## 5 Results and Discussion  
In this study, Python serves as the programming language for implementing our methodologies. Our central objective revolves around the integration of the Grey Wolf Optimizer (GWO) to optimize emotion features, thereby enhancing emotion recognition performance. To validate the efficacy of our approach, we conduct experiments across four distinct emotion datasets: Emotion Database, RAVDESS, TESS, and SAVEE. Our evaluation metrics encompass classification accuracy, precision, recall, and F1-score, providing comprehensive insights into the effectiveness of the proposed methodology. It can be seen from Table 3 that the GWO optimizer, when used with classifiers like SVM, LSTM, and CNN, surpasses the conventional classification methods on all evaluation metrics. In EmoDB, the traditional Kernelized SVM shows a classification accuracy of only 55%, which is the worst among the six models. The use of GWO improved accuracy to 85%. The difference in accuracy is an improvement of 30% for EmoDB and RAVDESS, whereas the precision doubled for EmoDB and RAVDESS and improved 7\(\times\) for SAVEE, which is a huge jump from that of traditional SVM classifiers. While the use of CNNs is not as accurate as SVMs, the GWO technique managed to improve all the measurement parameters of CNN as well. Figure 4a,b display the confusion matrix representing a set of emotions comprising six categories: anger, happiness, neutral, fear, disgust, and sadness for RAVDESS. The proposed model achieved an overall accuracy of 53% when evaluated using this particular emotion set and an accuracy of 60% when evaluated with the GWO technique. Upon analyzing the confusion matrix (Figure 4a,b), it becomes apparent that the accuracy rates for the fear and neutral classes are relatively high compared to those of the other classes. In contrast, the angry class exhibits a lower classification accuracy. Additionally, there is a significant misclassification of neutral as sad and disgust as happy. Despite the underperformance of the angry class, the proposed model demonstrated effective distinction among the other emotions within this emotion set. Figure 4b displays the classification after using GWO, which, as can be seen, lowers the misclassifications by a small degree. Figure 4c,d display the confusion matrix representing a set of emotions comprising six categories: anger, happiness, neutral, fear, disgust, and sadness for SAVEE. The proposed model achieved an accuracy of 57% when evaluated using this particular emotion set and an accuracy of 68% when evaluated with the Gray Wolf Optimization technique. Upon analyzing the confusion matrix (Figure 4c,d), it becomes apparent that the accuracy rates for the angry class are relatively high compared to the other classes, whereas the fear class  
exhibits the lowest classification accuracy. Despite the underperformance of all the classes, the proposed model demonstrated effective distinction among the other emotions within this emotion set. Figure 3(d) displays classification after using GWO, which, as can be seen, reduces the incidence of misclassifications by a small degree. Figure 3(e),f display the confusion matrix representing a set of emotions comprising six categories: anger, happiness, neutral, fear, disgust, and sadness for EmoDB. The proposed model achieved an overall accuracy of 75% when evaluated using this particular emotion set and an accuracy of 78% when evaluated with the Gray Wolf Optimization technique. Upon analyzing the confusion matrix, it becomes apparent that the accuracy rates for the disgust class are relatively high compared to the other classes, whereas the fear class exhibits a lower classification accuracy. The fear class is rarely classified accurately and is the lowest correctly identified class. Figure 3(f) shows the classification after using GWO, which, as can be seen, lowers the misclassifications by a small degree.