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 proposed 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 predictors 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.

Due to travelling on railway systems; there are many gaps and problems in cross areas. Therefore; it is necessary and very important to establish intelligent crossing systems in such areas. On the other hand, it is not possible for trains to stop or brake immediately against an obstacle due to their high speed and inertia. For this reason, it is necessary to work on the safety/warning of the other main factors and necessities (pedestrians and vehicles) in level crossings. This experimental investigation is carried out by using an experimental real-time train and crossing systems. The main vibration parameters are analysed by using neural networks. First, the dynamics of the train-rail system related to level crossings are examined, and the vibrations created by the train on rails are measured at different speeds. Then three types of proposed neural networks predictors, Levenberg-Marquardt backpropagation (LMBP), scaled conjugate gradient backpropagation (SCGB) and BFGS quasi-Newton backpropagation (BFGS) are used to predict the vibration of the train-rail system. From the results, it is seen that the proposed LMBP is more suitable for analysing and predicting the vibration of the train-rail system. It is clear that the speeds of the trains approaching the level crossing can be estimated from the vibration of the trains on the rails.