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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mohammed Adam Kunna Azrag
Universiti Teknologi MARA

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Review: machine and deep learning methods in Malaysia for COVID-19 Mohammed Adam Kunna Azrag; Jasni Mohamad Zain; Tuty Asmawaty Abdul Kadir; Marina Yusoff; Tao Hai
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp514-520

Abstract

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.
Co-Authors Jasni Mohamad Zain Marina Yusoff Tao Hai Tuty Asmawaty Abdul Kadir