The efficacy of any machine learning model is largely contingent on the quality of the features used for training. Hence, the extraction of robust and discriminative features from raw data is a critical step. This task, however, presents significant challenges. Although modern deep learning models are very advanced, they are often criticized for their black-box nature. The predictions made by these models are not readily interpretable in terms of the features that influenced them. In our research, we propose a nov…