“…The hyperparameters will be defined as all CNN features selected in the optimization process. The following features are considered as hyperparameters [26]: number of convolution layers, number of neurons in each layer, number of fully connected layers, number of filters in convolution layer and their size, batch normalization [29], activation function type, pooling type, pooling window size, and probability of dropout [28]. Additionally, the batch size X as well as the learning parameters: learning factor, cooldown, and patience, are treated as hyperparameters, and their values were optimized simultaneously with the others.…”