Balkan Journal of Electrical and Computer Engineering 2147-284X 2147-284X MUSA YILMAZ Artificial Intelligence Yapay Zeka A Novel Approach Based to Neural Network and Flower Pollination Algorithm to Predict Number of COVID-19 Cases https://orcid.org/0000-0002-0091-3606 Baştemur Kaya Ceren NEVSEHIR UNIVERSITY https://orcid.org/0000-0001-8576-7750 Kaya Ebubekir NEVSEHIR UNIVERSITY 10 30 2021 9 4 327 336 05 04 2021 08 09 2021 Copyright © 2013, Balkan Journal of Electrical and Computer Engineering 2013 Balkan Journal of Electrical and Computer Engineering

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 used 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 found. 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.

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