This book serves as the first guideline of the integrative approach, optimal for our new and young generations. Recent technology advancements in computer vision, IoT sensors, and analytics open the door to highly impactful innovations and applications as a result of effective and efficient integration of those. Such integration has brought to scientists and engineers a new approach —the integrative approach. This offers far more rapid development and scalable architecting when comparing to the traditional hardcore developmental approach. Featuring biomedical and healthcare challenges including COVID-19, we present a collection of carefully selective cases with significant added- values as a result of integrations, e.g., sensing with AI, analytics with different data sources, and comprehensive monitoring with many different sensors, while sustaining its readability.

This book constitutes the refereed proceedings of the First International Conference on Applied Intelligence and Informatics, AII 2021, held in Nottingham, UK, in July 2021. Due to the COVID-19 pandemic the conference was held in a fully virtual mode. The 26 full papers and 4 short papers presented were thoroughly reviewed and selected from the total 107 submissions. They are organized in the following topical sections: application of AI and informatics in disease detection; application of AI and informatics in healthcare; application of AI and informatics in pattern recognition; application of AI and informatics in network, security, and analytics; emerging applications of AI and informatics.

This book presents the best-selected research papers presented at the 3rd International Conference on Activity and Behavior Computing (ABC 2021), during 20–22 October 2021. The book includes works related to the field of vision- and sensor-based human action or activity and behavior analysis and recognition. It covers human activity recognition (HAR), action understanding, gait analysis, gesture recognition, behavior analysis, emotion, and affective computing, and related areas. The book addresses various challenges and aspects of human activity recognition—both in sensor-based and vision-based domains. It can be considered as an excellent treasury related to the human activity and behavior computing.

Modern hydrology is more interdisciplinary than ever. Staggering amounts and varieties of information pour in from GIS and remote sensing systems every day, and this information must be collected, interpreted, and shared efficiently. Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and Modeling introduces the tools, approache

This new volume discusses how integrating IoT devices and cyber-physical systems can help society by providing multiple efficient and affordable services to users. It covers the various applications of IoT-based cyber-physical systems, such as satellite imaging in relation to climate change, industrial control systems, e-healthcare applications, security uses, automotive and traffic monitoring and control, urban smart city planning, and more. The authors also outline the methods, tools, and algorithms for IoT-based cyber-physical systems and explore the integration of machine learning, blockchain, and Internet of Things-based cloud applications. With the continuous emerging new technologies and trends in IoT technology and CPS, this volume will be a helpful resource for scientists, researchers, industry professionals, faculty and students, and others who wish to keep abreast of new developments and new challenges for sustainable development in Industry 4.0.

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.

The rise of the Internet of Things (IoT) and Artificial Intelligence (AI) leads to the emergence of edge computing systems that push the training and deployment of AI models to the edge of networks for reduced bandwidth cost, improved responsiveness, and better privacy protection, allowing for the ubiquitous AI that can happen anywhere and anytime. Motivated by the above trend, this book introduces a new computing paradigm, the Social Edge Computing (SEC), that empowers human-centric edge intelligent applications by revolutionizing the computing, intelligence, and the training of the AI models at the edge. The SEC paradigm introduces a set of critical human-centric challenges such as the rational nature of edge device owners, pronounced heterogeneity of the edge devices, real-time AI at the edge, human and AI interaction, and the privacy of the edge users. The book addresses these challenges by presenting a series of principled models and systems that enable the confluence of the computing capabilities of devices and the domain knowledge of the people, while explicitly addressing the unique concerns and constraints from humans. Compared to existing books in the field of edge computing, the vision of this book is unique: we focus on the social edge computing (SEC), an emerging paradigm at the intersection of edge computing, AI, and social computing. This book discusses the unique vision, challenges and applications in SEC. To our knowledge, keeping humans in the loop of edge intelligence has not been systematically reviewed and studied in an existing book. The SEC vision generalizes the current machine-to-machine interactions in edge computing (e.g., mobile edge computing literature), and machine-to-AI interactions (e.g., edge intelligence literature) into a holistic human-machine-AI ecosystem.