At the heart of the optimization domain are mathematical modeling of the problem and the solution methodologies. The problems are becoming larger and with growing complexity. Such problems are becoming cumbersome when handled by traditional optimization methods. This has motivated researchers to resort to artificial intelligence (AI)-based, nature-inspired solution methodologies or algorithms.

The Handbook of AI-based Metaheuristics provides a wide-ranging reference to the theoretical and mathematical formulations of metaheuristics, including bio-inspired, swarm-based, socio-cultural, and physics-based methods or algorithms; their testing and validation, along with detailed illustrative solutions and applications; and newly devised metaheuristic algorithms.

This will be a valuable reference for researchers in industry and academia, as well as for all Master’s and PhD students working in the metaheuristics and applications domains.

At the heart of the optimization domain are mathematical modeling of the problem and the solution methodologies. The problems are becoming larger and with growing complexity. Such problems are becoming cumbersome when handled by traditional optimization methods. This has motivated researchers to resort to artificial intelligence (AI)-based, nature-inspired solution methodologies or algorithms.

The Handbook of AI-based Metaheuristics provides a wide-ranging reference to the theoretical and mathematical formulations of metaheuristics, including bio-inspired, swarm-based, socio-cultural, and physics-based methods or algorithms; their testing and validation, along with detailed illustrative solutions and applications; and newly devised metaheuristic algorithms.

This will be a valuable reference for researchers in industry and academia, as well as for all Master’s and PhD students working in the metaheuristics and applications domains.

Due to increasing industry 4.0 practices, massive industrial process data is now available for researchers for modelling and optimization. Artificial Intelligence methods can be applied to the ever-increasing process data to achieve robust control against foreseen and unforeseen system fluctuations. Smart computing techniques, machine learning, deep learning, computer vision, for example, will be inseparable from the highly automated factories of tomorrow. Effective cybersecurity will be a must for all Internet of Things (IoT) enabled work and office spaces.

This book addresses metaheuristics in all aspects of Industry 4.0. It covers metaheuristic applications in IoT, cyber physical systems, control systems, smart computing, artificial intelligence, sensor networks, robotics, cybersecurity, smart factory, predictive analytics and more.

Key features:

Metaheuristic Algorithms in Industry 4.0 provides a guiding light to engineers, researchers, students, faculty and other professionals engaged in exploring and implementing industry 4.0 solutions in various systems and processes.

Due to increasing industry 4.0 practices, massive industrial process data is now available for researchers for modelling and optimization. Artificial Intelligence methods can be applied to the ever-increasing process data to achieve robust control against foreseen and unforeseen system fluctuations. Smart computing techniques, machine learning, deep learning, computer vision, for example, will be inseparable from the highly automated factories of tomorrow. Effective cybersecurity will be a must for all Internet of Things (IoT) enabled work and office spaces.

This book addresses metaheuristics in all aspects of Industry 4.0. It covers metaheuristic applications in IoT, cyber physical systems, control systems, smart computing, artificial intelligence, sensor networks, robotics, cybersecurity, smart factory, predictive analytics and more.

Key features:

Metaheuristic Algorithms in Industry 4.0 provides a guiding light to engineers, researchers, students, faculty and other professionals engaged in exploring and implementing industry 4.0 solutions in various systems and processes.

Mechanical Engineering domain problems are generally complex, consisting of different design variables and constraints. These problems may not be solved using gradient-based optimization techniques. The stochastic nature-inspired optimization techniques have been proposed in this book to efficiently handle the complex problems. The nature-inspired algorithms are classified as bio-inspired, swarm, and physics/chemical-based algorithms.

Socio-inspired is one of the subdomains of bio-inspired algorithms, and Cohort Intelligence (CI) models the social tendencies of learning candidates with an inherent goal to achieve the best possible position. In this book, CI is investigated by solving ten discrete variable truss structural problems, eleven mixed variable design engineering problems, seventeen linear and nonlinear constrained test problems and two real-world applications from manufacturing domain. Static Penalty Function (SPF) is also adopted to handle the linear and nonlinear constraints, and limitations in CI and SPF approaches are examined.

