## 4 Results and Discussion  
The proposed model is simulated using Python 3.6.5 tool on PC i5-8600k, GeForce 1050 Ti 4 GB, 16 GB RAM, 250 GB SSD, and 1 TB HDD. The parameter settings are given as follows: learning rate: 0.01, dropout: 0.5, batch size: 5, epoch count: 50, and activation: ReLU. In this study, the emotion classification results of the CLBEDC-SND method are tested using a dataset comprising 3500 samples. The dataset holds samples under 7 different class labels as depicted in Table 1.  
The emotion classifier results of the CLBEDC-SND model are presented in the form of confusion matrix in Figure 3. The figure implied that the CLBEDC-SND model has properly identified all of the emotions that exist in the dataset. \begin{table}
\begin{tabular}{c c} \hline
**Class** & **No. of Samples** \\ \hline FEAR & 500 \\ \hline JOY & 500 \\ \hline ANGER & 500 \\ \hline SAD & 500 \\ \hline DISGUST & 500 \\ \hline GUILT & 500 \\ \hline SHAME & 500 \\ \hline
**Total Number of Samples** & **3500** \\ \hline \end{tabular}
\end{table} 
Table 1: Dataset details. Table 2 and Figure 4 provide overall emotion classification outcomes of the CLBEDC-SND method on entire dataset. The experimental values indicated the CLBEDC-SND method has achieved improved results under all classes. For instance, in FEAR class, the CLBEDC-SND model has offered \(accu_{y}\) of 98.49%, \(prec_{n}\) of 93.91%, \(rec_{a}\) of 95.60%, \(spec_{y}\) of 98.97%, and \(F_{score}\) of 94.75%. Moreover, in JOY class, the CLBEDC-SND approach has rendered \(accu_{y}\) of 99.03%, \(prec_{n}\) of 96.60%, \(rec_{a}\) of 96.60%, \(spec_{y}\) of 99.43%, and \(F_{score}\) of 96.60%. Likewise, in ANGER class, the CLBEDC-SND algorithm has provided \(accu_{y}\) of 98.54%, \(prec_{n}\) of 95.35%, \(rec_{a}\) of 94.40%, \(spec_{y}\) of 99.23%, and \(F_{score}\) of 94.87%. Figure 3: Confusion matrices of CLBEDC-SND approach (**a**) Entire dataset, (**b**) 70% of TR data, and (**c**) 30% of TS data. Table 3 and Figure 5 offer overall emotion classification outcomes of the CLBEDC-SND approach on 70% of training (TR) dataset. The experimental values showed that the CLBEDC-SND method have achieved improved results under all classes. For example, in FEAR class, the CLBEDC-SND algorithm has presented \(accu_{y}\) of 98.57%, \(prec_{n}\) of 94.21%, \(rec_{1}\) of 96.07%, \(spec_{y}\) of 99%, and \(F_{score}\) of 95.13%. Further, in JOY class, the CLBEDC-SND technique has rendered \(accu_{y}\) of 99.06%, \(prec_{n}\) of 95.89%, \(rec_{1}\) of 97.32%, \(spec_{y}\) of 99.34%, and \(F_{score}\) of 96.60%. Similarly, in ANGER class, the CLBEDC-SND approach has granted \(accu_{y}\) of 98.37%, \(prec_{n}\) of 94.90%, \(rec_{1}\) of 93.84%, \(spec_{y}\) of 99.14%, and \(F_{score}\) of 94.37%. Table 4 and Figure 6 demonstrate complete emotion classification outcomes of the CLBEDC-SND approach on 30% of testing (TS) dataset. The experimental values specified the CLBEDC-SND technique have gained improved results under all classes. For example, on FEAR class, the CLBEDC-SND approach has offered \(accu_{y}\) of 98.29%, \(prec_{n}\) of 93.15%, \(rec_{1}\) of 94.44%, \(spec_{y}\) of 98.90%, and \(F_{score}\) of 93.79%. Further, on JOY class, the CLBEDC-SND technique has granted \(accu_{y}\) of 98.95%, \(prec_{n}\) of 98.11%, \(rec_{1}\) of 95.12%, \(spec_{y}\) of 99.66%, and \(F_{score}\) of 96.59%. Likewise, on ANGER class, the CLBEDC-SND approach has presented \(accu_{y}\) of 98.95%, \(prec_{n}\) of 96.48%, \(rec_{1}\) of 95.80%, \(spec_{y}\) of 99.45%, and \(F_{score}\) of 96.14%.