A simple model architecture was designed to obtain good accuracy and prevent overfitting issues during training. This configuration consisted of an input layer with three neurons (MM, ln 𝑃 𝑣𝑎𝑝 , and experimental method), three hidden layers with the first having 16 neurons, the second having 32 neurons, and the third having 16 neurons, and an output layer with a single neuron unit (Figure ). The activation function used was ReLU (Rectified Linear Unit). The chosen optimizer was Adam with a default learning rate of 0.001. Additionally, regularization was applied to the kernel (i.e., the neural network weights) using the L1L2 approach, with both L1 and L2 penalties equal to 0.001.