Blockchain technology presents numerous advantages that include increased transparency, reduced transaction costs, faster transaction settlement, automation of information, increased traceability, improved customer experience, improved digital identity, better cyber security, and user-controlled networks. These potential applications are widespread and diverse including funds transfer, smart contracts, e-voting, efficient supply chain, and more in nearly every sector of society including finance, healthcare, law, trade, real estate, and other important areas. However, there are challenges and limitations that exist such as high energy consumption, limited scalability, complexity, security, network size, lack of regulations, and other critical issues. Nevertheless, blockchain is an attractive technology and has much to offer to the modern-day industry. Industry Use Cases on Blockchain Technology Applications in IoT and the Financial Sector investigates blockchain technology’s adoption and effectiveness in multiple industries and for the internet of things (IoT)-based applications, presents use cases from industrial and financial sectors as well as from other transaction-based services, and fills a gap in this respect by extending the existing body of knowledge in the suggested field. While highlighting topics such as cybersecurity, use cases, and models for blockchain implementation, this book is ideal for business managers, financial accountants, practitioners, researchers, academicians, and students interested in blockchain technology’s role and implementation in IoT and the financial sector.

Blockchain has the potential to revolutionize how people and organizations, who may not know or trust each other, share information and carry out transactions online. Nearly every institution on the planet wants to be a leader in blockchain technology as well as a home to significant platforms, applications, and companies. There is a need for a glocal policy to meet and support these goals as blockchain technology must embrace glocal values and ideals in its legal and regulatory frameworks. Glocal Policy and Strategies for Blockchain: Building Ecosystems and Sustainability discusses the features and advantages of blockchain technology, the innovative applications of blockchain technology, and the potent and limited aspects of blockchain technology. Covering topics such as digital change, international policy, and cyber security governance, this reference work is ideal for industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.

The book provides a comprehensive overview of cyber security in Industry 5.0, data security in emerging technologies, block chain technology, cloud computing security, evolving IoT and OT threats, and considerable data integrity in healthcare. The impact of security risks on various sectors is explored including artificial intelligence in national security, quantum com-puting for security, and AI-driven cyber security techniques. It explores how cyber security is applied across different areas of human life through computational modeling. The book concludes by presenting a roadmap for securing computing environments, addressing the complex interplay between advanced technologies and emerging security challenges, and offering insights into future trends and innovations for sustainable development. This book: • Analyzes the use of AI, support vector machines, and deep learning for dataclassification, vulnerability prediction, and defense. • Provides insights into data protection for Industry 4.0/5.0, cloud computing, and IoT/OT, focusing on risk mitigation. • Explores block chain’s role in smart nations, financial risk management, and the potential of quantum computing for security. • Examines AI’s applications in national security, including India’s AI strategy and securing smart cities. • Evaluate strategies for data integrity in healthcare, secure IoT platforms, and supply chain cyber security. The text is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

Digital transformation, also known as Industry 4.0, Smart Industry, and Smart Manufacturing, is at the top of leaders’ agendas. Such a transformation stimulates innovation in new products and services, the digital transformation of processes, and the creation of new business models and ecosystems. In the world of manufacturing, Industry 4.0 is based on various technological advances, among which we can mainly cite CPS (cyber-physical systems), IoT (Internet of Things), and IoS (internet of services). While engaging, this fourth wave also brings significant challenges for manufacturers. Business operations and the supply chain are becoming more vulnerable to cyber threats. Security Engineering for Embedded and Cyber-Physical Systems is an invaluable resource to discover cybersecurity and privacy techniques for embedded and cyber-physical systems. This book presents the latest studies and research results on all aspects of security engineering for embedded and cyber-physical systems. It also provides a premier interdisciplinary reference for researchers, practitioners, and educators to discover the most recent innovations, trends, concerns, and practical challenges encountered and solutions adopted in security engineering for embedded and cyber-physical systems. The book offers comprehensive coverage of the essential topics, including the following: Embedded and cyber-physical systems threats and vulnerabilities Security engineering techniques for embedded and cyber-physical systems Security engineering for embedded and cyber-physical systems and potential future-use cases Artificial intelligence techniques for cybersecurity and privacy Security engineering for Internet of Things Blockchain for cybersecurity in embedded and cyber-physical systems This book comprises a number of state-of-the-art contributions from both scientists and practitioners working in security engineering for embedded and cyber-physical systems. It aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this area or those interested in grasping its diverse facets and exploring the latest advances and future trends related to security engineering for embedded and cyber-physical systems.

This book comprises the proceedings of the 3rd International Conference on Computer Vision, High-Performance Computing, Smart Devices, and Networks (CHSN 2022). This book highlights high-quality research articles in machine learning, computer vision, and networks. The content of this volume gives the reader an up-to-date picture of the state-of-the-art connection between computational intelligence, machine learning, and IoT. The papers in this volume are peer-reviewed by experts in related areas. The book will serve as a valuable reference resource for academics and researchers across the globe.

Many organizations are currently undertaking digital transformation to improve their business processes and better achieve their goals. This Handbook provides a comprehensive overview of contemporary trends and research at the point where business process management and digital transformation meet. Presenting a multidisciplinary approach, it demonstrates the close link between these two fields through engagement with theory and practice.

Every industry will be positively affected by blockchain and AI technology at some point. However, blockchain is a misunderstood technology within the publishing realm. The scholarly publishing industry can significantly improve the flow of research, drive down costs, and introduce new efficiencies in the publishing industry with these new technologies. The scholarly publishing industry is in its early days of the digital transformation, and blockchain and AI technology could play a major role in this. However, the industry has been resistant to change. These reasons include but are not limited to staying with legacy systems, cost of new platforms, changing cultures, and understanding and adopting new technologies. With proper research and information provided, the publishing industry can adopt these technologies for beneficial advancements and the generation of a bright future. Transforming Scholarly Publishing With Blockchain Technologies and AI explores the changing landscape of scholarly publishing and how blockchain technologies and AI are slowly being integrated and used within the industry. This book covers both the benefits and challenges of implementing technology and provides both cases and new developments. Topics highlighted include business model developments, new efficiencies in scholarly publishing, blockchain in research libraries, knowledge discovery, and blockchain in academic publishing. This book is a valuable reference tool for publishers, IT specialists, technologists, publishing vendors, researchers, academicians, and students who are interested in how blockchain technologies and AI are transforming and developing a modern scholarly publishing industry.

"This edited book discusses data analytics and complex communication networks and recommends new methodologies, system architectures, and other solutions to prevail over the current limitations faced by the field"--

Multinational organizations have begun to realize that sentiment mining plays an important role for decision making and market strategy. The revolutionary growth of digital marketing not only changes the market game, but also brings forth new opportunities for skilled professionals and expertise. Currently, the technologies are rapidly changing, and artificial intelligence (AI) and machine learning are contributing as game-changing technologies. These are not only trending but are also increasingly popular among data scientists and data analysts. New Opportunities for Sentiment Analysis and Information Processing provides interdisciplinary research in information retrieval and sentiment analysis including studies on extracting sentiments from textual data, sentiment visualization-based dimensionality reduction for multiple features, and deep learning-based multi-domain sentiment extraction. The book also optimizes techniques used for sentiment identification and examines applications of sentiment analysis and emotion detection. Covering such topics as communication networks, natural language processing, and semantic analysis, this book is essential for data scientists, data analysts, IT specialists, scientists, researchers, academicians, and students.

Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches.