Edge computational intelligence is an interface between edge computing and artificial intelligence (AI) technologies. This interfacing represents a paradigm shift in the world of work by enabling a broad application areas and customer-friendly solutions. Edge computational intelligence technologies are just in their infancy. Edge Computational Intelligence for AI-Enabled IoT Systems looks at the trends and advances in edge computing and edge AI, the services rendered by them, related security and privacy issues, training algorithms, architectures, and sustainable AI-enabled IoT systems. Together, these technologies benefit from ultra-low latency, faster response times, lower bandwidth costs and resilience from network failure, and the book explains the advantages of systems and applications using intelligent IoT devices that are at the edge of a network and close to users. It explains how to make most of edge and cloud computing as complementary technologies or used in isolation for extensive and widespread applications. The advancement in IoT devices, networking facilities, parallel computation and 5G, and robust infrastructure for generalized machine learning have made it possible to employ edge computational intelligence in diverse areas and in diverse ways. The book begins with chapters that cover Edge AI services on offer as compared to conventional systems. These are followed by chapters that discuss security and privacy issues encountered during the implementation and execution of edge AI and computing services The book concludes with chapters looking at applications spread across different areas of edge AI and edge computing and also at the role of computational intelligence in AI-driven IoT systems.

The book discusses the evolution of future generation technologies through Internet of Things (IoT) in the scope of Artificial Intelligence (AI). The main focus of this volume is to bring all the related technologies in a single platform, so that undergraduate and postgraduate students, researchers, academicians, and industry people can easily understand the AI algorithms, machine learning algorithms, and learning analytics in IoT-enabled technologies. This book uses data and network engineering and intelligent decision support system-by-design principles to design a reliable AI-enabled IoT ecosystem and to implement cyber-physical pervasive infrastructure solutions. This book brings together some of the top IoT-enabled AI experts throughout the world who contribute their knowledge regarding different IoT-based technology aspects.

An essential book on the applications of AI and digital twin technology in the smart manufacturing sector. In the rapidly evolving landscape of modern manufacturing, the integration of cutting-edge technologies has become imperative for businesses to remain competitive and adaptive. Among these technologies, Artificial Intelligence (AI) stands out as a transformative force, revolutionizing traditional manufacturing processes and making the way for the era of smart manufacturing. At the heart of this technological revolution lies the concept of the Digital Twin—an innovative approach that bridges the physical and digital realms of manufacturing. By creating a virtual representation of physical assets, processes, and systems, organizations can gain unprecedented insights, optimize operations, and enhance decision-making capabilities. This timely book explores the convergence of AI and Digital Twin technologies to empower smart manufacturing initiatives. Through a comprehensive examination of principles, methodologies, and practical applications, it explains the transformative potential of AI-enabled Digital Twins across various facets of the manufacturing lifecycle. From design and prototyping to production and maintenance, AI-enabled Digital Twins offer multifaceted advantages that redefine traditional paradigms. By leveraging AI algorithms for data analysis, predictive modeling, and autonomous optimization, manufacturers can achieve unparalleled levels of efficiency, quality, and agility. This book explains how AI enhances the capabilities of Digital Twins by creating a powerful tool that can optimize production processes, improve product quality, and streamline operations. Note that the Digital Twin in this context is a virtual representation of a physical manufacturing system, including machines, processes, and products. It continuously collects real-time data from sensors and other sources, allowing it to mirror the physical system’s behavior and performance. What sets this Digital Twin apart is the incorporation of AI algorithms and machine learning techniques that enable it to analyze and predict outcomes, recommend improvements, and autonomously make adjustments to enhance manufacturing efficiency. This book outlines essential elements, like real-time monitoring of machines, predictive analytics of machines and data, optimization of the resources, quality control of the product, resource management, decision support (timely or quickly accurate decisions). Moreover, this book elucidates the symbiotic relationship between AI and Digital Twins, highlighting how AI augments the capabilities of Digital Twins by infusing them with intelligence, adaptability, and autonomy. Hence, this book promises to enhance competitiveness, reduce operational costs, and facilitate innovation in the manufacturing industry. By harnessing AI’s capabilities in conjunction with Digital Twins, manufacturers can achieve a more agile and responsive production environment, ultimately driving the evolution of smart factories and Industry 4.0/5.0. Audience This book has a wide audience in computer science, artificial intelligence, and manufacturing engineering, as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals.

The goal of this book is to explore various security paradigms such as Machine Learning, Big data, Cyber Physical Systems, and Blockchain to address both intelligence and reconfigurability in various IoT devices. The book further aims to address and analyze the state of the art of blockchain-based intelligent networks in IoT systems and related technologies including healthcare sector. AI can ease, optimize, and automate the blockchain-based decision-making process for better governance and higher performance in IoT systems. Considering the incredible progress made by AI models, a blockchain system powered by intelligent AI algorithms can detect the existence of any kind of attack and automatically invoke the required defense mechanisms. In case of unavoidable damage, AI models can help to isolate the compromised component from the blockchain platform and safeguard the overall system from crashing. Furthermore, AI models can also contribute toward the robustness and scalability of blockchain-based intelligent IoT networks. The book is designed to be the first-choice reference at university libraries, academic institutions, research and development centers, information technology centers, and any institutions interested in integration of AI and IoT. The intended audience of this book include UG/PG students, Ph.D. scholars of this fields, industry technologists, young entrepreneurs, professionals, network designers, data scientists, technology specialists, practitioners, and people who are interested in exploring the role of AI and blockchain technology in IoT systems.

Digital technologies are influencing the way we learn, live, work, and exist in different contexts of society in the digital age. There are a variety of learning systems that support innovative digital approaches, and universities and organizations around the world are investing in building their own e-learning platforms. Digital technologies are enabling wider access to education and new markets for student recruitment, resulting in increased income prospects for global higher education institutions. Technology enables numerous data and information sources, which give greater access to information and data. It also enables highly virtual environments, which impact teaching and the classroom. The widespread use and application of digital technologies in the teaching and learning process provoke pedagogical inquiry and mediation. It is in this context that Technologies for Sustainable Global Higher Education focuses on analyzing the application of digital technologies in the teaching–learning process. The chapters in this edited collection seek to answer questions relevant to the context of higher education, such as: What is the concept of digital technologies? How is digital technology used to mediate the learning process? What technologies are used to qualify education in higher education? This book provides answers to these questions by focusing on issues central to improving education through digital technologies, digital learning, and pedagogical practices in digital education. It also provides case studies of higher education institutions.

The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines: An overview of applications and computing devices Deep learning impacts in the field of AI Deep learning as state-of-the-art approach to AI Exploring deep learning architecture for cutting-edge AI solutions Operations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

The linguistics and language education world has recently experienced a rapidly proliferating interest in applying technology. This interdisciplinary area bridges the gap between two previously separate fields. Innovations and Applications of Technology in Language Education is a collection of 12 chapters by an international group of language and linguistics education experts. Although technology in language education is a global interest, its practices should be contextualized. This book covers how language educational technology is currently applied, discusses how it should be applied, and gives directions for its future development. Providing a critical review of respective current practices and perspectives, this book begins by presenting a set of research‐based principles for developing second language teachers’ professionalism. It then examines the use of technology to enhance students’ English language skills. Acknowledging the advantages and disadvantages of AI‐mediated communication, this book argues for the use of AI to facilitate communication in language education. It also proposes the use of AI to develop and administer language tests and suggests guidelines for practitioners to deploy AI in developing and administering language tests efficiently. This book concludes by discussing technology for specific purposes in second language education and the potential of computer‐mediated communication (CMC) to enhance interaction between students.