Ahmed A. Elngar – författare

Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide.

The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks.

Provides an introduction to the Internet of Medical Things through the principles and applications of machine learning Explains the functions and applications of machine learning in various applications such as ultrasound imaging, biomedical signal processing, robotics, and biomechatronics Includes coverage of the evolution of healthcare applications with machine learning, including Clinical Decision Support Systems, artificial intelligence in biomedical engineering, and AI-enabled connected health informatics, supported by real-world case studies

Applications of Computational Intelligence in Multi-Disciplinary Research provides the readers with a comprehensive handbook for applying the powerful principles, concepts, and algorithms of computational intelligence to a wide spectrum of research cases. The book covers the main approaches used in computational intelligence, including fuzzy logic, neural networks, evolutionary computation, learning theory, and probabilistic methods, all of which can be collectively viewed as soft computing. Other key approaches included are swarm intelligence and artificial immune systems. These approaches provide researchers with powerful tools for analysis and problem-solving when data is incomplete and when the problem under consideration is too complex for standard mathematics and the crisp logic approach of Boolean computing.

Provides an overview of the key methods of computational intelligence, including fuzzy logic, neural networks, evolutionary computation, learning theory, and probabilistic methods Includes case studies and real-world examples of computational intelligence applied in a variety of research topics, including bioinformatics, biomedical engineering, big data analytics, information security, signal processing, machine learning, nanotechnology, and optimization techniques Presents a thorough technical explanation on how computational intelligence is applied that is suitable for a wide range of multidisciplinary and interdisciplinary research

In recent years, blockchain development has grown quickly from the original Bitcoin protocol to the second-generation Ethereum platform, and to today’s process of building third-generation blockchains. During this evolution, we can see how blockchain technology has evolved from its original form as a distributed database to becoming a fully fledged, globally distributed, cloud computing platform. This book traces the past, present, and future of blockchain technology. Presents the knowledge and history of Bitcoin Offers blockchain applications Discusses developing working code for real-world blockchain applications Includes many real-life examples Covers the original Bitcoin protocol to the second-generation Ethereum platformBitcoin and Blockchain: History and Current Applications is a useful reference for students, business schools, research scholars, practitioners, and business analytics professionals.

Artificial Intelligence and Machine Learning in Business Management The focus of this book is to introduce artificial intelligence (AI) and machine learning (ML) technologies into the context of business management. The book gives insights into the implementation and impact of AI and ML to business leaders, managers, technology developers, and implementers.With the maturing use of AI or ML in the field of business intelligence, this book examines several projects with innovative uses of AI beyond data organization and access. It follows the Predictive Modeling Toolkit for providing new insight on how to use improved AI tools in the field of business. It explores cultural heritage values and risk assessments for mitigation and conservation and discusses on-shore and off-shore technological capabilities with spatial tools for addressing marketing and retail strategies, and insurance and healthcare systems.Taking a multidisciplinary approach for using AI, this book provides a single comprehensive reference resource for undergraduate, graduate, business professionals, and related disciplines.

Artificial Intelligence and Machine Learning in Business Management The focus of this book is to introduce artificial intelligence (AI) and machine learning (ML) technologies into the context of business management. The book gives insights into the implementation and impact of AI and ML to business leaders, managers, technology developers, and implementers.With the maturing use of AI or ML in the field of business intelligence, this book examines several projects with innovative uses of AI beyond data organization and access. It follows the Predictive Modeling Toolkit for providing new insight on how to use improved AI tools in the field of business. It explores cultural heritage values and risk assessments for mitigation and conservation and discusses on-shore and off-shore technological capabilities with spatial tools for addressing marketing and retail strategies, and insurance and healthcare systems.Taking a multidisciplinary approach for using AI, this book provides a single comprehensive reference resource for undergraduate, graduate, business professionals, and related disciplines.

Green engineering involves the designing, innovation, and commercialization of products and processes which promote sustainability without eliminating both efficiency and economic viability. This handbook focuses on sustainable development through green engineering and technology. It is intended to address the applications and issues involved in their practical implementation.A new range of renewable-energy technologies, modified to provide green engineering, will be described in this handbook. It will explore all green technologies required to provide green engineering for the future.These include, but are not limited to, green smart buildings, fuel-efficient transportation, paperless offices, and many more energy-efficient measures.Handbook of Sustainable Development through Green Engineering and Technology acts as a comprehensive reference book to use when identifying development for programs and sustainable initiatives within the current legislative framework. It aims to be of great interest to researchers, faculty members, and students across the globe.

