Focused on the latest mobile technologies, this book addresses specific features (such as IoT) and their adoptions that aim to enable excellence in business in Industry 4.0. Furthermore, this book explores how the adoption of these technologies is related to rising concerns about privacy and trusted communication issues that concern management and leaders of business organizations. Managing IoT and Mobile Technologies with Innovation, Trust, and Sustainable Computing not only targets IT experts and drills down on the technical issues but also provides readers from various groups with a well-linked concept about how the latest trends of mobile technologies are closely related to daily living and the workplace at managerial and even individual levels.

As the metaverse rapidly evolves into a virtual realm where digital interactions mirror and sometimes surpass physical reality, ensuring robust security paradigms becomes critical. The complex and interconnected nature of the metaverse begs better exploration into user privacy, cyber threat prevention, and virtual asset integrity. Establishing reliable security frameworks in the metaverse involves identity management, data protection, decentralized governance models, and the mitigation of virtual and augmented reality vulnerabilities. By developing and implementing robust security paradigms, those who use the metaverse can foster trust, promote innovation, and facilitate the safe and sustainable growth of the metaverse ecosystem. Metaverse Security Paradigms addresses the multifaceted security challenges within the metaverse and virtual worlds, while exploring privacy techniques and ethical implications. It delves into the technological, legal, and ethical dimensions of security in virtual environments. This book covers topics such as privacy systems, risk management, and artificial intelligence, and is a useful resource for IT professionals, business owners, computer engineers, security workers, researchers, scientists, and academicians.

The development of large language models has resulted in artificial intelligence advancements promising transformations and benefits across various industries and sectors. However, this progress is not without its challenges. The scale and complexity of these models pose significant technical hurdles, including issues related to bias, transparency, and data privacy. As these models integrate into decision-making processes, ethical concerns about their societal impact, such as potential job displacement or harmful stereotype reinforcement, become more urgent. Addressing these challenges requires a collaborative effort from business owners, computer engineers, policymakers, and sociologists. Fostering effective research for solutions to address AI ethical challenges may ensure that large language model developments benefit society in a positive way. Challenges in Large Language Model Development and AI Ethics addresses complex ethical dilemmas and challenges of the development of large language models and artificial intelligence. It analyzes ethical considerations involved in the design and implementation of large language models, while exploring aspects like bias, accountability, privacy, and social impacts. This book covers topics such as law and policy, model architecture, and machine learning, and is a useful resource for computer engineers, sociologists, policymakers, business owners, academicians, researchers, and scientists.

This book focuses on the key technology applied Internet of things and smart grid, which include some novel ICT technologies such as big data, edge computing, 5G, and wide area wireless communication technology. The mutual penetration, deep integration, and wide application of smart grid and IoT effectively integrate communication infrastructure resources and power system infrastructure resources, further realize energy conservation and emission reduction, improve the level of grid informatization, automation, and interaction, and improve grid operation capacity and quality of service. These key technologies are presented and studied in detail, which help readers deeply understand those key technologies to apply IoT and grid. The book benefits researchers, engineers, and graduate students in the fields of IoT and energy systems, etc.

This book focuses on green computing-based network security techniques and addresses the challenges involved in practical implementation. It also explores the idea of energy-efficient computing for network and data security and covers the security threats involved in social networks, data centers, IoT, and biomedical applications. Green Computing in Network Security: Energy Efficient Solutions for Business and Home includes analysis of green-security mechanisms and explores the role of green computing for secured modern internet applications. It discusses green computing-based distributed learning approaches for security and emphasizes the development of green computing-based security systems for IoT devices. Written with researchers, academic libraries, and professionals in mind so they can get up to speed on network security, the challenges, and implementation processes.

The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book consists of two sections: Section I contains the topics related to Applications of Machine Learning, and Section II addresses issues about Big Data, the Cloud and the Internet of Things. This brings all the related technologies into a single source so that undergraduate and postgraduate students, researchers, academicians and people in industry can easily understand them. Features Addresses the complete data science technologies workflow Explores basic and high-level concepts and services as a manual for those in the industry and at the same time can help beginners to understand both basic and advanced aspects of machine learning Covers data processing and security solutions in IoT and Big Data applications Offers adaptive, robust, scalable and reliable applications to develop solutions for day-to-day problems Presents security issues and data migration techniques of NoSQL databases

In the ever-evolving landscape of the modern world, the synergy between technology and management has become a cornerstone of innovation and progress. This book, Neural Networks Unleashed: From Basics to Advanced Models, is conceived to bridge the gap between emerging technological advancements in neural networks and their strategic application across industries. Our objective is to equip readers with the tools and insights necessary to excel in this dynamic intersection of fields. This book is structured to provide a comprehensive exploration of the methodologies and strategies that define neural networks, from foundational theories to advanced applications. We delve into the critical aspects that drive successful innovation in fields such as computer vision, natural language processing, and AI-driven automation. We have made a concerted effort to present complex concepts in a clear and accessible manner, making this work suitable for a diverse audience, including students, engineers, managers, and industry professionals. In authoring this book, we have drawn upon the latest research and best practices to ensure that readers not only gain a robust theoretical understanding but also acquire practical skills that can be applied in real-world scenarios. The chapters are designed to strike a balance between depth and breadth, covering topics ranging from neural network architectures and training techniques to their strategic management and application in various industries. Additionally, we emphasize the importance of effective communication, dedicating sections to the art of presenting innovative ideas and solutions in a precise and academically rigorous manner. The inspiration for this book arises from a recognition of the crucial role that neural networks play in shaping the future of technology and business. We are profoundly grateful to Chancellor Shri Shiv Kumar Gupta of Maharaja Agrasen Himalayan Garhwal University for his unwavering support and vision. His dedication to fostering academic excellence and promoting a culture of innovation has been instrumental in bringing this project to fruition. We hope this book will serve as a valuable resource and inspiration for those eager to deepen their understanding of how neural networks can be harnessed to drive innovation. We believe that the knowledge and insights contained within these pages will empower readers to lead the way in creating innovative solutions that will define the future of technology. Thank you for joining us on this journey. Authors