Table 1.

Hyperparameters for artificial neural network prediction model.

Hyperparameters	Values
Activation function at hidden layer	ReLU $f\left(x\right)=\mathit{max}\left(0,x\right)$
Activation function at output layer	ReLU $f\left(x\right)=\mathit{max}\left(0,x\right)$
Input parameters	$\mathit{P}$ (Perception of Fitting Factors by Consumers' Psychological Orientation) $\mathit{B}$ (Body Measurements)
Output parameters	$\mathit{R}$ (Pattern Parameters for Sketch Drafting)
Number of hidden layers	6
Number of hidden nodes in each layer	Layers 1 & 2: 70 Layers 3 $ 4: 140 Layers 5 & 6: 50
Number of training rows	70
Number of testing rows	50
Learning rate	0.001
Loss function	Mean Squared Error
Optimizer	Adaptive Moment Estimation (Adam)
Training Time for the Model	1–5 min