Artificial Intelligence and Machine Learning for COVID-19 [electronic resource] / edited by Fadi Al-Turjman.

9783030601881

1st ed. 2021.

Cham : Springer International Publishing : Imprint: Springer, 2021.

1 online resource (X, 266 p.) 105 illus., 91 illus. in color.

Access is available to the Yale community.

Access restricted by licensing agreement.

This book is dedicated to addressing the major challenges in fighting COVID-19 using artificial intelligence (AI) and machine learning (ML) - from cost and complexity to availability and accuracy. The aim of this book is to focus on both the design and implementation of AI-based approaches in proposed COVID-19 solutions that are enabled and supported by sensor networks, cloud computing, and 5G and beyond. This book presents research that contributes to the application of ML techniques to the problem of computer communication-assisted diagnosis of COVID-19 and similar diseases. The authors present the latest theoretical developments, real-world applications, and future perspectives on this topic. This book brings together a broad multidisciplinary community, aiming to integrate ideas, theories, models, and techniques from across different disciplines on intelligent solutions/systems, and to inform how cognitive systems in Next Generation Networks (NGN) should be designed, developed, and evaluated while exchanging and processing critical health information. Targeted readers are from varying disciplines who are interested in implementing the smart planet/environments vision via wireless/wired enabling technologies. Includes advances related to COVID-19 diagnosis and tracking through artificial intelligence and machine learning; Enriches the fields of AI and ML with new and innovative operational ideas aimed at aiding in efforts to combat and track COVID-19; Pertains to researchers, scientists, engineers, and practitioners in the field of computing and smart cities technologies.

Springer ENIN.

Books / Online

English

March 03, 2021