Green engineering involves the designing, innovation, and commercialization of products and processes which promote sustainability without eliminating both efficiency and economic viability. This handbook focuses on sustainable development through green engineering and technology. It is intended to address the applications and issues involved in their practical implementation.A new range of renewable-energy technologies, modified to provide green engineering, will be described in this handbook. It will explore all green technologies required to provide green engineering for the future.These include, but are not limited to, green smart buildings, fuel-efficient transportation, paperless offices, and many more energy-efficient measures.Handbook of Sustainable Development through Green Engineering and Technology acts as a comprehensive reference book to use when identifying development for programs and sustainable initiatives within the current legislative framework. It aims to be of great interest to researchers, faculty members, and students across the globe.

In recent years, blockchain development has grown quickly from the original Bitcoin protocol to the second-generation Ethereum platform, and to today’s process of building third-generation blockchains. During this evolution, we can see how blockchain technology has evolved from its original form as a distributed database to becoming a fully fledged, globally distributed, cloud computing platform. This book traces the past, present, and future of blockchain technology. Presents the knowledge and history of Bitcoin Offers blockchain applications Discusses developing working code for real-world blockchain applications Includes many real-life examples Covers the original Bitcoin protocol to the second-generation Ethereum platformBitcoin and Blockchain: History and Current Applications is a useful reference for students, business schools, research scholars, practitioners, and business analytics professionals.

Machine Learning and Models for Optimization in Cloud’s main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition.This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud.Key Features· Comprehensive introduction to cloud architecture and its service models.· Vulnerability and issues in cloud SAAS, PAAS and IAAS· Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models· Detailed study of optimization techniques, and fault management techniques in multi layered cloud.· Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network.· Advanced study of algorithms using artificial intelligence for optimization in cloud· Method for power efficient virtual machine placement using neural network in cloud· Method for task scheduling using metaheuristic algorithms.· A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment.This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence.

Machine Learning and Models for Optimization in Cloud’s main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition.This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud.Key Features· Comprehensive introduction to cloud architecture and its service models.· Vulnerability and issues in cloud SAAS, PAAS and IAAS· Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models· Detailed study of optimization techniques, and fault management techniques in multi layered cloud.· Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network.· Advanced study of algorithms using artificial intelligence for optimization in cloud· Method for power efficient virtual machine placement using neural network in cloud· Method for task scheduling using metaheuristic algorithms.· A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment.This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence.

Blockchain: Principles and Applications in IoT covers all the aspects of Blockchain and its application in IOT. The book focuses on Blockchain, its features, and the core technologies that are used to build the Blockchain network. The gradual flow of chapters traces the history of blockchain from cryptocurrencies to blockchain technology platforms and applications that are adopted by mainstream financial and industrial domains worldwide due to their ease of use, increased security and transparency.• Focuses on application of Blockchain on IoT domain• Focuses on Blockchain as a data repository• Most books on Blockchain cover bitcoins and crypto currency. This book will also cover blockchain in other areas like healthcare, supply chain management, etc• Covers consensus algorithms like PAROX, RAFT etc. and its applicationsThis book is primarily aimed at graduates and researchers in computer science and IT.

Blockchain: Principles and Applications in IoT covers all the aspects of Blockchain and its application in IOT. The book focuses on Blockchain, its features, and the core technologies that are used to build the Blockchain network. The gradual flow of chapters traces the history of blockchain from cryptocurrencies to blockchain technology platforms and applications that are adopted by mainstream financial and industrial domains worldwide due to their ease of use, increased security and transparency.• Focuses on application of Blockchain on IoT domain• Focuses on Blockchain as a data repository• Most books on Blockchain cover bitcoins and crypto currency. This book will also cover blockchain in other areas like healthcare, supply chain management, etc• Covers consensus algorithms like PAROX, RAFT etc. and its applicationsThis book is primarily aimed at graduates and researchers in computer science and IT.

