## 4 Results and Discussion
### Result Analysis
Figure 4: Average outcome of IMFL-IDSCS system under 80:20 of TR/TS databases. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Class** & \(Acc_{y}\) & \(Prec_{n}\) & \(Reca_{1}\) & \(F_{score}\) & MCC \\ \hline \multicolumn{5}{l}{**Training Phase (70\%)**} \\ \hline Dos & 98.80 & 97.88 & 98.85 & 98.36 & 97.42 \\ R2l & 99.65 & 82.73 & 69.93 & 75.79 & 75.89 \\ Probe & 99.39 & 95.57 & 97.99 & 96.77 & 96.44 \\ U2r & 99.96 & 00.00 & 00.00 & 00.00 & 00.00 \\ Normal & 99.03 & 99.49 & 98.69 & 99.09 & 98.06 \\ \hline \multicolumn{5}{l}{**Average**} & **99.37** & **75.14** & **73.09** & **74.00** & **73.56** \\ \hline \multicolumn{5}{l}{**Testing Phase (30\%)**} \\ \hline Dos & 98.88 & 97.93 & 98.99 & 98.46 & 97.58 \\ R2l & 99.58 & 78.23 & 68.39 & 72.98 & 72.94 \\ Probe & 99.37 & 95.54 & 97.76 & 96.64 & 96.30 \\ U2r & 99.96 & 00.00 & 00.00 & 00.00 & \(-\)0.01 \\ Normal & 99.10 & 99.59 & 98.72 & 99.15 & 98.19 \\ \hline \multicolumn{5}{l}{**Average**} & **99.38** & **74.26** & **72.77** & **73.45** & **73.00** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Result analysis of IMFL-IDSCS system under 70:30 of TR/TS databases. The training loss (TLS) and validation loss (VLS) of the IMFL-IDSCS methodology are tested on ID performance in Figure 7. The figure displays that the IMFL-IDSCS algorithm revealed better performance with the least values of TLS and VLS. It is seen that the IMFL-IDSCS technique reduced the VLS outcomes. A clear precision-recall examination of the IMFL-IDSCS method in the test database is portrayed in Figure 8. The results specified that the IMFL-IDSCS methodology had enhanced precision-recall values in class labels. The detailed ROC study of the IMFL-IDSCS method under the test database is given in Figure 9. The outcomes denoted by the IMFL-IDSCS approach showed its ability to classify different classes under the test database. outcome of the IMFL-IDSCS model. For instance, based on \(accu_{y}\), the IMFL-IDSCS model provided a maximum \(accu_{y}\) of 99.31%, whereas the LKM-OFLS, K-means-OFLS, FCM-OFLS, MLP, and PCA-NN models gained reduced performance with \(accu_{y}\) of 89.34%, 91.43%, 93.38%, 91.46%, and 90.08%, respectively. Additionally, based on \(F_{score}\), the IMFL-IDSCS approach presented a maximum \(F_{score}\) of 81. In Figure 11, a close inspection of the IMFL-IDSCS method with other ML methods in terms of \(prec_{n}\) and \(reca_{l}\) is given. The outcomes exhibit the enhanced outcome of the IMFL-IDSCS approach. For instance, based on \(prec_{n}\), the IMFL-IDSCS methodology presented maximum \(prec_{n}\) of 92.03%, whereas the LKM-OFLS, K-means-OFLS, FCM-OFLS, MLP, and PCA-NN methods obtained reduced performance with \(prec_{n}\) of 84.64%, 85.74%, 82.74%, 86.61%, and 84.56%, correspondingly. Similarly, based on \(reca_{l}\), the IMFL-IDSCS approach \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Methods** & \(Acc_{y}\) & \(Prec_{n}\) & \(Reca_{l}\) & \(F_{score}\) \\ \hline IMFL-IDSCS & **99.31** & **92.03** & **78.25** & **81.80** \\ LKM-OFLS [17] & 89.34 & 84.64 & 74.68 & 78.26 \\ K-means-OFLS [17] & 91.43 & 85.74 & 75.51 & 78.33 \\ MLP [29] & 91.46 & 86.61 & 76.76 & 74.99 \\ PCA-NN [30] & 90.08 & 84.56 & 76.06 & 77.54 \\ FCM-OFLS [31] & 93.38 & 82.74 & 74.43 & 75.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of IMFL-IDSCS system with other approaches. Figure 10: \(Acc_{y}\) and \(F_{score}\) analysis of IMFL-IDSCS system with other approaches. Figure 9: ROC curve analysis of IMFL-IDSCS system. rendered maximum \(reca_{l}\) of 78.25%. In contrast, the LKM-OFLS, K-means-OFLS, FCM-OFLS, MLP, and PCA-NN techniques achieved reduced performance with \(reca_{l}\) of 74.68%, 75.51%, 74.43%, 76.76%, and 76.06% correspondingly. These results highlight the superior outcomes of the IMFL-IDSCS model. The enhanced performance of the proposed model is due to the inclusion of the ECOA-FS model, which decreases the computational burden and increases the classification rate.