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href="https://www.academia.edu/16028874/Age_related_neural_activity_during_allocentric_spatial_memory">Age-related neural activity during allocentric spatial memory</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/16028874" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="35a40004feedfb290e68076cb32c6468" rel="nofollow" data-download="{"attachment_id":42779626,"asset_id":16028874,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen 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brain potential (ERP) sign of the brain's evaluation of novelty</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/7163225" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="81853e51d1cb412a6d3b707c8e89b24c" rel="nofollow" data-download="{"attachment_id":48575935,"asset_id":7163225,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" 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Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=26066","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="61474" href="https://www.academia.edu/Documents/in/Brain">Brain</a><script data-card-contents-for-ri="61474" type="text/json">{"id":61474,"name":"Brain","url":"https://www.academia.edu/Documents/in/Brain?f_ri=26066","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=7163225]'), work: {"id":7163225,"title":"The novelty P3: an event-related brain potential (ERP) sign of the brain's evaluation of 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networks</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This study is concerned with understanding of the formation of ore deposits (precious and base metals) and contributes to the exploration and discovery of new occurrences using artificial neural networks. From the different digital data... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_65868556" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This study is concerned with understanding of the formation of ore deposits (precious and base metals) and contributes to the exploration and discovery of new occurrences using artificial neural networks. 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href="https://www.academia.edu/23984056/Knowledge_recovery_for_continental_scale_mineral_exploration_by_neural_networks">Knowledge recovery for continental-scale mineral exploration by neural networks</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This study is concerned with understanding of the formation of ore deposits (precious and base metals) and contributes to the exploration and discovery of new occurrences using artificial neural networks. From the different digital data... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_23984056" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This study is concerned with understanding of the formation of ore deposits (precious and base metals) and contributes to the exploration and discovery of new occurrences using artificial neural networks. 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other physical parameters like conduit inclination, pipe material, pipe... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_76485962" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Identification of flow pattern during the simultaneous flow of two immiscible liquids requires knowledge of the flow rate of each fluid as well as knowledge of other physical parameters like conduit inclination, pipe material, pipe diameter, viscosity of the oil, wetting characteristics of the pipe, design of the entry mixer, and fluid-fluid interfacial tension. 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systems","url":"https://www.academia.edu/Documents/in/Dynamic_systems?f_ri=26066"},{"id":254570,"name":"Interpolation","url":"https://www.academia.edu/Documents/in/Interpolation?f_ri=26066"},{"id":314271,"name":"Fuzzy System","url":"https://www.academia.edu/Documents/in/Fuzzy_System?f_ri=26066"},{"id":366369,"name":"Time Series Forecasting","url":"https://www.academia.edu/Documents/in/Time_Series_Forecasting?f_ri=26066"},{"id":506858,"name":"Nonlinear system","url":"https://www.academia.edu/Documents/in/Nonlinear_system?f_ri=26066"},{"id":634757,"name":"Dynamic Systems","url":"https://www.academia.edu/Documents/in/Dynamic_Systems-1?f_ri=26066"},{"id":780669,"name":"Input Output","url":"https://www.academia.edu/Documents/in/Input_Output?f_ri=26066"},{"id":868912,"name":"Dynamic System","url":"https://www.academia.edu/Documents/in/Dynamic_System?f_ri=26066"},{"id":1327249,"name":"Least Square Method","url":"https://www.academia.edu/Documents/in/Least_Square_Method?f_ri=26066"},{"id":2004933,"name":"Hybrid System","url":"https://www.academia.edu/Documents/in/Hybrid_System?f_ri=26066"},{"id":2595821,"name":"Least squares method","url":"https://www.academia.edu/Documents/in/Least_squares_method?f_ri=26066"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_69633638" data-work_id="69633638" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/69633638/Offline_Cursive_Handwriting_Recognition_System_based_on_Hybrid_Markov_Model_and_Neural_Networks">Offline Cursive Handwriting Recognition System based on Hybrid Markov Model and Neural Networks</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">An offline cursive handwriting recognition system, based on hybrid of Neural Networks (NN) and Hidden Markov Models (HMM), is described in this paper. Applying SegRec principle, the recognizer does not make hard decision at the character... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_69633638" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">An offline cursive handwriting recognition system, based on hybrid of Neural Networks (NN) and Hidden Markov Models (HMM), is described in this paper. Applying SegRec principle, the recognizer does not make hard decision at the character segmentation process. Instead, it delays the character segmentation to the recognition stage by generating a segmentation graph that describes all possible ways to segment a word into letters. To recognize a word, the NN computes the observation probabilities for each segmentation candidates (SCs) in the segmentation graph. Then, using concatenated letter-HMMs, a likelihood is computed for each word in the lexicon by multiplying the probabilities over the best paths through the graph. We present in detail two approaches to train the word recognizer: 1). character-level training 2). word-level training. The recognition performances of the two systems are discussed. I.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/69633638" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="46ba9831584a88731497a41195340804" rel="nofollow" data-download="{"attachment_id":79653507,"asset_id":69633638,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/79653507/download_file?st=MTczMzI2MTE3Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="32440316" href="https://univ-nantes.academia.edu/CViardgaudin">C. 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Applying SegRec principle, the recognizer does not make hard decision at the character segmentation process. Instead, it delays the character segmentation to the recognition stage by generating a segmentation graph that describes all possible ways to segment a word into letters. To recognize a word, the NN computes the observation probabilities for each segmentation candidates (SCs) in the segmentation graph. Then, using concatenated letter-HMMs, a likelihood is computed for each word in the lexicon by multiplying the probabilities over the best paths through the graph. We present in detail two approaches to train the word recognizer: 1). character-level training 2). word-level training. The recognition performances of the two systems are discussed. I.","downloadable_attachments":[{"id":79653507,"asset_id":69633638,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":32440316,"first_name":"C.","last_name":"Viard-gaudin","domain_name":"univ-nantes","page_name":"CViardgaudin","display_name":"C. Viard-gaudin","profile_url":"https://univ-nantes.academia.edu/CViardgaudin?f_ri=26066","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=26066","nofollow":false},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=26066","nofollow":false},{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=26066","nofollow":false},{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=26066","nofollow":false},{"id":26870,"name":"Image segmentation","url":"https://www.academia.edu/Documents/in/Image_segmentation?f_ri=26066"},{"id":54123,"name":"Artificial Neural Networks","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Networks?f_ri=26066"},{"id":68937,"name":"Hidden Markov Models","url":"https://www.academia.edu/Documents/in/Hidden_Markov_Models?f_ri=26066"},{"id":108243,"name":"Character Segmentation","url":"https://www.academia.edu/Documents/in/Character_Segmentation?f_ri=26066"},{"id":143539,"name":"hidden Markov model","url":"https://www.academia.edu/Documents/in/hidden_Markov_model?f_ri=26066"},{"id":167397,"name":"Image recognition","url":"https://www.academia.edu/Documents/in/Image_recognition?f_ri=26066"},{"id":185625,"name":"Handwriting Recognition","url":"https://www.academia.edu/Documents/in/Handwriting_Recognition?f_ri=26066"},{"id":195152,"name":"Hmm","url":"https://www.academia.edu/Documents/in/Hmm?f_ri=26066"},{"id":2050770,"name":"Markov model","url":"https://www.academia.edu/Documents/in/Markov_model?f_ri=26066"},{"id":3142462,"name":"Neural nets","url":"https://www.academia.edu/Documents/in/Neural_nets?f_ri=26066"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_68660215" data-work_id="68660215" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/68660215/Monitoring_students_actions_and_using_teachers_expertise_in_implementing_and_evaluating_the_neural_network_based_fuzzy_diagnostic_model">Monitoring students' actions and using teachers' expertise in implementing and evaluating the neural network-based fuzzy diagnostic model</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this paper, the implementation of a neural network-based fuzzy modeling approach to assess aspects of students&#x27; learning style in the discovery learning environment Vectors in Physics and Mathematics is presented. Fuzzy logic is... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_68660215" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper, the implementation of a neural network-based fuzzy modeling approach to assess aspects of students&#x27; learning style in the discovery learning environment Vectors in Physics and Mathematics is presented. Fuzzy logic is used to provide a linguistic description of ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/68660215" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="bc27b0e5518c2a337ac1d28a589ab45a" rel="nofollow" data-download="{"attachment_id":79064889,"asset_id":68660215,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/79064889/download_file?st=MTczMzI2MTE3Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="131536458" href="https://independent.academia.edu/%CE%9C%CE%B1%CF%81%CE%AF%CE%B1%CE%A3%CE%B1%CE%BC%CE%B1%CF%81%CE%AC%CE%BA%CE%BF%CF%85">Μαρία Σαμαράκου</a><script data-card-contents-for-user="131536458" type="text/json">{"id":131536458,"first_name":"Μαρία","last_name":"Σαμαράκου","domain_name":"independent","page_name":"ΜαρίαΣαμαράκου","display_name":"Μαρία Σαμαράκου","profile_url":"https://independent.academia.edu/%CE%9C%CE%B1%CF%81%CE%AF%CE%B1%CE%A3%CE%B1%CE%BC%CE%B1%CF%81%CE%AC%CE%BA%CE%BF%CF%85?f_ri=26066","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_68660215 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="68660215"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 68660215, container: ".js-paper-rank-work_68660215", }); 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class="complete hidden">This paper introduces a novel knowledge based neural network models that incorporate and adapt both existing logistic regression formulas and kernel functions in there structures to improve the learning and adaptation ability of a connectionist model when there is an existing knowledge on the problem in the form of a logistic regression. 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Different from standard feed-forward neural networks, the proposed","downloadable_attachments":[{"id":77644134,"asset_id":66463136,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":158086,"first_name":"Nikola","last_name":"Kasabov, Professor","domain_name":"aut","page_name":"NikolaKasabovProfessor","display_name":"Nikola Kasabov, Professor","profile_url":"https://aut.academia.edu/NikolaKasabovProfessor?f_ri=26066","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=26066","nofollow":false},{"id":32433,"name":"Logistic Regression","url":"https://www.academia.edu/Documents/in/Logistic_Regression?f_ri=26066","nofollow":false},{"id":39714,"name":"Incremental learning","url":"https://www.academia.edu/Documents/in/Incremental_learning?f_ri=26066","nofollow":false},{"id":227354,"name":"Renal Function","url":"https://www.academia.edu/Documents/in/Renal_Function?f_ri=26066","nofollow":false},{"id":246163,"name":"Knowledge base","url":"https://www.academia.edu/Documents/in/Knowledge_base?f_ri=26066"},{"id":383422,"name":"Local Knowledge","url":"https://www.academia.edu/Documents/in/Local_Knowledge?f_ri=26066"},{"id":862300,"name":"Parameter Optimization","url":"https://www.academia.edu/Documents/in/Parameter_Optimization?f_ri=26066"},{"id":1368234,"name":"Gradient Descent Method","url":"https://www.academia.edu/Documents/in/Gradient_Descent_Method?f_ri=26066"},{"id":1729304,"name":"Kernel Function","url":"https://www.academia.edu/Documents/in/Kernel_Function?f_ri=26066"},{"id":1837730,"name":"Neural Network Model","url":"https://www.academia.edu/Documents/in/Neural_Network_Model?f_ri=26066"},{"id":2003344,"name":"Feed Forward Neural Network","url":"https://www.academia.edu/Documents/in/Feed_Forward_Neural_Network?f_ri=26066"},{"id":2117111,"name":"Gaussian kernel","url":"https://www.academia.edu/Documents/in/Gaussian_kernel?f_ri=26066"},{"id":2366663,"name":"Glomerular Filtration Rate","url":"https://www.academia.edu/Documents/in/Glomerular_Filtration_Rate?f_ri=26066"},{"id":2968128,"name":"Fuzzy Model","url":"https://www.academia.edu/Documents/in/Fuzzy_Model?f_ri=26066"},{"id":3672744,"name":"connectionist models","url":"https://www.academia.edu/Documents/in/connectionist_models?f_ri=26066"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_65774246" data-work_id="65774246" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/65774246/Automated_Maintenance_Approach_for_Industrial_Machineries_by_Soft_Computing_Techniques_at_Offline_Monitoring_Process">Automated Maintenance Approach for Industrial Machineries by Soft Computing Techniques at Offline Monitoring Process</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Fault diagnosis of industrial machineries become very much important for improving the quality of the manufacturing as well as for reducing the cost for product testing. In modern manufacturing scenario, a fast and reliable diagnosis... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_65774246" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Fault diagnosis of industrial machineries become very much important for improving the quality of the manufacturing as well as for reducing the cost for product testing. In modern manufacturing scenario, a fast and reliable diagnosis system has turned into a challenging issue in the complex industrial environment. In this work, the diagnosis of gearbox is considered as a mean of health monitoring system by used lubricant. The proposed methodology has been performed on the basis of wear particle analysis in gearbox at offline stage. Possible wear characterization has been done by image vision system to interpret into soft computing techniques like fuzzy inference and neural network mechanisms. Basically, the maintenance policy has been taken with the help of fuzzy expert system, which has been described in the present work.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/65774246" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="82db173f5ec8f2aef3b21381a11321a0" rel="nofollow" data-download="{"attachment_id":77225155,"asset_id":65774246,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/77225155/download_file?st=MTczMzI2MTE3Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="173580174" href="https://independent.academia.edu/SUROJITGHOSH24">SUROJIT GHOSH</a><script data-card-contents-for-user="173580174" type="text/json">{"id":173580174,"first_name":"SUROJIT","last_name":"GHOSH","domain_name":"independent","page_name":"SUROJITGHOSH24","display_name":"SUROJIT GHOSH","profile_url":"https://independent.academia.edu/SUROJITGHOSH24?f_ri=26066","photo":"https://0.academia-photos.com/173580174/49563149/37542527/s65_surojit.ghosh.png"}</script></span></span></li><li class="js-paper-rank-work_65774246 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="65774246"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 65774246, container: ".js-paper-rank-work_65774246", }); 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In modern manufacturing scenario, a fast and reliable diagnosis system has turned into a challenging issue in the complex industrial environment. In this work, the diagnosis of gearbox is considered as a mean of health monitoring system by used lubricant. The proposed methodology has been performed on the basis of wear particle analysis in gearbox at offline stage. Possible wear characterization has been done by image vision system to interpret into soft computing techniques like fuzzy inference and neural network mechanisms. Basically, the maintenance policy has been taken with the help of fuzzy expert system, which has been described in the present work.","downloadable_attachments":[{"id":77225155,"asset_id":65774246,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":173580174,"first_name":"SUROJIT","last_name":"GHOSH","domain_name":"independent","page_name":"SUROJITGHOSH24","display_name":"SUROJIT GHOSH","profile_url":"https://independent.academia.edu/SUROJITGHOSH24?f_ri=26066","photo":"https://0.academia-photos.com/173580174/49563149/37542527/s65_surojit.ghosh.png"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=26066","nofollow":false},{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering?f_ri=26066","nofollow":false},{"id":1185,"name":"Image Processing","url":"https://www.academia.edu/Documents/in/Image_Processing?f_ri=26066","nofollow":false},{"id":6132,"name":"Soft Computing","url":"https://www.academia.edu/Documents/in/Soft_Computing?f_ri=26066","nofollow":false},{"id":10685,"name":"Fuzzy Expert System","url":"https://www.academia.edu/Documents/in/Fuzzy_Expert_System?f_ri=26066"},{"id":14305,"name":"Industrial Engineering","url":"https://www.academia.edu/Documents/in/Industrial_Engineering?f_ri=26066"},{"id":20307,"name":"PRODUCTION ENGINEERING","url":"https://www.academia.edu/Documents/in/PRODUCTION_ENGINEERING?f_ri=26066"},{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=26066"},{"id":27010,"name":"Fuzzy Inference","url":"https://www.academia.edu/Documents/in/Fuzzy_Inference?f_ri=26066"},{"id":117101,"name":"Fault diagnosis","url":"https://www.academia.edu/Documents/in/Fault_diagnosis?f_ri=26066"},{"id":213658,"name":"Health monitoring","url":"https://www.academia.edu/Documents/in/Health_monitoring?f_ri=26066"},{"id":314271,"name":"Fuzzy System","url":"https://www.academia.edu/Documents/in/Fuzzy_System?f_ri=26066"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network?f_ri=26066"},{"id":3480612,"name":"Vision system","url":"https://www.academia.edu/Documents/in/Vision_system?f_ri=26066"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_61755560" data-work_id="61755560" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/61755560/Simultaneous_kinetic_determination_of_thiocyanate_and_sulfide_using_eigenvalue_ranking_and_correlation_ranking_in_principal_component_wavelet_neural_network">Simultaneous kinetic determination of thiocyanate and sulfide using eigenvalue ranking and correlation ranking in principal component-wavelet neural network</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/61755560" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7aec78360499a98f257a2e4a0a91ad28" rel="nofollow" data-download="{"attachment_id":74711465,"asset_id":61755560,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/74711465/download_file?st=MTczMzI2MTE3Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="207598" href="https://iut.academia.edu/ensafi">Ali A. 