Computational Methods for Industrial Applications – serie

This reference text provides the theoretical foundations, the emergence, and the application areas of Blockchain in an easy-to-understand manner that would be highly helpful for the researchers, academicians, and industry professionals to understand the disruptive potentials of Blockchain. It explains Blockchain concepts related to Industry 4.0, Smart Healthcare, and the Internet of Things (IoT) and explores Smart Contracts and Consensus algorithms. This book will serve as an ideal reference text for graduate students and academic researchers in electrical engineering, electronics and communication engineering, computer engineering, and information technology.This book • Discusses applications of blockchain technology in diverse sectors such as industry 4.0, education, finance, and supply chain. • Provides theoretical concepts, applications, and research advancements in the field of blockchain. • Covers industry 4.0 digitization platform and blockchain for data management in industry 4.0 in a comprehensive manner. • Emphasizes analysis and design of consensus algorithms, fault tolerance, and strategy to choose the correct consensus algorithm.• Introduces security issues in the industrial internet of things, internet of things, blockchain integration, and blockchain-based applications. The text presents in-depth coverage of theoretical concepts, applications and advances in the field of blockchain technology. This book will be an ideal reference for graduate students and academic researchers in diverse engineering fields such as electrical, electronics and communication, computer, and information technology.

This reference text provides the theoretical foundations, the emergence, and the application areas of Blockchain in an easy-to-understand manner that would be highly helpful for the researchers, academicians, and industry professionals to understand the disruptive potentials of Blockchain. It explains Blockchain concepts related to Industry 4.0, Smart Healthcare, and the Internet of Things (IoT) and explores Smart Contracts and Consensus algorithms. This book will serve as an ideal reference text for graduate students and academic researchers in electrical engineering, electronics and communication engineering, computer engineering, and information technology.This book • Discusses applications of blockchain technology in diverse sectors such as industry 4.0, education, finance, and supply chain. • Provides theoretical concepts, applications, and research advancements in the field of blockchain. • Covers industry 4.0 digitization platform and blockchain for data management in industry 4.0 in a comprehensive manner. • Emphasizes analysis and design of consensus algorithms, fault tolerance, and strategy to choose the correct consensus algorithm.• Introduces security issues in the industrial internet of things, internet of things, blockchain integration, and blockchain-based applications. The text presents in-depth coverage of theoretical concepts, applications and advances in the field of blockchain technology. This book will be an ideal reference for graduate students and academic researchers in diverse engineering fields such as electrical, electronics and communication, computer, and information technology.

The book explores the concepts and challenges in developing novel approaches using the Internet of Things, intelligent systems, machine intelligence systems, and data analytics in various industrial sectors such as manufacturing, smart agriculture, smart cities, food processing, environment, defense, stock market and healthcare. Further, it discusses the latest improvements in the industrial sectors using machine intelligence learning and intelligent systems techniques, especially robotics. Features: • Highlights case studies and solutions to industrial problems using machine learning and intelligent systems. • Covers applications in smart agriculture, smart healthcare, intelligent machines for disaster management, and smart manufacturing. • Provides the latest methodologies using machine intelligence systems in the early forecasting of weather. • Examines the research challenges and identifies the gaps in data collection and data analysis, especially imagery, signal, and speech. • Provides applications of digitization and smart processing using the Internet of Things and effective intelligent agent systems in manufacturing. • Discusses a systematic and exhaustive analysis of intelligent software effort estimation models.It will serve as an ideal reference text for graduate students, post-graduate students, IT Professionals, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

The book explores the concepts and challenges in developing novel approaches using the Internet of Things, intelligent systems, machine intelligence systems, and data analytics in various industrial sectors such as manufacturing, smart agriculture, smart cities, food processing, environment, defense, stock market and healthcare. Further, it discusses the latest improvements in the industrial sectors using machine intelligence learning and intelligent systems techniques, especially robotics. Features: • Highlights case studies and solutions to industrial problems using machine learning and intelligent systems. • Covers applications in smart agriculture, smart healthcare, intelligent machines for disaster management, and smart manufacturing. • Provides the latest methodologies using machine intelligence systems in the early forecasting of weather. • Examines the research challenges and identifies the gaps in data collection and data analysis, especially imagery, signal, and speech. • Provides applications of digitization and smart processing using the Internet of Things and effective intelligent agent systems in manufacturing. • Discusses a systematic and exhaustive analysis of intelligent software effort estimation models.It will serve as an ideal reference text for graduate students, post-graduate students, IT Professionals, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

