Table 4. Hyperparameters configuration of the proposed classification models.

Model	Hyperparameters	Value
Multilayer Perceptron	Number of neurons in hidden layers (1–3), layer 4 and hidden layers (5–7)	512, 1024, 512
	Activation function used in hidden layers	ReLU
	Optimizer and learning rate	Adam and 0.0001
Convolutional Neural Network	Number of filters in Conv2D layers	32
	Stride in Conv2D layers	(1,1)
	Pool size in MaxPool2D layers	(2,2)
	Stride in MaxPool 2D layers	(2,2)
	Kernel size in Conv2D layers 1 and 2	(3,3) and (2,2)
	Number of neurons in Dense layers (1–4)	64, 128, 128, 64
	Activation function used in Dense layers	ReLU
	Optimizer and Learning Rate	Adam and 0.0001
LSTM	Number of memory cells in LSTM layers 1 and 2	64 and 128
	Number of neurons in Dense layers (1–3)	64, 256, 128
	Activation function used in LSTM layers 1 and 2	tanh
	Activation function used in Dense layers (1–3)	ReLU
	Optimizer and Learning Rate	Adam and 0.0001
ResNet-50	Number of neurons in Dense layers (1–6)	256, 128, 64, 512, 512, 512
	Activation function used in Dense layers (1–6)	ReLU
	Optimizer and Learning Rate	Adam and 0.0001
Efficient Net B0	Number of neurons in Dense layers (1–3)	256, 128, 64
	Activation function used in Dense layers (1–3)	ReLU
	Optimizer and Learning Rate	Adam and 0.0001