Constraint Handling in Cohort Intelligence Algorithm is a valuable reference to practitioners working in the industry as well as to students and researchers in the area of optimization methods.

Mechanical Engineering domain problems are generally complex, consisting of different design variables and constraints. These problems may not be solved using gradient-based optimization techniques. The stochastic nature-inspired optimization techniques have been proposed in this book to efficiently handle the complex problems. The nature-inspired algorithms are classified as bio-inspired, swarm, and physics/chemical-based algorithms.

Socio-inspired is one of the subdomains of bio-inspired algorithms, and Cohort Intelligence (CI) models the social tendencies of learning candidates with an inherent goal to achieve the best possible position. In this book, CI is investigated by solving ten discrete variable truss structural problems, eleven mixed variable design engineering problems, seventeen linear and nonlinear constrained test problems and two real-world applications from manufacturing domain. Static Penalty Function (SPF) is also adopted to handle the linear and nonlinear constraints, and limitations in CI and SPF approaches are examined.

Constraint Handling in Cohort Intelligence Algorithm is a valuable reference to practitioners working in the industry as well as to students and researchers in the area of optimization methods.

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 studies the role of Artificial Intelligence (AI) in journalism. It traces the origin, growth and development of the media and communication industry in the globalized world and discusses the implications of technologies such as Augmented Reality, Virtual Reality and Extended Reality which have helped foster a communication revolution across the globe. The volume discusses technology-centric media theories in the context of AI and examines if AI has been a boon or bane for data journalism. It also looks at artificial intelligence in beat reporting, and citizen journalism, and analyses the social-cultural implications of artificial intelligence driven journalism and the ethical concerns arising from it.

An important contribution, this book will be indispensable for students and researchers of media studies, communication studies, journalism, social media, technology studies, and digital humanities. It will also be useful for media professionals.

This book studies the role of Artificial Intelligence (AI) in journalism. It traces the origin, growth and development of the media and communication industry in the globalized world and discusses the implications of technologies such as Augmented Reality, Virtual Reality and Extended Reality which have helped foster a communication revolution across the globe. The volume discusses technology-centric media theories in the context of AI and examines if AI has been a boon or bane for data journalism. It also looks at artificial intelligence in beat reporting, and citizen journalism, and analyses the social-cultural implications of artificial intelligence driven journalism and the ethical concerns arising from it.

An important contribution, this book will be indispensable for students and researchers of media studies, communication studies, journalism, social media, technology studies, and digital humanities. It will also be useful for media professionals.

Since 2011, the Fourth Industrial Revolution (IR4.0) has played a significant role in education, research, and industry. Data technologies have also evolved rapidly to cater to the rapidly growing size of the data as well as to enhance the security of the shared data through multiple resources and platforms.

Intelligent Systems of Computing and Informatics aims to develop a new Intelligent Systems of Computing and Informatics (ISCI) to cater to the needs of industries in line with the United Nations’ Sustainable Development Goals (SDGs) of affordable and clean energy and sustainable cities and communities.

Comprising 20 chapters by experts from all over the world, this book covers such topics as data technologies, machine learning, signal and image processing, software systems efficiency, computer networking, Internet of Things, and computational intelligence for real-life problems.

Key Features:

This book is aimed at postgraduate students, researchers working in various research and development (R&D) agencies, and practitioners, as well as scientists that have an interest in ISCI.

Since 2011, the Fourth Industrial Revolution (IR4.0) has played a significant role in education, research, and industry. Data technologies have also evolved rapidly to cater to the rapidly growing size of the data as well as to enhance the security of the shared data through multiple resources and platforms.

Intelligent Systems of Computing and Informatics aims to develop a new Intelligent Systems of Computing and Informatics (ISCI) to cater to the needs of industries in line with the United Nations’ Sustainable Development Goals (SDGs) of affordable and clean energy and sustainable cities and communities.

Comprising 20 chapters by experts from all over the world, this book covers such topics as data technologies, machine learning, signal and image processing, software systems efficiency, computer networking, Internet of Things, and computational intelligence for real-life problems.

Key Features:

This book is aimed at postgraduate students, researchers working in various research and development (R&D) agencies, and practitioners, as well as scientists that have an interest in ISCI.

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.

