## 4 Results and Discussion
### Compilation and Comparison of Results
Beyond 30 features, however, its accuracy slightly decreases, with the lowest recorded at 45 features (0.916), then showing an increase at 50 features (0.933). The standard GAN model shows fluctuating performance, starting with 0.895 at 5 features and reaching its highest accuracy at 45 features (0.940). The CNN model exhibits a somewhat inconsistent performance, beginning with an accuracy of 0.872 at 5 features, peaking at 25 features (0.915), then experiencing a slight decrease with further increase in features, achieving an accuracy of 0.925 at 50 features. The DBN model starts with 0.894 accuracy at 5 features, peaking twice at 25 and 35 features with an accuracy of 0.928 and 0.930, respectively. It then shows a dip at 40 features, and a slight increase to reach 0.922 at 50 features. All models show variations in performance at different feature counts. Nevertheless, the proposed GAN consistently outperforms the other models in most feature counts, achieving the highest accuracy of all models at 30 features (0.960). Figure 3c illustrates the accuracy comparison between our proposed BGM-GAN and the related works (standard GAN, CNN, and DBN) across varying numbers of features. All models were trained for 90 epochs. The proposed GAN exhibits high accuracy starting from 0.934 at 5 features, peaking at 10 features with an accuracy of 0.951. While there is a slight decrease in accuracy at 30 features (0.931), it picks up again reaching a second peak at 40 features (0.953) and then decreases slightly towards 50 features (0.922). The standard GAN begins with 0.882 accuracy at 5 features and peaks at 30 features (0.927). Its performance remains fairly stable thereafter, with the final accuracy at 50 features being 0.927. The CNN model's performance starts at 0.874 accuracy for 5 features and progressively increases to reach a peak at 40 features (0.924), and then slightly decreases at 50 features (0.916). The DBN model exhibits a steady increase in performance, from an initial 0.884 accuracy at 5 features to its peak at 30 features (0.932). Thereafter, the accuracy decreases, reaching 0.916 at 50 features. All the models exhibit fluctuations in accuracy at different feature counts. However, the proposed GAN consistently shows higher performance in comparison to the other models in most feature counts, reaching the highest accuracy of all models at 10 and 40 features (0.951 and 0.953, respectively). Figure 3d displays an accuracy comparison between our proposed BGM-GAN and the related works (standard GAN, CNN, and DBN), across varying numbers of features. All models were trained for 120 epochs. Starting with the proposed GAN, it shows a consistent increase in accuracy with a slight dip at 30 features (0.963), before reaching the highest accuracy of 0.989 at 50 features. The standard GAN shows fluctuations with its performance peaking at 35 features with an accuracy of 0.940. However, it ends with a slightly lower  
accuracy of 0.927 at 50 features. The CNN model displays an initial increase in accuracy, peaking at 25 features (0.940). After this peak, there is a decrease at 30 features (0.915) followed by a minor increase and a subsequent drop, ending with the accuracy of 0.899 at 50 features. Finally, the DBN model starts at 0.908 at 5 features shows its peak performance at 35 features (0.946) and then displays a slight decrease, ending with an accuracy of 0.929 at 50 features. The proposed GAN outperforms the other models across the range of features, achieving the highest accuracy of all models at 50 features (0.989). The standard GAN, CNN, and DBN models demonstrate fluctuating performance across different feature counts, with their peak accuracies achieved at 35, 25, and 35 features, respectively. Figure 2(e) shows an accuracy comparison between our proposed BGM-GAN and the related models (standard GAN, CNN, and DBN), across varying numbers of features. All these models were trained for 150 epochs.