## 4 Results and Discussion
### Compilation and Comparison of Results
The proposed GAN displays a consistent increase in accuracy, starting from 0.942 at 5 features and peaking at 0.984 at 50 features. The accuracy dips slightly at 15 features (0.950), then increases consistently, except for a minor decrease at 40 features (0.976). The standard GAN shows varying performance, with its highest accuracy achieved at 25 features (0.961) and 50 features (0.945), and the lowest accuracy at 15 features (0.879). The CNN model exhibits a steady increase in accuracy, peaking at 45 features (0.949), followed by a substantial decrease to 0.899 at 50 features. The DBN model displays a somewhat fluctuating performance, beginning at 0.882 for 5 features, peaking at 35 features (0.944), and ending with an accuracy of 0.930 at 50 features. It can be observed that the proposed GAN consistently outperforms the other models across the range of features, achieving the highest accuracy of all models at 50 features (0.984). The standard GAN, CNN, and DBN models demonstrate variable performance across different feature counts, with their peak accuracies occurring at 25, 45, and 35 features, respectively. Figure 2(f) shows the accuracy comparison between our proposed BGM-GAN and the related works (standard GAN, CNN, and DBN) over various feature counts. Each model was trained for 180 epochs. The proposed GAN shows consistently high accuracy. Starting at 0.938 at 5 features, the accuracy generally increases to reach the highest accuracy of 0.994 at 50 features. There is a slight dip at 30 features (0.967), but the model then continues to increase its performance. The standard GAN has some fluctuation, starting with an accuracy of 0.887 at 5 features, reaching a peak at 20 features (0.939), and ending at 0.930 for 50 features. The CNN model also fluctuates, with its highest accuracy achieved at 30 features (0.952) and the lowest at 5 features (0.882). Its accuracy dips after 30 features and ends at 0.935 for 50 features. The DBN model starts at 0.892 at 5 features, peaks at 30 features (0.947), then slightly decreases, ending with an accuracy of 0.931 at 50 features. It can be noticed that the proposed GAN consistently outperforms the other models across the range of features, reaching the highest accuracy at 50 features (0.994). The standard GAN, CNN, and DBN models display more fluctuation in their performance across different feature counts, with the peak accuracies achieved at 20, 30, and 30 features, respectively. Figure 2(g) depicts an accuracy comparison between our proposed BGM-GAN and the related works (standard GAN, CNN, and DBN) for different numbers of features. All models were trained for 210 epochs. The proposed GAN shows a consistent increase in accuracy as the number of features increases, beginning at 0.926 at 5 features and reaching a peak at 0.995 at 50 features, indicating the robustness and efficacy of our proposed model. The standard GAN, on the other hand, exhibits a fluctuating performance with its highest accuracy achieved at 30 features (0.979). The accuracy slightly decreases afterwards, but it still manages to end with a relatively high accuracy of 0.977 at 50 features. The CNN model starts at 0.905 at 5 features and peaks at 25 features with an accuracy of 0.959. Thereafter, the accuracy tends to decrease with a minor increase at 40 features (0.967), finally ending with an accuracy of 0.940 at 50 features. The DBN model starts at 0.927 at 5 features, dips slightly at 10 features (0.897), and then rises steadily till 25 features (0.950). It further increases to 0.940 at 30 features, then experiences fluctuations, eventually ending with an accuracy of 0.935 at 50 features. It can be observed that the proposed GAN outperforms the other models across the feature range, reaching the highest accuracy of all  
models at 50 features (0.995). The standard GAN, CNN, and DBN models display variable performance across different feature counts, with their peak accuracies achieved at 30, 25, and 25 features, respectively.