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class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container 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 Dr. SHRWAN RAM</h3></div><div class="js-work-strip profile--work_container" data-work-id="125474693"><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/125474693/Glioma_Tumor_Detection_Through_Faster_Region_Based_Convolutional_Neural_Networks_Using_Transfer_Learning"><img alt="Research paper thumbnail of Glioma Tumor Detection Through Faster Region-Based Convolutional Neural Networks Using Transfer Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/119510867/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/125474693/Glioma_Tumor_Detection_Through_Faster_Region_Based_Convolutional_Neural_Networks_Using_Transfer_Learning">Glioma Tumor Detection Through Faster Region-Based Convolutional Neural Networks Using Transfer Learning</a></div><div class="wp-workCard_item"><span>European Journal of Molecular & Clinical Medicine</span><span>, Nov 27, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Glioma Tumor is generally found in the brain and spinal cord. This tumor begins in glial cells 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">Glioma Tumor is generally found in the brain and spinal cord. This tumor begins in glial cells that cover the nerve cells and control the function of that. The Glioma tumor is classified based on glial cells involved in the Glioma tumor formation. The tumor affects the normal activity of the patients such as loss of memory, difficulties in speech, confuse the identification of objects, and also causes difficulties to maintain the balance of the body. The early detection of Glioma tumor helps healthcare practitioners to suggest a suitable treatment for the disease. The detection of a Glioma tumor is a challenging task. Many types of approaches had been proposed by the researchers and academicians for accurately detecting the Glioma tumor. Accurately detecting the brain tumor is still a big challenge. Because of recent advances in image processing and computer vision, healthcare professionals are using sophisticated disease diagnostic tools for disorders/disease prediction. The Neurosurgeons and Neuro-Physicians use the magnetic resonance imaging technique to identify multiple brain tumors. The approaches to computer vision play a significant role in the automated identification of different Brain tumors. This research paper explores the Convolutional neural network-based Faster R-CNN approach for the Glioma tumor detection using four pre-trained deep networks such as Alexnet, Resnet18, Resnet50, and Googlenet. The proposed approach of object detection as compared to other R-CNN approaches is more efficient and accurate having higher precision. The proposed model detects the Glioma tumor with 99.9% accuracy. The pre-trained networks used to train the tumor detection model are Alexnet, Resnet18, and Resnet50, and Googlenet. As compare to Alexnet, resnet18, and Googlenet deep networks, the Resnet50 Pre-trained network performed well with higher accuracy of detection.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1d13066f8aa60a46503b926b82285e81" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119510867,"asset_id":125474693,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119510867/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="125474693"><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="125474693"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474693; 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$(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="125474692"><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/125474692/Object_Detection_and_Classification_Through_Deep_Learning_Approaches"><img alt="Research paper thumbnail of Object Detection and Classification Through Deep Learning Approaches" class="work-thumbnail" src="https://attachments.academia-assets.com/119510866/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/125474692/Object_Detection_and_Classification_Through_Deep_Learning_Approaches">Object Detection and Classification Through Deep Learning Approaches</a></div><div class="wp-workCard_item"><span>Journal of emerging technologies and innovative research</span><span>, Sep 1, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, we implemented the image classification and object detection. This paper presents ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, we implemented the image classification and object detection. This paper presents a deep learning approach for traffic light detection in adapting a single shot detection(SSD) approach and image classification of two categories of bicycle by retraining inceptionv3 model both using an open source tool called TensorFlow Object Detection API. We reviewed the current literature on convolutional object detection and tested the implementability of one of the methods and discovered that convolutional object detection is still evolving as a technology despite that convolutional object detection has outranked other object detection methods. To implement object detection and image classification there is free availability of datasets and pretrained networks it is possible to create a functional implementation of a deep neural network without access to specialist hardware.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bcfc2d4ac45400a8af8b37a54848dceb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119510866,"asset_id":125474692,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119510866/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="125474692"><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="125474692"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474692; 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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="125474691"><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/125474691/Classification_of_Pituitary_Tumor_and_Multiple_Sclerosis_Brain_Lesions_through_Convolutional_Neural_Networks"><img alt="Research paper thumbnail of Classification of Pituitary Tumor and Multiple Sclerosis Brain Lesions through Convolutional Neural Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/125474691/Classification_of_Pituitary_Tumor_and_Multiple_Sclerosis_Brain_Lesions_through_Convolutional_Neural_Networks">Classification of Pituitary Tumor and Multiple Sclerosis Brain Lesions through Convolutional Neural Networks</a></div><div class="wp-workCard_item"><span>IOP conference series</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Automatic classification of Brain Tumor and brain Lesions has become a very important step in the...