This book seamlessly connects the topics of Industry 4.0 and cyber security. It discusses the risks and solutions of using cyber security techniques for Industry 4.0.Cyber Security and Operations Management for Industry 4.0 covers the cyber security risks involved in the integration of Industry 4.0 into businesses and highlights the issues and solutions. The book offers the latest theoretical and practical research in the management of cyber security issues common in Industry 4.0 and also discusses the ethical and legal perspectives of incorporating cyber security techniques and applications into the day-to-day functions of an organization. Industrial management topics related to smart factories, operations research, and value chains are also discussed.This book is ideal for industry professionals, researchers, and those in academia who are interested in learning more about how cyber security and Industry 4.0 are related and can work together.

This book seamlessly connects the topics of Industry 4.0 and cyber security. It discusses the risks and solutions of using cyber security techniques for Industry 4.0.Cyber Security and Operations Management for Industry 4.0 covers the cyber security risks involved in the integration of Industry 4.0 into businesses and highlights the issues and solutions. The book offers the latest theoretical and practical research in the management of cyber security issues common in Industry 4.0 and also discusses the ethical and legal perspectives of incorporating cyber security techniques and applications into the day-to-day functions of an organization. Industrial management topics related to smart factories, operations research, and value chains are also discussed.This book is ideal for industry professionals, researchers, and those in academia who are interested in learning more about how cyber security and Industry 4.0 are related and can work together.

This book looks at industry change patterns and innovations (such as artificial intelligence, machine learning, big data analysis, and blockchain support and efficiency technology) that are speeding up industrial transformation, industrial infrastructure, biodiversity, and productivity.This book focuses on real-world industrial applications and case studies to provide for a wider knowledge of intelligent manufacturing. It also offers insights into manufacturing, logistics, and supply chain, where systems have undergone an industrial transformation. It discusses current research of machine learning along with blockchain techniques that can fill the gap between research and industrial exposure. It goes on to cover the effects that the Fourth Industrial Revolution has on industrial infrastructures and looks at the current industry change patterns and innovations that are accelerating industrial transformation activities.Researchers, scholars, and students from different countries will appreciate this book for its real-world applications and knowledge acquisition. This book targets manufacturers, industry owners, product developers, scientists, logistics, and supply chain engineers.Focuses on real-world industrial applications and case studies to provide for a wider knowledge of intelligent manufacturingOffers insights into manufacturing, logistics, and supply chain where systems have undergone an industrial transformationDiscusses current research of machine learning along with blockchain techniques that can fill the gap between research and industrial exposureCovers the effects that the 4th Industrial Revolution has on industrial infrastructuresLooks at industry change patterns and innovations that are speeding up industrial transformation activities

This book looks at industry change patterns and innovations (such as artificial intelligence, machine learning, big data analysis, and blockchain support and efficiency technology) that are speeding up industrial transformation, industrial infrastructure, biodiversity, and productivity.This book focuses on real-world industrial applications and case studies to provide for a wider knowledge of intelligent manufacturing. It also offers insights into manufacturing, logistics, and supply chain, where systems have undergone an industrial transformation. It discusses current research of machine learning along with blockchain techniques that can fill the gap between research and industrial exposure. It goes on to cover the effects that the Fourth Industrial Revolution has on industrial infrastructures and looks at the current industry change patterns and innovations that are accelerating industrial transformation activities.Researchers, scholars, and students from different countries will appreciate this book for its real-world applications and knowledge acquisition. This book targets manufacturers, industry owners, product developers, scientists, logistics, and supply chain engineers.Focuses on real-world industrial applications and case studies to provide for a wider knowledge of intelligent manufacturingOffers insights into manufacturing, logistics, and supply chain where systems have undergone an industrial transformationDiscusses current research of machine learning along with blockchain techniques that can fill the gap between research and industrial exposureCovers the effects that the 4th Industrial Revolution has on industrial infrastructuresLooks at industry change patterns and innovations that are speeding up industrial transformation activities

This book covers advances and applications of smart technologies including the Internet of Things (IoT), artificial intelligence, and deep learning in areas such as manufacturing, production, renewable energy, and healthcare. It also covers wearable and implantable biomedical devices for healthcare monitoring, smart surveillance, and monitoring applications such as the use of an autonomous drone for disaster management and rescue operations. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas such as electrical engineering, electronics and communications engineering, computer engineering, and information technology.• Covers concepts, theories, and applications of artificial intelligence and deep learning, from the perspective of the Internet of Things.• Discusses powers predictive analysis, predictive maintenance, and automated processes for making manufacturing plants more efficient, profitable, and safe.• Explores the importance of blockchain technology in the Internet of Things security issues.• Discusses key deep learning concepts including trust management, identity management, security threats, access control, and privacy.• Showcases the importance of intelligent algorithms for cloud-based Internet of Things applications.This text emphasizes the importance of innovation and improving the profitability of manufacturing plants using smart technologies such as artificial intelligence, deep learning, and the Internet of Things. It furtherdiscusses applications of smart technologies in diverse sectors such as agriculture, smart home, production, manufacturing, transport, and healthcare.

