## 4 Performance Evaluation
\begin{table}
\begin{tabular}{l l l l l l} \hline \multicolumn{2}{l}{**Training Phase (70\%)**} & & & & \\ \hline
**Labels** & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** & **F-Score** \\ \hline Class-1 & 96.43 & 90.76 & 95.43 & 96.76 & 93.04 \\ \hline Class-2 & 94.43 & 90.70 & 87.15 & 96.93 & 88.89 \\ \hline Class-3 & 97.14 & 94.15 & 94.15 & 98.11 & 94.15 \\ \hline Class-4 & 94.86 & 90.17 & 89.14 & 96.76 & 89.66 \\ \hline
**Average** & **95.71** & **91.45** & **91.47** & **97.14** & **91.43** \\ \hline \end{tabular}
\end{table} 
Table 3: Crowd classification outcome of the AOTL-CDA3S system with different measures under 70% of the TR database. Figure 5: Crowd classification outcome of the AOTL-CDA3S system under 70% of the TR database. The training accuracy (\(TR_{acc}\)) and validation accuracy (\(VL_{acc}\)) acquired by the AOTL-CDA3S approach under test database is shown in Figure 7. The simulation value denoted by the AOTL-CDA3S method has attained maximal values of \(TR_{acc}\) and \(VL_{acc}\); the \(VL_{acc}\) is greater than \(TR_{acc}\). The training loss (\(TR_{loss}\)) and validation loss (\(VL_{loss}\)) reached by the AOTL-CDA3S method under the test database are displayed in Figure 8. The simulation values displayed by the AOTL-CDA3S technique have exhibited minimal values of \(TR_{loss}\) and \(VL_{loss}\). Seemingly, the \(VL_{loss}\) is less than \(TR_{loss}\). \begin{table}
\begin{tabular}{l l l l l l} \hline \multicolumn{6}{l}{**Testing Phase (30\%)**} \\ \hline
**Labels** & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** & **F-Score** \\ \hline Class-1 & 98.33 & 96.05 & 97.33 & 98.67 & 96.69 \\ \hline Class-2 & 97.33 & 98.46 & 90.14 & 99.56 & 94.12 \\ \hline Class-3 & 98.67 & 95.18 & 100.00 & 98.19 & 97.53 \\ \hline Class-4 & 97.67 & 94.74 & 96.00 & 98.22 & 95.36 \\ \hline
**Average** & **98.00** & **96.11** & **95.87** & **98.66** & **95.93** \\ \hline \end{tabular}
\end{table} 
Table 4: Crowd classification outcome of the AOTL-CDA3S system with different measures under 30% of the TS database. Figure 6: Crowd classification outcome of the AOTL-CDA3S system under 30% of the TS database. A clear precision-recall inspection of the AOTL-CDA3S technique under the test database is depicted in Figure 9. The figure denotes that the AOTL-CDA3S approach has resulted in enhanced values of precision-recall values in every class label. Figure 8: \(TR_{\text{loss}}\) and \(VL_{\text{loss}}\) analysis of the AOTL-CDA3S system. Figure 7: \(TR_{\text{acc}}\) and \(VL_{\text{acc}}\) analysis of the AOTL-CDA3S system. A brief ROC study of the AOTL-CDA3S approach under the test database is represented in Figure 10. The outcomes exemplified by the AOTL-CDA3S method have revealed its capability in classifying different classes in the test database. Lastly, a comprehensive comparative study of the AOTL-CDA3S model was made with recent models in Table 5 and Figure 11[12]. These results affirm that the AOTL-CDA3S  
Figure 10: ROC curve analysis of AOTL-CDA3S system. Figure 9: Precision-recall analysis of the AOTL-CDA3S system. model has reached improved performance over other models. For instance, based on \(accu_{y}\), the AOTL-CDA3S model has gained a higher \(accu_{y}\) of 98%, whereas the Gabor, BoW-SRP, BoW-LBP, GLCM-SVM, GoogleNet, VGGNet, and MDTL-ICDDC models have reached reduced \(accu_{y}\) of 71.95%, 79.95%, 84.26%, 80.20%, 84.51%, 84.49%, and 96.94% respectively. Additionally, based on \(prec_{n}\), the AOTL-CDA3S method has obtained a higher \(prec_{n}\) of 96.11%, whereas the Gabor, BoW-SRP, BoW-LBP, GLCM-SVM, GoogleNet, VGGNet, and MDTL-ICDDC methods have attained a reduced \(prec_{n}\) of 61.88%, 68.25%, 74.36%, 75.57%, 83.16%, 85.72%, and 93.24% correspondingly. The AOTL-CDA3S model has attained maximum crowd density classification performance. Since the SqueezeNet model has 50 times less parameters than AlexNet with effective accuracy, the proposed model reaches better performance over other models.