## 6 Results and Discussion
### Comparative Analysis
#### 6.4.1 Analysis with 100 Nodes  
Figure 5a illustrates the analysis of testing accuracy. For 60% of preparation samples, Table 3 shows the testing accuracy observed by deep learning, CNN, Hybrid DNN, and the SPWO-based Deep maxout network have values of 0.706, 0.728, 0.804, and 0.858, respectively, which show an effective performance. With deep learning, CNN, and Hybrid DNN, the results are: 18%, 15%, and 6%, respectively. With 70% of the preparation samples, the testing accuracy of deep learning, CNN, Hybrid DNN, and the proposed model are 0.721, 0.753, 0.845, and 0.890, respectively, which implies an increase in performance using deep learning, CNN, and Hybrid DNN of 19%, 15%, and 5%, respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Description** \\ \hline Initial energy & 1 \\ \hline Transmitter Energy & 0.0006 \\ \hline Receiver Energy & 0.0006 \\ \hline Number of nodes & 100, 150 \\ \hline Width of simulation area & 100 m \\ \hline Height of simulation area & 100 m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters utilized for the experimentation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Training Data (\%)**} & \multicolumn{4}{c}{**Testing Accuracy (Performance \%)**} \\ \cline{2-5}  & **Deep Learning** & **CNN** & **Hybrid DNN** & **SPWO-Based Deep Maxout Network** \\ \hline
60\% & 0.706 (18\%) & 0.708 (15\%) & 0.804 (6\%) & 0.858 \\ \hline
70\% & 0.721 (19\%) & 0.753 (15\%) & 0.845 (6\%) & 0.890 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Analysis of Testing Accuracy. Table 4 displays the analysis and the sensitivity is shown in Figure 5. By assuming preparation samples of 70%, sensitivity captured by deep learning, CNN, Hybrid DNN, and the proposed approach is 0.744, 0.773, 0.882, and 0.918, respectively, implying an efficient performance using deep learning, CNN, and Hybrid DNN of 19%, 16%, and 4%, respectively. With 90% preparation samples, the sensitivity of deep learning, CNN, Hybrid DNN, and the SPWO-based Deep maxout network is 0.810, 0.833, 0.911, and 0.953, which implies a higher performance using Deep learning, CNN, and Hybrid DNN by 15%, 13%, and 4%, respectively. Table 5 presents the analysis and the specificity is revealed in Figure 5. With 80% of data samples, specificity observed with deep learning, the results of CNN, Hybrid DNN, and the SPWO-based Deep maxout network are 0.743, 0.760, 0.862, and 0.891, respectively, resulting in an efficient performance improvement by deep learning, CNN, and Hybrid DNN of 17%, 15%, and 3%. By considering 90% preparation samples, the specificity computed by deep learning, CNN, Hybrid DNN, and SPWO-based Deep maxout network is 0.770, 0.796, 0.883, and 0.915, respectively, which results in an efficiency of the proposed model by analyzing with deep learning, CNN, and Hybrid DNN of 16%, 13%, and 3%, respectively. Figure 5 depicts the analysis evaluation based on energy, which is shown in Table 6. For 1000 rounds, the energy observed by SFG, MARP, Crow Whale-ETR, and the proposed SPWO is 0.2966 J, 0.2283 J, 0.0737 J, and 0.3685 J. Table 7 refers to the analysis performed in terms of delay, which is shown in Figure 5. By assuming rounds to be 1000, the delay faced by SFG, MARP, Crow Whale-ETR, and the proposed SPWO is 0.673 s, 0.753 s, 0.889 s, and 0.596 s, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No. of Rounds**} & \multicolumn{4}{c}{**Energy (in J units)**} \\ \cline{2-5}  & **SFG** & **MARP** & **CrowWhale-ETR** & **Proposed SPWO** \\ \hline
1000 & 0.2966 J & 0.2283 J & 0.0737 J & 0.3685 J \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Depicts Analysis Evaluation.