Document Type
Article
Source of Publication
IEEE Access
Publication Date
1-1-2020
Abstract
© 2013 IEEE. Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses.
DOI Link
ISSN
Publisher
Institute of Electrical and Electronics Engineers Inc.
Volume
8
First Page
128776
Last Page
128795
Disciplines
Computer Sciences | Education | Medicine and Health Sciences
Keywords
artificial intelligence, Big data, cloud computing, deep learning, the IoT
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Hussain, Adedoyin Ahmed; Bouachir, Ouns; Al-Turjman, Fadi; and Aloqaily, Moayad, "AI Techniques for COVID-19" (2020). All Works. 384.
https://zuscholars.zu.ac.ae/works/384
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series