## 4 Simulation Results
### Proposed Emotion Recognition Algorithm  
The highest accuracy of 70.43% and 76.05% for valence and arousal scenarios is produced by the BS model using CNN as the feature extractor and the SCFs as represented in Table 5. Therefore, we propose an intelligent algorithm for emotion recognition as shown in Figure 4.  
Figure 4: Proposed flow of emotion detection algorithm.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref., Year** & **Method** & **Ac (\%)** & **F1 (\%)** & **Cons** & **Pros** \\ \hline
[14], 2021 & - Scalogram images constructed by CVT and preselected ID channels. & 61.5/58.5 & NA & - Preselection of 10 channels - Low detection accuracy - Reliability improved by cross-dataset validation. \\ \hline
[19], 2023 & - Welch method for signal decomposition. & & & - Only PSD considered for - Policy extraction. - PCA for dimension reduction and quantum SVM for classification. & 65.6/75.0 & 7.0/60.0 & - Positive extraction. - Time-consuming for kernel selection of quantum SVM. - Effective PCA for feature selection. \\ \hline \multirow{4}{*}{[21], 2023} & - Generation of different sub-bands for the construction of topographic maps & & & - Only a method of Cartesian coordinates for sub-band construction. & - Effective topographic maps converted from sub-bands as the input. \\  & - Spatial feature extraction using CNN & 67.5/68.3 & 68.0/68.2 & & - Time-consuming for DL parameter and structure optimization and LSTM. & - Better deep learning representatives of CNN and structure optimization \\ \hline
[21], 2021 & - Segmentation of EEG signals. & & & - Low detection accuracy. - Combination of CNN, RNN, GAN for emotion detection. & 56.4/58.5 & 70.8/72.0 & - Low detection accuracy. - Time-consuming for training. Difficulties of parameter selection processes. & - A combination of different models to construct a detection network. \\ \hline \multirow{4}{*}{[20], 2023} & - 3D spatial-spectral feature extraction using the Welch method applied to different sub-bands. & & & - Time-consuming for distance & - Deep features improved by multiple learning processes. & - Deep features improved by multiple learning processes. \\  & - Generation of adjacency matrix using contextual loss & 57.7/58.3 & NA & - Preselection of DL model. & - Effective 3D spatial-spectral feature with adjacency matrix using trainable adjacency relation for optimization. \\  & - computation with GCN. & & & - Low detection accuracy. - Attribution & - Proposal of BLSTM with hyper-parameter optimization. & - Optimal training. \\ \hline
[27], 2021 & - Utility of different DL and ML for the comparison. & 58.4/NA & 56.0/NA & - Time consuming for DL parameter optimization & - Utility of a feature for the proposed algorithm. & - Feature quality improvement by dual GA for channel and feature selection and hybrid learning process. & - Feature quality improvement by learning process. & - Feature quality improvement by dual GA for learning process. & - Gradient search for hyperparameter optimization. \\ \hline Proposed algorithm & - Dual GA for feature and channel selection. - CNN as the feature extractor. - BS as the classifier & 70.4/76.1 & 81.8/85.1 & - Time-consuming for GA and CV procedures. - Small dataset. & - Investigation of a large number of input features. - Similarity optimal features extracted from individual sub-bands. & - Similarity optimal features extracted from individual sub-bands. & - Performance comparison of various ML and DL models. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of the proposed algorithm to the existing works for valence/arousal on the DEAP dataset.