At the heart of the optimization domain are mathematical modeling of the problem and the solution methodologies. The problems are becoming larger and with growing complexity. Such problems are becoming cumbersome when handled by traditional optimization methods. This has motivated researchers to resort to artificial intelligence (AI)-based, nature-inspired solution methodologies or algorithms.

The Handbook of AI-based Metaheuristics provides a wide-ranging reference to the theoretical and mathematical formulations of metaheuristics, including bio-inspired, swarm-based, socio-cultural, and physics-based methods or algorithms; their testing and validation, along with detailed illustrative solutions and applications; and newly devised metaheuristic algorithms.

This will be a valuable reference for researchers in industry and academia, as well as for all Master’s and PhD students working in the metaheuristics and applications domains.

At the heart of the optimization domain are mathematical modeling of the problem and the solution methodologies. The problems are becoming larger and with growing complexity. Such problems are becoming cumbersome when handled by traditional optimization methods. This has motivated researchers to resort to artificial intelligence (AI)-based, nature-inspired solution methodologies or algorithms.

The Handbook of AI-based Metaheuristics provides a wide-ranging reference to the theoretical and mathematical formulations of metaheuristics, including bio-inspired, swarm-based, socio-cultural, and physics-based methods or algorithms; their testing and validation, along with detailed illustrative solutions and applications; and newly devised metaheuristic algorithms.

This will be a valuable reference for researchers in industry and academia, as well as for all Master’s and PhD students working in the metaheuristics and applications domains.

Due to increasing industry 4.0 practices, massive industrial process data is now available for researchers for modelling and optimization. Artificial Intelligence methods can be applied to the ever-increasing process data to achieve robust control against foreseen and unforeseen system fluctuations. Smart computing techniques, machine learning, deep learning, computer vision, for example, will be inseparable from the highly automated factories of tomorrow. Effective cybersecurity will be a must for all Internet of Things (IoT) enabled work and office spaces.

This book addresses metaheuristics in all aspects of Industry 4.0. It covers metaheuristic applications in IoT, cyber physical systems, control systems, smart computing, artificial intelligence, sensor networks, robotics, cybersecurity, smart factory, predictive analytics and more.

Key features:

Metaheuristic Algorithms in Industry 4.0 provides a guiding light to engineers, researchers, students, faculty and other professionals engaged in exploring and implementing industry 4.0 solutions in various systems and processes.

Due to increasing industry 4.0 practices, massive industrial process data is now available for researchers for modelling and optimization. Artificial Intelligence methods can be applied to the ever-increasing process data to achieve robust control against foreseen and unforeseen system fluctuations. Smart computing techniques, machine learning, deep learning, computer vision, for example, will be inseparable from the highly automated factories of tomorrow. Effective cybersecurity will be a must for all Internet of Things (IoT) enabled work and office spaces.

This book addresses metaheuristics in all aspects of Industry 4.0. It covers metaheuristic applications in IoT, cyber physical systems, control systems, smart computing, artificial intelligence, sensor networks, robotics, cybersecurity, smart factory, predictive analytics and more.

Key features:

Metaheuristic Algorithms in Industry 4.0 provides a guiding light to engineers, researchers, students, faculty and other professionals engaged in exploring and implementing industry 4.0 solutions in various systems and processes.

Mechanical Engineering domain problems are generally complex, consisting of different design variables and constraints. These problems may not be solved using gradient-based optimization techniques. The stochastic nature-inspired optimization techniques have been proposed in this book to efficiently handle the complex problems. The nature-inspired algorithms are classified as bio-inspired, swarm, and physics/chemical-based algorithms.

Socio-inspired is one of the subdomains of bio-inspired algorithms, and Cohort Intelligence (CI) models the social tendencies of learning candidates with an inherent goal to achieve the best possible position. In this book, CI is investigated by solving ten discrete variable truss structural problems, eleven mixed variable design engineering problems, seventeen linear and nonlinear constrained test problems and two real-world applications from manufacturing domain. Static Penalty Function (SPF) is also adopted to handle the linear and nonlinear constraints, and limitations in CI and SPF approaches are examined.

