Kwok Tai Chui – författare

Approx.428 pagesApprox.428 pages

Artificial Intelligence and Big Data Analytics for Smart Healthcare serves as a key reference for practitioners and experts involved in healthcare as they strive to enhance the value added of healthcare and develop more sustainable healthcare systems. It brings together insights from emerging sophisticated information and communication technologies such as big data analytics, artificial intelligence, machine learning, data science, medical intelligence, and, by dwelling on their current and prospective applications, highlights managerial and policymaking challenges they may generate.

The book is split into five sections: big data infrastructure, framework and design for smart healthcare; signal processing techniques for smart healthcare applications; business analytics (descriptive, diagnostic, predictive and prescriptive) for smart healthcare; emerging tools and techniques for smart healthcare; and challenges (security, privacy, and policy) in big data for smart healthcare. The content is carefully developed to be understandable to different members of healthcare chain to leverage collaborations with researchers and industry.

Presents a holistic discussion on the new landscape of data driven medical technologies including Big Data, Analytics, Artificial Intelligence, Machine Learning, and Precision Medicine Discusses such technologies with case study driven approach with reference to real world application and systems, to make easier the understanding to the reader not familiar with them Encompasses an international collaboration perspective, providing understandable knowledge to professionals involved with healthcare to leverage productive partnerships with technology developers

The integration of fog computing with the resource-limited Internet of Things (IoT) network formulates the concept of the fog-enabled IoT system. Due to a large number of IoT devices, the IoT is a main source of Big Data. A large volume of sensing data is generated by IoT systems such as smart cities and smart-grid applications. A fundamental research issue is how to provide a fast and efficient data analytics solution for fog-enabled IoT systems. Big Data Analytics in Fog-Enabled IoT Networks: Towards a Privacy and Security Perspective focuses on Big Data analytics in a fog-enabled-IoT system and provides a comprehensive collection of chapters that touch on different issues related to healthcare systems, cyber-threat detection, malware detection, and the security and privacy of IoT Big Data and IoT networks.This book also emphasizes and facilitates a greater understanding of various security and privacy approaches using advanced artificial intelligence and Big Data technologies such as machine and deep learning, federated learning, blockchain, and edge computing, as well as the countermeasures to overcome the vulnerabilities of the fog-enabled IoT system.

The integration of fog computing with the resource-limited Internet of Things (IoT) network formulates the concept of the fog-enabled IoT system. Due to a large number of IoT devices, the IoT is a main source of Big Data. A large volume of sensing data is generated by IoT systems such as smart cities and smart-grid applications. A fundamental research issue is how to provide a fast and efficient data analytics solution for fog-enabled IoT systems. Big Data Analytics in Fog-Enabled IoT Networks: Towards a Privacy and Security Perspective focuses on Big Data analytics in a fog-enabled-IoT system and provides a comprehensive collection of chapters that touch on different issues related to healthcare systems, cyber-threat detection, malware detection, and the security and privacy of IoT Big Data and IoT networks.This book also emphasizes and facilitates a greater understanding of various security and privacy approaches using advanced artificial intelligence and Big Data technologies such as machine and deep learning, federated learning, blockchain, and edge computing, as well as the countermeasures to overcome the vulnerabilities of the fog-enabled IoT system.

Edge computing and intelligence is a new technical discipline that researches and develops theories, methods, technologies, and application systems for enabling fast and efficient processing and intelligent decision-making using the big data generated from the Internet of Things (IoT) system. The edge computing and intelligence–enabled IoT system helps in alleviating the resource limitations of IoT devices and facilitates large-scale deployment of IoT systems for different applications such as smart cities, smart grids, smart homes, smart healthcare systems, smart video surveillance, and e-healthcare.Due to the deployment of the IoT devices in the open environment and their interaction with other IoT devices through unstable and unreliable wireless channels, as well as the placement of edge nodes near IoT systems, IoT devices offer many chances for malicious attackers to compromise them through collusion attacks, denial-of-service attacks, and many other types of attacks. Analysis of the network traffic and IoT data at the edge nodes using the latest artificial intelligence techniques will address a number of issues, such as anomaly detection in IoT data, data poisoning attacks, and other security and privacy issues related to IoT systems. In this book, we will provide an overview of the fundamental concepts of edge computing, edge intelligence using advanced machine- and deep-learning models, the architecture of edge computing–based IoT systems, and edge computing–based cyber threat intelligence solutions for IoT systems.

Collaboration in business allows for equitable opportunities and inclusive growth as the economy rises while also permitting partnering organizations to adopt and utilize the latest successful practices and management. However, a market in stasis may require a displacement in order to allow businesses to grow and create new alliances and partnerships toward a shared economy. There is a need for studies that seek to understand the necessity of market disruption and the best supervisory methods for remaining relevant and profitable in a time of change.Managerial Practices and Disruptive Innovation in Asia is an essential reference source that explores successful executive behavior and business operations striving toward a more inclusive economy. Featuring research on topics such as employee welfare, brand orientation, and entrepreneurship, this publication is ideally designed for human resources developers, policymakers, IT specialists, economists, executives, managers, corporate directors, information technologists, and academicians seeking current research focusing on innovative business factors and sustainable economies in Asia.

Businesses in the Asia-Pacific communities provide enormous opportunities for local entrepreneurs to develop and collectively collaborate with other economies. However, several challenges and success factors exist for effective business operations in the region.Innovative Management and Business Practices in Asia is a collection of innovative research that enhances understanding and collaboration in business, management, and technology in Asia for the present and in the future. While highlighting topics including corporate culture, international trade, and business administration, this book is ideally designed for managers, executives, CEOs, board members, corporate professionals, managing directors, deans, decision makers, professors, researchers, policymakers, industry practitioners, and students.