Review : Machine and deep learning methods in Malaysia for COVID-19
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 u...
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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/38363/ http://umpir.ump.edu.my/id/eprint/38363/1/Review_Machine%20and%20deep%20learning%20methods%20in%20Malaysia%20for%20COVID-19.pdf |
| _version_ | 1848825489851940864 |
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| author | Mohammed Adam Kunna, Azrag Jasni, Mohamad Zain Tuty Asmawaty, Abdul Kadir Marina, Yusoff Hai, Tao |
| author_facet | Mohammed Adam Kunna, Azrag Jasni, Mohamad Zain Tuty Asmawaty, Abdul Kadir Marina, Yusoff Hai, Tao |
| author_sort | Mohammed Adam Kunna, Azrag |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:29:45Z |
| format | Article |
| id | ump-38363 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:29:45Z |
| publishDate | 2023 |
| publisher | Institute of Advanced Engineering and Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-383632023-09-04T06:36:01Z http://umpir.ump.edu.my/id/eprint/38363/ Review : Machine and deep learning methods in Malaysia for COVID-19 Mohammed Adam Kunna, Azrag Jasni, Mohamad Zain Tuty Asmawaty, Abdul Kadir Marina, Yusoff Hai, Tao QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) 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. Institute of Advanced Engineering and Science 2023-07 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/38363/1/Review_Machine%20and%20deep%20learning%20methods%20in%20Malaysia%20for%20COVID-19.pdf 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) https://doi.org/10.11591/ijeecs.v31.i1.pp514-520 https://doi.org/10.11591/ijeecs.v31.i1.pp514-520 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) Mohammed Adam Kunna, Azrag Jasni, Mohamad Zain Tuty Asmawaty, Abdul Kadir Marina, Yusoff Hai, Tao Review : Machine and deep learning methods in Malaysia for COVID-19 |
| title | Review : Machine and deep learning methods in Malaysia for COVID-19 |
| title_full | Review : Machine and deep learning methods in Malaysia for COVID-19 |
| title_fullStr | Review : Machine and deep learning methods in Malaysia for COVID-19 |
| title_full_unstemmed | Review : Machine and deep learning methods in Malaysia for COVID-19 |
| title_short | Review : Machine and deep learning methods in Malaysia for COVID-19 |
| title_sort | review : machine and deep learning methods in malaysia for covid-19 |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) |
| url | http://umpir.ump.edu.my/id/eprint/38363/ http://umpir.ump.edu.my/id/eprint/38363/ http://umpir.ump.edu.my/id/eprint/38363/ http://umpir.ump.edu.my/id/eprint/38363/1/Review_Machine%20and%20deep%20learning%20methods%20in%20Malaysia%20for%20COVID-19.pdf |