[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120899-en":3,"doc-seo-120899-105":29,"detail-sidebar-cat-0-en-105":89},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120899,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Convolutional Neural Network based Machine Learning for Ameloglyphics: A Forensic Analysis","This research paper explores the application of Convolutional Neural Network (CNN) based machine learning for ameloglyphics, a forensic analysis of tooth prints. The primary aim is to analyze tooth prints using CNN technology and subsequently correlate the observed patterns with gender and age. The study outlines several objectives, including the manual classification of ameloglyphic patterns in both deciduous and permanent teeth, training a machine learning model to recognize these patterns via a CNN algorithm, testing the model's accuracy, and finally, establishing correlations between ameloglyphic patterns and demographic factors like gender and age. The methodology involves sampling extracted deciduous and permanent teeth, followed by acid etching, enamel print creation, image acquisition using high-resolution microscopy, manual classification, and then CNN training and testing. Results indicate a validation accuracy of 83.33% for both deciduous and permanent samples. The study concludes that ameloglyphic analysis through CNN-based machine learning offers a relatively accurate, cost-effective, and time-efficient approach for forensic applications.","Convolutional Neural Network based Machine Learning  \nfor Ameloglyphics: A Forensic Analysis  \nSanjana Shetty, Sowmya SV, Dominic Augustine, Sai prasad Alva, Mukul Sai ni  \nAIM:  \nTo analyse tooth prints through Convolutional Neural Network based technology and correlate the patterns with gender and age.  \nOBJECTIVES:  \n1. To record and manually classify theameloglyphic patterns of deciduous and permanent teeth.  \n2. To train the machine in classification through a Convoluted Neural Network algorithm.  \n3. To test the accuracy of the machine in identifying ameloglyphic patterns.  \n4. To correlate the ameloglyphic patterns with gender and age.  \nMETHODOLOGY:  \n1. SAMPLING  \nEXTRACTED DECIDUOUS TEETH  \n(N= 39)  \nEXTRACTED PERMANENT TEETH  \n(N=51)  \n2, ACID ETCHING WITH 37% PHOSPHORIC ACID  \n3. ENAMEL PRINTS ON CELLULOSE ACETATE STRIPS  \n4. IMAGE ACQUISITION USING  \nOLYMPUS RESEARCH MICROSCOPE BX53F2, TOKYO,  \nJAPAN  \n5. MANUAL CLASSIFICATION  \n6. CNN TRAINING AND TESTING  \nCONCLUSION:  \nAmeloglyphic analysis via CNN based machine learning was found to be relatively accurate, cost effective and time efficient  \nPoster No.: 310, South Jordan Campus  \nRESULTS:  \nVALIDATION ACCURACY FOR DECIDUOUS SAMPLES: 83.33%  \nVALIDATION ACCURACY FOR PERMANENT SAMPLES: 83.33%  \n| \u003Cbr>\u003Cbr>DECIDUOUS TEETH GENDER CORRELATION\u003Cbr>DECIDUOUS TEETH-GENDER CORRELATION |  |  |  |\n| --- | --- | --- | --- |\n|  | \u003Cbr>Linear Pattern | \u003Cbr>Non-Linear Pattern | \u003Cbr>Fish scale Pattern |\n| \u003Cbr>MALE | \u003Cbr>05 | \u003Cbr>10 | \u003Cbr>04 |\n| \u003Cbr>FEMALE | \u003Cbr>04 | \u003Cbr>16 | \u003Cbr>00 |\n\nX² (6, N = 51) = 2.1, p = . 003","cbCaidxpWSn7Ou2N","https://ap.wps.com/l/cbCaidxpWSn7Ou2N","pdf",443942,1,"English","en",105,"# Convolutional Neural Network based Machine Learning for Ameloglyphics: A Forensic Analysis\n## AIM\n## OBJECTIVES\n## METHODOLOGY\n## RESULTS\n## CONCLUSION","[{\"question\":\"What is the main objective of this research?\",\"answer\":\"The main objective is to analyze tooth prints using Convolutional Neural Network (CNN) based technology and correlate the patterns with gender and age.\"},{\"question\":\"What was the methodology used in this study?\",\"answer\":\"The methodology involved sampling deciduous and permanent teeth, acid etching, creating enamel prints, acquiring images via microscopy, manual classification, and finally, CNN training and testing.\"},{\"question\":\"What were the accuracy results of the CNN model?\",\"answer\":\"The validation accuracy for both deciduous and permanent tooth samples was found to be 83.33%.\"}]","Convolutional Neural Network based Machine Learning for Ameloglyphics: A Forensic Analysis | 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is the main objective of this research?","Question",{"text":73,"@type":74},"The main objective is to analyze tooth prints using Convolutional Neural Network (CNN) based technology and correlate the patterns with gender and age.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"What was the methodology used in this study?",{"text":78,"@type":74},"The methodology involved sampling deciduous and permanent teeth, acid etching, creating enamel prints, acquiring images via microscopy, manual classification, and finally, CNN training and testing.",{"name":80,"@type":71,"acceptedAnswer":81},"What were the accuracy results of the CNN model?",{"text":82,"@type":74},"The validation accuracy for both deciduous and permanent tooth samples was found to be 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