UID:
almahu_9949315537702882
Format:
VIII, 248 p. 85 illus., 59 illus. in color.
,
online resource.
Edition:
1st ed. 2022.
ISBN:
9783031045974
Series Statement:
Studies in Computational Intelligence, 1023
Content:
This Springer book provides a perfect platform to submit chapters that discuss the prospective developments and innovative ideas in artificial intelligence and machine learning techniques in the diagnosis of COVID-19. COVID-19 is a huge challenge to humanity and the medical sciences. So far as of today, we have been unable to find a medical solution (Vaccine). However, globally, we are still managing the use of technology for our work, communications, analytics, and predictions with the use of advancement in data science, communication technologies (5G & Internet), and AI. Therefore, we might be able to continue and live safely with the use of research in advancements in data science, AI, machine learning, mobile apps, etc., until we can find a medical solution such as a vaccine. We have selected eleven chapters after the vigorous review process. Each chapter has demonstrated the research contributions and research novelty. Each group of authors must fulfill strict requirements.
Note:
Uncertainty Propagation and Salient Features Maps in Deep Learning Architectures for Supporting Covid-19 Diagnosis -- A review of Machine Learning techniques to detect and treat COVID-19 using EHR data -- Machine Learning-Based Emerging Technologies in the Post Pandemic Scenario -- Biomedical Data Driven COVID-19 Prediction using Machine Learning Approach -- Impact of COVID-19 on Indian Agriculture -- Coordination of Covid-19 vaccination: an optimization problem and related tools derived from telecommunications systems.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783031045967
Additional Edition:
Printed edition: ISBN 9783031045981
Additional Edition:
Printed edition: ISBN 9783031045998
Language:
English
Subjects:
Computer Science
DOI:
10.1007/978-3-031-04597-4
URL:
https://doi.org/10.1007/978-3-031-04597-4
URL:
Volltext
(URL des Erstveröffentlichers)