The text highlights a comprehensive survey that focuses on all security aspects and challenges facing the Internet of Things systems, including outsourcing techniques for partial computations on edge or cloud while presenting case studies to map security challenges. It further covers three security aspects including Internet of Things device identification and authentication, network traffic intrusion detection, and executable malware files detection.This book:Presents a security framework model design named Behavioral Network Traffic Identification and Novelty Anomaly Detection for the IoT InfrastructuresHighlights recent advancements in machine learning, deep learning, and networking standards to boost Internet of Things securityBuilds a near real-time solution for identifying Internet of Things devices connecting to a network using their network traffic traces and providing them with sufficient access privilegesDevelops a robust framework for detecting IoT anomalous network trafficCovers an anti-malware solution for detecting malware targeting embedded devicesIt will serve as an ideal text for senior undergraduate and graduate students, and professionals in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis.Features:Focuses on neuron modeling, development, and direction of neural circuits to explain perception, behavior, and biologically inspired intelligent agents for decision makingShowcases important aspects such as human behavior prediction using smart technologies and understanding the modeling of nervous systemsDiscusses nature-inspired algorithms such as swarm intelligence, ant colony optimization, and multi-agent systemsPresents information-theoretic, control-theoretic, and decision-theoretic approaches in neuroscience.Includes case studies in functional magnetic resonance imaging (fMRI) and neural data analysisThis reference text addresses different applications of computational neuro-sciences using artificial intelligence, deep learning, and other machine learning techniques to fine-tune the models, thereby solving the real-life problems prominently. It will further discuss important topics such as neural rehabili-tation, brain-computer interfacing, neural control, neural system analysis, and neurobiologically inspired self-monitoring systems. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, information technology, and biomedical engineering.

The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis.Features:Focuses on neuron modeling, development, and direction of neural circuits to explain perception, behavior, and biologically inspired intelligent agents for decision makingShowcases important aspects such as human behavior prediction using smart technologies and understanding the modeling of nervous systemsDiscusses nature-inspired algorithms such as swarm intelligence, ant colony optimization, and multi-agent systemsPresents information-theoretic, control-theoretic, and decision-theoretic approaches in neuroscience.Includes case studies in functional magnetic resonance imaging (fMRI) and neural data analysisThis reference text addresses different applications of computational neuro-sciences using artificial intelligence, deep learning, and other machine learning techniques to fine-tune the models, thereby solving the real-life problems prominently. It will further discuss important topics such as neural rehabili-tation, brain-computer interfacing, neural control, neural system analysis, and neurobiologically inspired self-monitoring systems. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, information technology, and biomedical engineering.

Artificial Intelligence based Solutions for Industrial Applications aims to examine the utilization of artificial intelligence (AI) technologies to tackle difficult industrial issues and offers readers a thorough understanding of how these technologies are being employed to address intricate industrial challenges and to stimulate innovation. This book explores the fundamental principles of artificial intelligence (AI) and its practical use in industrial environments. This book improves understanding of core concepts, the present state of the art and real-time implementation of AI in many industrial applications. This book describes the detailed implementation of AI in the industrial sector as well as related case studies for in-depth understanding. Basic concepts, related work reviews, illustrations, empirical results, and tables are integrated within each chapter to give the readers the opportunity to gain maximum knowledge and to easily understand the methodology and results presented.This book introduces a variety of smart algorithms to help in filtering important information and to solve problems in the application domains. Application of machine learning and deep learning in the industry demonstrates the capabilities by which it may be used to solve practical problems in the 'Fourth Industrial Revolution', and it equips readers with the necessary knowledge and tools to design solutions by themselves with the help of theory and practical examples dealt with. The fourth industrial revolution and its consequences on society and organizations are discussed in this book.Features:Detailed understanding of the industrial application of AI.Discussion of core concepts of different machine learning and deep learning techniques such as artificial neural networks, support vector machines, K –nearest neighbour, decision tree, logistic regression, and many more.Detailed study on various industrial applications of machine learning and deep learning in healthcare, education, entertainment, share market, manufacturing, and many more.Case studies on industrial application of AISummataion of the fourth industrial revolution and its consquences on society and organizationsThis book is primarily written for graduate students, engineers, and academic researchers, industrial practitioners, and anyone who wants to optimize production processes, explore AI technology, or stay ahead in the industrial field. It covers the complexities of AI in industrial contexts from core basic understanding to complex implementation.

