{"title":"基于同一潜在证据的高效指纹分析和DNA分析在法医学上的应用","authors":"Jyothi Johnson, R. Chitra, A. Anusha Bamini","doi":"10.1109/STCR55312.2022.10009376","DOIUrl":null,"url":null,"abstract":"The latent ridge impressions (Finger Prints (FPs)) and DNA profiling have been regarded as mutually exclusive for the analysis of Forensic Evidence (FE). However, these dual evaluations were excluded due to the processing and sensitivity problems. Thus, effectual FP Analysis (FPA) and DNA profiling from similar latent evidence were proposed for forensic applications. The features from the FP and the DNA components within the FP are regarded here. The features were merged; then, it is inputted to the new Zone-out Regularization-centric Improved Artificial Neural Network (ZR-IANN) that exhibited precise predictions of whether the suspect is an imposter or a genuine one. The overall recognition accuracy of 98.54% was attained by the proposed technique. Hence, the proposed methodology surpasses other prevailing techniques.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Efficient Fingerprint Analysis and DNA Profiling from the Same Latent Evidence for the Forensic Applications\",\"authors\":\"Jyothi Johnson, R. Chitra, A. Anusha Bamini\",\"doi\":\"10.1109/STCR55312.2022.10009376\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The latent ridge impressions (Finger Prints (FPs)) and DNA profiling have been regarded as mutually exclusive for the analysis of Forensic Evidence (FE). However, these dual evaluations were excluded due to the processing and sensitivity problems. Thus, effectual FP Analysis (FPA) and DNA profiling from similar latent evidence were proposed for forensic applications. The features from the FP and the DNA components within the FP are regarded here. The features were merged; then, it is inputted to the new Zone-out Regularization-centric Improved Artificial Neural Network (ZR-IANN) that exhibited precise predictions of whether the suspect is an imposter or a genuine one. The overall recognition accuracy of 98.54% was attained by the proposed technique. Hence, the proposed methodology surpasses other prevailing techniques.\",\"PeriodicalId\":338691,\"journal\":{\"name\":\"2022 Smart Technologies, Communication and Robotics (STCR)\",\"volume\":\"142 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Smart Technologies, Communication and Robotics (STCR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/STCR55312.2022.10009376\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Smart Technologies, Communication and Robotics (STCR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STCR55312.2022.10009376","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Efficient Fingerprint Analysis and DNA Profiling from the Same Latent Evidence for the Forensic Applications
The latent ridge impressions (Finger Prints (FPs)) and DNA profiling have been regarded as mutually exclusive for the analysis of Forensic Evidence (FE). However, these dual evaluations were excluded due to the processing and sensitivity problems. Thus, effectual FP Analysis (FPA) and DNA profiling from similar latent evidence were proposed for forensic applications. The features from the FP and the DNA components within the FP are regarded here. The features were merged; then, it is inputted to the new Zone-out Regularization-centric Improved Artificial Neural Network (ZR-IANN) that exhibited precise predictions of whether the suspect is an imposter or a genuine one. The overall recognition accuracy of 98.54% was attained by the proposed technique. Hence, the proposed methodology surpasses other prevailing techniques.