Table 3.

Hyperparameters used for machine and deep learning models.

Algorithm	Hyperparameters
LR	solver=saga, C=2.0, max_iter=100, penalty=’l2’, multi_clas=multinomial
SVM	kernel=’linear’, C=2.0, random_state=500
RF	n_estimators=200,max_depth=50, random_state=2
DT	max_depth=50, random_state=2
LSTM	Input layer, Hidden layer, Output layer, optimizer=adam, Dropout=0.5 loss=categorical_crossentropy, activation= ReLU, Softmax, epoches=10
CNN	Conv2D (filter=16, 32, 64, 128, kernel=2x2), maxpooling2D=2x2, optimizer=adam, loss=categorical_crossentropy, Dropout=0.5, epoches=200