Table 1.

Proposed Model Layer Hyperparameter Details.

Layers	Hyperparameter Settings	Output Shape
Input_1	shape = (31)	(31)
Embedding	Input dim = 22
	Output dim = 32	(31, 32)
	Input shape = (31 )
LSTM	units = 32
	Kernal reg = L2 (1 × 10${}^{-4}$)	(31, 32)
	Recurrent reg = L2 (1 × 10${}^{-4}$)
	Bias reg = L2 (1 × 10${}^{-4}$)
Dropout	Rate = 0.2	(31, 32)
MaxPooling1D	Pool size = 2	(15, 32)
Flatten_1	Just flatten the matrix	(480)
Input_2	shape = (31, 5)	(31, 5)
Conv1D	filters = 16
	kernal_size = 3	(29, 16)
	Activation = relu
MaxPooling1D	Pool size = 2	(14, 16)
Dropout	Rate = 0.2	(14, 16)
Flatten_2	Just flatten the matrix	(224)
Concatenate	concatenate the Flatten_1 and Flatten_2	(704)
Dense	Activation = relu	(16)
	Units = 16
Dropout	Rate = 0.4	(16)
Dense	Activation = softmax	(2)
	Units = 2