This book covers advances and applications of smart technologies including the Internet of Things (IoT), artificial intelligence, and deep learning in areas such as manufacturing, production, renewable energy, and healthcare. It also covers wearable and implantable biomedical devices for healthcare monitoring, smart surveillance, and monitoring applications such as the use of an autonomous drone for disaster management and rescue operations. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas such as electrical engineering, electronics and communications engineering, computer engineering, and information technology.• Covers concepts, theories, and applications of artificial intelligence and deep learning, from the perspective of the Internet of Things.• Discusses powers predictive analysis, predictive maintenance, and automated processes for making manufacturing plants more efficient, profitable, and safe.• Explores the importance of blockchain technology in the Internet of Things security issues.• Discusses key deep learning concepts including trust management, identity management, security threats, access control, and privacy.• Showcases the importance of intelligent algorithms for cloud-based Internet of Things applications.This text emphasizes the importance of innovation and improving the profitability of manufacturing plants using smart technologies such as artificial intelligence, deep learning, and the Internet of Things. It furtherdiscusses applications of smart technologies in diverse sectors such as agriculture, smart home, production, manufacturing, transport, and healthcare.

Integration of AI-Based Manufacturing and Industrial Engineering Systems with the Internet of Things describes how AI techniques, such as deep learning, cognitive computing, and Machine Learning, can be used to analyze massive volumes of data produced by IoT devices in manufacturing environments.The potential benefits and challenges associated with the integration of AI and IoT in industrial environments are explored throughout the book as the authors delve into various aspects of the integration process. The role of IoT-enabled sensors, actuators, and smart devices in capturing real-time data from manufacturing processes, supply chains, and equipment is discussed along with how data can be processed and analyzed using AI algorithms to derive actionable insights, optimize production, improve quality control, and enhance overall operational efficiency.A valuable resource for researchers, practitioners, and professionals involved in the fields of AI, IoT, manufacturing systems, and industrial engineering, and combines theoretical foundations, practical applications, and case studies.

Integration of AI-Based Manufacturing and Industrial Engineering Systems with the Internet of Things describes how AI techniques, such as deep learning, cognitive computing, and Machine Learning, can be used to analyze massive volumes of data produced by IoT devices in manufacturing environments.The potential benefits and challenges associated with the integration of AI and IoT in industrial environments are explored throughout the book as the authors delve into various aspects of the integration process. The role of IoT-enabled sensors, actuators, and smart devices in capturing real-time data from manufacturing processes, supply chains, and equipment is discussed along with how data can be processed and analyzed using AI algorithms to derive actionable insights, optimize production, improve quality control, and enhance overall operational efficiency.A valuable resource for researchers, practitioners, and professionals involved in the fields of AI, IoT, manufacturing systems, and industrial engineering, and combines theoretical foundations, practical applications, and case studies.

Technology Innovation Pillars for Industry 4.0: Challenges, Improvements, and Case Studies discusses the latest innovations in the application of technologies to Industry 4.0 and the nine pillars and how they relate, support, and bridge the gap between the digital and physical worlds we now live in.This book discusses each of the nine pillars and the roles they play in the rapid transformation of the design and operation, and offers applications and case studies supporting Industry 4.0 technologies. It presents the supply chain organizational activities utilizing cyber- physical systems architectures and talks about the advantages of intelligent manufacturing and the ability to proactively detect and respond to events, to improve quality and yield, reduce downtime, and lead to better overall equipment effectiveness among other advantages in smart factory operations.This reference book provides a great resource for undergraduate and graduate students, industrial and manufacturing engineers, and engineers of related disciplines along with business professionals, explaining what the nine pillars are and how they relate to Industry 4.0 and smart factories.

