## 4 Results and Discussion  
In this section, the experimental validation of the MDTL-ICDDC model is tested with a dataset comprising 1000 images under four class labels. The MDTL-ICDDC model is simulated using Python 3.6.5 tool on a 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. Few sample images are demonstrated in Figure 4. The details related to the dataset are given in Table 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Labels** & **Class Name** & **No. of Instances** \\ \hline Class-0 & Dense Crowd & 250 \\ \hline Class-1 & Medium Dense Crowd & 250 \\ \hline Class-2 & Sparse Crowd & 250 \\ \hline Class-3 & No Crowd & 250 \\ \hline Total No. of Instances & 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset details. Figure 5 demonstrates a set of confusion matrices formed by the MDTL-ICDDC model on distinct epoch counts. On epoch 200, the MDTL-ICDDC model has recognized 223, 220, 236, and 243 images under classes 0-3 respectively. Moreover, on epoch 600, the MDTL-ICDDC technique has recognized 206, 203, 234, and 241 images under classes 0-3 respectively. At the same time, on epoch 1000, the MDTL-ICDDC approach has recognized 197, 178, 233, and 234 images under classes 0-3 respectively. In line with, epoch 1200, the MDTL-ICDDC system has recognized 227, 178, 236, and 242 images under classes 0-3 respectively. Table 2 and Figure 6 offer a detailed crowd density classification outcome of the MDTL-ICDDC model under distinct epochs and classes. The results indicated that the MDTL-ICDDC model has gained effectual outcomes under all classes and epochs. For instance, with 200 epochs, the MDTL-ICDDC model has provided average \(accu_{y}\), \(prec_{n}\), \(rec_{d}\), \(F_{score}\), and \(G_{measure}\) of 96.10%, 92.20%, 92.20%, 92.18%, and 92.19% respectively. Likewise, with 600 epochs, the MDTL-ICDDC technique has obtainable average \(accu_{y}\), \(prec_{n}\), \(rec_{d}\), \(F_{score}\), and \(G_{measure}\) of 94.20%, 88.38%, 88.40%, 88.31%, and 88.35% respectively. Similarly, with 1000 epochs, the MDTL-ICDDC system has provided average \(accu_{y}\), \(prec_{n}\), \(rec_{d}\), \(F_{score}\), and \(G_{measure}\) of 92.10%, 84.06%, 84.20%, 83.94%, and 84.04% respectively. Eventually, with 1200 epochs, the MDTL-ICDDC methodology has offered average \(accu_{y}\), \(prec_{n}\), \(rec_{d}\), \(F_{score}\), and \(G_{measure}\) of 96.40%, 92.82%, 92.80%, 92.80%, and 92.80% respectively. Figure 4: Sample Crowd Density Images. (**a**) Dense Crowd, (**b**) Medium Dense Crowd, (**c**) Sparse Crowd, (**d**) No Crowd. The training accuracy (TA) and validation accuracy (VA) attained by the MDTL-ICDDC method on test dataset are demonstrated in Figure 7. The experimental outcome implied that the MDTL-ICDDC model has gained maximal values of TA and VA. Specfically, the VA seemed superior to the TA. The training loss (TL) and validation loss (VL) achieved by the MDTL-ICDDC model on test dataset are displayed in Figure 8. The experimental outcome exposed that the MDTL-ICDDC technique has been able least values of TL and VL. Specifically, the VL seemed lower than the TL. A brief precision-recall examination of the MDTL-ICDDC method on test dataset is portrayed in Figure 9. An observation of the figure shows that the MDTL-ICDDC system has been able to achieve maximal precision-recall performance under all classes. A detailed ROC investigation of the MDTL-ICDDC approach on test dataset is depicted in Figure 10. The results indicated that the MDTL-ICDDC model has exhibited its ability in categorizing four different classes 0-3 on the test dataset. Table 3 and Figure 11 inspect a comparative \(prec_{n}\) inspection of the MDTL-ICDDC model with other models under distinct classes [30,31]. The experimental results indicated that the Gabor and BoW-SRP models have shown lower classification results with least average \(prec_{n}\) of 61.83% and 68.33% respectively.