Ihsan, Mohd Yassin and Azlee, Zabidi and Megat Syahirul Amin, Ali and Rahimi, Baharom (2021) PSO-Optimized CoVID-19 MLP-NARX mortality prediction model. In: IEACon 2021 - 2021 IEEE Industrial Electronics and Applications Conference. 2nd IEEE Industrial Electronics and Applications Conference, IEACon 2021 , 22 - 23 November 2021 , Virtual, Online. pp. 308-312.. ISBN 978-172819253-6 (Published)
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Abstract
Mortality prediction models localized for Malaysia is limited, warranting a research gap to study further. A predictive model for CoVID-19 mortality prediction is presented in this paper. The model utilized the MLP-NARX structure. Parameters for the model were optimized using PSO. Prediction results yielded average MSE value of 8.1141× 10× {-7} with acceptable validation results.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | CoVID-19; Multi-Layer Perceptron (MLP); Nonlinear Auto-Regressive with Exogeneous Inputs (NARX); Particle Swarm Optimization (PSO) |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Faculty/Division: | Faculty of Computing |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 30 Oct 2024 04:40 |
| Last Modified: | 30 Oct 2024 04:40 |
| URI: | http://umpir.ump.edu.my/id/eprint/42393 |
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