An Efficient Deep Learning with Optimization Algorithm for Emotion Recognition in Social Networks

Emotion recognition, or computers' ability to interpret people's emotional states, is a rapidly expanding topic with many life-improving applications. However, most imagebased emotion recognition algorithms have flaws since people can disguise their emotions by changing their facial expressions. As a result, brain signals are being used to detect human emotions with increased precision. However, most proposed systems could do better because electroencephalogram (EEG) signals are challenging to classify using typical machine learning and deep recommendation systems, online learning, and data mining all benefit from emotion recognition in photos. However, there are challenges with removing irrelevant text aspects during emotion extraction. As a consequence, emotion prediction is inaccurate. This paper proposes Radial Basis Function Networks (RBFN) with Blue Monkey Optimization to address such challenges in human emotion recognition (BMO). The proposed RBFN-BMO detects faces on large-scale images before analyzing face landmarks to predict facial expressions for emotional acknowledgment. Patch cropping and neural networks comprise the two stages of the RBFN-BMO. Pre-processing, feature extraction, rating, and organizing are the four categories of the proposed model. In the ranking stage, appropriate features are extracted from the pre-processed information, the data are then classed, and accurate output is obtained from the classification phase. This study compares the results of the proposed RBFNBMO algorithm to the previous state-of-the-art algorithms using publicly available datasets derived from the RBFN-BMO model. Furthermore, we demonstrated the efficacy of our framework in comparison to previous works. The results show that the projected method can progress the rate of emotion recognition on datasets of various sizes.