Before training, weight parameters in the model were randomly initialized using the Xavier initialization method, which involves the use of uniformly distributed weights with a standard deviation depends on the number of parameters in a layer, in order to alleviate the vanishing gradient problem. Once the training began, the Adam optimizer was used to minimize the loss function to update weight parameters. The loss here is proportional to the mean squared error between the predicted and experimental values. To minimize overfitting, additional dropout layers were implemented after the convolution layer and each hidden layer. Other hyper-parameters include the batch size of 999 and the learning rate of 0.001. The early stopping method was used to obtain the best trained model. All models were implemented using Keras ().