## 6 Results and Discussion
### Comparative Analysis
#### 6.4.2 Analysis with 150 nodes  
Figure 5(a) illustrates the evaluation of testing accuracy. With 70% preparation samples, Table 8 shows that the testing accuracy measured by Deep learning, CNN, Hybrid DNN, and SPWO-based Deep maxout network is 0.719, 0.749, 0.841, and 0.888, respectively, resulting in a performance of the developed classification scheme by analysis with deep learning, CNN, and Hybrid DNN of 19%, 16%, and 5%, respectively. For 90% preparation samples, testing accuracy observed using deep learning, CNN, Hybrid DNN, and the SPWO-based Deep maxout network is 0.791, 0.809, 0.890, and 0.928, respectively, which implies a higher performance with deep learning, CNN, and Hybrid DNN of 15%, 13%, and 4%. Table 9 refers to the analysis using sensitivity, which is portrayed in Figure 5(b). For preparation samples considered at 80%, the sensitivity captured by deep learning, CNN, Hybrid DNN, and SPWO-based Deep maxout network is 0.771, 0.789, 0.90, and 0.930, respectively, which shows a performance improvement using deep learning, CNN, and Hybrid DNN of 17%, 15%, and 3%. With 90% preparation samples, the sensitivity of deep learning, CNN, Hybrid DNN, and the proposed model is 0.808, 0.828, 0.910, and 0.949, respectively, which implies a higher performance using deep learning, CNN, and Hybrid DNN of 15%, 13%, and 4%. Table 10 shows the analysis based on specificity, which is shown in Figure 5(c). When 80% of data samples are considered, the specificity observed with deep learning, CNN, Hybrid DNN, and the SPWO-based Deep maxout network is 0.741, 0.759, 0.859, and 0.888, respectively, which results in a proficient performance by deep learning, CNN, and Hybrid DNN of 17%, 14%, and 3%. By considering 90% preparation samples, specificity computed by deep learning, CNN, Hybrid DNN, and the SPWO-based Deep maxout network is 0.768, 0.791, 0.878, and 0.910, respectively. The results of the proposed model by analyzing with deep learning, CNN, and Hybrid DNN are 16%, 13%, and 4%, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Training Data (\%)**} & \multicolumn{4}{c}{**Specificity (Performance \%)**} \\ \cline{2-5}  & **Deep Learning** & **CNN** & **Hybrid DNN** &
\begin{tabular}{c} **SPWO-Based Deep** \\ **Maxout Network** \\ \end{tabular} \\ \hline
80\% & 0.741 (17\%) & 0.759 (14\%) & 0.859 (3\%) & 0.888 \\ \hline
90\% & 0.768 (16\%) & 0.791 (13\%) & 0.878 (4\%) & 0.910 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Analysis with Sensitivity. \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** &
\begin{tabular}{c} **SPWO-based Deep** \\ **Maxout Network** \\ \end{tabular} \\ \hline
70\% & 0.719 (19\%) & 0.749 (16\%) & 0.841 (5\%) & 0.888 \\ \hline
90\% & 0.791 (15\%) & 0.809 (13\%) & 0.890 (4\%) & 0.928 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The Analysis of Testing Accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Training Data (\%)**} & \multicolumn{4}{c}{**Sensitivity (Performance \%)**} \\ \cline{2-5}  & **Deep Learning** & **CNN** & **Hybrid DNN** &
\begin{tabular}{c} **SPWO-based Deep** \\ **Maxout Network** \\ \end{tabular} \\ \hline
80\% & 0.771 (17\%) & 0.789 (15\%) & 0.900 (3\%) & 0.930 \\ \hline
90\% & 0.808 (15\%) & 0.828 (13\%) & 0.910 (4\%) & 0.949 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Analysis with Sensitivity. Table 11 presents an analysis performed with energy, which is shown in Figure 6d. For 1000 rounds, the energy observed by SFG, MARP, Crow Whale-ETR, and proposed SPWO is 0.2906 J, 0.2240 J, 0.1973 J, and 0.3697 J, respectively. Figure 6e shows the delay measure, which is also shown in Table 12. The delay faced by methods such as SFG, MARP, Crow Whale-ETR, and the proposed SPWO, when considering the rounds as 1000, are 0.6812 s, 0.7576 s, 0.7648 s, and 0.6007 s, respectively.