Optimizing Emotion Recognition of Non-Intrusive E-Walking Dataset
DOI:
https://doi.org/10.56294/dm2023162Keywords:
Metaheuristic Optimization, Emotion Recognition (ER), Chiropteran Mahi Metaheuristic, E-WalkingAbstract
Emotion recognition being a complex task because of its valuable usages in critical fields like Robotics, human-computer interaction and mental health has recently gathered huge attention. The selection and optimization of suitable feature sets that can accurately capture the underlying emotional states is one of the critical challenges in Emotion Recognition. Metaheuristic optimization techniques have shown promise in addressing this challenge by efficiently exploring the large and complex feature space. This research paper proposes a novel framework for emotion recognition that uses metaheuristic optimization. The key idea behind metaheuristic optimization is to explore the search space in an intelligent way, by generating candidate solutions and iteratively improving them until an optimal or near-optimal solution is found. The accuracy & robustness of emotion identification systems can be enhanced by optimizing the metaheuristic optimization. The major contribution of this research is to develop a Chiropteran Mahi Metaheuristic optimization which emphasizes the weights updating in the classifier for improving the accuracy of the proposed system
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