Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification
The world health organization (WHO) proclaimed the COVID-19, commonly known as the coronavirus disease 2019, was a pandemic in March 2020. When people are in close proximity to one another, the virus spreads mostly through the air. It causes some symptoms in the affected person. COVID-19 symptoms ar...
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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2022
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| Online Access: | http://eprints.utm.my/98695/1/NoorHidayahZakaria2022_ComparisonofMetaHeuristicAlgorithms.pdf |
| _version_ | 1796866671271477248 |
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| author | Mohamad Aseri, Nur Azieta Ismail, Mohd. Arfian Fakharudin, Abdul Sahli Ibrahim, Ashraf Osman Kasim, Shahreen Zakaria, Noor Hidayah Sutikno, Tole |
| author_facet | Mohamad Aseri, Nur Azieta Ismail, Mohd. Arfian Fakharudin, Abdul Sahli Ibrahim, Ashraf Osman Kasim, Shahreen Zakaria, Noor Hidayah Sutikno, Tole |
| author_sort | Mohamad Aseri, Nur Azieta |
| collection | ePrints |
| description | The world health organization (WHO) proclaimed the COVID-19, commonly known as the coronavirus disease 2019, was a pandemic in March 2020. When people are in close proximity to one another, the virus spreads mostly through the air. It causes some symptoms in the affected person. COVID-19 symptoms are quite variable, ranging from none to severe sickness. As a result, the fuzzy method is seen favourably as a tool for determining the severity of a person’s COVID-19 sickness. However, when applied to a large situation, manually generating a fuzzy parameter is challenging. This could be because of the identification of a large number of fuzzy parameters. A mechanism, such as an automatic procedure, is consequently required to identify the right fuzzy parameters. The meta-heuristic algorithm is regarded as a viable strategy. Five meta-heuristic algorithms were analyzed and utilized in this article to classify the severity of COVID-19 sickness data. The performance of the five meta-heuristic algorithms was evaluated using the COVID-19 symptoms dataset. The COVID-19 symptom dataset was created in accordance with WHO and the Indian ministry of health and family welfare criteria. The findings provide the average classification accuracy for each approach. |
| first_indexed | 2024-03-05T21:15:39Z |
| format | Article |
| id | utm.eprints-98695 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:15:39Z |
| publishDate | 2022 |
| publisher | Institute of Advanced Engineering and Science |
| record_format | dspace |
| spelling | utm.eprints-986952023-02-02T05:48:09Z http://eprints.utm.my/98695/ Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification Mohamad Aseri, Nur Azieta Ismail, Mohd. Arfian Fakharudin, Abdul Sahli Ibrahim, Ashraf Osman Kasim, Shahreen Zakaria, Noor Hidayah Sutikno, Tole QA75 Electronic computers. Computer science The world health organization (WHO) proclaimed the COVID-19, commonly known as the coronavirus disease 2019, was a pandemic in March 2020. When people are in close proximity to one another, the virus spreads mostly through the air. It causes some symptoms in the affected person. COVID-19 symptoms are quite variable, ranging from none to severe sickness. As a result, the fuzzy method is seen favourably as a tool for determining the severity of a person’s COVID-19 sickness. However, when applied to a large situation, manually generating a fuzzy parameter is challenging. This could be because of the identification of a large number of fuzzy parameters. A mechanism, such as an automatic procedure, is consequently required to identify the right fuzzy parameters. The meta-heuristic algorithm is regarded as a viable strategy. Five meta-heuristic algorithms were analyzed and utilized in this article to classify the severity of COVID-19 sickness data. The performance of the five meta-heuristic algorithms was evaluated using the COVID-19 symptoms dataset. The COVID-19 symptom dataset was created in accordance with WHO and the Indian ministry of health and family welfare criteria. The findings provide the average classification accuracy for each approach. Institute of Advanced Engineering and Science 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/98695/1/NoorHidayahZakaria2022_ComparisonofMetaHeuristicAlgorithms.pdf Mohamad Aseri, Nur Azieta and Ismail, Mohd. Arfian and Fakharudin, Abdul Sahli and Ibrahim, Ashraf Osman and Kasim, Shahreen and Zakaria, Noor Hidayah and Sutikno, Tole (2022) Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification. IAES International Journal of Artificial Intelligence, 11 (1). pp. 50-64. ISSN 2089-4872 http://dx.doi.org/10.11591/ijai.v11.i1.pp50-64 DOI: 10.11591/ijai.v11.i1.pp50-64 |
| spellingShingle | QA75 Electronic computers. Computer science Mohamad Aseri, Nur Azieta Ismail, Mohd. Arfian Fakharudin, Abdul Sahli Ibrahim, Ashraf Osman Kasim, Shahreen Zakaria, Noor Hidayah Sutikno, Tole Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification |
| title | Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification |
| title_full | Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification |
| title_fullStr | Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification |
| title_full_unstemmed | Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification |
| title_short | Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification |
| title_sort | comparison of meta heuristic algorithms for fuzzy modelling of covid 19 illness severity classification |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/98695/1/NoorHidayahZakaria2022_ComparisonofMetaHeuristicAlgorithms.pdf |
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