Ahmed, Marzia and Mohd Herwan, Sulaiman and Ahmad Johari, Mohamad (2023) Improved barnacle mating optimizer-based least square support vector machine to predict COVID-19 confirmed cases with total vaccination. Cybernetics and Information Technologies, 23 (1). 125 -140. ISSN 1311-9702. (Published)
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Abstract
Every country must have an accurate and efficient forecasting model to avoid and manage the epidemic. This paper suggests an upgrade to one of the evolutionary algorithms inspired by nature, the Barnacle Mating Optimizer (BMO). First, the exploration phase of the original BMO is enhanced by enforcing and replacing the sperm cast equation through Levy flight. Then, the Least Square Support Vector Machine (LSSVM) is partnered with the improved BMO (IBMO). This hybrid approach, IBMO-LSSVM, has been deployed effectively for time-series forecasting to enhance the RBF kernel-based LSSVM model since vaccination started against COVID-19 in Malaysia. In comparison to other well-known algorithms, our outcomes are superior. In addition, the IBMO is assessed on 19 conventional benchmarks and the IEEE Congress of Evolutionary Computation Benchmark Test Functions (CECC06, 2019 Competition). In most cases, IBMO outputs are better than comparison algorithms. However, in other circumstances, the outcomes are comparable.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Barnacles mating optimizer; COVID-19 confirmed case; Least Square Support Vector Machine (LSSVM); Levy flight; Meta-heuristic; Total vaccinations |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 26 Jun 2023 02:56 |
| Last Modified: | 26 Jun 2023 02:56 |
| URI: | http://umpir.ump.edu.my/id/eprint/37860 |
| Download Statistic: | View Download Statistics |
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