Technology Innovation Pillars for Industry 4.0: Challenges, Improvements, and Case Studies discusses the latest innovations in the application of technologies to Industry 4.0 and the nine pillars and how they relate, support, and bridge the gap between the digital and physical worlds we now live in.This book discusses each of the nine pillars and the roles they play in the rapid transformation of the design and operation, and offers applications and case studies supporting Industry 4.0 technologies. It presents the supply chain organizational activities utilizing cyber- physical systems architectures and talks about the advantages of intelligent manufacturing and the ability to proactively detect and respond to events, to improve quality and yield, reduce downtime, and lead to better overall equipment effectiveness among other advantages in smart factory operations.This reference book provides a great resource for undergraduate and graduate students, industrial and manufacturing engineers, and engineers of related disciplines along with business professionals, explaining what the nine pillars are and how they relate to Industry 4.0 and smart factories.

This book presents the role of AI-Driven Digital Twin in the Industry 4.0 ecosystem by focusing on Smart Manufacturing, sustainable development, and many other applications. It also discusses different case studies and presents an in-depth understanding of the benefits and limitations of using AI and Digital Twin for industrial developments.AI-Driven Digital Twin and Industry 4.0: A Conceptual Framework with Applications introduces the role of Digital Twin in Smart Manufacturing and focuses on the Digital Twin framework throughout. It provides a summary of the various AI applications in the Industry 4.0 environment and emphasizes the role of advanced computational and communication technologies. The book offers demonstrative examples of AI-Driven Digital Twin in various application domains and includes AI techniques used to analyze the environmental impact of industrial operations along with examples. The book reviews the major challenges in the deployment of AI-Driven Digital Twin in the Industry 4.0 ecosystem and presents an understanding of how AI is used in the designing of Digital Twin for various applications. The book also enables familiarity with various industrial applications of computational and communication technologies and summarizes the ongoing research and innovations in the areas of AI, Digital Twin, and Smart Manufacturing while also tracking the various research challenges along with future advances.This reference book is a must-read and is very beneficial to students, researchers, academicians, industry experts, and professionals working in related fields.

This book presents the role of AI-Driven Digital Twin in the Industry 4.0 ecosystem by focusing on Smart Manufacturing, sustainable development, and many other applications. It also discusses different case studies and presents an in-depth understanding of the benefits and limitations of using AI and Digital Twin for industrial developments.AI-Driven Digital Twin and Industry 4.0: A Conceptual Framework with Applications introduces the role of Digital Twin in Smart Manufacturing and focuses on the Digital Twin framework throughout. It provides a summary of the various AI applications in the Industry 4.0 environment and emphasizes the role of advanced computational and communication technologies. The book offers demonstrative examples of AI-Driven Digital Twin in various application domains and includes AI techniques used to analyze the environmental impact of industrial operations along with examples. The book reviews the major challenges in the deployment of AI-Driven Digital Twin in the Industry 4.0 ecosystem and presents an understanding of how AI is used in the designing of Digital Twin for various applications. The book also enables familiarity with various industrial applications of computational and communication technologies and summarizes the ongoing research and innovations in the areas of AI, Digital Twin, and Smart Manufacturing while also tracking the various research challenges along with future advances.This reference book is a must-read and is very beneficial to students, researchers, academicians, industry experts, and professionals working in related fields.

Blockchain and distributed-ledger technologies enable new modes of communication, synchronization, and transfer of value with a broad impact on Internet of Things (IoT), Data Science, society, industry, commerce, and government. This book studies the potential impact of blockchain and distributed-ledger technologies on IoT. It highlights the application of possible solutions in the domain of blockchain and IoT system security, including cryptology, distributed systems, law, formal methods, code verification and validation, software, and systems metrics. As the field is growing fast, the book adapts to the changing research landscape, integrates and cross-links studies and citations in related subfields, and provides an overview of these fields and how they complement each other.• Highlights how the security aspect of the integration of blockchain with IoT will help to design secure data solutions for various domains• Offers fundamental knowledge of the blockchain concept and its usage in real-life applications• Presents current and future trends on the IoT and blockchain with an efficient, scalable, and sustainable approach• Reviews future developments in blockchain and IoT in future job opportunities• Discusses how blockchain for IoT systems can help a varied range of end-users to access computational and storage resourcesThis book is intended for postgraduate students and researchers in the departments of computer science, working in the areas of IoT, blockchain, deep learning, machine learning, image processing, and big data.

