Table 6.

Notations of Algorithm 1 and their descriptions.

Notation	Description
X1, X2, X3,…., X17	The seventeen input features
${\text{N}}_{\text{h}}$	The number of neurons in the hidden layers
m	The total number of samples in the MUCHD dataset
α	An arbitrary scaling factor
Ni	The number of neurons in the input layer
No	The number of neurons in the output layer
W	Weights
$\mathrm{\theta }$ 0, $\mathrm{\theta }$ 1, …, $\mathrm{\theta }$ 17	The eighteen input weights
Z	A linear transfer function
b	bias node
T	Transpose
i	A counter
n	The total number of input features
g	The activation function
$\widehat{y}$	The predicted output
ReLU	Rectified Linear unit
$\sigma$	the Sigmoid function
$L\left({\widehat{y}}^{\left(i\right)},,,y^{\left(i\right)}\right)$	Loss Function
Y	Actual output
$J(\mathrm{\theta },\text{b})$	Cost Function
${\mathit{dW}}_i+$	Accumulative weight
$db+$	Accumulative bias