Mangal Singh – författare

This book examines the latest developments in Artificial Intelligence (AI)-based metaheuristics algorithms with applications in information security for digital media. It highlights the importance of several security parameters, their analysis, and validations for different practical applications. Drawing on multidisciplinary research including computer vision, machine learning, artificial intelligence, and modified/newly developed metaheuristics algorithms, it will enhance information security for society. It includes state-of-the-art research with illustrations and exercises throughout.

This book examines the latest developments in Artificial Intelligence (AI)-based metaheuristics algorithms with applications in information security for digital media. It highlights the importance of several security parameters, their analysis, and validations for different practical applications. Drawing on multidisciplinary research including computer vision, machine learning, artificial intelligence, and modified/newly developed metaheuristics algorithms, it will enhance information security for society. It includes state-of-the-art research with illustrations and exercises throughout.

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 networks and systems, analyze and classify malware, and identify potential sources of malware attacks. This book also explores AI’s role in digital forensics investigations, including data recovery, incident response and management, real‑time monitoring, automated response analysis, ethical and legal considerations, and visualization. By covering these topics, this book will provide a valuable resource for researchers, students, and cybersecurity and digital forensics professionals interested in learning about the latest advances in deep learning and AI techniques and their applications.

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 networks and systems, analyze and classify malware, and identify potential sources of malware attacks. This book also explores AI’s role in digital forensics investigations, including data recovery, incident response and management, real‑time monitoring, automated response analysis, ethical and legal considerations, and visualization. By covering these topics, this book will provide a valuable resource for researchers, students, and cybersecurity and digital forensics professionals interested in learning about the latest advances in deep learning and AI techniques and their applications.

This book aims to provide a collection of state-of-the-art scientific and technical research papers related to machine learning-based algorithms in the field of optimization and engineering design. The theoretical and practical development for numerous engineering applications such as smart homes, ICT-based irrigation systems, academic success prediction, future agro-industry for crop production, disease classification in plants, dental problems and solutions, loan eligibility processing, etc., and their implementation with several case studies and literature reviews are included as self-contained chapters. Additionally, the book intends to highlight the importance of study and effectiveness in addressing the time and space complexity of problems and enhancing accuracy, analysis, and validations for different practical applications by acknowledging the state-of-the-art literature survey. The book targets a larger audience by exploring multidisciplinary research directions such as computer vision, machine learning, artificial intelligence, modified/newly developed machine learning algorithms, etc., to enhance engineering design applications for society. State-of-the-art research work with illustrations and exercises along with pseudo-code has been provided here.