## 4 Performance Evaluation  
The proposed model is simulated using the Python 3.6.5 tool. The proposed model is tested on the PC i5-8600k, GeForce 1050 Ti 4 GB, 16 GB RAM, 250 GB SSD, and 1 TB HDD. The parameter settings are given as follows: learning rate: 0.01, dropout: 0.5, batch size: 5, epoch count: 50, and activation: ReLU. The experimental validation of the AOTL-CDA3S model is carried out using a crowd dataset with 1000 samples as represented in Table 1. The dataset holds 250 samples under every class. Figure 2 demonstrates some sample images. 167 samples to C1, 156 samples to C2, 161 samples to C3, and 156 samples to C4. Furthermore, with 30% of the TS database, the AOTL-CDA3S technique has recognized 73 samples to C1, 64 samples to C2, 79 samples to C3, and 72 samples to C4. Table 2 and Figure 4 illustrate brief crowd classification results of the AOTL-CDA3S model on the entire data. The AOTL-CDA3S model has shown enhanced results under each class. For instance, in class-1, the AOTL-CDA3S model has obtained \(accu_{y}\) of 97%, \(prec_{n}\) of 92.31%, \(sens_{y}\) of 96%, \(spec_{y}\) of 97.33%, and \(F_{score}\) of 94.12%. Meanwhile, in class-3, the AOTL-CDA3S approach has gained \(accu_{y}\) of 97.60%, \(prec_{n}\) of 94.49%, \(sens_{y}\) of 96%, \(spec_{y}\) of 98.13%, and \(F_{score}\) of 95.24%. Concurrently, in class-4, the AOTL-CDA3S method has gained \(accu_{y}\) of 95.70%, \(prec_{n}\) of 91.57%, \(sens_{y}\) of 91.20%, \(spec_{y}\) of 97.20%, and \(F_{score}\) of 91.38%. Figure 3: Confusion matrices of AOTL-CDA3S system (**a**) Entire database, (**b**) 70% of TR database, and (**c**) 30% of TS database. Table 3 and Figure 5 exemplify detailed crowd classification results of the AOTL-CDA3S approach on 70% of the TR database. The AOTL-CDA3S technique has exhibited enhanced results under each class. For example, in class-1, the AOTL-CDA3S methodology has attained \(accu_{y}\) of 96.43%, \(prec_{n}\) of 90.76%, \(sens_{y}\) of 95.43%, \(spec_{y}\) of 96.76%, and \(F_{score}\) of 93.04%. Meanwhile, in class-3, the AOTL-CDA3S approach has gained \(accu_{y}\) of 97.14%, \(prec_{n}\) of 94.15%, \(sens_{y}\) of 94.15%, \(spec_{y}\) of 98.11%, and \(F_{score}\) of 94.15%. Concurrently, in class-4, the AOTL-CDA3S technique has achieved \(accu_{y}\) of 94.86%, \(prec_{n}\) of 90.17%, \(sens_{y}\) of 89.14%, \(spec_{y}\) of 96.76%, and \(F_{score}\) of 89.66%. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Entire Dataset** & & & & & \\ \hline
**Labels** & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** & **F-Score** \\ \hline Class-1 & 97.00 & 92.31 & 96.00 & 97.33 & 94.12 \\ \hline Class-2 & 95.30 & 92.83 & 88.00 & 97.73 & 90.35 \\ \hline Class-3 & 97.60 & 94.49 & 96.00 & 98.13 & 95.24 \\ \hline Class-4 & 95.70 & 91.57 & 91.20 & 97.20 & 91.38 \\ \hline
**Average** & **96.40** & **92.80** & **92.80** & **97.60** & **92.77** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Crowd classification outcome of the AOTL-CDA3S system with different measures under the entire database. Figure 4: Crowd classification outcome of the AOTL-CDA3S system under the entire database. Table 4 and Figure 6 demonstrate the brief crowd classification results of the AOTL-CDA3S algorithm on 30% of the TS database. The AOTL-CDA3S approach has displayed enhanced results under each class. For example, in class-1, the AOTL-CDA3S technique has gained \(accu_{y}\) of 98.33%, \(prec_{n}\) of 96.05%, \(sens_{y}\) of 97.33%, \(spec_{y}\) of 98.67%, and \(F_{score}\) of 96.69%. Meanwhile, in class-3, the AOTL-CDA3S approach has reached \(accu_{y}\) of 98.67%, \(prec_{n}\) of 95.18%, \(sens_{y}\) of 100%, \(spec_{y}\) of 98.19%, and \(F_{score}\) of 97.53%. Concurrently, in class-4, the AOTL-CDA3S technique has reached \(accu_{y}\) of 97.67%, \(prec_{n}\) of 94.74%, \(sens_{y}\) of 96%, \(spec_{y}\) of 98.22%, and \(F_{score}\) of 95.36%.