A novel approach based to neural network and flower pollination algorithm to predict number of Covıd-19 cases

Abstract

Flower Pollination Algorithm (FPA) is one of the popular heuristic algorithms that model pollination in the natural environment. Since 2012, it has been used in the solution of many difficult real world problems and successful results have been achieved. In this study, FPA is utilized for the training of neural network to predict number of COVID-19 cases. Namely, a model based on FPA and neural network (FPA_NN) is proposed. Within the scope of application, the data belonging to Turkey are estimated using the proposed model. A data set is created with the data between 1 April 2020 and 15 September 2020. A time series is created with these data and the nonlinear dynamic systems are obtained to model the problem. In order to determine the performance of the proposed model, RMSE (root mean square error) are used. The output graphs of the results are also examined in detail. The results are compared with neural network approaches based on PSO and HS. The Wilcoxon signed rank test is utilized to determine the significance of the results. The results show that FPA is generally more effective than PSO and HS to predict number of COVID-19 cases based on neural network