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 Fourth Industrial Revolution revolves around cyber-physical systems and artificial intelligence. Little is certain about this new wave of innovation, which leaves industrialists and educators in the lurch without much guidance on adapting to this new digital landscape. Society must become more agile and place a higher emphasis on lifelong learning to master new technologies in order to stay ahead of the changes and overcome challenges to become more globally competitive. Promoting Inclusive Growth in the Fourth Industrial Revolution is a collection of innovative research that focuses on the role of formal education in preparing students for uncertain futures and for societies that are changing at great speed in terms of their abilities to drive job creation, economic growth, and prosperity for millions in the future. Featuring coverage on a broad range of topics including economics, higher education, and safety and regulation, this book is ideally designed for teachers, managers, entrepreneurs, economists, policymakers, academicians, researchers, students, and professionals in the fields of human resources, organizational design, learning design, information technology, and e-learning.

5G is the upcoming generation of the wireless network that will be the advanced version of 4G LTE+ providing all the features of a 4G LTE network and connectivity for IoT devices with faster speed and lower latency. The 5G network is going to be a service-oriented network, connecting billions of IoT devices and mobile phones through the wireless network, and hence, it needs a special emphasis on security. Security is the necessary enabler for the continuity of the wireless network business, and in 5G, network security for IoT devices is the most important aspect. As IoT is gaining momentum, people can remotely operate or instruct their network devices. Therefore, there is a need for robust security mechanisms to prevent unauthorized access to the devices. >Evolution of Software-Defined Networking Foundations for IoT and 5G Mobile Networks is a collection of innovative research on the security challenges and prevention mechanisms in high-speed mobile networks. The book explores the threats to 5G and IoT and how to implement effective security architecture for them. While highlighting topics including artificial intelligence, mobile technology, and ubiquitous computing, this book is ideally designed for cybersecurity experts, network providers, computer scientists, communication technologies experts, academicians, students, and researchers.

Many processes in nature arise from the interaction of periodic phenomena with random phenomena. The results are processes that are not periodic, but whose statistical functions are periodic functions of time. These processes are called cyclostationary and are an appropriate mathematical model for signals encountered in many fields including communications, radar, sonar, telemetry, acoustics, mechanics, econometrics, astronomy, and biology. Cyclostationary Processes and Time Series: Theory, Applications, and Generalizations addresses these issues and includes the following key features.

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors' firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions.

Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the "big data." Deep learning (DL) is a subset of machine learning that processes "big data" to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface.

Computing in Communication Networks: From Theory to Practice provides comprehensive details and practical implementation tactics on the novel concepts and enabling technologies at the core of the paradigm shift from store and forward (dumb) to compute and forward (intelligent) in future communication networks and systems. The book explains how to create virtualized large scale testbeds using well-established open source software, such as Mininet and Docker. It shows how and where to place disruptive techniques, such as machine learning, compressed sensing, or network coding in a newly built testbed. In addition, it presents a comprehensive overview of current standardization activities. Specific chapters explore upcoming communication networks that support verticals in transportation, industry, construction, agriculture, health care and energy grids, underlying concepts, such as network slicing and mobile edge cloud, enabling technologies, such as SDN/NFV/ ICN, disruptive innovations, such as network coding, compressed sensing and machine learning, how to build a virtualized network infrastructure testbed on one's own computer, and more.

The internet of things (IoT) has drawn great attention from both academia and industry, since it offers a challenging notion of creating a world where all things around us are connected to the internet and communicate with each other with minimal human intervention. Another component for helping IoT to succeed is cloud computing. The combination of cloud computing and IoT will enable new monitoring services and powerful processing of sensory data streams. These applications, alongside implementation details and challenges, should also be explored for successful mainstream adoption. IoT is also fueled by the advancement of digital technologies, and the next generation era will be cloud-based IoT systems. Integration and Implementation of the Internet of Things Through Cloud Computing studies, analyzes, and presents cloud-based IoT-related technologies, protocols, and standards along with recent research and development in cloud-based IoT. It also presents recent emerging trends and technological advances of cloud-based IoT, innovative applications, and the challenges and implications for society. The chapters included take a strong look at the societal and social aspects of this technology along with its implementations and technological analyses. This book is intended for IT specialists, technologists, practitioners, researchers, academicians, and students who are interested in the next era of IoT through cloud computing.

