Comparison of meta-heuristic algorithms for fuzzy modelling of covid-19 illness’ severity classification

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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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
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