Unlike PR, the multi-layer structure of ANN introduces non-linearity, and the use of non-linear activation functions further enhances its ability to model complex decision boundaries. However, as input data complexity or dimensionality increases, the required number of neurons and connections grows significantly, leading to higher computational costs and an increased risk of overfitting .