</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">Automatic classification of Brain Tumor and brain Lesions has become a very important step in the field of medical image analytics. The machine learning/Deep learning approaches are playing a tremendous role in the field of medical imaging classification, due to the drastic changes in the field of computing power and image analytics techniques. The deep learning, which is the subfield of machine learning, is playing the major role in the automatic classification of Magnetic Resonance Images (MRIs) having various brain abnormalities. Convolutional Neural Networks are widely used for the classification and detection of various brain disorders. In this research paper, Convolutional Neural Networks are designed with considering various learning parameters for the classification of Multiple Sclerosis Brain Lesions and Pituitary Tumor. In the proposed research, T1-weighted Contrast-enhanced Magnetic Resonance images are preprocessed with various image-preprocessing approaches such as to resize the images, to convert the images into suitable image format so that the experimental work can be performed with deep learning in the Matlab environment. The Experiment is conducted with the dataset of Multiple Sclerosis and Pituitary Tumor each of having 718 and 930T1-weighted MRI images respectively. The experimental results we achieved 99.7% classification accuracy of pituitary Tumor, and 99.2% accuracy of Multiple Sclerosis brain Lesions. The average accuracy of both classifications is 99.55%. The precision of the classification of Pituitary Tumor is 99.7, recall value is 99.7 and the f1_score of the classification is 99.7%. Similarly, the Precision of the classification of Multiple Sclerosis Brain Lesions is 99.15%, the recall value is 99.15%, and the f1_score is 99.15%. The purposed approach of the Convolutional Neural Network architecture exhibited outstanding performance as compared to other research outcomes.</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="125474691"><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="125474691"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474691; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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</script> <div class="js-work-strip profile--work_container" data-work-id="125474690"><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/125474690/Pre_trained_Deep_Networks_for_Faster_Region_Based_CNN_Model_for_Pituitary_Tumor_Detection"><img alt="Research paper thumbnail of Pre-trained Deep Networks for Faster Region-Based CNN Model for Pituitary Tumor Detection" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/125474690/Pre_trained_Deep_Networks_for_Faster_Region_Based_CNN_Model_for_Pituitary_Tumor_Detection">Pre-trained Deep Networks for Faster Region-Based CNN Model for Pituitary Tumor Detection</a></div><div class="wp-workCard_item"><span>Lecture notes in networks and systems</span><span>, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="125474690"><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="125474690"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474690; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=125474690]").text(description); $(".js-view-count[data-work-id=125474690]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 125474690; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='125474690']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=125474690]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":125474690,"title":"Pre-trained Deep Networks for Faster Region-Based CNN Model for Pituitary Tumor Detection","internal_url":"https://www.academia.edu/125474690/Pre_trained_Deep_Networks_for_Faster_Region_Based_CNN_Model_for_Pituitary_Tumor_Detection","owner_id":321190784,"coauthors_can_edit":true,"owner":{"id":321190784,"first_name":"Dr. SHRWAN","middle_initials":null,"last_name":"RAM","page_name":"DrSHRWANRAM","domain_name":"independent","created_at":"2024-08-10T20:13:28.636-07:00","display_name":"Dr. SHRWAN RAM","url":"https://independent.academia.edu/DrSHRWANRAM"},"attachments":[]}, 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="125474689"><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/125474689/Building_Machine_Learning_Based_Diseases_Diagnosis_System_Considering_Various_Features_of_Datasets"><img alt="Research paper thumbnail of Building Machine Learning Based Diseases Diagnosis System Considering Various Features of Datasets" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/125474689/Building_Machine_Learning_Based_Diseases_Diagnosis_System_Considering_Various_Features_of_Datasets">Building Machine Learning Based Diseases Diagnosis System Considering Various Features of Datasets</a></div><div class="wp-workCard_item"><span>Advances in Intelligent Systems and Computing</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Millions of people worldwide suffer from late diagnosis of diseases. Machine learning algorithms ...</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">Millions of people worldwide suffer from late diagnosis of diseases. Machine learning algorithms can significantly help in solving healthcare systems that can assist physicians in early diagnosis of diseases. Algorithms in Machine Learning provide the ways to classify data efficiently, at great speed and with high accuracy. Many types of machine learning algorithms are widely adopted and implemented for the early detection of various diseases; these algorithms are like Decision Tree, Naive Bayes, Support Vector Machine, and Logistic Regression. The results show that there is no particular algorithm available which provides best accuracy in all kind of the healthcare data classification. Most appropriate method can be chosen only after analyzing the nature of the datasets. All the available machine learning techniques are used based on their performances in terms of accuracy and comprehensibility. The datasets considered in this paper are on breast cancer, dermatology, chronic kidney disorder, and biomechanical analysis of orthopedic patients. Data sets from UCI machine learning repository were taken to show applications of Machine Learning on wide variety of Life Sciences data. The four algorithms are implemented with considering various parameters of classification.