remove_a_Chinese_IUD"><img alt="Research paper thumbnail of More on how to remove a Chinese IUD" class="work-thumbnail" src="https://attachments.academia-assets.com/71959068/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" rel="nofollow" href="https://www.academia.edu/56704382/More_on_how_to_remove_a_Chinese_IUD">More on how to remove a Chinese IUD</a></div><div class="wp-workCard_item"><span>Journal of Family Planning and Reproductive Health Care</span><span>, 2003</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9a8da9e0cbe45d3206783d49c361e368" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":71959068,"asset_id":56704382,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/71959068/download_file?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="56704382"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="56704382"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 56704382; 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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="56704372"><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/56704372/An_Optimized_Approach_to_Vehicle_Type_Classification_Using_a_Convolutional_Neural_Network"><img alt="Research paper thumbnail of An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network" class="work-thumbnail" src="https://attachments.academia-assets.com/71958995/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/56704372/An_Optimized_Approach_to_Vehicle_Type_Classification_Using_a_Convolutional_Neural_Network">An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network</a></div><div class="wp-workCard_item"><span>Computers, Materials & Continua</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Vehicle type classification is considered a central part of an intelligent traffic system. In rec...</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">Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used to quickly learn the features by recording a small number of training images from vehicle frontal view images. The proposed system employs extensive dataaugmentation techniques for effective training while avoiding the problem of data shortage. In order to capture rich and discriminative information of vehicles, the convolutional neural network is fine-tuned for the classification of vehicle types using the augmented data. The network extracts the feature maps from the entire dataset and generates a label for each object (vehicle) in an image, which can help in vehicle-type detection and classification. Experimental results on a public dataset and our own dataset demonstrated that the proposed method is quite effective in detection and classification of different types of vehicles. The experimental results show that the proposed model achieves 96.04% accuracy on vehicle type classification.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3bd6edcbadd894dae30f7af1fe7ffd33" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":71958995,"asset_id":56704372,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/71958995/download_file?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="56704372"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="56704372"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 56704372; 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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="41996230"><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/41996230/Detection_and_classification_of_Vehicle_Type_by_using_Convolution_Neural_Network"><img alt="Research paper thumbnail of Detection and classification of Vehicle-Type by using Convolution Neural Network" class="work-thumbnail" src="https://attachments.academia-assets.com/62120321/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/41996230/Detection_and_classification_of_Vehicle_Type_by_using_Convolution_Neural_Network">Detection and classification of Vehicle-Type by using Convolution Neural Network</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/AltafHussain216">Altaf Hussain</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MohammadKamil26">Mohammad Kamil</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/NoreenKhan16">Noreen Khan</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this era of automation, deep learning has a vital role in computer vision for objects detectio...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this era of automation, deep learning has a vital role in computer vision for objects detection. Deep learning provides powerful tools that are able to learn semantics, and high-level deep features to address the problems that exist in traditional architectures of hand-crafted feature extraction techniques like HOG and SIFT. In this paper we proposed an automatic surveillance and auditing system for detecting eight categories of automobiles i.e. bus, car, truck, bike, horse buggy, rickshaws, and van that can help vehicle tracking systems for commercialized parking areas. A transfer learning technique has been used in this research to quickly learn the features by recording a small number of training images. A convolution neural network is used, to fine-tune the accuracy of classification for a given set of images. The network extracts the feature maps from all the data set and generate a label for each object (vehicle) in the image that can help in vehicle-type detection and classification. The experimental results showed that the proposed system is working accurately and efficiently by giving 91.09% accuracy.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="db21b1bccf12281dbc068388649d2b93" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":62120321,"asset_id":41996230,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/62120321/download_file?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="41996230"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="41996230"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 41996230; 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