Bøger i Intelligent Systems Reference Library serien

Artificial Intelligence (AI) has transformed many aspects of our daily activities. Health and well-being of humans stand as one of the key domains where AI has achieved significant progresses, saving time, costs, and potentially lives, as well as fostering economic resilience, particularly under the COVID-19 pandemic environments. This book is a sequel of the Handbook of Artificial Intelligence in Healthcare. The first volume of the Handbook is dedicated to present advances and applications of AI methodologies in several specific areas, i.e., signal, image, and video processing as well as information and data analytics. In this second volume of the Handbook, general practicality challenges and future prospects of AI methodologies pertaining to healthcare and related domains are presented in Part 1 and Part 2, respectively.It is envisaged that the selected studies will provide readers a general perspective on the issues, challenges, and opportunities in designing, developing, and implementing AI-based tools and solutions in the healthcare sector, bringing benefits to transform and advance health and well-being development of humans..

In these applications, the cloud computing provides a common workplace for IoT and big data, big data provides data analytics technology and IoT provides the source of data.

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.

Big data and data science are transforming our world today in ways we could not have imagined at the beginning of the twenty-first century.

This book is a truly comprehensive, timely, and very much needed treatise on the conceptualization of analysis, and design of contactless & multimodal sensor-based human activities, behavior understanding & intervention.

This handbook on Artificial Intelligence (AI) in healthcare consists of two volumes. The first volume is dedicated to advances and applications of AI methodologies in specific healthcare problems, while the second volume is concerned with general practicality issues and challenges and future prospects in the healthcare context.

The book discusses Explainable (XAI) and Responsive Artificial Intelligence (RAI) for biomedical and healthcare applications. It will discuss the advantages in dealing with big and complex data by using explainable AI concepts in the field of biomedical sciences. The book explains both positive as well as negative findings obtained by explainable AI techniques. It features real time experiences by physicians and medical staff for applied deep learning based solutions. The book will be extremely useful for researchers and practitioners in advancing their studies.

This book focuses on research and development aspects of building data analytics workflows that address various challenges of e-learning applications.This book represents a guideline for building a data analysis workflow from scratch. Each chapter presents a step of the entire workflow, starting from an available dataset and continuing with building interpretable models, enhancing models, and tackling aspects of evaluating engagement and usability. The related work shows that many papers have focused on machine learning usage and advancement within e-learning systems. However, limited discussions have been found on presenting a detailed complete roadmap from the raw dataset up to the engagement and usability issues. Practical examples and guidelines are provided for designing and implementing new algorithms that address specific problems or functionalities. This roadmap represents a potential resource for various advances of researchers and practitioners in educational data mining and learning analytics.

Dieses forschungsorientierte Buch enthält wichtige Beiträge zur Gestaltung der digitalen Transformation. Es umfasst die folgenden Hauptabschnitte in 20 Kapiteln:- Digitale Transformation- Digitales Geschäft- Digitale Architektur- Entscheidungshilfe- Digitale Anwendungen Es konzentriert sich auf digitale Architekturen für intelligente digitale Produkte und Dienstleistungen und ist eine wertvolle Ressource für Forscher, Doktoranden, Postgraduierte, Absolventen, Studenten, Akademiker und Praktiker, die sich für die digitale Transformation interessieren.

This book provides essential future directions for IoT and Big Data research. Thus, there is a new global interest in these applications in various domains such as health, agriculture, energy, security and retail.

Deep Architectures in Visual Transfer Learning.- Deep Reinforcement Learning: A New Frontier in Computer Vision Research.- Deep Learning for Data-driven Predictive Maintenance.- Multi-Criteria Fuzzy Goal Programming under Multi-Uncertainty.- Skeleton-based Human Action Recognition on Large-Scale Datasets.