In the digital age, the relentless growth of data centers and cloud computing has given rise to a pressing dilemma. The power consumption of these facilities is spiraling out of control, emitting massive amounts of carbon dioxide, and contributing to the ever-increasing threat of global warming. Studies show that data centers alone are responsible for nearly eighty million metric tons of CO2 emissions worldwide, and this figure is poised to skyrocket to a staggering 8000 TWh by 2030 unless we revolutionize our approach to computing resource management. The root of this problem lies in inefficient resource allocation within cloud environments, as service providers often over-provision computing resources to avoid Service Level Agreement (SLA) violations, leading to both underutilization of resources and a significant increase in energy consumption. Computational Intelligence for Green Cloud Computing and Digital Waste Management stands as a beacon of hope in the face of the environmental and technological challenges we face. It introduces the concept of green computing, dedicated to creating an eco-friendly computing environment. The book explores innovative, intelligent resource management methods that can significantly reduce the power consumption of data centers. From machine learning and deep learning solutions to green virtualization technologies, this comprehensive guide explores innovative approaches to address the pressing challenges of green computing. Whether you are an educator teaching about green computing, an environmentalist seeking sustainability solutions, an industry professional navigating the digital landscape, a resolute researcher, or simply someone intrigued by the intersection of technology and sustainability, this book offers an indispensable resource.

Organizations face a pressing challenge in today's rapidly evolving economies: navigating the ethical complexities of adopting Artificial Intelligence (AI) and related technologies. As AI becomes increasingly integral to operations, transparency, fairness, accountability, and privacy concerns are more critical than ever. Organizations need practical guidance to develop and implement AI ethics strategies effectively. Cases on AI Ethics in Business offers a comprehensive solution by examining AI Ethics through theoretical lenses and innovative practices. It provides a roadmap for organizations to address ethical challenges in AI adoption, offering insights from leaders in the field. With a focus on theory-to-practice, the book equips readers with actionable strategies and frameworks to navigate the ethical implications of AI, ensuring responsible and sustainable AI deployment.

The confluence of Artificial Intelligence of Things (AIoT) and Semantic Web technologies is nothing short of revolutionary. The profound impact of this synergy extends far beyond the realms of industry, research, and society; it shapes the very fabric of our future. Semantic Web Technologies and Applications in Artificial Intelligence of Things is a meticulously crafted reference that not only acknowledges this significance but also serves as a guide for those navigating the complexities of Industry 4.0 and AIoT. This curated compendium of cutting-edge technologies acts as a veritable knowledge base for future developments. As academics, scholars, and industry professionals, the ideal audience of this book, will find meticulously curated content that caters to their diverse interests and expertise, covering topics ranging from smart agriculture, manufacturing, industry, health sciences, and government. Seasoned academics, students, and visionary industry leaders, will find this book to be an indispensable guide that paves the way for innovation and progress.

This book focuses on big data in business intelligence, data management, machine learning, cloud computing, and smart cities. It also provides an interdisciplinary platform to present and discuss recent innovations, trends, and concerns in the fields of big data and analytics. Big Data Analysis for Green Computing: Concepts and Applications presents the latest technologies and covers the major challenges, issues, and advances of big data and data analytics in green computing. It explores basic as well as high-level concepts. It also includes the use of machine learning using big data and discusses advanced system implementation for smart cities. The book is intended for business and management educators, management researchers, doctoral scholars, university professors, policymakers, and higher academic research organizations.

Convergence Strategies for Green Computing and Sustainable Development presents a comprehensive exploration of the potential of emerging technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), fog computing, and cloud computing, to aid in fostering a sustainable future. It examines how these technologies can reduce the impact of unsustainability in societies, the environment, and natural resources, offering invaluable insights into harnessing their power for positive change. Convergence Strategies for Green Computing and Sustainable Development serves as a comprehensive strategy that holistically understands, transforms, and develops technological systems in society. This book caters to a diverse range of readers, including graduate students, researchers, working professionals seeking knowledge, and industry experts seeking information about new trends. With its recommended topics and comprehensive table of contents, readers can gain in-depth knowledge about sustainable cloud computing, artificial intelligence and machine learning for sustainable development, sustainable wireless systems and networks, and the crucial role of green IoT and Edge-AI in driving a sustainable digital transition.

In the dynamic intersection of cutting-edge technology and evolving business strategies, the fusion of Artificial Intelligence (AI) and the Internet of Things (IoT) has given rise to a revolutionary paradigm known as Artificial Intelligence of Things (AIoT). This emerging technological powerhouse offers boundless possibilities while presenting formidable challenges, and organizations worldwide are wrestling with its integration into their core operations. Artificial Intelligence of Things (AIoT) for Productivity and Organizational Transition introduces the fundamental concepts underpinning AIoT integration and the evolving landscape of AIoT research and development, then delves deep into the theoretical foundations of AIoT, examining it through the lenses of economics, behavioral science, technology, psychology, and organizational theory. Within its pages, readers will discover insights into the tools, methods, design factors, user interfaces, and techniques driving AIoT innovation.

In today's rapidly evolving digital landscape, extended reality (XR) technologies have emerged as transformative tools that blur the boundaries between the physical and digital worlds. However, with this innovation comes a host of complex challenges. From ethical considerations in virtual reality research to legal and regulatory hurdles in extended reality, navigating this dynamic terrain requires a deep understanding of technology and human behavior. The lack of comprehensive resources that bridge these disciplines poses a significant barrier to those seeking to harness the full potential of immersive technologies. Multidisciplinary Applications of Extended Reality for Human Experience addresses this critical gap by offering a multidisciplinary exploration of XR's impact on society. Through expert contributions from diverse fields, including psychology, sociology, design, and culture, this book provides a comprehensive understanding of how XR technologies are reshaping human perceptions and interactions. It serves as a foundational text for academics, researchers, and industry professionals seeking to navigate the complexities of immersive technologies and their profound implications for human life.