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)
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Comparison of meta-heuristic algorithms for fuzzy modelling of covid-19 illness’.pdf Available under License Creative Commons Attribution Share Alike. Download (521kB) | Preview |
Abstract
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.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Covid-19; Differential evolution; Fuzzy logic; Genetic algorithm; Meta-heuristic; Particle swarm optimization; TLBO Algorithm |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TJ Mechanical engineering and machinery |
| Faculty/Division: | Faculty of Computing |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 15 Apr 2022 04:02 |
| Last Modified: | 15 Apr 2022 04:02 |
| URI: | http://umpir.ump.edu.my/id/eprint/33572 |
| Download Statistic: | View Download Statistics |
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