Chang V. AI and Machine Learning Methods in COVID-19...2021 torrent

Textbook in PDF format

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.
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
Covid-19 Face Mask Detection Using Deep Learning Techniques
Computational Intelligence in Identification of Some FDA Approved Drug Compounds for Treatment of COVID-19
Biomedical Data Driven COVID-19 Prediction Using Machine Learning Approach
Vaccination, Lock-Down, Measures and Time-Varying Reproduction Number Based Estimation and Control of COVID-19 Outbreak in Turkey
Diabetes Analysis with a Dataset Using Machine Learning
Impact of Covid-19 on Indian Agriculture
COVID-19 Patients Management and Triaging Using Machine Learning Techniques
Coordination of Covid-19 Vaccation:​ An Optimization Problem and Related Tools Derived from Telecommunicatio​ns Systems
Christophe Gaie and Markus Muec