Firstly, due to their lack of trainable parameters, traditional techniques might not perform well under high-dimensional data or when the distribution varies greatly across different regions in the data space. Secondly, substantial feature selection is often required as a crucial pre-processing step to decrease dimensionality and eliminate redundant and irrelevant features. The primary objective is to mitigate overfitting, which can impact the model's prediction and generalisation capacity. In order to overcome the limitations associated with these two aspects of traditional models, we employ two other neural network-based OCC algorithms.