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href="https://www.academia.edu/47332580/Preliminary_considerations_for_wearable_sensors_for_astronauts_in_exploration_scenarios">Preliminary considerations for wearable sensors for astronauts in exploration scenarios</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">... donning. This system is designed to be embedded in Bio-Suit, a revolutionary space suit concept developed for many years by Prof. Dava ... exploration. I.Bio-SUIT SYSTEM The Bio-Suit System is a project developed by Prof. Dava ...</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/47332580" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="216544301" href="https://independent.academia.edu/TrottiG">G. 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u-tcGrayDarkest"><div class="summarized">We investigate symbolic sequences and in particular information carriers as e.g. books and DNA–strings. First the higher order Shannon entropies are calculated, a characteristic root law is detected. Then the algorithmic entropy is... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_45383041" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We investigate symbolic sequences and in particular information carriers as e.g. books and DNA–strings. First the higher order Shannon entropies are calculated, a characteristic root law is detected. Then the algorithmic entropy is estimated by using Lempel–Ziv compression algorithms. In the third section the correlation function for distant letters, the low frequency Fourier spectrum and the characteristic scaling exponents are calculated. We show that all these measures are able to detect long–range correlations. However, as demonstrated by shuffling experiments, different measures operate on different length scales. The longest correlations found in our analysis comprise a few hundreds or thousands of letters and may be understood as long–wave fluctuations of the composition. 1</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/45383041" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b0cc81c8262105e24a1990b49ef5f15e" rel="nofollow" data-download="{"attachment_id":65908839,"asset_id":45383041,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/65908839/download_file?st=MTczMzI2MTE3Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3155192" href="https://ohio.academia.edu/AlexanderNeiman">Alexander Neiman</a><script data-card-contents-for-user="3155192" type="text/json">{"id":3155192,"first_name":"Alexander","last_name":"Neiman","domain_name":"ohio","page_name":"AlexanderNeiman","display_name":"Alexander Neiman","profile_url":"https://ohio.academia.edu/AlexanderNeiman?f_ri=26066","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_45383041 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="45383041"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 45383041, container: ".js-paper-rank-work_45383041", }); 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Network","url":"https://www.academia.edu/Documents/in/Recurrent_Neural_Network?f_ri=26066"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine?f_ri=26066"},{"id":543171,"name":"Lyapunov function","url":"https://www.academia.edu/Documents/in/Lyapunov_function?f_ri=26066"},{"id":575846,"name":"Upper Bound","url":"https://www.academia.edu/Documents/in/Upper_Bound?f_ri=26066"},{"id":1121354,"name":"Linear Equations","url":"https://www.academia.edu/Documents/in/Linear_Equations?f_ri=26066"},{"id":2217833,"name":"Gradient methods","url":"https://www.academia.edu/Documents/in/Gradient_methods?f_ri=26066"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_5706776" data-work_id="5706776" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/5706776/Combining_Multi_scale_Character_Recognition_and_Linguistic_Knowledge_for_Natural_Scene_Text_OCR">Combining Multi-scale Character Recognition and Linguistic Knowledge for Natural Scene Text OCR</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Understanding text captured in real-world scenes is a challenging problem in the field of visual pattern recognition and continues to generate a significant interest in the OCR (Optical Character Recognition) community. This paper... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_5706776" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Understanding text captured in real-world scenes is a challenging problem in the field of visual pattern recognition and continues to generate a significant interest in the OCR (Optical Character Recognition) community. This paper proposes a novel method to recognize scene texts avoiding the conventional character segmentation step. The idea is to scan the text image with multi-scale windows and apply a robust recognition model, relying on a neural classification approach, to every window in order to recognize valid characters and identify non valid ones. Recognition results are represented as a graph model in order to determine the best sequence of characters. Some linguistic knowledge is also incorporated to remove errors due to recognition confusions. The designed method is evaluated on the ICDAR 2003 database of scene text images and outperforms state-of-the-art approaches.