</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="125474689"><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="125474689"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474689; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=125474689]").text(description); 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</script> <div class="js-work-strip profile--work_container" data-work-id="125474688"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/125474688/Pre_trained_Deep_Networks_for_Faster_Region_Based_CNN_Model_for_Pituitary_Tumor_Detection"><img alt="Research paper thumbnail of Pre-trained Deep Networks for Faster Region-Based CNN Model for Pituitary Tumor Detection" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/125474688/Pre_trained_Deep_Networks_for_Faster_Region_Based_CNN_Model_for_Pituitary_Tumor_Detection">Pre-trained Deep Networks for Faster Region-Based CNN Model for Pituitary Tumor Detection</a></div><div class="wp-workCard_item"><span>Lecture Notes in Networks and Systems</span><span>, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="125474688"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="125474688"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474688; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=125474688]").text(description); $(".js-view-count[data-work-id=125474688]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 125474688; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='125474688']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=125474688]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":125474688,"title":"Pre-trained Deep Networks for Faster Region-Based CNN Model for Pituitary Tumor Detection","internal_url":"https://www.academia.edu/125474688/Pre_trained_Deep_Networks_for_Faster_Region_Based_CNN_Model_for_Pituitary_Tumor_Detection","owner_id":321190784,"coauthors_can_edit":true,"owner":{"id":321190784,"first_name":"Dr. SHRWAN","middle_initials":null,"last_name":"RAM","page_name":"DrSHRWANRAM","domain_name":"independent","created_at":"2024-08-10T20:13:28.636-07:00","display_name":"Dr. SHRWAN RAM","url":"https://independent.academia.edu/DrSHRWANRAM"},"attachments":[]}, 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="125474687"><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/125474687/Comparative_Study_of_K_Nearest_Neighbor_Classification_and_J48_Decision_Tree_Algorithm_with_and_Without_Clustering_Considering_Different_Data_Parameters"><img alt="Research paper thumbnail of Comparative Study of K-Nearest Neighbor Classification and J48 Decision Tree Algorithm with and Without Clustering Considering Different Data Parameters" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/125474687/Comparative_Study_of_K_Nearest_Neighbor_Classification_and_J48_Decision_Tree_Algorithm_with_and_Without_Clustering_Considering_Different_Data_Parameters">Comparative Study of K-Nearest Neighbor Classification and J48 Decision Tree Algorithm with and Without Clustering Considering Different Data Parameters</a></div><div class="wp-workCard_item"><span>International Journal of Advance Engineering and Research Development</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Data mining is an area where computer science, machine learning and statistics meet and where the...</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">Data mining is an area where computer science, machine learning and statistics meet and where the goal is to discover and extract information such as relations and patterns that’s hidden inside the data. The volume of data is increasing exponentially and analyzing such a large volume of data has become one of the big challenges for IT industries. The data has become the asset of every enterprise. The mining of such large volume of data provides the valuable information regarding to the specific field for which data are collected. There are many types of data mining techniques available and used to extract the valuable hidden patterns from the large volume of data. The patterns extracted from the data become the part of knowledge base for the decision support system. The main goal of the data mining is to find out the relevant and more valuable information from the data and building the knowledge base. In this paper we are considering the K-means Clustering algorithm for classifying the data on the basis of similarity. This is one type of the unsupervised machine learning technique. The Clusters produced by the K-means clustering are further classified using Supervised machine learning techniques, Such as K-nearest Neighbour method and decision tree algorithm. Keywords— Data mining, hidden patterns, knowledge base, decision support system, K-means Clustering algorithm, Unsupervised machine learning, Supervised machine learning, Knearest Neighbour method, decision tree algorithm.</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="125474687"><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="125474687"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474687; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=125474687]").text(description); $(".js-view-count[data-work-id=125474687]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 125474687; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='125474687']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=125474687]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":125474687,"title":"Comparative Study of K-Nearest Neighbor Classification and J48 Decision Tree Algorithm with and Without Clustering Considering Different Data Parameters","internal_url":"https://www.academia.edu/125474687/Comparative_Study_of_K_Nearest_Neighbor_Classification_and_J48_Decision_Tree_Algorithm_with_and_Without_Clustering_Considering_Different_Data_Parameters","owner_id":321190784,"coauthors_can_edit":true,"owner":{"id":321190784,"first_name":"Dr. SHRWAN","middle_initials":null,"last_name":"RAM","page_name":"DrSHRWANRAM","domain_name":"independent","created_at":"2024-08-10T20:13:28.636-07:00","display_name":"Dr. SHRWAN RAM","url":"https://independent.academia.edu/DrSHRWANRAM"},"attachments":[]}, 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="125474686"><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/125474686/Object_Detection_and_Classification_Through_Deep_Learning_Approaches"><img alt="Research paper thumbnail of Object Detection and Classification Through Deep Learning Approaches" class="work-thumbnail" src="https://attachments.academia-assets.com/119510861/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/125474686/Object_Detection_and_Classification_Through_Deep_Learning_Approaches">Object Detection and Classification Through Deep Learning Approaches</a></div><div class="wp-workCard_item"><span>Journal of emerging technologies and innovative research</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, we implemented the image classification and object detection. This paper presents ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, we implemented the image classification and object detection. This paper presents a deep learning approach for traffic light detection in adapting a single shot detection(SSD) approach and image classification of two categories of bicycle by retraining inceptionv3 model both using an open source tool called TensorFlow Object Detection API. We reviewed the current literature on convolutional object detection and tested the implementability of one of the methods and discovered that convolutional object detection is still evolving as a technology despite that convolutional object detection has outranked other object detection methods. To implement object detection and image classification there is free availability of datasets and pretrained networks it is possible to create a functional implementation of a deep neural network without access to specialist hardware. KEYWORDS-Object detection, Deep learning, Convolutional neural network, TensorFlow Object Detection API, SS...