Geospatial Data Science in Healthcare for Society 5.0


Book Description

The book introduces a variety of latest techniques designed to represent, enhance, and empower multi-disciplinary approaches of geographic information system (GIS), artificial intelligence (AI), deep learning (DL), machine learning, and cloud computing research in healthcare. It provides a unique compendium of the current and emerging use of geospatial data for healthcare and reflects the diversity, complexity, and depth and breadth of this multi-disciplinary area. This book addresses various aspects of how smart healthcare devices can be used to detect and analyze diseases. Further, it describes various tools and techniques to evaluate the efficacy, suitability, and efficiency of geospatial data for health-related applications. It features illustrative case studies, including future applications and healthcare challenges. This book is beneficial for computer science and engineering students and researchers, medical professionals, and anyone interested in using geospatial data in healthcare. It is also intended for experts, offering them a valuable retrospective and a global vision for the future, as well as for non-experts who are curious to learn about this important subject. The book presents an effort to draw how we can build health-related applications using geospatial big data and their subsequent analysis.




Emerging Trends, Techniques, and Applications in Geospatial Data Science


Book Description

With the emergence of smart technology and automated systems in today’s world, big data is being incorporated into many applications. Trends in data can be detected and objects can be tracked based on the real-time data that is utilized in everyday life. These connected sensor devices and objects will provide a large amount of data that is to be analyzed quickly, as it can accelerate the transformation of smart technology. The accuracy of prediction of artificial intelligence (AI) systems is drastically increasing by using machine learning and other probability and statistical approaches. Big data and geospatial data help to solve complex issues and play a vital role in future applications. Emerging Trends, Techniques, and Applications in Geospatial Data Science provides an overview of the basic concepts of data science, related tools and technologies, and algorithms for managing the relevant challenges in real-time application domains. The book covers a detailed description for readers with practical ideas using AI, the internet of things (IoT), and machine learning to deal with the analysis, modeling, and predictions from big data. Covering topics such as field spectra, high-resolution sensing imagery, and spatiotemporal data engineering, this premier reference source is an excellent resource for data scientists, computer and IT professionals, managers, mathematicians and statisticians, health professionals, technology developers, students and educators of higher education, librarians, researchers, and academicians.




Artificial Intelligence in Healthcare


Book Description

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data




Advances in Computational Intelligence for the Healthcare Industry 4.0


Book Description

In the dynamic environment of healthcare, the fusion of Computational Intelligence and Healthcare Industry 4.0 has enabled remarkable advancements in disease detection and analysis. However, a critical challenge persists – the limitations of current computational intelligence approaches in dealing with small sample sizes. This setback hampers the performance of these innovative models, hindering their potential impact on medical applications. As we stand at the crossroads of technological innovation and healthcare evolution, the need for a solution becomes paramount. Advances in Computational Intelligence for the Healthcare Industry 4.0 is a comprehensive guide addressing the very heart of this challenge. Designed for academics, researchers, healthcare professionals, and stakeholders in Healthcare Industry 4.0, this book serves as a source of innovation. It not only illuminates the complexities of computational intelligence in healthcare but also provides a roadmap for overcoming the limitations posed by small sample sizes. From fundamental principles to innovative concepts, this book offers a holistic perspective, shaping the future of healthcare through the lens of computational intelligence and Healthcare Industry 4.0.







Intelligent Systems in Digital Transformation


Book Description

This book states that intelligent digital transformation is the process of using artificial intelligence techniques in digital technologies such as machine learning, natural language processing, automation and robotics to transform existing non-digital business processes and services to meet with the evolving market and customer expectations. This book including 26 chapters, each written by their experts, focuses on revealing the reflection of digital transformation in our business and social life under emerging conditions through intelligent systems. Intelligent digital transformation examples from almost all sectors including health, education, manufacturing, tourism, insurance, smart cities, banking, energy and transportation are introduced by theory and applications. The intended readers are managers responsible for digital transformation, intelligent systems researchers, lecturers, and MSc and PhD students studying digital transformation.







Spatial Big Data Science


Book Description

Emerging Spatial Big Data (SBD) has transformative potential in solving many grand societal challenges such as water resource management, food security, disaster response, and transportation. However, significant computational challenges exist in analyzing SBD due to the unique spatial characteristics including spatial autocorrelation, anisotropy, heterogeneity, multiple scales and resolutions which is illustrated in this book. This book also discusses current techniques for, spatial big data science with a particular focus on classification techniques for earth observation imagery big data. Specifically, the authors introduce several recent spatial classification techniques, such as spatial decision trees and spatial ensemble learning. Several potential future research directions are also discussed. This book targets an interdisciplinary audience including computer scientists, practitioners and researchers working in the field of data mining, big data, as well as domain scientists working in earth science (e.g., hydrology, disaster), public safety and public health. Advanced level students in computer science will also find this book useful as a reference.




Society 5.0


Book Description

This open access book introduces readers to the vision on future cities and urban lives in connection with “Society 5.0”, which was proposed in the 5th Basic Science and Technology Plan by Japan’s national government for a technology-based, human-centered society, emerging from the fourth industrial revolution. The respective chapters summarize the findings and suggestions of joint research projects conducted by H-UTokyo Lab. Through the research collaboration and discussion, this book explores the future urban lives under the concept of “Society 5.0”, characterized by the key phrases of data-driven society, knowledge-intensive society, and non-monetary society, and suggests the directionality to which the concept should aim as Japan’s technology-led national vision. Written by Hitachi’s researchers as well as academics from a wide range of fields, including engineering, economics, psychology and philosophy at The University of Tokyo, the book is a must read for members of the general public interested in urban planning, students, professionals and researchers in engineering and economics.




Handbook of Research on Consumer Behavioral Analytics in Metaverse and the Adoption of a Virtual World


Book Description

Although there are various studies on theories and analytical techniques to address consumer behavior change in the current world, tracking consumer behavior change in the metaverse and the adoption of the metaverse remains a challenge that requires discussion. The advent of the metaverse will have a profound influence on consumer behavior, from how people make decisions and create brand connections to how they feel about their avatar embodiment and their purchases in the metaverse. The Handbook of Research on Consumer Behavioral Analytics in Metaverse and the Adoption of a Virtual World investigates the social, behavioral, and psychological factors that influence metaverse adoption. The focus then shifts to concepts, theories, and analytical approaches for detecting changes in consumer behavior in the metaverse. Covering topics such as e-commerce markets, user experience, and immersive technologies, this major reference work is an excellent resource for business executives, entrepreneurs, data analysts, marketers, advertisers, government officials, social media professionals, librarians, students and educators of higher education, researchers, and academicians.