Hybrid multi-verse optimizer for covid19 confirmed cases prediction: cases in Malaysia - Zuriani, Mustaffa; Mohd Herwan, Sulaiman; Bariah, Yusob (IEEE, 2021)
The year of 2019 ended with shocking news when the World Health Organization (WHO) was at first notified by the pneumonia cases by an unknown source in Wuhan City, China. These cases are later appalling the world globally, known as Covid19. In no time, the virus spread all over the world, which consequently caused many countries to declare lockdown for some time. Even though there is time the number of cases is small and economic activities can operate in an optimum range, nonetheless until today, the number of cases is still up and down which finally caused many countries to face with sequence of wave, including Malaysia. Between the active activities of vaccination, the precaution steps in preventing the virus keep on spreading is vital. Concerning the highlighted issue, this paper demonstrates a hybrid Multi-verse Optimizer-Least Squares Support Vector Machines (MVO-LSSVM) for confirmed cases of Covid19 prediction in Malaysia. The proposed model was realized on Malaysia daily data of confirmed cases recorded by WHO. Compared against two comparable prediction models namely hybrid Grey Wolf Optimizer (GWO) with LSSVM (GWO-LSSVM) and Salp Swarm Algorithm with LSSVM (SSA-LSSVM), the obtained results demonstrated the superiority of MVO-LSSVM over the identified algorithms by producing lower prediction error rates.