This book discusses the basic principles of sustainable development in a smart city ecosystem to better serve the life of citizens. It examines smart city systems driven by emerging IoT-powered technologies and the other dependent platforms. AI, IoT Technologies, Big Data Solutions, Cloud Platforms, and Cybersecurity Techniques in Developing Smart Cities, discusses the design and implementation of the core components of the Smart City ecosystem. The editors discuss the effective management and development of smart city infrastructures, starting with planning and integrating complex models and diverse frameworks into an ecosystem. Specifically the chapters examines the core infrastructure elements including activities of the public and private services as well as innovative ICT solutions, Computer Vision, IoT technologies, Data tools, Cloud services, AR/VR technologies, Cybersecurity techniques, Treatment solution of the environmental water pollution, and other intelligent devices for supporting sustainable living in the smart environment. The chapters also discuss machine vision models and implementation as well as real-time robotic applications. Upon reading the book users will be able handle the challenges and improvements of security for smart systems, and will have the know-how to analysis and visualize data using big data tools and visualization applications. The book will provide the technologies, solutions as well as designs about the application design of the smart cities with advanced tools and techniques to students, researchers, engineers and academics.

The book focuses on the recent developments in the areas of error reduction, resource optimization, and revenue growth in sustainable manufacturing using machine learning. It presents the integration of smart technologies such as machine learning in the field of Industry 4.0 for better quality products and efficient manufacturing methods. Focusses on machine learning applications in Industry 4.0 ecosystem, such as resource optimization, data analysis, and predictions. Highlights the importance of the explainable machine learning model in the manufacturing processes. Presents the integration of machine learning and big data analytics from an industry 4.0 perspective. Discusses advanced computational techniques for sustainable manufacturing. Examines environmental impacts of operations and supply chain from an industry 4.0 perspective. This book provides scientific and technological insight into sustainable manufacturing by covering a wide range of machine learning applications fault detection, cyber-attack prediction, and inventory management. It further discusses resource optimization using machine learning in industry 4.0, and explainable machine learning models for industry 4.0. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the fields including mechanical engineering, manufacturing engineering, production engineering, aerospace engineering, and computer engineering.

This book brings insight to the HR management system and offers data-centric approaches and AI-enabled applications for the design and implementation strategies used for workforce development and management. Designing Workforce Management Systems for Industry 4.0: Data-Centric and AI-Enabled Approaches focuses on the mechanisms of proposing solutions along with architectural concepts, design principles, smart solutions, and intelligent predictions with visualization simulation. Data visualization for the metrics of management systems and robotic process automation applications and tools are also offered. This book is also useful as a reference for those involved in AI-enabled applications, data analytics, data visualization, as well as systems engineering and systems designing.

Cloud computing, the Internet of Things (IoT), and big data are three significant technological trends affecting the world's largest corporations. This book discusses big data, cloud computing, and the IoT, with a focus on the benefits and implementation problems. In addition, it examines the many structures and applications pertinent to these disciplines. Also, big data, cloud computing, and the IoT are proposed as possible study avenues. Features: Informs about cloud computing, IoT and big data, including theoretical foundations and the most recent empirical findings Provides essential research on the relationship between various technologies and the aggregate influence they have on solving real-world problems Ideal for academicians, developers, researchers, computer scientists, practitioners, information technology professionals, students, scholars, and engineers exploring research on the incorporation of technological innovations to address contemporary societal challenges

Today, relevant data are typically delivered to cloud-based servers for storing and analysis in order to extract key features and enable enhanced applications beyond the basic transmission of raw data and to realize the possibilities associated with the impending Internet of Things (IoT). To allow for quicker, more efficient, and expanded privacy-preserving services, a new trend called Fog Computing has emerged: moving these responsibilities to the network's edge. Traditional centralized cloud computing paradigms confront new problems posed by IoT application growth, including high latency, limited storage, and outages due to a lack of available resources. Fog Computing puts the cloud and IoT devices closer together to address these issues. Instead of sending IoT data to the cloud, the fog processes and stores it locally at IoT devices. Unlike the cloud, fog-based services have a faster reaction time and better quality overall. Fog Computing, Cloud Computing, and their connectivity with the IoT are discussed in this book, with an emphasis on the advantages and implementation issues. It also explores the various architectures and appropriate IoT applications. Fog Computing, Cloud Computing, and Internet of Things are being suggested as potential research directions. Features: A systematic overview of the state-of-the-art in Cloud Computing, Fog Computing, and Internet of Things Recent research results and some pointers to future advancements in architectures and methodologies Detailed examples from clinical studies using several different data sets

Explains smart city ecosystem and AI-centric solutions Presents the application of design principles and computer vision models for operating smart cities and security systems Discusses how to integrate the AI-based controls systems to make the IoT devices smarter Explains data engineering and visualization patterns for monitoring smart city systems Discusses self-driving car models and transportation infrastructures

The recent advancements in the field of the internet of things (IoT), AI, big data, blockchain, augmented reality (AR)/virtual reality (VR), cloud platforms, quantum computing, cybersecurity, and telecommunication technology enabled the promotion of conventional computer-aided industry to the metaverse ecosystem that is powered by AR/VR-driven technologies. In this paradigm shift, the integrated technologies of IoT and AI play a vital role to connect the cyberspace of computing systems and virtual environments. AR/VR supports a huge range of industrial applications such as logistics, the food industry, and manufacturing utilities. AI-Based Technologies and Applications in the Era of the Metaverse discusses essential components of the metaverse ecosystem such as concepts, methodologies, technologies, modeling, designs, statistics, implementation, and maintenance. Covering key topics such as machine learning, deep learning, quantum computing, and blockchain, this premier reference source is ideal for computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.