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.

The COVID-19 pandemic has forced organizations and individuals to embrace new practices such as social distancing and remote working. During these unprecedented times, many have increasingly relied on the internet for work, shopping, and healthcare. However, while the world focuses on the health and economic threats posed by the COVID-19 pandemic, cyber criminals are capitalizing on this crisis as the world has become more digitally dependent and vulnerable than ever. Cybersecurity Crisis Management and Lessons Learned From the COVID-19 Pandemic provides cutting-edge research on the best guidelines for preventing, detecting, and responding to cyber threats within educational, business, health, and governmental organizations during the COVID-19 pandemic. It further highlights the importance of focusing on cybersecurity within organizational crisis management. Covering topics such as privacy and healthcare, remote work, and personal health data, this premier reference source is an indispensable resource for startup companies, health and business executives, ICT procurement managers, IT professionals, libraries, students and educators of higher education, entrepreneurs, government officials, social media experts, researchers, and academicians.

Emerging scientific and industrial applications in today's world require significant computing power. Modern software tools are available for such platforms but are relatively complex and require the use of innovative programming models. One promising area in modern software design is the development, analysis, and implementation of algorithms and adaptive methods. These advancements in programming are promising but lack relevant research and understanding. Formal and Adaptive Methods for Automation of Parallel Programs Construction: Emerging Research and Opportunities is an essential reference source that solves the problem of the development of efficient models, methods, and tools for parallel programming automation based on the algebra of algorithms, term rewriting, and auto-tuning paradigms. The results of this book will help to further develop and improve existing research on design, synthesis, and optimization of sequential and parallel algorithms and programs. Featuring research on topics such as auto-tuning methods, graphics processing, and algorithmic language, this book is ideally designed for mathematicians, software engineers, data scientists, researchers, academicians, and students seeking coverage on developing tools for automated design and parallel programs.

The COVID-19 pandemic caused educational institutions to close for the safety of students and staff and to aid in prevention measures around the world to slow the spread of the outbreak. Closures of schools and the interruption of education affected billions of enrolled students of all ages, leading to nearly the entire student population to be impacted by these measures. Consequently, this changed the educational landscape. Emergency remote education (ERE) was put into practice to ensure the continuity of education and caused the need to reinterpret pedagogical approaches. The crisis revealed flaws within our education systems and exemplified how unprepared schools were for the educational crisis both in K-12 and higher education contexts. These shortcomings require further research on education and emerging pedagogies for the future. The Handbook of Research on Emerging Pedagogies for the Future of Education: Trauma-Informed, Care, and Pandemic Pedagogy evaluates the interruption of education, reports best-practices, identifies the strengths and weaknesses of educational systems, and provides a base for emerging pedagogies. The book provides an overview of education in the new normal by distilling lessons learned and extracting the knowledge and experience gained through the COVID-19 global crisis to better envision the emerging pedagogies for the future of education. The chapters cover various subjects that include mathematics, English, science, and medical education, and span all schooling levels from preschool to higher education. The target audience of this book will be composed of professionals, researchers, instructional designers, decision-makers, institutions, and most importantly, main-actors from the educational landscape interested in interpreting the emerging pedagogies and future of education due to the pandemic.

Technology is used in various forms within today’s modern market. Businesses and companies, specifically, are beginning to manage their effectiveness and performance using intelligent systems and other modes of digitization. The rise of artificial intelligence and automation has caused organizations to re-examine how they utilize their personnel and how to train employees for new skillsets using these technologies. These responsibilities fall on the shoulders of human resources, creating a need for further understanding of autonomous systems and their capabilities within organizational progression. Transforming Human Resource Functions With Automation is a collection of innovative research on the methods and applications of artificial intelligence and autonomous systems within human resource management and modern alterations that are occurring. While highlighting topics including cloud-based systems, robotics, and social media, this book is ideally designed for managers, practitioners, researchers, executives, policymakers, strategists, academicians, and students seeking current research on advancements within human resource strategies through the implementation of information technology and automation.

As digital technology continues to revolutionize the world, businesses are also evolving by adopting digital technologies such as artificial intelligence, digital marketing, and analytical methods into their daily practices. Due to this growing adoption, further study on the potential solutions modern technology provides to businesses is required to successfully apply it across industries. AI-Driven Intelligent Models for Business Excellence explores various artificial intelligence models and methods for business applications and considers algorithmic approaches for business excellence across numerous fields and applications. Covering topics such as business analysis, deep learning, machine learning, and analytical methods, this reference work is ideal for managers, business owners, computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

Infrastructure Computer Vision delves into this field of computer science that works on enabling computers to see, identify, process images and provide appropriate output in the same way that human vision does. However, implementing these advanced information and sensing technologies is difficult for many engineers. This book provides civil engineers with the technical detail of this advanced technology and how to apply it to their individual projects.

Innovations in Artificial Intelligence and Human Computer Interaction in the Digital Era investigates the interaction and growing interdependency of the HCI and AI fields, which are not usually addressed in traditional approaches. Chapters explore how well AI can interact with users based on linguistics and user-centered design processes, especially with the advances of AI and the hype around many applications. Other sections investigate how HCI and AI can mutually benefit from a closer association and the how the AI community can improve their usage of HCI methods like “Wizard of Oz” prototyping and “Thinking aloud” protocols. Moreover, HCI can further augment human capabilities using new technologies. This book demonstrates how an interdisciplinary team of HCI and AI researchers can develop extraordinary applications, such as improved education systems, smart homes, smart healthcare and map Human Computer Interaction (HCI) for a multidisciplinary field that focuses on the design of computer technology and the interaction between users and computers in different domains.