Bioinspiration is recognized by the World Health Organization as having great promise in transforming and democratizing health systems while improving the quality, safety, and efficiency of standard healthcare in order to offer patients the tremendous opportunity to take charge of their own health. This phenomenon can enable great medical breakthroughs by helping healthcare providers improve patient care, make accurate diagnoses, optimize treatment protocols, and more. Unfortunately, the consequences can be serious if those who finance, design, regulate, or use artificial intelligence (AI) technologies for health do not prioritize ethical principles and obligations in terms of human rights and preservation of the private life. Advanced Bioinspiration Methods for Healthcare Standards, Policies, and Reform is the fruit of the fusion of AI and medicine, which brings together the latest empirical research findings in the areas of AI, bioinspiration, law, ethics, and medicine. It assists professionals in optimizing the potential benefits of AI models and bioinspired algorithms in health issues while mitigating potential dangers by examining the complex issues and innovative solutions that are linked to healthcare standards, policies, and reform. Covering topics such as genetic algorithms, health surveillance cameras, and hybrid classification algorithms, this premier reference source is an excellent resource for AI specialists, hospital administrators, health professionals, healthcare scientists, students and educators of higher education, government officials, researchers, and academicians.

Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.

This book introduces the concept of Event Mining for building explanatory models from analyses of correlated data. Such a model may be used as the basis for predictions and corrective actions. The idea is to create, via an iterative process, a model that explains causal relationships in the form of structural and temporal patterns in the data. The first phase is the data-driven process of hypothesis formation, requiring the analysis of large amounts of data to find strong candidate hypotheses. The second phase is hypothesis testing, wherein a domain expert's knowledge and judgment is used to test and modify the candidate hypotheses. The book is intended as a primer on Event Mining for data-enthusiasts and information professionals interested in employing these event-based data analysis techniques in diverse applications. The reader is introduced to frameworks for temporal knowledge representation and reasoning, as well as temporal data mining and pattern discovery. Also discussed are the design principles of event mining systems. The approach is reified by the presentation of an event mining system called EventMiner, a computational framework for building explanatory models. The book contains case studies of using EventMiner in asthma risk management and an architecture for the objective self. The text can be used by researchers interested in harnessing the value of heterogeneous big data for designing explanatory event-based models in diverse application areas such as healthcare, biological data analytics, predictive maintenance of systems, computer networks, and business intelligence.

Advances in digital technologies continue to impact all areas of life, including the business sector. Digital transformation is ascertained to usher in the digitalized economy and involves new concepts and management tools that must be considered in the context of management science and practice. For business leaders to ensure their companies remain competitive and relevant, it is essential for them to utilize these innovative technologies and strategies. The Handbook of Research on Digital Transformation Management and Tools highlights new digital concepts within management, such as digitalization and digital disruption, and addresses the paradigm shift in management science incurred by the digital transformation towards the digitalized economy. Covering a range of important topics such as cultural economy, online consumer behavior, sustainability, and social media, this major reference work is crucial for managers, business owners, researchers, scholars, academicians, practitioners, instructors, and students.

Artificial intelligence serves as a catalyst for transformation in the field of education. This shift in the educational paradigm has a profound impact on the way we live, interact with each other, and define our values. Thus, there is a need for an earnest inquiry into the cultural repercussions of this phenomenon that extends beyond superficial analyses of AI-based applications in education. Cultural and Social Implications of Artificial Intelligence in Education addresses the need for a scholarly exploration of the cultural and social impacts of the rapid expansion of artificial intelligence in the field of education including potential consequences these impacts could have on culture, social relations, and values. The content within this publication covers such topics as ethics, critical thinking, and augmented intelligence and is designed for educators, academicians, administrators, researchers, and professionals.