DESIGN OF NEURAL PREDICTORS FOR PREDICTING AND ANALYSING COVID-19 CASES IN DIFFERENT REGIONS
Küçük Resim Yok
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Acad Sciences Czech Republic, Inst Computer Science
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Nowadays, some unexpected viruses are affecting people with many troubles. COVID-19 virus is spread in the world very rapidly. However, it seems that predicting cases and death fatalities is not easy. Artificial neural networks are employed in many areas for predicting the system's parameters in simulation or real-time approaches. This paper presents the design of neural predictors for analysing the cases of COVID-19 in three countries. Three countries were selected because of their different regions. Especially, these major countries' cases were selected for predicting future effects. Furthermore, three types of neural network predictors were employed to analyse COVID-19 cases. NAR-NN is one of the pro-posed neural networks that have three layers with one input layer neurons, hidden layer neurons and an output layer with fifteen neurons. Each neuron consisted of the activation functions of the tan-sigmoid. The other proposed neural network, ANFIS, consists of five layers with two inputs and one output and ARIMA uses four iterative steps to predict. The proposed neural network types have been selected from many other types of neural network types. These neural network structures are feed-forward types rather than recurrent neural networks. Learning time is better and faster than other types of networks. Finally, three types of neural pre-dictors were used to predict the cases. The R2 and MSE results improved that three types of neural networks have good performance to predict and analyse three region cases of countries.
Açıklama
Anahtar Kelimeler
COVID-19, NAR-NN, ANFIS, ARIMA, prediction, modelling of the pandemic
Kaynak
Neural Network World
WoS Q Değeri
Q4
Scopus Q Değeri
Q4
Cilt
32
Sayı
5
