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

IAES International Journal of Artificial Intelligence · Vol. 11 No. 1 (2022) · hlm. 50–64

DOI
10.11591/ijai.v11.i1.pp50-64
ISSN
2252-8938, 2089-4872
Terbit
2022-03-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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 metaheuristic 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.

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  • University of Technology Malaysia
  • Universiti Teknikal Malaysia Melaka
  • Universiti Malaysia Pahang Al-Sultan Abdullah
  • Universiti Malaysia Perlis
  • Universitas Ahmad Dahlan
  • Islamic Azad University Yasuj
  • Diponegoro University
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Rekan penulisOrganisasiArtikel bersama
Auzani JidinUniversiti Teknikal Malaysia Melaka39
Nik Rumzi Nik IdrisUniversity of Technology Malaysia31
Mohd Hatta JopriUniversiti Teknikal Malaysia Melaka18
Awang JusohUniversity of Technology Malaysia17
Mohammad JannatiUniversity of Technology Malaysia16
Mohd Junaidi Abdul AzizUniversity of Technology Malaysia16
Kasrul Abdul KarimUniversiti Teknikal Malaysia Melaka13
Mustafa ManapUniversiti Teknikal Malaysia Melaka12
Mohamed SalemUniversiti Sains Malaysia10
Lina HandayaniUniversitas Ahmad Dahlan10
Abdul Rahim AbdullahUniversiti Teknikal Malaysia Melaka9
Mahmoud ZadehbagheriIslamic Azad University Yasuj8
Siti Azura Ahmad TarusanUniversiti Teknikal Malaysia Melaka7
Nor Azizah Mohd YusoffUniversiti Teknikal Malaysia Melaka7
Shahrin Md AyobUniversity of Technology Malaysia7
Aiman Zakwan JidinUniversiti Teknikal Malaysia Melaka6
Mochammad FactaDiponegoro University6
Mohammad Faridun Naim TajuddinUniversiti Malaysia Perlis6
Ibrahim AlhamrouniUniversity of Kuala Lumpur6
M. Rahimi YusoffUniversiti Teknikal Malaysia Melaka5
Hendril Satrian PurnamaAfiliasi tidak diketahui5
Watra ArsadiandoAfiliasi tidak diketahui5
Azralmukmin AzmiUniversiti Malaysia Perlis5
Norjulia Mohamad NordinUniversity of Technology Malaysia5
Munawar Agus RiyadiDiponegoro University5
Imam Much Ibnu SubrotoSultan Agung Islamic University4
Muhammad AdoUniversity of Technology Malaysia4
Anggit PamungkasAfiliasi tidak diketahui4
Ibrahim Mohd AlsofyaniUniversity of Technology Malaysia4
Mohammad Javad KianiIslamic Azad University Yasuj4
Vahdat NazerianUniversity of Mazandaran4
Muhammad Zaid AihsanUniversiti Malaysia Perlis4
Chibuike Peter OhanuUniversity of Nigeria4

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification</dc:title>
	<dc:creator>Mohamad Aseri, Nur Azieta</dc:creator>
	<dc:creator>Ismail, Mohd Arfian</dc:creator>
	<dc:creator>Fakharudin, Abdul Sahli</dc:creator>
	<dc:creator>Ibrahim, Ashraf Osman</dc:creator>
	<dc:creator>Kasim, Shahreen</dc:creator>
	<dc:creator>Zakaria, Noor Hidayah</dc:creator>
	<dc:creator>Sutikno, Tole</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">COVID-19; Differential evolution; Fuzzy logic; Genetic algorithm; Meta-heuristic; Particle swarm optimization; TLBO Algorithm</dc:subject>
	<dc:description xml:lang="en-US">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 metaheuristic 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.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2022-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/21017</dc:identifier>
	<dc:identifier>10.11591/ijai.v11.i1.pp50-64</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 11, No 1: March 2022; 50-64</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v11.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/21017/13304</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>