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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
  • Universiti Teknologi MARA
  • Universiti Malaysia Pahang Al-Sultan Abdullah
  • Qiannan Normal College for Nationalities
  • Nanchang Institute of Science and Technology

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations

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.

Original languageEnglish
Pages (from-to)514-520
Number of pages7
JournalIndonesian Journal of Electrical Engineering and Computer Science
Volume31
Issue number1
DOIs
StatePublished - Jul 2023
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • COVID-19
  • Deep learning
  • Global pandemic
  • Machine learning
  • Vaccine

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