# A Hybrid Learning Framework for Enhancing Bridge Damage Prediction
## 3 Results and Discussion
### Discussion  
In this section, we display a detailed analysis of the results of the proposed models on the utilized datasets. Many factors affect model performance, such as preprocessing operations and their vital role in classification performance, feature selection, dimensionality reduction, and selecting an appropriate classifier. Also, the number of features, image quality, and lighting conditions all impact classification performance. We evaluated the performance of the proposed algorithm using MobileNet model. Table 2 explains the performance of the proposed model using difference evaluation metrics using four sizes of features for each dataset. As shown in Table 2, the BCD dataset with feature sizes of 100 performed the best, with an accuracy of 99.99% and a high recall of 99.99%, with a precision and F1 score of 100%, ROC-AUC of 99.99% and a 0.01 error rate. The best performance was attained by the Bridge dataset with a feature size of 300, with 99.97% for all performance metrics and an error rate of 0.03. The DIMEC-Crack dataset with feature sizes of 100 performed the best, with a high precision reaching 100%, which means there were no false positives, and 99.99% for all performance metrics, with an error rate reaching 0.01%. Figure 3 shows the plot of the average of the best accuracy and loss. Figure 4 shows the confusion matrix of the best results for each dataset. Table 3 presents the accuracy scores for each dataset and compares them with related studies that classify the same datasets. The difference in methodologies prevents a direct comparison with these studies, so we will only compare accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Performance**} & \multicolumn{4}{c}{**Number of Visual Words**} \\ \cline{3-6}  & & **Metrics** & **50** & **100** & **200** & **300** \\ \hline \multirow{6}{*}{BCD} & Accuracy & 99.98 & **99.99** & 99.98 & 99.97 \\  & Precision & 99.97 & **100** & 100 & 100 \\ \cline{1-1}  & Recall & 100 & **99.99** & 99.97 & 99.97 \\ \cline{1-1}  & F1-score & 99.99 & **100** & 99.98 & 99.98 \\ \cline{1-1}  & ROC\(-\)AUC & 99.97 & **99.99** & 99.98 & 99.98 \\ \cline{1-1}  & Error rate & 0.02 & **0.01** & 0.02 & 0.03 \\ \hline \multirow{6}{*}{Bridge dataset} & Accuracy & 99.82 & 99.84 & 99.96 & **99.97** \\  & Precision & 99.78 & 99.90 & 99.97 & **99.97** \\ \cline{1-1}  & Recall & 99.81 & 99.75 & 99.94 & **99.97** \\ \cline{1-1}  & F1-score & 99.79 & 99.83 & 99.95 & **99.97** \\ \cline{1-1}  & ROC\(-\)AUC & 99.82 & 99.83 & 99.96 & **99.97** \\ \cline{1-1}  & Error rate & 0.18 & 0.16 & 0.04 & **0.03** \\ \hline \hline \multirow{6}{*}{DIMEC- Crack} & Accuracy & 99.98 & **99.99** & 99.98 & 99.97 \\ \cline{1-1}  & Precision & 100 & **100** & 100 & 99.99 \\ \cline{1-1}  & Recall & 99.97 & **99.99** & 99.98 & 99.97 \\ \cline{1-1}  & F1-score & 99.99 & **99.99** & 99.99 & 99.98 \\ \cline{1-1}  & ROC\(-\)AUC & 99.99 & **99.99** & 99.99 & 99.97 \\ \cline{1-1}  & Error rate & 0.02 & **0.01** & 0.02 & 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of the proposed model.