Apoorva S Shastri – 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.

With the emergence of the data economy, information has become integral to business excellence. Every enterprise, irrespective of its domain of interest, carries and processes a lot of data in their day-to-day activities. Converting massive datasets into insightful information plays an important role in developing better business solutions. Data intelligence and its analysis pose several challenges in data representation, building knowledge systems, issue resolution and predictive systems for trend analysis and decisionmaking. The data available could be of any modality, especially when data is associated with healthcare, biomedical, finance, retail, cybersecurity, networking, supply chain management, manufacturing, etc. The optimization of such systems is therefore crucial to leveraging the best outcomes and conclusions. To this end, AI-based nature-inspired optimization methods or approximation-based optimization methods are becoming very powerful. Notable metaheuristics include genetic algorithms, differential evolution, ant colony optimization, particle swarm optimization, artificial bee colony, grey wolf optimizer, political optimizer, cohort intelligence and league championship algorithm. This book provides a systematic discussion of AI-based metaheuristics application in a wide range of areas, including big data intelligence and predictive analytics, enterprise analytics, graph optimization algorithms, machine learning and ensemble learning, computer vision enterprise practices and data benchmarking.

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 provides a comprehensive review of the latest developments in optimization based learning algorithms within the field of electrical engineering. It discusses several optimization algorithms such as random forest algorithm, metaheuristic algorithm, scaled conjugate gradient descent algorithm, artificial bee colony algorithm etc.

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.