## 3 Experiments and Analysis
### Performance Testing and Comparative Analysis
#### 3.3.1 Comparative Analysis of Loss Function Performance  
In Section 3.3.1, we analyze the performance of various loss functions through a comparative study. The sample set \(S\), comprising various capability institutions, is utilized to pinpoint the optimal hyperparameters as delineated in Table 1; the resultant findings are cataloged in Table 4. Subsequently, the dimensionality-reduced dataset, denoted as \(S^{\prime}\), is partitioned into a training set (\(S^{\prime}_{\text{train}}\) ) and a test set (\(S^{\prime}_{\text{test}}\) ) based on a 7:3 ratio. Following this, \(S^{\prime}_{\text{train}}\) is fed into the neural network, employing \(L_{\text{$n-$ Adaptive-Focal}}\) (\(p_{i},y_{i}\)) as the loss function during the training phase. This strategy facilitates the development of the Encoder-Adaptive-Focal deep neural network for dynamic capability evaluation. To verify the efficacy of the Weighted Adaptive Focal Loss (WAFL) introduced in this study for capability evaluation, we conducted experiments to compare the Encoder-Adaptive-Focal deep neural network capability evaluation model constructed herein with Encoder-DNN and Encoder-Focal models. The comparative results are displayed in Table 5.  
To further scrutinize the effectiveness of the Weighted Adaptive Focal Loss (WAFL) and to comply with the reviewer's suggestion, we executed additional experiments employing a 60:40 training-testing split. The Encoder-Adaptive-Focal model was once again juxtaposed against the Encoder-DNN and Encoder-Focal models to ascertain any potential enhancement in performance with this altered data split. The comparative findings are encapsulated in Table 6 below. The results delineated in Table 6 signify a marginal improvement in model performance across all three models with the 60:40 data split, particularly showcasing a slight enhancement in the Encoder-Adaptive-Focal model's F1 score and AUC values. This exploration underscores the importance of data split ratios in honing the efficacy of our proposed model and provides a promising avenue for further optimization in our capability evaluation methodology. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models/Performance** & **Accuracy** & **F1 Score** & **AUC** \\ \hline Encoder-Adaptive-Focal & 0.852 & 0.904 & 0.725 \\ Encoder-DNN & 0.835 & 0.881 & 0.665 \\ Encoder-Focal & 0.843 & 0.889 & 0.731 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Enhanced Comparison of Loss Functions with 60:40 Training-Testing Split. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Value** \\ \hline \(Layers\) & 5 \\ \(C_{l}\) & 7, 5, 5, 3, 5, respectively \\ \(g_{l}\) & Relu for the hidden layer and Softmax for the output layer \\ \(B\) & 64 \\ \(E\) & 300 \\ \(\varepsilon\) & 0.005 \\ \(o\) & Adam \\ \(\rho\) & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hyperparameter search results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models/Performance** & **Accuracy** & **F1 Score** & **AUC** \\ \hline Encoder-Adaptive-Focal & 0.844 & 0.896 & 0.713 \\ Encoder-DNN & 0.828 & 0.875 & 0.652 \\ Encoder-Focal & 0.836 & 0.883 & 0.719 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Initial Comparison of Loss Functions in Different Models.