Forensic Intelligence And Deep Learning Solutions In Crime Investigation
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Forensic Intelligence and Deep Learning Solutions in Crime Investigation
The massive advancement in various sectors of technology including forensic science is no exception. Integration of deep learning (DL) and artificial intelligence (AI) in forensic intelligence plays a vital role in the transformational shift in the effective approach towards the investigation of crimes and solving criminal investigations with foolproof evidence. As crimes grow increasingly sophisticated, traditional investigative tactics may be inadequate to grapple with the complexities of transnational criminal organizations. DL uses scientific tools for the recognition of patterns, image and speech analysis, and predictive modeling among others which are necessary to help solve crimes. By studying fingerprints, behavioral profiling, and DNA in digital forensics, AI powered tools provide observations that were inconceivable before now. Forensic Intelligence and Deep Learning Solutions in Crime Investigation discusses the numerous potential applications of deep learning and AI in forensic science. It explores how deep learning algorithms and AI technologies transform the role that forensic scientists and investigators play by enabling them to efficiently process and analyze vast amounts of data with very high accuracy in a short duration. Covering topics such as forensic ballistics, evidence processing, and crime scene analysis, this book is an excellent resource for forensic scientists, investigators, law enforcement, criminal justice professionals, computer scientists, legal professionals, policy makers, professionals, researchers, scholars, academicians, and more.
Confluence of AI, Machine, and Deep Learning in Cyber Forensics
Developing a knowledge model helps to formalize the difficult task of analyzing crime incidents in addition to preserving and presenting the digital evidence for legal processing. The use of data analytics techniques to collect evidence assists forensic investigators in following the standard set of forensic procedures, techniques, and methods used for evidence collection and extraction. Varieties of data sources and information can be uniquely identified, physically isolated from the crime scene, protected, stored, and transmitted for investigation using AI techniques. With such large volumes of forensic data being processed, different deep learning techniques may be employed. Confluence of AI, Machine, and Deep Learning in Cyber Forensics contains cutting-edge research on the latest AI techniques being used to design and build solutions that address prevailing issues in cyber forensics and that will support efficient and effective investigations. This book seeks to understand the value of the deep learning algorithm to handle evidence data as well as the usage of neural networks to analyze investigation data. Other themes that are explored include machine learning algorithms that allow machines to interact with the evidence, deep learning algorithms that can handle evidence acquisition and preservation, and techniques in both fields that allow for the analysis of huge amounts of data collected during a forensic investigation. This book is ideally intended for forensics experts, forensic investigators, cyber forensic practitioners, researchers, academicians, and students interested in cyber forensics, computer science and engineering, information technology, and electronics and communication.
Machine and Deep Learning Solutions for Achieving the Sustainable Development Goals
Achieving the United Nations' Sustainable Development Goals (SDGs) requires innovative solutions that address global challenges such as climate change, poverty, and social inequality. Artificial intelligence (AI), machine learning, and data-driven technologies offer transformative potential by optimizing resource management, improving healthcare outcomes, and enhancing decision-making processes. However, integrating AI into sustainable development efforts presents ethical, technical, and policy-related challenges that must be carefully navigated. A multidisciplinary approach is essential to ensure these technologies are applied inclusively and responsibly, maximizing their positive societal impact. Machine and Deep Learning Solutions for Achieving the Sustainable Development Goals enhances understanding and application of machine learning, deep learning, data mining and AI technologies in the context of the SDGs. It fills the gap by linking theory and practice and addresses both the opportunities and challenges inherent in this intersection. Covering topics such as demand side management, agricultural productivity, and smart manufacturing, this book is an excellent resource for engineers, computer scientists, practitioners, policymakers, professionals, researchers, scholars, academicians, and more.