Enhanced Emotion Recognition Using a Hybrid Autoencoder-LSTM Model Optimized with a Hybrid ACO-WOA Algorithm for Hyperparameter Tuning

• Published in: International journal of advanced computer science & applications Vol. 16; no. 4
• Main Authors: Waiker, Vinod, Ramesh, Janjhyam Venkata Naga, Bala, Kiran, Krishnaiah, V. V. Jaya Rama, Jackulin, T., Muniyandy, Elangovan, Shahin, Osama R.
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2025
• Subjects: Accuracy; Algorithms; Ant colony optimization; Automation; College professors; Computer science; Datasets; Deep learning; Emotion recognition; Emotions; Engineering; Machine learning; Optimization algorithms; Social networks; Tuning
• ISSN: 2158-107X; 2156-5570; 2156-5570
• DOI: 10.14569/IJACSA.2025.0160486

Emotion recognition is vital in the human Computer interaction because it improves interaction. Therefore, this paper proposes an improved method for emotion recognition regarding the Hybrid Autoencoder-Long Short-Term Memory (LSTM) model and the newly developed hybrid approach of the Ant Colony Optimization (ACO) and Whale Optimization Algorithm (WOA) for hyperparameters tuning. In this case, Autoencoder can reduce input data dimensionality for input data and find the features relevant for the model’s work. In addition, LSTM is able to work with temporal structures of sequential inputs like speech and videos. The contribution of this research lies in the novel combination method of ACO-WOA which aims at tweaking hyperparameters of Autoencoder-LSTM model. Global aspect of ACO and WOA thereby improve the search efficiency and the accuracy of the proposed emotion recognition system and its generalization capacity. In context with the benchmark dataset for the experimentations of emotion recognition, it has established the efficiency of the proposed model in terms of the conventional methods. Recall rates in recognitive intended various emotions and different modalities were also higher in the hybrid Autoencoder-LSTM model. The optimization algorithms like the ACO-WOA also supported in reducing the computational cost which arose due to hyperparameters tuning. The implementation of this paper is done through Python Software. This implementation shows a high accuracy of 94.12% and 95.94% for audio datasets and image datasets respectively when compared with other deep learning models of Conv LSTM and VGG16. Therefore, the research shows that the presented hybrid approach can be a useful solution for successfully employing emotion recognition for enhancing the creation of the empathetic AI systems and for improving user interactions within various fields including healthcare, entertainment, and customer support.