To enhance the model and get the best results, we ran a number of tests and adjusted the hyperparameters. After 100 epochs of end-to-end training, the CSMLP model's weights were kept from the epoch with the greatest F1 score. We observed significant label imbalance in both the IGD and Subset-IGD datasets. To mitigate this, we used two resampling techniques: ML-RUS and ML-ROS. Table represents the classification results obtained on the baseline dataset and after resampling it. After resampling the datasets at varying percentages, we noted a considerable increase in the F1 scores for all four models; this highlights the effectiveness of resampling techniques in mitigating label imbalances in multi-label datasets. On the IGD_FP dataset, the combination of resampling with RF, BR, and CC performed noticeably better than any of the models used by Saini et al. . The CSMLP performed noticeably better than any other model. Deep learning models like CSMLP can accommodate extremely complicated data that typical machine learning models are unable to capture due to their many adjustable parameters. The CSMLP model trained on IGD_FP resampled with ML-ROS (10%) achieved the highest F1 score of 0.410 on the smell dataset, underscoring the potential of deep learning in olfaction. Additionally, the CSMLP algorithm with resampling outperformed all models trained on different modalities. We also observed a comparative performance of the models on the Subset-IGD_VS and Subset-IGD_MS datasets, Subset-IGD_VS performed better than Subset-IGD_MS, highlighting the crucial role of spectral data in explaining the olfaction mechanism. Table shows the effect of resampling on various imbalance measures, for brevity we show scores for datasets resampled with ML-ROS (10%, 20%, 30%) and ML-RUS (10%, 20%, 30%) as they gave the highest F1 scores and compared their scores with baseline model without any sampling. Table shows that both CVIR and MeanIR have significantly reduced after the resampling on both the dataset with ML-ROS and ML-RUS.