Table 2. Hyperparameters and layer configurations for the CNN model.

Type of Layer	Tuning hyperparameter	Value
Convolutional	—	—
Convolutional	dropout	[0.00, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50]
Convolutional	—	—
Convolutional	number of filters	[32, 64]
Max Pooling	dropout	[0.00, 0.50, 0.10, 0.15, 0.20]
Flatten	—	—
Dense	- units	[32, 64, 96….512]
	-activation	[relu, tanh, sigmoid]
Dropout	rate	[0.00, 0.50, 0.10, 0.15, 0.20]
Adam optimization compile	learning rate	min − value = 1e−4
		max − value = 1e−2
		sampling = LOG