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The availability of machine-learning algorithms, and the immense computational power required to develop robust models with high accuracy, has driven researchers to conduct extensive studies in forensic science, particularly in the identification and examination of evidence found at crime scenes. Machine Learning in Forensic Evidence Examination discusses methodologies for the application of machine learning to the field of forensic science. Evidence analysis is the cornerstone of forensic investigations, examined for either classification or individualization based on distinct characteristics. Artificial intelligence offers a powerful advantage by efficiently processing large datasets with ...
The six-volume set, CCIS 2424 - 2429, constitutes the refereed proceedings of the Third International Conference on Advances in Smart Computing and Information Security, ASCIS 2024, held in Rajkot, Gujarat, India, in October 16–18, 2024. The 138 full papers and 43 short papers presented in these six volumes were carefully reviewed and selected from 667 submissions.The papers presented in these six volumes are organized in the following topical sections: Part I, II, III, IV: Artificial Intelligence & Machine Learning Part V: Smart Computing; Network and Cloud Computing. Part VI: Cyber Security; Computer Application for Sustainability.
Vol. 2-33 include Papers read at the annual conference of the Indian Economic Association.
This book aims to provide a comprehensive overview of the applications of Artificial Intelligence (AI) in the area of Cybersecurity and Digital Forensics. The various chapters of this book are written to explore how cutting‐edge technologies can be used to improve the detection, prevention, and investigation of cybercrime and help protect digital assets. Digital Defence covers an overview of deep learning and AI techniques and their relevance to cybersecurity and digital forensics, discusses common cyber threats and vulnerabilities, and how deep learning and AI can detect and prevent them. It focuses on how deep learning/artificial learning techniques can be used for intrusion detection in...
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