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f38236790e3461876dc789505bfc8f5d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119510861,"asset_id":125474686,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119510861/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="125474686"><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="125474686"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474686; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=125474686]").text(description); $(".js-view-count[data-work-id=125474686]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 125474686; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='125474686']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "f38236790e3461876dc789505bfc8f5d" } } $('.js-work-strip[data-work-id=125474686]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":125474686,"title":"Object Detection and Classification Through Deep Learning Approaches","internal_url":"https://www.academia.edu/125474686/Object_Detection_and_Classification_Through_Deep_Learning_Approaches","owner_id":321190784,"coauthors_can_edit":true,"owner":{"id":321190784,"first_name":"Dr. SHRWAN","middle_initials":null,"last_name":"RAM","page_name":"DrSHRWANRAM","domain_name":"independent","created_at":"2024-08-10T20:13:28.636-07:00","display_name":"Dr. SHRWAN RAM","url":"https://independent.academia.edu/DrSHRWANRAM"},"attachments":[{"id":119510861,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/119510861/thumbnails/1.jpg","file_name":"JETIRA006301.pdf","download_url":"https://www.academia.edu/attachments/119510861/download_file","bulk_download_file_name":"Object_Detection_and_Classification_Thro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/119510861/JETIRA006301-libre.pdf?1731396188=\u0026response-content-disposition=attachment%3B+filename%3DObject_Detection_and_Classification_Thro.pdf\u0026Expires=1739835243\u0026Signature=Tr3yuVWtO14DJ7iVpeo5ogfiJuWwkDeKAygHbZZlLQI~lBgohrYnp9sqyUQRwGXeg5RbgVLAhq71wxN1X17~J4Ucjptx3RV335-3iHCJoxeQsfk1gGWzR3ywgyMTxRCRCRDv8c0mLExxVMdJZpnllacbHM6lHSM5z3~y6G5p5jYota2nHg4lzEyZE8oSfa~S~LkEZZyCMiRua0tKnbMQgB57-hjRbenHv2Ry7-HUirWM7ZjY8T-WjLtEAV5ZE7lY4wCJaNh35EopwHBf7oBh92jN4-6vCPwc82yfH95hnULgVdWI3QiaiE5zIMYglo-X~IRUTGmlwnaGDPz6NRxOWw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":119510862,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/119510862/thumbnails/1.jpg","file_name":"JETIRA006301.pdf","download_url":"https://www.academia.edu/attachments/119510862/download_file","bulk_download_file_name":"Object_Detection_and_Classification_Thro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/119510862/JETIRA006301-libre.pdf?1731396193=\u0026response-content-disposition=attachment%3B+filename%3DObject_Detection_and_Classification_Thro.pdf\u0026Expires=1739835243\u0026Signature=Cnik9B1dbEoNju0sQVGX8xHGqbdu3w~g4rAgnGGAO1RtFRisDh4jXApqp80mk82nwJKfPecYLkWxsmLwoQTHRgkgChHlmlaIYxDXjf0-g8NfHK~Urdm-B1FfPr0z-j7gvoeksp5uNnoLK1fjb7Imz34x2fnkXop5D0aYMY20XGT4avwz4dE68Ml6Eoqb8LKL53gP5tKxLIgZLAY8CADmXFT9i61TNB4YsvJxfsf9M7~06yzsxkZS1gfB-2pEcOUS4rLHBWOipsVzDqMEQE-BrGQnz25cfMFhBXSu3vfPJHw4cjW~xB78FEOrT3PNEjL79i~T6LQDv5zy006FOJq6Tw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="125474685"><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/125474685/A_Comparative_Study_of_Multilayer_Perceptron_Radial_Basis_Function_Networks_and_Logistic_Regression_for_Healthcare_Data_Classification"><img alt="Research paper thumbnail of A Comparative Study of Multilayer Perceptron, Radial Basis Function Networks and Logistic Regression for Healthcare Data Classification" class="work-thumbnail" src="https://attachments.academia-assets.com/119510860/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/125474685/A_Comparative_Study_of_Multilayer_Perceptron_Radial_Basis_Function_Networks_and_Logistic_Regression_for_Healthcare_Data_Classification">A Comparative Study of Multilayer Perceptron, Radial Basis Function Networks and Logistic Regression for Healthcare Data Classification</a></div><div class="wp-workCard_item"><span>International Journal of Advance Engineering and Research Development</span><span>, Mar 31, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The Healthcare databases are becoming more important nowadays. Many Healthcare institutions are m...</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 Healthcare databases are becoming more important nowadays. Many Healthcare institutions are maintaining the large volume of healthcare databases to provide the best clinical services and insurance claims. The profits of Healthcare insurance companies are totally depending on the care of their customers. It is predicted by the healthcare department of United States of America that the early detection of any disease and its cause is very important strategy to save the big amount of insurance claim. Therefore Healthcare data classification approach has become the dominant process to save the big amount of budget allocation for the government sector. There are many types of classification approaches used for classification and prediction. In this research paper mainly multilayered Perceptron, Radial basis function networks and Logistic Regression are used to classify the Healthcare databases and on the basis of classification trends the decision are taken. All these approaches of data classification are covered. in this paper.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="299f6fa57ed88e8287b997d7c9ec8a10" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119510860,"asset_id":125474685,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119510860/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="125474685"><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="125474685"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474685; 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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="125474660"><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/125474660/Glioma_Tumor_Detection_Through_Faster_Region_Based_Convolutional_Neural_Networks_Using_Transfer_Learning"><img alt="Research paper thumbnail of Glioma Tumor Detection Through Faster Region-Based Convolutional Neural Networks Using Transfer Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/119510819/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/125474660/Glioma_Tumor_Detection_Through_Faster_Region_Based_Convolutional_Neural_Networks_Using_Transfer_Learning">Glioma Tumor Detection Through Faster Region-Based Convolutional Neural Networks Using Transfer Learning</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Glioma Tumor is generally found in the brain and spinal cord. This tumor begins in glial cells 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">Glioma Tumor is generally found in the brain and spinal cord. This tumor begins in glial cells that cover the nerve cells and control the function of that. The Glioma tumor is classified based on glial cells involved in the Glioma tumor formation. The tumor affects the normal activity of the patients such as loss of memory, difficulties in speech, confuse the identification of objects, and also causes difficulties to maintain the balance of the body. The early detection of Glioma tumor helps healthcare practitioners to suggest a suitable treatment for the disease. The detection of a Glioma tumor is a challenging task. Many types of approaches had been proposed by the researchers and academicians for accurately detecting the Glioma tumor. Accurately detecting the brain tumor is still a big challenge. Because of recent advances in image processing and computer vision, healthcare professionals are using sophisticated disease diagnostic tools for disorders/disease prediction. The Neuros...