Table 3.

Classification models parameter values.

Classification Model	Parameters Values
MLP	Loss = binary_crossentropy
	optimizer = Adam
	epochs = 50
	batch_size = 32
	layers = 2
	Hidden layers = 200
	Relu
	softmax
	Dropout = 0.5
SVM	Kernel = RBF
	C = 1000
	Gamma = 0.7
DT	Criterion = gini
	Splitter = best
	Max_depth = None
	min_samples_split = 2
	min_samples_leaf = 1
	min_weight_fraction_leaf = 0.0
	max_features = None
	random_state = None
	max_leaf_nodes = None
	min_impurity_decrease = 0.0
	min_impurity_split = 0
	class_weight = none
	ccp_alpha = 0.0
RF	n_estimators = 100
	criterion = gini
	max_depth = none
	min_samples_split = 2
	min_samples_leaf = 1
	min_weight_fraction_leaf = 0.0
	max_features = “auto”
	max_leaf_nodes = None
	min_impurity_decrease = 0.0
	min_impurity_split = None
	bootstrap = True
	oob_score = False
	n_jobs = None
	random_state = None
	verbose = 0
	warm_start = False
	class_weight = None
	ccp_alpha = 0.0
	max_samples = None
CNN	batch_size = 100
	epochs = 500
	momentum = 0.8
	SGD Optimizer