Table 2.

Parameter settings.

Algorithm	Parameters	Values
KNN	Number of neighbours	5
	Distance function	Euclidean distance
	(N × D) training data	N, no. of samples; D, dimensionality of each data point
	(M × D) testing data	M, no. of data points
NB	Model	Gaussian base distribution
	N	Size of data
DT	Splitting criterion	Gini
	Minimum instances per leaf	2
ANN	Size of input layer	Size of data
	Type of ANN	Feed-forward
	Number of neurons	20
	Training and testing set	75% of training and 25% of testing set
FCNN	Input	56 × 28
	Fuzzification	2 × (input)-Gaussian MF
	In and out channel range	1 to 100
	Stride and padding	1 & 0
	Conv3x d	2 × (in & out channels, kernel size = (3 × 128), stride & padding), ReLU, Max_Pooling (55 × 1)
	Conv4x d	2 × (in & out channels, kernel size = (4 × 128), stride & padding), ReLU, Max_Pooling (54 × 1)
	Conv5x d	2 × (in & out channels, kernel size = (5 × 128), stride & padding), ReLU, Max_Pooling (53 × 1)
	Defuzzification	2 × 128