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e60835959220c9fb8664202e6c0fb59d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119510819,"asset_id":125474660,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119510819/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="125474660"><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="125474660"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474660; 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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="20162436" id="papers"><div class="js-work-strip profile--work_container" data-work-id="125474693"><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/125474693/Glioma_Tumor_Detection_Through_Faster_Region_Based_Convolutional_Neural_Networks_Using_Transfer_Learning"><img alt="Research paper thumbnail of Glioma Tumor Detection Through Faster Region-Based Convolutional Neural Networks Using Transfer Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/119510867/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/125474693/Glioma_Tumor_Detection_Through_Faster_Region_Based_Convolutional_Neural_Networks_Using_Transfer_Learning">Glioma Tumor Detection Through Faster Region-Based Convolutional Neural Networks Using Transfer Learning</a></div><div class="wp-workCard_item"><span>European Journal of Molecular & Clinical Medicine</span><span>, Nov 27, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Glioma Tumor is generally found in the brain and spinal cord. This tumor begins in glial cells 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">Glioma Tumor is generally found in the brain and spinal cord. This tumor begins in glial cells that cover the nerve cells and control the function of that. The Glioma tumor is classified based on glial cells involved in the Glioma tumor formation. The tumor affects the normal activity of the patients such as loss of memory, difficulties in speech, confuse the identification of objects, and also causes difficulties to maintain the balance of the body. The early detection of Glioma tumor helps healthcare practitioners to suggest a suitable treatment for the disease. The detection of a Glioma tumor is a challenging task. Many types of approaches had been proposed by the researchers and academicians for accurately detecting the Glioma tumor. Accurately detecting the brain tumor is still a big challenge. Because of recent advances in image processing and computer vision, healthcare professionals are using sophisticated disease diagnostic tools for disorders/disease prediction. The Neurosurgeons and Neuro-Physicians use the magnetic resonance imaging technique to identify multiple brain tumors. The approaches to computer vision play a significant role in the automated identification of different Brain tumors. This research paper explores the Convolutional neural network-based Faster R-CNN approach for the Glioma tumor detection using four pre-trained deep networks such as Alexnet, Resnet18, Resnet50, and Googlenet. The proposed approach of object detection as compared to other R-CNN approaches is more efficient and accurate having higher precision. The proposed model detects the Glioma tumor with 99.9% accuracy. The pre-trained networks used to train the tumor detection model are Alexnet, Resnet18, and Resnet50, and Googlenet. As compare to Alexnet, resnet18, and Googlenet deep networks, the Resnet50 Pre-trained network performed well with higher accuracy of detection.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1d13066f8aa60a46503b926b82285e81" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119510867,"asset_id":125474693,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119510867/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="125474693"><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="125474693"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474693; 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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="125474692"><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/125474692/Object_Detection_and_Classification_Through_Deep_Learning_Approaches"><img alt="Research paper thumbnail of Object Detection and Classification Through Deep Learning Approaches" class="work-thumbnail" src="https://attachments.academia-assets.com/119510866/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/125474692/Object_Detection_and_Classification_Through_Deep_Learning_Approaches">Object Detection and Classification Through Deep Learning Approaches</a></div><div class="wp-workCard_item"><span>Journal of emerging technologies and innovative research</span><span>, Sep 1, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, we implemented the image classification and object detection. This paper presents ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, we implemented the image classification and object detection. This paper presents a deep learning approach for traffic light detection in adapting a single shot detection(SSD) approach and image classification of two categories of bicycle by retraining inceptionv3 model both using an open source tool called TensorFlow Object Detection API. We reviewed the current literature on convolutional object detection and tested the implementability of one of the methods and discovered that convolutional object detection is still evolving as a technology despite that convolutional object detection has outranked other object detection methods. To implement object detection and image classification there is free availability of datasets and pretrained networks it is possible to create a functional implementation of a deep neural network without access to specialist hardware.