Constraint Handling in Cohort Intelligence Algorithm is a valuable reference to practitioners working in the industry as well as to students and researchers in the area of optimization methods.

Mechanical Engineering domain problems are generally complex, consisting of different design variables and constraints. These problems may not be solved using gradient-based optimization techniques. The stochastic nature-inspired optimization techniques have been proposed in this book to efficiently handle the complex problems. The nature-inspired algorithms are classified as bio-inspired, swarm, and physics/chemical-based algorithms.

Socio-inspired is one of the subdomains of bio-inspired algorithms, and Cohort Intelligence (CI) models the social tendencies of learning candidates with an inherent goal to achieve the best possible position. In this book, CI is investigated by solving ten discrete variable truss structural problems, eleven mixed variable design engineering problems, seventeen linear and nonlinear constrained test problems and two real-world applications from manufacturing domain. Static Penalty Function (SPF) is also adopted to handle the linear and nonlinear constraints, and limitations in CI and SPF approaches are examined.

Constraint Handling in Cohort Intelligence Algorithm is a valuable reference to practitioners working in the industry as well as to students and researchers in the area of optimization methods.

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 studies the role of Artificial Intelligence (AI) in journalism. It traces the origin, growth and development of the media and communication industry in the globalized world and discusses the implications of technologies such as Augmented Reality, Virtual Reality and Extended Reality which have helped foster a communication revolution across the globe. The volume discusses technology-centric media theories in the context of AI and examines if AI has been a boon or bane for data journalism. It also looks at artificial intelligence in beat reporting, and citizen journalism, and analyses the social-cultural implications of artificial intelligence driven journalism and the ethical concerns arising from it.

An important contribution, this book will be indispensable for students and researchers of media studies, communication studies, journalism, social media, technology studies, and digital humanities. It will also be useful for media professionals.

This book studies the role of Artificial Intelligence (AI) in journalism. It traces the origin, growth and development of the media and communication industry in the globalized world and discusses the implications of technologies such as Augmented Reality, Virtual Reality and Extended Reality which have helped foster a communication revolution across the globe. The volume discusses technology-centric media theories in the context of AI and examines if AI has been a boon or bane for data journalism. It also looks at artificial intelligence in beat reporting, and citizen journalism, and analyses the social-cultural implications of artificial intelligence driven journalism and the ethical concerns arising from it.

An important contribution, this book will be indispensable for students and researchers of media studies, communication studies, journalism, social media, technology studies, and digital humanities. It will also be useful for media professionals.

Since 2011, the Fourth Industrial Revolution (IR4.0) has played a significant role in education, research, and industry. Data technologies have also evolved rapidly to cater to the rapidly growing size of the data as well as to enhance the security of the shared data through multiple resources and platforms.

Intelligent Systems of Computing and Informatics aims to develop a new Intelligent Systems of Computing and Informatics (ISCI) to cater to the needs of industries in line with the United Nations’ Sustainable Development Goals (SDGs) of affordable and clean energy and sustainable cities and communities.

Comprising 20 chapters by experts from all over the world, this book covers such topics as data technologies, machine learning, signal and image processing, software systems efficiency, computer networking, Internet of Things, and computational intelligence for real-life problems.

Key Features:

This book is aimed at postgraduate students, researchers working in various research and development (R&D) agencies, and practitioners, as well as scientists that have an interest in ISCI.

Since 2011, the Fourth Industrial Revolution (IR4.0) has played a significant role in education, research, and industry. Data technologies have also evolved rapidly to cater to the rapidly growing size of the data as well as to enhance the security of the shared data through multiple resources and platforms.

Intelligent Systems of Computing and Informatics aims to develop a new Intelligent Systems of Computing and Informatics (ISCI) to cater to the needs of industries in line with the United Nations’ Sustainable Development Goals (SDGs) of affordable and clean energy and sustainable cities and communities.

Comprising 20 chapters by experts from all over the world, this book covers such topics as data technologies, machine learning, signal and image processing, software systems efficiency, computer networking, Internet of Things, and computational intelligence for real-life problems.

Key Features:

This book is aimed at postgraduate students, researchers working in various research and development (R&D) agencies, and practitioners, as well as scientists that have an interest in ISCI.

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.