## 4 Performance Evaluation
The training loss (TRL) and validation loss (VLL) reached by the MODAE-RCM method under the test database are established in Figure 9. The simulation outcome denotes that the MODAE-RCM system has established the lowest values of TRL and VLL. Specifically, the VLL is lesser than the TRL. A clear precision-recall inspection of the MODAE-RCM technique under the test database is represented in Figure 10. The figure denotes that the MODAE-RCM approach has resulted in enhanced values of precision-recall values in every class label. Table 5 provides the overall road classification performance of the MODAE-RCM model with other existing models [17]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Sensitivity** & **Specificity** & **F1-Score** \\ \hline MODAE-RCM & 99.30 & 98.25 & 99.56 & 98.24 \\ \hline SGD & 74.11 & 73.46 & 93.46 & 71.13 \\ \hline RMSProp & 97.65 & 97.51 & 99.15 & 97.28 \\ \hline Adagrad & 79.99 & 80.51 & 95.07 & 80.86 \\ \hline Adam & 98.62 & 98.09 & 98.57 & 97.89 \\ \hline Adadelta & 71.37 & 71.39 & 92.51 & 71.51 \\ \hline Adamax & 95.39 & 96.27 & 98.59 & 95.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of the MODAE-RCM system and other approaches. Figure 7: Road classification outcomes of MODAE-RCM system under 30% of TS database. Figure 11 reports a brief \(accu_{y}\) examination of the MODAE-RCM with compared approaches. The figure represents that the SGD, Adagrad, and Adadelta models have reported lower classifier results. The RMSProp and Adamax models have shown reasonable \(accu_{y}\) values. Although [PERSON] attempted to gain considerable \(accu_{y}\) of 98.62%, the MODAE-RCM model has accomplished a maximum \(accu_{y}\) of 99.30%. Figure 8: TRA and VLA analysis of the MODAE-RCM system. Figure 9: TRL and VLL analysis of the MODAE-RCM system. Figure 12 reports a brief \(sens_{y}\) investigation of the MODAE-RCM with compared approaches. The figure represents that the SGD, Adagrad, and Adadelta models have reported lower classifier results. The RMSProp and Adamax methods also exposed re  
Figure 11: \(Accu_{y}\) analysis of the MODAE-RCM system and other approaches. Figure 10: Precision-recall analysis of the MODAE-RCM system. sonable \(sens_{y}\) values. Although [PERSON] attempted to gain considerable \(sens_{y}\) of 98.09%, the MODAE-RCM method has accomplished a maximum \(sens_{y}\) of 98.25%. Figure 13 portrays a brief \(spec_{y}\) inspection of the MODAE-RCM system with compared approaches. The figure signifies that the SGD, Adagrad, and Adadelta models have reported lower classifier results. The Adam and Adamax approaches have also revealed reasonable \(spec_{y}\) values. Although the RMSProp attempted to gain a considerable \(spec_{y}\) of 99.15%, the MODAE-RCM method has established a maximum \(spec_{y}\) of 99.56%. Figure 14 reports a brief \(F_{score}\) inspection of the MODAE-RCM system with compared approaches. The figure denotes that the SGD, Adagrad, and Adadelta techniques have reported lower classifier results. The RMSProp and Adamax models have also displayed reasonable \(F_{score}\) values. Although [PERSON] attempted to obtain a considerable \(F_{score}\) of 97.89%, the MODAE-RCM method has established a maximum \(spec_{y}\) of 98.24%. After examining the aforementioned results, it was assured that the MODAE-RCM approach has reached maximum road classification performance. Figure 12: \(sens_{y}\) analysis of MODAE-RCM system and other approaches. Figure 14: \(F1_{score}\) analysis of the MODAE-RCM system and other approaches. Figure 13: \(Spec_{y}\) analysis of the MODAE-RCM system and other approaches.