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bcfc2d4ac45400a8af8b37a54848dceb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119510866,"asset_id":125474692,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119510866/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="125474692"><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="125474692"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474692; 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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="125474691"><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/125474691/Classification_of_Pituitary_Tumor_and_Multiple_Sclerosis_Brain_Lesions_through_Convolutional_Neural_Networks"><img alt="Research paper thumbnail of Classification of Pituitary Tumor and Multiple Sclerosis Brain Lesions through Convolutional Neural Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/125474691/Classification_of_Pituitary_Tumor_and_Multiple_Sclerosis_Brain_Lesions_through_Convolutional_Neural_Networks">Classification of Pituitary Tumor and Multiple Sclerosis Brain Lesions through Convolutional Neural Networks</a></div><div class="wp-workCard_item"><span>IOP conference series</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Automatic classification of Brain Tumor and brain Lesions has become a very important step in the...</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">Automatic classification of Brain Tumor and brain Lesions has become a very important step in the field of medical image analytics. The machine learning/Deep learning approaches are playing a tremendous role in the field of medical imaging classification, due to the drastic changes in the field of computing power and image analytics techniques. The deep learning, which is the subfield of machine learning, is playing the major role in the automatic classification of Magnetic Resonance Images (MRIs) having various brain abnormalities. Convolutional Neural Networks are widely used for the classification and detection of various brain disorders. In this research paper, Convolutional Neural Networks are designed with considering various learning parameters for the classification of Multiple Sclerosis Brain Lesions and Pituitary Tumor. In the proposed research, T1-weighted Contrast-enhanced Magnetic Resonance images are preprocessed with various image-preprocessing approaches such as to resize the images, to convert the images into suitable image format so that the experimental work can be performed with deep learning in the Matlab environment. The Experiment is conducted with the dataset of Multiple Sclerosis and Pituitary Tumor each of having 718 and 930T1-weighted MRI images respectively. The experimental results we achieved 99.7% classification accuracy of pituitary Tumor, and 99.2% accuracy of Multiple Sclerosis brain Lesions. The average accuracy of both classifications is 99.55%. The precision of the classification of Pituitary Tumor is 99.7, recall value is 99.7 and the f1_score of the classification is 99.7%. Similarly, the Precision of the classification of Multiple Sclerosis Brain Lesions is 99.15%, the recall value is 99.15%, and the f1_score is 99.15%. The purposed approach of the Convolutional Neural Network architecture exhibited outstanding performance as compared to other research outcomes.</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="125474691"><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="125474691"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474691; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=125474691]").text(description); $(".js-view-count[data-work-id=125474691]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 125474691; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='125474691']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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); 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</script> <div class="js-work-strip profile--work_container" data-work-id="125474690"><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/125474690/Pre_trained_Deep_Networks_for_Faster_Region_Based_CNN_Model_for_Pituitary_Tumor_Detection"><img alt="Research paper thumbnail of Pre-trained Deep Networks for Faster Region-Based CNN Model for Pituitary Tumor Detection" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/125474690/Pre_trained_Deep_Networks_for_Faster_Region_Based_CNN_Model_for_Pituitary_Tumor_Detection">Pre-trained Deep Networks for Faster Region-Based CNN Model for Pituitary Tumor Detection</a></div><div class="wp-workCard_item"><span>Lecture notes in networks and systems</span><span>, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="125474690"><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="125474690"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474690; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=125474690]").text(description); $(".js-view-count[data-work-id=125474690]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 125474690; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='125474690']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=125474690]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":125474690,"title":"Pre-trained Deep Networks for Faster Region-Based CNN Model for Pituitary Tumor Detection","internal_url":"https://www.academia.edu/125474690/Pre_trained_Deep_Networks_for_Faster_Region_Based_CNN_Model_for_Pituitary_Tumor_Detection","owner_id":321190784,"coauthors_can_edit":true,"owner":{"id":321190784,"first_name":"Dr. SHRWAN","middle_initials":null,"last_name":"RAM","page_name":"DrSHRWANRAM","domain_name":"independent","created_at":"2024-08-10T20:13:28.636-07:00","display_name":"Dr. SHRWAN RAM","url":"https://independent.academia.edu/DrSHRWANRAM"},"attachments":[]}, 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="125474689"><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/125474689/Building_Machine_Learning_Based_Diseases_Diagnosis_System_Considering_Various_Features_of_Datasets"><img alt="Research paper thumbnail of Building Machine Learning Based Diseases Diagnosis System Considering Various Features of Datasets" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/125474689/Building_Machine_Learning_Based_Diseases_Diagnosis_System_Considering_Various_Features_of_Datasets">Building Machine Learning Based Diseases Diagnosis System Considering Various Features of Datasets</a></div><div class="wp-workCard_item"><span>Advances in Intelligent Systems and Computing</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Millions of people worldwide suffer from late diagnosis of diseases. Machine learning algorithms ...</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">Millions of people worldwide suffer from late diagnosis of diseases. Machine learning algorithms can significantly help in solving healthcare systems that can assist physicians in early diagnosis of diseases. Algorithms in Machine Learning provide the ways to classify data efficiently, at great speed and with high accuracy. Many types of machine learning algorithms are widely adopted and implemented for the early detection of various diseases; these algorithms are like Decision Tree, Naive Bayes, Support Vector Machine, and Logistic Regression. The results show that there is no particular algorithm available which provides best accuracy in all kind of the healthcare data classification. Most appropriate method can be chosen only after analyzing the nature of the datasets. All the available machine learning techniques are used based on their performances in terms of accuracy and comprehensibility. The datasets considered in this paper are on breast cancer, dermatology, chronic kidney disorder, and biomechanical analysis of orthopedic patients. Data sets from UCI machine learning repository were taken to show applications of Machine Learning on wide variety of Life Sciences data. The four algorithms are implemented with considering various parameters of classification.