## 6 Simulation Results
### Detecting Backscatter Signals
Figures 14 and 15 depict a comparison of architectures in the training and validation datasets, respectively. Based on the figures, the ranking of the architectures from highest to lowest is as follows: HABC-DCNN, OA-DCNN, GWO-DCNN, BBO-DCNN, ABC-DCNN, PSO-DCNN, RNN, standard DCNN, and LSTM. The outcomes show that the suggested architectures have been effectively trained using meta-heuristic algorithms, as evidenced by the stability of accuracy across different hybrid DL architectures in both the test and train datasets. According to Figure 16, the ROC curves of various architectures are depicted, representing a useful tool for evaluating model performance and comparing different classifiers. The area under the curve (AUC) of HABC-DCNN outperforms other architectures, which is clearly visible in the graph. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**DL Models**} & \multicolumn{2}{c}{**Train**} & \multicolumn{3}{c}{**Test**} \\ \cline{2-7}  & **Sensitivity** & **Specificity** & **Accuracy** & **Sensitivity** & **Specificity** & **Accuracy** \\ \hline HABC-DCNN & 99.74\% & 96.48\% & 99.26\% & 98.89\% & 95.24\% & 98.32\% \\ \hline OA-DCNN & 98.85\% & 95.91\% & 98.79\% & 98.32\% & 94.92\% & 98.16\% \\ \hline GWO-DCNN & 98.34\% & 95.21\% & 98.27\% & 98.03\% & 94.36\% & 97.34\% \\ \hline ABC-DCNN & 97.76\% & 94.82\% & 97.67\% & 96.74\% & 93.88\% & 96.61\% \\ \hline BBO-DCNN & 97.82\% & 94.72\% & 97.73\% & 96.84\% & 93.76\% & 96.72\% \\ \hline PSO-DCNN & 97.42\% & 94.31\% & 97.23\% & 96.51\% & 93.21\% & 95.96\% \\ \hline RNN & 96.14\% & 93.29\% & 96.52\% & 95.27\% & 92.35\% & 94.86\% \\ \hline Standard DCNN & 95.91\% & 92.88\% & 96.43\% & 95.39\% & 92.09\% & 94.51\% \\ \hline LSTM & 95.18\% & 92.43\% & 96.19\% & 94.95\% & 92.28\% & 94.29\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of architectures in the test and train datasets. Figure 14: Comparison of DL models in training datasets. Figure 15: Comparison of DL models in validation datasets. Mean square error (MSE) criteria are also used to compare the proposed models in Table 5. The HABC-DCNN architecture has a lower MSE than the other architectures, indicating that the proposed approach is effective for use in this problem. In the proposed HABC, the migration operator of the BBO is used to improve the exploitation of the algorithm. Moving towards the global best of PSO is also proposed to improve the exploration of the ABC. As a result, the algorithm is aided in steering clear of getting stuck in local minimal. Figure 17 illustrates that the HABC-DCNN architecture converges faster than the other architectures. At epoch = 100, the HABC-DCNN architecture has achieved nearly the lowest MSE, while other architectures have higher MSE. However, the HABC-DCNN architecture exhibits high stability and rapid convergence as the epochs proceed. \begin{table}
\begin{tabular}{l l l} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**MSE**} \\ \cline{2-3}  & **Training Datasets** & **Validation Datasets** \\ \hline HABC-DCNN & 0.00008 & 0.00096 \\ \hline OA-DCNN & 0.00086 & 0.00896 \\ \hline GWO-DCNN & 0.02186 & 0.19652 \\ \hline ABC-DCNN & 0.12452 & 0.47562 \\ \hline BBO-DCNN & 0.10592 & 0.35896 \\ \hline PSO-DCNN & 0.21745 & 0.59856 \\ \hline RNN & 0.42691 & 0.69853 \\ \hline Standard DCNN & 0.55239 & 0.75263 \\ \hline LSTM & 0.59852 & 0.88745 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The MSE values of the different architectures. Figure 16: Comparison of the ROC curves of DL architectures.