Machine learning applications to Covid-19: a state-of-the-art survey

Date
2022
Authors
Kamalov, Firuz
Cherukuri, Aswani Kumar
Thabtah, Fadi
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
There exists a large and rapidly growing body of literature related to applications of machine learning to Covid-19. Given the substantial volume of research, there is a need to organize and categorize the literature. In this paper, we provide the most up-to-date review as of the beginning of 2022. We propose an application-based taxonomy to group the existing literature and provide an analysis of the research in each category. We discuss the progress as well as the pitfalls of the existing research, and propose keys for improvement. © 2022 IEEE.
Description
This conference paper is not available at CUD collection. The version of scholarly record of this paper is published in 2022 Advances in Science and Engineering Technology International Conferences (ASET) (2022), available online at: https://doi.org/10.1109/ASET53988.2022.9734959
Keywords
CNN, Covid-19, deep learning, LSTM, machine learning, review
Citation
Kamalov, F., Cherukuri, A. K., & Thabtah, F. (2022). Machine learning applications to covid-19: A state-of-the-art survey. 2022 Advances in Science and Engineering Technology International Conferences (ASET). https://doi.org/10.1109/ASET53988.2022.9734959