COVID-19 Confirmed Cases Prediction in China Based on Barnacles Mating Optimizer-Least Squares Support Vector Machines

Mustaffa, Zuriani; Sulaiman, Mohd Herwan

doi:10.2478/cait-2021-0043

Abstract

The Covid19 has significantly changed the global landscape in every aspect including economy, social life, and many others. After almost two years of living with the pandemic, new challenges are faced by the research community. It may take some time before the world can be declared as totally safe from the virus. Therefore, prediction of Covid19 confirmed cases is vital for the sake of proper prevention and precaution steps. In this study, a hybrid Barnacles Mating Optimizer with Least Square Support Vector Machines (BMO-LSSVM) is proposed for prediction of Covid19 confirmed cases. The employed data are the Covid19 cases in China which are defined in daily periodicity. The BMO was utilized to obtain optimal values of LSSVM hyper-parameters. Later, with the optimized values of the hyper-parameters, the prediction task will be executed by LSSVM. Through the experiments, the study recommends the superiority of BMO-LSSVM over the other identified hybrid algorithms.

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