The text emphasizes the need for data pre-processing, classification and prediction, cluster analysis, mining multimedia, and advanced machine learning techniques for scientific programming in Industry 5.0.Addresses how the convergence of intelligent systems and 5G wireless systems will solve industrial problems such as autonomous robots, and self-driving carsHighlights the methods of smart things in collaborative autonomous fleets and platforms for integrating applications across different business and industry domainsDiscusses important topics such as the Internet of robotic things, cloud robotics, and cognitive architecture for cyber-physical roboticsExplains image compression, and advanced machine learning techniques for scientific programming in Industry 5.0Presents a detailed discussion of smart manufacturing techniques, industrial Internet of things, and supply chain management in Industry 5.0The text is primarily written for graduate students and academic researchers in the fields of industrial engineering, manufacturing engineering, electrical engineering, production engineering, and mechanical engineering.

The text emphasizes the need for data pre-processing, classification and prediction, cluster analysis, mining multimedia, and advanced machine learning techniques for scientific programming in Industry 5.0.Addresses how the convergence of intelligent systems and 5G wireless systems will solve industrial problems such as autonomous robots, and self-driving carsHighlights the methods of smart things in collaborative autonomous fleets and platforms for integrating applications across different business and industry domainsDiscusses important topics such as the Internet of robotic things, cloud robotics, and cognitive architecture for cyber-physical roboticsExplains image compression, and advanced machine learning techniques for scientific programming in Industry 5.0Presents a detailed discussion of smart manufacturing techniques, industrial Internet of things, and supply chain management in Industry 5.0The text is primarily written for graduate students and academic researchers in the fields of industrial engineering, manufacturing engineering, electrical engineering, production engineering, and mechanical engineering.

This reference text covers the theory and applications of soft computing and machine learning and presents readers with the intelligent fuzzy and neutrosophic rules that require situations where classical modeling approaches cannot be utilized, such as when there is incomplete, unclear, or imprecise information at hand or inadequate data. It further illustrates topics such as image processing, and power system analysis.This book:Discusses soft computing techniques including fuzzy Logic, rough sets, neutrosophic sets, neural networks, generative adversarial networks, and evolutionary computationExamines novel and contemporary advances in the fields of soft computing, fuzzy computing, neutrosophic computing, and machine learning systems, as well as their applications in real lifeServes as a comprehensive reference for applying machine learning and neutrosophic sets in real-world applications such as smart cities, healthcare, and the Internet of ThingsCovers topics such as image processing, bioinformatics, natural language processing, supply chain management, and cyberneticsIllustrates classification of neutrosophic machine learning, neutrosophic reinforcement learning, and applications of neutrosophic machine learning in emerging industriesThe text is written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

This comprehensive volume explores cutting-edge technologies, groundbreaking methodologies, and insightful ethical considerations. It brings together leading minds and research across diverse domains, offering readers a glimpse into the future of healthcare. From leveraging AI in diagnostics and personalized treatments to advancing telemedicine and remote patient monitoring, the text addresses real-world challenges and opportunities in the rapidly evolving healthcare industry.Key features:Innovative approaches to early disease detection, including Alzheimer’s and cancer, using machine learning and deep learning techniques.State-of-the-art applications of design thinking, meta-heuristics, and federated learning in healthcare.Ethical perspectives on the use of AI technologies and their impact on businesses and patient care.A deep dive into the role of IoT, NLP, and AI-driven image processing in revolutionizing healthcare delivery.Generalized models analyzing trade credits and their implications for healthcare supply chains.Whether you're a researcher, healthcare professional, or technology enthusiast, this book serves as an invaluable resource for understanding and shaping the future of health technologies. Packed with diverse perspectives and actionable insights, it’s a must-read for anyone passionate about making a difference in healthcare.