Artificial intelligence (AI) and mechatronics are booming areas where most of the industrial sectors are becoming smart nowadays. This handbook includes material of multidisciplinary content from the AI, mechanical, and electronics engineering domains, among others. It gives insights into various application sectors discussing current global developments in mechatronics employing AI technology and addressing the complexity of current issues and the effects of diverse mechatronics systems.Handbook of AI-Based Mechatronics Systems and Smart Solutions in Industrial Automation focuses on system automation, predictive analysis, preventive analysis, and real-time decision-making systems for next-generation automation. It discusses the advancements of mechatronics systems using AI applications along with the global approach toward smart industrial automation and presents the impact of AI on today’s work of autonomous and industrial automation. The book discusses future research potential and is beneficial to manufacturing, healthcare, and finance disputes, while it offers AI algorithms to analyze large amounts of data and identifies patterns, trends, and anomalies for accurate predictions and optimization processes. The handbook also addresses use cases and case studies related to AI in mechatronics along with applications.Scholars in the field of AI in mechatronics and related applications will find this book useful. In particular, attention is drawn to both fundamental ideas and important practical contexts. Readers interested in the most recent findings in the field of problem-oriented processing approaches in mechatronics, including those in academia, data science, industry, research, and graduate and undergraduate students, will find this fascinating handbook extremely interesting.

The field of industrial engineering has undergone a transformation in recent years, driven by emerging technologies such as Artificial Intelligence (AI), Machine Learning, and the Internet of Things (IoT). To keep pace with these advancements, a shift toward AI-driven development has become a necessity.From case studies to national and international best practices, Intelligent Manufacturing: Exploring AI, Blockchain, and Smart Technologies in Industry 4.0 delves into the challenges of incorporating AI-driven frameworks and standards into the smart industry landscape. It explores the integration of these advanced technologies into Industry 4.0 and presents a comprehensive overview of their potential impact on industrial processes. Topics covered include security management, standard specifications, and sustainable manufacturing, highlighting the latest innovations in industrial engineering design. The book goes on to discuss the practical applications of AI-driven technologies in manufacturing, such as simulation, optimization, and sustainable development of industrial processes. A variety of real-world examples are offered, and a discussion of how these technologies are being deployed in different industries provides a clear understanding of their applications and benefits.This book is an essential guide for students, researchers, and industry professionals seeking to understand the evolving landscape of intelligent manufacturing and industrial engineering.

By enabling the conversion of traditional manufacturing systems into contemporary digitalized ones, Internet of Things (IoT) adoption in manufacturing creates huge economic prospects through reshaping industries. Modern businesses can more readily implement new data-driven strategies and deal with the pressure of international competition thanks to Industrial IoT. But as the use of IoT grows, the amount of created data rises, turning industrial data into Industrial Big Data.Internet of Things and Big Data Analytics-Based Manufacturing shows how Industrial Big Data can be produced as a result of IoT usage in manufacturing, considering sensing systems and mobile devices. Different IoT applications that have been developed are demonstrated and it is shown how genuine industrial data can be produced, leading to Industrial Big Data. This book is organized into four sections discussing IoT and technology, the future of Big Data, algorithms, and case studies demonstrating the use of IoT and Big Data in a variety of industries, including automation, industrial manufacturing, and healthcare.This reference title brings all related technologies into a single source so that researchers, undergraduate and postgraduate students, academicians, and those in the industry can easily understand the topic and further their knowledge.

The industrial landscape is changing rapidly, and so is global society. This change is driven by the growing adoption of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) technologies. IIoT and AI are transforming the way industrial engineering is done, enabling new levels of automation, productivity, and efficiency. However, as IIoT and AI become more pervasive in the industrial world, they also offer new security risks that must be addressed to ensure the reliability and safety of critical systems.Industrial Internet of Things Security: Protecting AI-Enabled Engineering Systems in Cloud and Edge Environments provides a comprehensive guide to IIoT security, covering topics such as network architecture, risk management, data security, and compliance. It addresses the unique security challenges that the cloud and edge environments pose, providing practical guidance for securing IIoT networks in these contexts. It includes numerous real-world case studies and examples, providing readers with practical insights into how IIoT security and AI-enabled industrial engineering are being implemented in various industries. Best practices are emphasized for the readers to ensure the reliability, safety, and security of their systems while also learning the latest developments in IIoT security for AI-enabled industrial engineering systems in this rapidly evolving field.By offering step-by-step guidance for the implantation process along with best practices, this book becomes a valuable resource for practitioners and engineers in the areas of industrial engineering, IT, computer engineering, and anyone looking to secure their IIoT network against cyber threats.