</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="125474689"><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="125474689"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474689; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=125474689]").text(description); 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</script> <div class="js-work-strip profile--work_container" data-work-id="125474688"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/125474688/Pre_trained_Deep_Networks_for_Faster_Region_Based_CNN_Model_for_Pituitary_Tumor_Detection"><img alt="Research paper thumbnail of Pre-trained Deep Networks for Faster Region-Based CNN Model for Pituitary Tumor Detection" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/125474688/Pre_trained_Deep_Networks_for_Faster_Region_Based_CNN_Model_for_Pituitary_Tumor_Detection">Pre-trained Deep Networks for Faster Region-Based CNN Model for Pituitary Tumor Detection</a></div><div class="wp-workCard_item"><span>Lecture Notes in Networks and Systems</span><span>, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="125474688"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="125474688"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474688; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=125474688]").text(description); $(".js-view-count[data-work-id=125474688]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 125474688; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='125474688']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=125474688]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":125474688,"title":"Pre-trained Deep Networks for Faster Region-Based CNN Model for Pituitary Tumor Detection","internal_url":"https://www.academia.edu/125474688/Pre_trained_Deep_Networks_for_Faster_Region_Based_CNN_Model_for_Pituitary_Tumor_Detection","owner_id":321190784,"coauthors_can_edit":true,"owner":{"id":321190784,"first_name":"Dr. SHRWAN","middle_initials":null,"last_name":"RAM","page_name":"DrSHRWANRAM","domain_name":"independent","created_at":"2024-08-10T20:13:28.636-07:00","display_name":"Dr. SHRWAN RAM","url":"https://independent.academia.edu/DrSHRWANRAM"},"attachments":[]}, 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="125474687"><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/125474687/Comparative_Study_of_K_Nearest_Neighbor_Classification_and_J48_Decision_Tree_Algorithm_with_and_Without_Clustering_Considering_Different_Data_Parameters"><img alt="Research paper thumbnail of Comparative Study of K-Nearest Neighbor Classification and J48 Decision Tree Algorithm with and Without Clustering Considering Different Data Parameters" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/125474687/Comparative_Study_of_K_Nearest_Neighbor_Classification_and_J48_Decision_Tree_Algorithm_with_and_Without_Clustering_Considering_Different_Data_Parameters">Comparative Study of K-Nearest Neighbor Classification and J48 Decision Tree Algorithm with and Without Clustering Considering Different Data Parameters</a></div><div class="wp-workCard_item"><span>International Journal of Advance Engineering and Research Development</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Data mining is an area where computer science, machine learning and statistics meet and where the...</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">Data mining is an area where computer science, machine learning and statistics meet and where the goal is to discover and extract information such as relations and patterns that’s hidden inside the data. The volume of data is increasing exponentially and analyzing such a large volume of data has become one of the big challenges for IT industries. The data has become the asset of every enterprise. The mining of such large volume of data provides the valuable information regarding to the specific field for which data are collected. There are many types of data mining techniques available and used to extract the valuable hidden patterns from the large volume of data. The patterns extracted from the data become the part of knowledge base for the decision support system. The main goal of the data mining is to find out the relevant and more valuable information from the data and building the knowledge base. In this paper we are considering the K-means Clustering algorithm for classifying the data on the basis of similarity. This is one type of the unsupervised machine learning technique. The Clusters produced by the K-means clustering are further classified using Supervised machine learning techniques, Such as K-nearest Neighbour method and decision tree algorithm. Keywords— Data mining, hidden patterns, knowledge base, decision support system, K-means Clustering algorithm, Unsupervised machine learning, Supervised machine learning, Knearest Neighbour method, decision tree algorithm.</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="125474687"><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="125474687"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474687; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=125474687]").text(description); $(".js-view-count[data-work-id=125474687]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 125474687; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='125474687']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=125474687]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":125474687,"title":"Comparative Study of K-Nearest Neighbor Classification and J48 Decision Tree Algorithm with and Without Clustering Considering Different Data Parameters","internal_url":"https://www.academia.edu/125474687/Comparative_Study_of_K_Nearest_Neighbor_Classification_and_J48_Decision_Tree_Algorithm_with_and_Without_Clustering_Considering_Different_Data_Parameters","owner_id":321190784,"coauthors_can_edit":true,"owner":{"id":321190784,"first_name":"Dr. SHRWAN","middle_initials":null,"last_name":"RAM","page_name":"DrSHRWANRAM","domain_name":"independent","created_at":"2024-08-10T20:13:28.636-07:00","display_name":"Dr. SHRWAN RAM","url":"https://independent.academia.edu/DrSHRWANRAM"},"attachments":[]}, 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="125474686"><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/125474686/Object_Detection_and_Classification_Through_Deep_Learning_Approaches"><img alt="Research paper thumbnail of Object Detection and Classification Through Deep Learning