## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Entire Dataset**} & \\ \hline
**Labels** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F-Score** \\ \hline FEAR & 98.49 & 93.91 & 95.60 & 98.97 & 94.75 \\ \hline JOY & 99.03 & 96.60 & 96.60 & 99.43 & 96.60 \\ \hline ANGER & 98.54 & 95.35 & 94.40 & 99.23 & 94.87 \\ \hline SAD & 99.06 & 96.61 & 96.80 & 99.43 & 96.70 \\ \hline DISCUST & 98.63 & 95.93 & 94.40 & 99.33 & 95.16 \\ \hline GUILT & 98.89 & 95.28 & 97.00 & 99.20 & 96.13 \\ \hline SHAME & 98.80 & 96.36 & 95.20 & 99.40 & 95.77 \\ \hline
**Average** & **98.78** & **95.72** & **95.71** & **99.29** & **95.71** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result analysis of CLBEDC-SND approach with distinct class labels under entire dataset. Figure 4: Result analysis of CLBEDC-SND approach under entire dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Testing Phase (30\%)**} \\ \hline
**Labels** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F-Score** \\ \hline FEAR & 98.29 & 93.15 & 94.44 & 98.90 & 93.79 \\ \hline JOY & 98.95 & 98.11 & 95.12 & 99.66 & 96.59 \\ \hline ANGER & 98.95 & 96.48 & 95.80 & 99.45 & 96.14 \\ \hline SAD & 98.48 & 93.24 & 95.83 & 98.90 & 94.52 \\ \hline DISGUST & 98.95 & 94.74 & 96.92 & 99.24 & 95.82 \\ \hline GUILT & 98.67 & 95.43 & 96.53 & 99.09 & 95.98 \\ \hline SHAME & 98.76 & 97.28 & 94.08 & 99.55 & 95.65 \\ \hline
**Average** & **98.72** & **95.49** & **95.53** & **99.25** & **95.50** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Result analysis of CLBEDC-SND approach with distinct class labels under 30% of TS data. Figure 5: Result analysis of CLBEDC-SND approach under 70% of TR data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Training Phase (70\%)**} \\ \hline
**Labels** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F-Score** \\ \hline FEAR & 98.57 & 94.21 & 96.07 & 99.00 & 95.13 \\ \hline JOY & 99.06 & 95.89 & 97.32 & 99.34 & 96.60 \\ \hline ANGER & 98.37 & 94.90 & 93.84 & 99.14 & 94.37 \\ \hline SAD & 99.31 & 98.02 & 97.19 & 99.67 & 97.60 \\ \hline DISGUST & 98.49 & 96.38 & 93.51 & 99.38 & 94.92 \\ \hline GUILT & 98.98 & 95.21 & 97.25 & 99.25 & 96.22 \\ \hline SHAME & 98.82 & 95.97 & 95.69 & 99.33 & 95.83 \\ \hline
**Average** & **98.80** & **95.80** & **95.84** & **99.30** & **95.81** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Result analysis of CLBEDC-SND approach with distinct class labels under 70% of TR data. The training accuracy (TRA) and validation accuracy (VLA) attained by the CLBEDC-SND algorithm on test dataset is exemplified in Figure 7. The experimental result implicit the CLBEDC-SND algorithm has gained maximum values of TRA and VLA. Seemingly the VLA is greater than TRA. The training loss (TRL) and validation loss (VLL) reached by the CLBEDC-SND method on test dataset are given in Figure 8. The TRL and VLL values needs to be lower for enhanced classification results. The experimental outcome showed the CLBEDC-SND technique has established minimal values of TRL and VLL. Particularly, the VLL is lower than TRL. Figure 6: Result analysis of CLBEDC-SND approach under 30% of TS data. Figure 7: TRA and VLA analysis of CLBEDC-SND approach. A clear precision-recall inspection of the CLBEDC-SND algorithm on test dataset is portrayed in Figure 9. The precision-recall curve demonstrates the tradeoff between precision and recall for various threshold values. A high area under the curve denotes increased high recall and high precision, where high precision related to a low false positive rate, and high recall related to a low false negative rate. The figure denoted the CLBEDC-SND technique has resulted in enhanced values of precision-recall values under all classes. A brief ROC study of the CLBEDC-SND algorithm on the test dataset is portrayed in Figure 10. The outcomes denoted that the CLBEDC-SND method has shown its ability in categorizing distinct classes on test dataset.