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/5706776" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="acd2c39d5c6810be46657cbaaccd43e4" rel="nofollow" data-download="{"attachment_id":49173317,"asset_id":5706776,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/49173317/download_file?st=MTczMzI2MTE3Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="8269647" href="https://irisa.academia.edu/PascaleSebillot">Pascale Sebillot</a><script data-card-contents-for-user="8269647" type="text/json">{"id":8269647,"first_name":"Pascale","last_name":"Sebillot","domain_name":"irisa","page_name":"PascaleSebillot","display_name":"Pascale Sebillot","profile_url":"https://irisa.academia.edu/PascaleSebillot?f_ri=26066","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_5706776 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="5706776"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 5706776, container: ".js-paper-rank-work_5706776", }); 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This paper proposes a novel method to recognize scene texts avoiding the conventional character segmentation step. The idea is to scan the text image with multi-scale windows and apply a robust recognition model, relying on a neural classification approach, to every window in order to recognize valid characters and identify non valid ones. Recognition results are represented as a graph model in order to determine the best sequence of characters. Some linguistic knowledge is also incorporated to remove errors due to recognition confusions. The designed method is evaluated on the ICDAR 2003 database of scene text images and outperforms state-of-the-art approaches.","downloadable_attachments":[{"id":49173317,"asset_id":5706776,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":8269647,"first_name":"Pascale","last_name":"Sebillot","domain_name":"irisa","page_name":"PascaleSebillot","display_name":"Pascale Sebillot","profile_url":"https://irisa.academia.edu/PascaleSebillot?f_ri=26066","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":5109,"name":"Pattern Recognition","url":"https://www.academia.edu/Documents/in/Pattern_Recognition?f_ri=26066","nofollow":false},{"id":5111,"name":"Character Recognition","url":"https://www.academia.edu/Documents/in/Character_Recognition?f_ri=26066","nofollow":false},{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=26066","nofollow":false},{"id":80596,"name":"Design 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type="text/json">{"id":47347,"name":"Football","url":"https://www.academia.edu/Documents/in/Football?f_ri=26066","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="99499" href="https://www.academia.edu/Documents/in/Complex_network">Complex network</a>, <script data-card-contents-for-ri="99499" type="text/json">{"id":99499,"name":"Complex network","url":"https://www.academia.edu/Documents/in/Complex_network?f_ri=26066","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="154235" href="https://www.academia.edu/Documents/in/Community_Structure">Community Structure</a><script data-card-contents-for-ri="154235" type="text/json">{"id":154235,"name":"Community Structure","url":"https://www.academia.edu/Documents/in/Community_Structure?f_ri=26066","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=14301027]'), work: {"id":14301027,"title":"Network 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networks to modelling nonlinear relationships in ecology</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/23863134" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="fc2dbeef2a77ab9e755eb829cbf76d38" rel="nofollow" data-download="{"attachment_id":44256681,"asset_id":23863134,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" 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Modelling","url":"https://www.academia.edu/Documents/in/Ecological_Modelling?f_ri=26066","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="64073" href="https://www.academia.edu/Documents/in/Brown_trout">Brown trout</a><script data-card-contents-for-ri="64073" type="text/json">{"id":64073,"name":"Brown trout","url":"https://www.academia.edu/Documents/in/Brown_trout?f_ri=26066","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=23863134]'), work: {"id":23863134,"title":"Application of neural networks to modelling nonlinear relationships in 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such as security systems, medical systems, entertainment, etc. Face... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_70779232" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Face Recognition has been identified as one of the attracting research areas and it has drawn the attention of many researchers due to its varying applications such as security systems, medical systems, entertainment, etc. Face recognition is the preferred mode of identification by humans: it is natural, robust and non-intrusive. A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. In this paper we have developed and illustrated a recognition system for human faces using a novel Kohonen self-organizing map (SOM) or Self-Organi...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/70779232" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5717f7c9b7e8e1fe6d1e6730966eda93" rel="nofollow" data-download="{"attachment_id":80385182,"asset_id":70779232,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/80385182/download_file?st=MTczMzI2MTE3Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="128935072" href="https://mitpune.academia.edu/DrShamlaMantri">Dr. Shamla Mantri</a><script data-card-contents-for-user="128935072" type="text/json">{"id":128935072,"first_name":"Dr. Shamla","last_name":"Mantri","domain_name":"mitpune","page_name":"DrShamlaMantri","display_name":"Dr. Shamla Mantri","profile_url":"https://mitpune.academia.edu/DrShamlaMantri?f_ri=26066","photo":"https://0.academia-photos.com/128935072/46085496/143497789/s65_dr._shamla.mantri.jpg"}</script></span></span></li><li class="js-paper-rank-work_70779232 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="70779232"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 70779232, container: ".js-paper-rank-work_70779232", }); 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type="text/json">{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=26066","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="244976" href="https://www.academia.edu/Documents/in/Transfer_Function">Transfer Function</a><script data-card-contents-for-ri="244976" type="text/json">{"id":244976,"name":"Transfer Function","url":"https://www.academia.edu/Documents/in/Transfer_Function?f_ri=26066","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=67221242]'), work: {"id":67221242,"title":"VLSI implementation of a fully parallel stochastic neural 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Data mining is... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_45642439" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Crime remains to continue to be a serious threat to all groups and peoples throughout the world together with the complexity in technology and procedures that are being manipulated to allow extremely complex criminal acts. Data mining is now an essential tool for examining, reducing, and avoiding crime and is manipulated by both government and private institutions across the globe which is the method of revealing hidden information from Big Data. The data mining methods themselves are temporarily presented to the reader and this information includes the social network analysis, neural networks, naive Bayes rule, support vector machines, decision trees, association rule mining, clustering, entity extraction, and amongst others. The main objective of this article is to offer a concise analysis of the data mining applications in crime. Finally, the article evaluates applications of data mining in crime, including a considerable quantity of the study to date, displayed in chronological order with a summary table of numerous crucial information mining applications in the crime area as a directory of reference.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/45642439" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5295111c9b9ea9318e9771a93f7ece92" rel="nofollow" data-download="{"attachment_id":66138166,"asset_id":45642439,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/66138166/download_file?st=MTczMzI2MTE3Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="39122404" href="https://iaeme.academia.edu/publication">IAEME Publication</a><script data-card-contents-for-user="39122404" type="text/json">{"id":39122404,"first_name":"IAEME","last_name":"Publication","domain_name":"iaeme","page_name":"publication","display_name":"IAEME Publication","profile_url":"https://iaeme.academia.edu/publication?f_ri=26066","photo":"https://0.academia-photos.com/39122404/12178523/13563629/s65_iaeme.publication.jpg"}</script></span></span></li><li class="js-paper-rank-work_45642439 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="45642439"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 45642439, container: ".js-paper-rank-work_45642439", }); 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Data mining is now an essential tool for examining, reducing, and avoiding crime and is manipulated by both government and private institutions across the globe which is the method of revealing hidden information from Big Data. The data mining methods themselves are temporarily presented to the reader and this information includes the social network analysis, neural networks, naive Bayes rule, support vector machines, decision trees, association rule mining, clustering, entity extraction, and amongst others. The main objective of this article is to offer a concise analysis of the data mining applications in crime. 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