A novel hybrid evolutionary mating algorithm for Covid19 confirmed cases prediction based on vaccination

Ahmed, Marzia and Ahmad Johari, Mohamad and Rahman, Mostafijur and Mohd Herwan, Sulaiman and Abul Kashem, Mohammod (2023) A novel hybrid evolutionary mating algorithm for Covid19 confirmed cases prediction based on vaccination. In: 2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023, 16 - 17 June 2023 , Gazipur, Bangladesh. pp. 1-6.. ISBN 979-8-3503-1600-1

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Microorganisms may cause illness when they enter the body, multiply, and spread to other parts. The rapid spread of COVID-19 to neighboring countries is examined in this research. Anticipating a positive COVID-19 occurrence helps in determining risks and creating countermeasures. As a result, developing robust mathematical models with small error margins for predictions is crucial. Based on these findings, a combined method of evaluating confirmed cases of COVID-19 with universal immunization is recommended. First, the best hyperparameter values of the RBF kernel-based LSSVM (least square support vector machine) were determined using the most recent Evolutionary Mating Algorithm (EMA). After that, LSSVM will complete the task of prediction. This hybrid method has been utilized for time series forecasting in Malaysia since the country's immunization program against COVID-19 got underway. We evaluate our results next to those of well-known methodologies in nature-inspired metaheuristics.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Metaheuristics; Optimization; Prediction; Machine Learning; Covid-19 case
Subjects: R Medicine > RA Public aspects of medicine
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: 08 Nov 2023 03:27
Last Modified: 08 Nov 2023 03:27
URI: http://umpir.ump.edu.my/id/eprint/37861
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