Optimized Extreme Learning Machine for Forecasting Confirmed Cases of COVID-19
Ahmed Mudheher Hasan, Aseel Ghazi Mahmoud, Zainab Hasan, Ministry of Health and Environment of Iraq · International journal of intelligent engineering and systems · 2021
Recently, a new challenge to the researchers has been emerged due to the spread of a new outbreak called novel corona-virus (COVID-19) to portend the confirmed cases.Since discovering the first certain cases in Wuhan, China, the COVID-19 has been widely spreading and expanding to other provinces and other countries via travellers into various countries around the world.COVID-19 virus is not a problem of only developing countries, but also of developed countries.Artificial Intelligence (AI) techniques can be profitable to predict such; parameters, risks and influence of an outbreak.Therefore, accurate prognosis can be helpful to control the spread of such viruses and provide crucial information for identifying the type of virus interventions and intensity.Here are develop an intelligent model depicting COVID-19 transmission and resulting confirmed cases.The epidemic curve of COVID-19 cases was modelled.The main key idea of predicting the confirmed cases is based on two factors 1) cumulative number of confirmed cases and 2) the daily confirmed cases instead of using only one factor as previous research.In this study, a comparison between different intelligent techniques has been conducted.To assess the effectiveness of these intelligent models, a recorded data of 5 months has been used for training in various countries.Results obtained show the superiority of ELM model in accurate prediction and outperforms other intelligent techniques.We have used a Social Spider Optimization (SSO) method to optimize the ELM parameters.The prediction results show the superiority of the proposed intelligent predictors with accuracy greater than 93%.Therefore, medical personnel can take defensive steps earlier.