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://umpir.ump.edu.my/id/eprint/33572/ http://umpir.ump.edu.my/id/eprint/33572/1/Comparison%20of%20meta-heuristic%20algorithms%20for%20fuzzy%20modelling%20of%20covid-19%20illness%E2%80%99.pdf |
| _version_ | 1848824288296042496 |
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| author | Nur Azieta, Mohamad Aseri Mohd Arfian, Ismail Abdul Sahli, Fakharudin Ashraf Osman, Ibrahim Shahreen, Kasim Noor Hidayah, Zakaria Sutikno, Tole |
| author_facet | Nur Azieta, Mohamad Aseri Mohd Arfian, Ismail Abdul Sahli, Fakharudin Ashraf Osman, Ibrahim Shahreen, Kasim Noor Hidayah, Zakaria Sutikno, Tole |
| author_sort | Nur Azieta, Mohamad Aseri |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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 | 2025-11-15T03:10:39Z |
| format | Article |
| id | ump-33572 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:10:39Z |
| publishDate | 2022 |
| publisher | Institute of Advanced Engineering and Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-335722022-04-15T04:02:13Z http://umpir.ump.edu.my/id/eprint/33572/ Comparison of meta-heuristic algorithms for fuzzy modelling of covid-19 illness’ severity classification Nur Azieta, Mohamad Aseri Mohd Arfian, Ismail Abdul Sahli, Fakharudin Ashraf Osman, Ibrahim Shahreen, Kasim Noor Hidayah, Zakaria Sutikno, Tole QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TJ Mechanical engineering and machinery 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 2022-03 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/33572/1/Comparison%20of%20meta-heuristic%20algorithms%20for%20fuzzy%20modelling%20of%20covid-19%20illness%E2%80%99.pdf Nur Azieta, Mohamad Aseri and Mohd Arfian, Ismail and Abdul Sahli, Fakharudin and Ashraf Osman, Ibrahim and Shahreen, Kasim and Noor Hidayah, Zakaria 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. (Published) https://doi.org/10.11591/ijai.v11.i1.pp50-64 https://doi.org/10.11591/ijai.v11.i1.pp50-64 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TJ Mechanical engineering and machinery Nur Azieta, Mohamad Aseri Mohd Arfian, Ismail Abdul Sahli, Fakharudin Ashraf Osman, Ibrahim Shahreen, Kasim Noor Hidayah, Zakaria 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 QA76 Computer software T Technology (General) TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/33572/ http://umpir.ump.edu.my/id/eprint/33572/ http://umpir.ump.edu.my/id/eprint/33572/ http://umpir.ump.edu.my/id/eprint/33572/1/Comparison%20of%20meta-heuristic%20algorithms%20for%20fuzzy%20modelling%20of%20covid-19%20illness%E2%80%99.pdf |