More individuals than ever are utilizing internet technologies to work from home, teach and learn, shop, interact with peers, review medical records, and more. While it is certainly convenient to conduct such tasks via the internet, this increased internet presence has also led to a rise in the search and availability of personal information, which in turn is resulting in more cyber-attacks, privacy breaches, and information leaks. Cyber criminals are using such opportunities to attack governments, organizations, and individuals, making it necessary to anticipate, assess, and mitigate privacy and security threats during this infodemic. The Handbook of Research on Technical, Privacy, and Security Challenges in a Modern World discusses the design and development of different machine learning systems, including next generation applications, in order to mitigate cyber-attacks and address security challenges in everyday technologies. It further explores select methods and algorithms of learning for implementing better security methods in fields such as business and healthcare. It recognizes the future of privacy and the importance of preserving data through recommended practice, feedback loops, and smart agents. Covering topics such as face mask detection, gesture recognition, and botnet attacks and detection, this major reference work is a dynamic resource for medical professionals, healthcare administrators, government officials, business executives and managers, IT managers, students and faculty of higher education, librarians, researchers, and academicians.

With new technologies, such as computer vision, internet of things, mobile computing, e-governance and e-commerce, and wide applications of social media, organizations generate a huge volume of data and at a much faster rate than several years ago. Big data in large-/small-scale systems, characterized by high volume, diversity, and velocity, increasingly drives decision making and is changing the landscape of business intelligence. From governments to private organizations, from communities to individuals, all areas are being affected by this shift. There is a high demand for big data analytics that offer insights for computing efficiency, knowledge discovery, problem solving, and event prediction. To handle this demand and this increase in big data, there needs to be research on innovative and optimized machine learning algorithms in both large- and small-scale systems. Applications of Big Data in Large- and Small-Scale Systems includes state-of-the-art research findings on the latest development, up-to-date issues, and challenges in the field of big data and presents the latest innovative and intelligent applications related to big data. This book encompasses big data in various multidisciplinary fields from the medical field to agriculture, business research, and smart cities. While highlighting topics including machine learning, cloud computing, data visualization, and more, this book is a valuable reference tool for computer scientists, data scientists and analysts, engineers, practitioners, stakeholders, researchers, academicians, and students interested in the versatile and innovative use of big data in both large-scale and small-scale systems.

This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts.  Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models.  After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.

The emergent phenomena of virtual reality, augmented reality, and mixed reality is having an impact on ways people communicate with technology and with each other. Schools and higher education institutions are embracing these emerging technologies and implementing them at a rapid pace. The challenge, however, is to identify well-defined problems where these innovative technologies can support successful solutions and subsequently determine the efficacy of effective virtual learning environments. Emerging Technologies in Virtual Learning Environments is an essential scholarly research publication that provides a deeper look into 3D virtual environments and how they can be developed and applied for the benefit of student learning and teacher training. This book features a wide range of topics in the areas of science, technology, engineering, arts, and math to ensure a blend of both science and humanities research. Therefore, it is ideal for curriculum developers, instructional designers, teachers, school administrators, higher education faculty, professionals, researchers, and students studying across all academic disciplines.

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis.

Safety and security are crucial to the operations of nuclear power plants, but cyber threats to these facilities are increasing significantly. Instrumentation and control systems, which play a vital role in the prevention of these incidents, have seen major design modifications with the implementation of digital technologies. Advanced computing systems are assisting in the protection and safety of nuclear power plants; however, significant research on these computational methods is deficient. Cyber Security and Safety of Nuclear Power Plant Instrumentation and Control Systems is a pivotal reference source that provides vital research on the digital developments of instrumentation and control systems for assuring the safety and security of nuclear power plants. While highlighting topics such as accident monitoring systems, classification measures, and UAV fleets, this publication explores individual cases of security breaches as well as future methods of practice. This book is ideally designed for engineers, industry specialists, researchers, policymakers, scientists, academicians, practitioners, and students involved in the development and operation of instrumentation and control systems for nuclear power plants, chemical and petrochemical industries, transport, and medical equipment.

Recent advances in information and communication technologies have enhanced the standards of metropolitan planning and development. With the increase in mobile communication, this will help to deliver innovative new services and apps in the field of urban e-planning. New Approaches, Methods, and Tools in Urban E-Planning is a key resource for the latest academic research on recent innovations in urban e-planning, citizen e-participation, the use of social media, and new forms of data collection and idea generation for urban planning. Presenting broad coverage among a variety of pertinent views and themes such as ethnography, e-consultation, and civic engagement, this book is ideally designed for planners, policymakers, researchers, and graduate students interested in how recent technological advancements are enhancing the traditional practices in e-planning.

Throughout the world, artificial intelligence is reshaping businesses, trade interfaces, economic activities, and society as a whole. In recent years, scholarly research on artificial intelligence has emerged from a variety of empirical and applied domains of knowledge. Computer scientists have developed advanced deep learning algorithms to leverage its utility in a variety of fields such as medicine, energy, travel, education, banking, and business management. Although a growing body of literature is shedding light on artificial intelligence-enabled difficulties, there is still much to be gained by applying fresh theory-driven techniques to this vital topic. Revolutionizing Business Practices Through Artificial Intelligence and Data-Rich Environments provides a comprehensive understanding of the business systems, platforms, procedures, and mechanisms that underpin different stakeholders' experiences with reality-enhancing technologies and their transformative application in management. The book also identifies areas in various business processes where artificial intelligence intervention would not only transform the business but would also make the business more sustainable. Covering key topics such as blockchain, business automation, and manufacturing, this reference work is ideal for computer scientists, business owners, managers, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.

Digital technologies are transforming economies and societies around the world. As such, markets demand new types of skills and competences that students must learn in order to be successful. IT and emerging technologies can be integrated into educational institutions to improve teaching methods and academic results as well as digital literacy. IT and the Development of Digital Skills and Competences in Education compiles critical research into one comprehensive reference source that explores the new demands of labor markets in the digital economy, how educational institutions can respond to these new opportunities and threats, the development of new teaching and learning methods, and the development of digital skills and competences. Through new theories, research findings, and case studies, the book seeks to incite new perspectives to understandings of the challenges and opportunities of the utilization of IT in the education sector around the world. Due to innovative topics that include digital competence, disruptive technologies, and digital transformation, this book is an ideal reference for academicians, directors of schools, vice-chancellors, education and IT experts, CEOs, policymakers in the field of education and IT, researchers, and students.

With technology creating a more competitive market, the global economy has been continually evolving in recent years. These technological developments have drastically changed the ways organizations manage their resources, as they are constantly seeking innovative methods to implement new systems. Because of this, there is an urgent need for empirical research that studies advancing theories and applications that organizations can use to successfully handle information and supplies. Novel Theories and Applications of Global Information Resource Management is a pivotal reference source that provides vital research on developing practices for businesses to effectively manage their assets on a global scale. While highlighting topics such as enterprise systems, library management, and information security, this publication explores the implementation of technological innovation into business techniques as well as the methods of controlling information in a contemporary society. This book is ideally designed for brokers, accountants, marketers, researchers, data scientists, financiers, managers, and academicians seeking current research on global resource management.