Approaches" class="work-thumbnail" src="https://attachments.academia-assets.com/119510861/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/125474686/Object_Detection_and_Classification_Through_Deep_Learning_Approaches">Object Detection and Classification Through Deep Learning Approaches</a></div><div class="wp-workCard_item"><span>Journal of emerging technologies and innovative research</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, we implemented the image classification and object detection. This paper presents ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, we implemented the image classification and object detection. This paper presents a deep learning approach for traffic light detection in adapting a single shot detection(SSD) approach and image classification of two categories of bicycle by retraining inceptionv3 model both using an open source tool called TensorFlow Object Detection API. We reviewed the current literature on convolutional object detection and tested the implementability of one of the methods and discovered that convolutional object detection is still evolving as a technology despite that convolutional object detection has outranked other object detection methods. To implement object detection and image classification there is free availability of datasets and pretrained networks it is possible to create a functional implementation of a deep neural network without access to specialist hardware. KEYWORDS-Object detection, Deep learning, Convolutional neural network, TensorFlow Object Detection API, SS...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f38236790e3461876dc789505bfc8f5d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119510861,"asset_id":125474686,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119510861/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="125474686"><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="125474686"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474686; 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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="125474685"><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/125474685/A_Comparative_Study_of_Multilayer_Perceptron_Radial_Basis_Function_Networks_and_Logistic_Regression_for_Healthcare_Data_Classification"><img alt="Research paper thumbnail of A Comparative Study of Multilayer Perceptron, Radial Basis Function Networks and Logistic Regression for Healthcare Data Classification" class="work-thumbnail" src="https://attachments.academia-assets.com/119510860/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/125474685/A_Comparative_Study_of_Multilayer_Perceptron_Radial_Basis_Function_Networks_and_Logistic_Regression_for_Healthcare_Data_Classification">A Comparative Study of Multilayer Perceptron, Radial Basis Function Networks and Logistic Regression for Healthcare Data Classification</a></div><div class="wp-workCard_item"><span>International Journal of Advance Engineering and Research Development</span><span>, Mar 31, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The Healthcare databases are becoming more important nowadays. Many Healthcare institutions are m...</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 Healthcare databases are becoming more important nowadays. Many Healthcare institutions are maintaining the large volume of healthcare databases to provide the best clinical services and insurance claims. The profits of Healthcare insurance companies are totally depending on the care of their customers. It is predicted by the healthcare department of United States of America that the early detection of any disease and its cause is very important strategy to save the big amount of insurance claim. Therefore Healthcare data classification approach has become the dominant process to save the big amount of budget allocation for the government sector. There are many types of classification approaches used for classification and prediction. In this research paper mainly multilayered Perceptron, Radial basis function networks and Logistic Regression are used to classify the Healthcare databases and on the basis of classification trends the decision are taken. All these approaches of data classification are covered. in this paper.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="299f6fa57ed88e8287b997d7c9ec8a10" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119510860,"asset_id":125474685,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119510860/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="125474685"><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="125474685"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474685; 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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="125474660"><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/125474660/Glioma_Tumor_Detection_Through_Faster_Region_Based_Convolutional_Neural_Networks_Using_Transfer_Learning"><img alt="Research paper thumbnail of Glioma Tumor Detection Through Faster Region-Based Convolutional Neural Networks Using Transfer Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/119510819/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/125474660/Glioma_Tumor_Detection_Through_Faster_Region_Based_Convolutional_Neural_Networks_Using_Transfer_Learning">Glioma Tumor Detection Through Faster Region-Based Convolutional Neural Networks Using Transfer Learning</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Glioma Tumor is generally found in the brain and spinal cord. This tumor begins in glial cells 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">Glioma Tumor is generally found in the brain and spinal cord. This tumor begins in glial cells that cover the nerve cells and control the function of that. The Glioma tumor is classified based on glial cells involved in the Glioma tumor formation. The tumor affects the normal activity of the patients such as loss of memory, difficulties in speech, confuse the identification of objects, and also causes difficulties to maintain the balance of the body. The early detection of Glioma tumor helps healthcare practitioners to suggest a suitable treatment for the disease. The detection of a Glioma tumor is a challenging task. Many types of approaches had been proposed by the researchers and academicians for accurately detecting the Glioma tumor. Accurately detecting the brain tumor is still a big challenge. Because of recent advances in image processing and computer vision, healthcare professionals are using sophisticated disease diagnostic tools for disorders/disease prediction. The Neuros...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e60835959220c9fb8664202e6c0fb59d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119510819,"asset_id":125474660,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119510819/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="125474660"><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="125474660"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125474660; 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