## 5 Experimental Results
### Window Size Manipulation + Attention Mechanism + FGSM  
In this model, window size manipulation was applied to the model created by applying the attention mechanism algorithm, and noise was added to the data using the FGSM method. After these trials, we observed that the optimum window size for this model was in the 80-100 range, as represented in Table 3. When evaluated based on the mean absolute error (MAE), which reflects the average magnitude of the errors in the model's predictions, we observe that the model achieves its most accurate predictions within this window size range. Some improvement in the model's overall performance may have been observed with window size manipulation. However, it is essential to note that the sensitivity and precision values of the model are still low, and the maximum absolute error value is high. This indicates that the model may be more susceptible to certain types of errors. The parameters of the proposed attention mechanism + FGSM model are as follows: batch_size = 32, n_epochs = 32, epsilon = 0.01, optimizer = AdamW optimizer, loss function = mean squared error (MSE). The loss value is calculated between the model's prediction and the true answer. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **Precision** & **Accuracy** & **Recall** & **F1** & **MAE** & **MAD** & **Maximum Absolute Error** \\ \hline GRU & 0.0752 & 0.2031 & 0.1057 & 0.0527 & 0.0121 & 0.0052 & 21.5777 \\ \hline LSTM & 0.0592 & 0.1560 & 0.0958 & 0.0333 & 0.0154 & 0.0070 & 20.9665 \\ \hline CNN & 0.0356 & 0.0067 & 0.0789 & 0.0144 & 0.0214 & 0.0068 & 24.8914 \\ \hline MLP & 0.0688 & 0.5837 & 0.0470 & 0.0144 & 0.0403 & 0.0087 & 20.5822 \\ \hline Transformer & 0.0848 & 0.1031 & 0.1422 & 0.0606 & 0.0149 & 0.0053 & 21.0068 \\ \hline Attention Mechanism & 0.0975 & 0.2601 & 0.1031 & 0.0307 & 0.0130 & 0.0054 & 20.5695 \\ \hline RESNET & 0.1777 & 0.4559 & 0.0958 & 0.0333 & 0.0077 & 0.0040 & 21.6432 \\ \hline Random Forest & 0.1417 & 0.9928 & 0.1556 & 0.1475 & 0.0827 & 0.0 & 55 \\ \hline G. Naive Bayes & 0.1031 & 0.9865 & 0.1044 & 0.1037 & 0.0870 & 0.0 & 45 \\ \hline Gan & 0.1005 & 0.7370 & 0.1190 & 0.0686 & 0.0123 & 0.0056 & 21.000 \\ \hline A3C & 0.0441 & 0.2971 & 0.0805 & 0.0280 & 0.0138 & 0.0060 & 20.1652 \\ \hline VAE & 0.0538 & 0.1248 & 0.0703 & 0.0175 & 0.0106 & 0.0042 & 20.2315 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Patch performance results for standard models. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Window Size** & **Precision** & **Accuracy** & **Recall** & **F1 Score** & **MAE** & **MAD** & **Maximum Absolute Error** \\ \hline
20–40 & 0.138 & 0.341 & 0.143 & 0.113 & 0.0165 & 0.0017 & 21.672 \\ \hline
40–60 & 0.101 & 0.217 & 0.140 & 0.079 & 0.0122 & 0.0085 & 20.3738 \\ \hline
60–80 & 0.079 & 0.140 & 0.138 & 0.115 & 0.0096 & 0.0031 & 20.4977 \\ \hline
80–100 & 0.132 & 0.795 & 0.138 & 0.097 & 0.0065 & 0.0025 & 20.7388 \\ \hline
100–120 & 0.071 & 0.541 & 0.081 & 0.047 & 0.0133 & 0.0048 & 20.8360 \\ \hline
120–140 & 0.082 & 0.213 & 0.077 & 0.028 & 0.0082 & 0.0032 & 20.5589 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hybrid model performance based on FGSM method. The hybrid model performance based on the PGD method is shown in Table 4. These results show that increasing the window size to within (100-120) significantly affects the model's performance. The precision, accuracy, and sensitivity metrics are slightly reduced compared to the previous state (80-100). However, the F1 score still needs to be higher, indicating that the model fails to strike an ideal balance between precision and sensitivity. The errors have decreased somewhat in the model's error metrics, indicating that the model's predictions are closer to the actual values. These results show that the window size significantly affects the model performance, and this hyperparameter must be carefully selected to optimize the model's overall performance. The proposed attention mechanism + PGD model's parameters are epsilon = 0.01, alpha = 0.01, num_steps = 3.