Review : Machine and deep learning methods in Malaysia for COVID-19

Mohammed Adam Kunna, Azrag and Jasni, Mohamad Zain and Tuty Asmawaty, Abdul Kadir and Marina, Yusoff and Hai, Tao (2023) Review : Machine and deep learning methods in Malaysia for COVID-19. Indonesian Journal of Electrical Engineering and Computer Science, 31 (1). pp. 514-520. ISSN 2502-4752. (Published)

Review_Machine and deep learning methods in Malaysia for COVID-19.pdf
Available under License Creative Commons Attribution Share Alike.

Download (227kB) | Preview


The global pandemic of the coronavirus disease COVID-19 has impacted a variety of operations. This dilemma is also attributable to the lockdown measures taken by the afflicted nations. The entire or partial shutdown enacted by nations across the globe affected the majority of hospitals and clinics until the pandemic was contained. The judgements made by the authorities of each impacted nation vary based on a number of variables, including the nation's severity of reported cases, the availability of vaccines, beds in intensive care unit (ICU), staff number, patient number, and medicines. Consequently, this work offers a thorough analysis of the most recent machine learning (ML) and deep learning (DL) approaches for COVID-19 that can assist the medical field in offering quick and exact COVID-19 diagnosis in Malaysia. This research aims to review the machine learning and deep learning methods that were used to help diagnose COVID-19 in Malaysia.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: COVID-19; Deep learning; Global pandemic; Machine learning; Vaccine
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Faculty/Division: Faculty of Computing
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 04 Sep 2023 06:36
Last Modified: 04 Sep 2023 06:36
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item