## 4 Experimental Results
### Classification Analysis  
The proposed model starts with an LMA; in the first experiment, we will evaluate which combination of three pre-trained CNN models performed better on both selected datasets. Table 2 demonstrates the ten late model aggregation performances combined across Kaggle and FigShare datasets, measured by accuracy (A), recall (R), precision (P), F1-score, and RMSE. The results highlight significant variability, with accuracy values ranging from 52.8% to 85.0% on the Kaggle dataset and 56.9% to 85.3% on the FigShare dataset. Among the combinations, IN + DE + MO demonstrates the best performance, achieving an accuracy of 85.0% on Kaggle dataset and 85.3% on FigShare dataset, with precision of 84.2% and 83.4%, recall of 83.8% and 82.0%, and F1-scores of 84.0% and 82.7%. It also recorded the lowest RMSE of 6.6% on the Kaggle dataset and 9.5% on the FigShare dataset. The second-best combination is IN + E0 + DA, which achieves an accuracy of 82.1% on Kaggle dataset and 82.2% on FigShare dataset, with precision rates of 82.7% and 82.1%, recall rates of 82.8% and 81.8%, and F1-scores of 82.1% and 81.9%, along with RMSE values of 14.8% on Kaggle dataset and 10.1% on FigShare dataset. DE + DA + MO ranks third with an accuracy of 81.5% on the Kaggle dataset and 80.0% on FigShare dataset, supportedby precision rates of 81.4% and 81.1%, recall rates of 80.3% and 81.3%, and F1-scores of 82.3% and 81.2%, with RMSE values of 12.8% on Kaggle dataset and 12.4% on FigShare dataset. These top three combinations stand out for their strong classification performance across the datasets. As we have found the best combination of pre-trained CNN models to be integrated into the LMA module, we will test the best combination (In + De + Mo) to evaluate the impact of data augmentation techniques. Table 3 compares the performance of the best combination in LMA on different employed augmentation techniques with the original dataset. During this evaluation, SMOTE demonstrates the most substantial improvements in classification performance, achieving the highest accuracy of 89.3% on the Kaggle dataset and 89.6% on the FigShare dataset. It consistently outperforms in precision, recall, and F1-scores while maintaining the lowest RMSE values of 4.2% on the Kaggle dataset and 6.9% on the FigShare dataset, indicating a significant reduction in prediction errors. Essential augmentation further enhances the results compared to the original dataset, with an accuracy of 87.1% on the Kaggle dataset and 87.5% on the FigShare dataset. It also improves the precision, recall, and F1-scores while reducing the RMSE to 5.5% on the Kaggle dataset and 8.3% on the FigShare dataset. While providing a baseline accuracy of 85.0% on the Kaggle dataset and 85.3% on the FigShare dataset, the original dataset shows higher RMSE values of 6.6% on the Kaggle dataset and 9.5% on the FigShare dataset. These findings emphasize the effectiveness of augmentation techniques like SMOTE and essential augmentation in improving classification outcomes compared to using the original dataset alone. Now that we have compared the impact of employed augmentation techniques, we will compare the performance of the proposed GATransformer model on both datasets. Table 4 illustrates the performance of the GATransformer model on the Kaggle and FigShare datasets under different augmentation techniques, including the original dataset, essential augmentation, and SMOTE. The results indicate progressive improvement in the classification performance with augmentation techniques. Using the original dataset, the model achieves an accuracy of 92.0% on the Kaggle dataset and 92.2% on the FigShare dataset, supported by precision rates of 91.5% and 91.0%, recall rates of 91.2% and 90.8%, and F1-scores of 91.3% and 90.9%. However, the RMSE values remain relatively higher at 8.0%