The text highlights a comprehensive survey that focuses on all security aspects and challenges facing the Internet of Things systems, including outsourcing techniques for partial computations on edge or cloud while presenting case studies to map security challenges. It further covers three security aspects including Internet of Things device identification and authentication, network traffic intrusion detection, and executable malware files detection.This book:Presents a security framework model design named Behavioral Network Traffic Identification and Novelty Anomaly Detection for the IoT InfrastructuresHighlights recent advancements in machine learning, deep learning, and networking standards to boost Internet of Things securityBuilds a near real-time solution for identifying Internet of Things devices connecting to a network using their network traffic traces and providing them with sufficient access privilegesDevelops a robust framework for detecting IoT anomalous network trafficCovers an anti-malware solution for detecting malware targeting embedded devicesIt will serve as an ideal text for senior undergraduate and graduate students, and professionals in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

Artificial Intelligence based Solutions for Industrial Applications aims to examine the utilization of artificial intelligence (AI) technologies to tackle difficult industrial issues and offers readers a thorough understanding of how these technologies are being employed to address intricate industrial challenges and to stimulate innovation. This book explores the fundamental principles of artificial intelligence (AI) and its practical use in industrial environments. This book improves understanding of core concepts, the present state of the art and real-time implementation of AI in many industrial applications. This book describes the detailed implementation of AI in the industrial sector as well as related case studies for in-depth understanding. Basic concepts, related work reviews, illustrations, empirical results, and tables are integrated within each chapter to give the readers the opportunity to gain maximum knowledge and to easily understand the methodology and results presented.This book introduces a variety of smart algorithms to help in filtering important information and to solve problems in the application domains. Application of machine learning and deep learning in the industry demonstrates the capabilities by which it may be used to solve practical problems in the 'Fourth Industrial Revolution', and it equips readers with the necessary knowledge and tools to design solutions by themselves with the help of theory and practical examples dealt with. The fourth industrial revolution and its consequences on society and organizations are discussed in this book.Features:Detailed understanding of the industrial application of AI.Discussion of core concepts of different machine learning and deep learning techniques such as artificial neural networks, support vector machines, K –nearest neighbour, decision tree, logistic regression, and many more.Detailed study on various industrial applications of machine learning and deep learning in healthcare, education, entertainment, share market, manufacturing, and many more.Case studies on industrial application of AISummataion of the fourth industrial revolution and its consquences on society and organizationsThis book is primarily written for graduate students, engineers, and academic researchers, industrial practitioners, and anyone who wants to optimize production processes, explore AI technology, or stay ahead in the industrial field. It covers the complexities of AI in industrial contexts from core basic understanding to complex implementation.

This reference text covers the theory and applications of soft computing and machine learning and presents readers with the intelligent fuzzy and neutrosophic rules that require situations where classical modeling approaches cannot be utilized, such as when there is incomplete, unclear, or imprecise information at hand or inadequate data. It further illustrates topics such as image processing, and power system analysis.This book:Discusses soft computing techniques including fuzzy Logic, rough sets, neutrosophic sets, neural networks, generative adversarial networks, and evolutionary computationExamines novel and contemporary advances in the fields of soft computing, fuzzy computing, neutrosophic computing, and machine learning systems, as well as their applications in real lifeServes as a comprehensive reference for applying machine learning and neutrosophic sets in real-world applications such as smart cities, healthcare, and the Internet of ThingsCovers topics such as image processing, bioinformatics, natural language processing, supply chain management, and cyberneticsIllustrates classification of neutrosophic machine learning, neutrosophic reinforcement learning, and applications of neutrosophic machine learning in emerging industriesThe text is written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

This reference text covers deep learning-based communication frameworks for multiuser detection and sparse channel estimation and engages in a discussion on deep learning-based ultra-dense cell communication and sensor networks and ad-hoc communication. It further presents concepts and theories related to high-speed communication systems, which are important in intelligent wireless communications.Features:• Discusses machine learning-based network management strategy in wireless systems, and machine learning-inspired big data analytics frameworks for wireless network applications.• Presents high-speed communication systems, deep learning for wireless networks, security aspects in wireless networks, and decision-making for wireless networks.• Highlights the importance of using deep reinforcement learning in intelligent wireless networks and deep reinforcement learning-based mobile data offloading frameworks.• Covers novel network architectures for distributed edge learning, and privacy issues in distributed edge learning.• Illustrates experimentation and deep learning-based simulations in networking systems, deep learning-based communication frameworks for multiuser detection and sparse channel estimation.The book is written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.