November 30, 2025International Journal of Humanoid Robotics

Enhancing Textual Emotion Recognition to Aid People with Disabilities Using Brown-Bear Optimization with Stacked Supervised Autoencoder Model

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Authors

MAMohammed Abdullah Al-Hagery IAIbrahim Abunadi MAMalak Zayed AlamriJouf University

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Implication

This approach demonstrates improved robustness and feature extraction in emotion recognition systems for supporting people with disabilities, suggesting promising advancements in NLP.

Key Points

Emotion recognition systems show improved robustness by enhancing feature extraction through advanced techniques.
The model leverages deep learning and word embedding methods to effectively capture semantic relationships in textual data.
Brown-Bear Optimization fine-tunes hyperparameters in a Stacked Supervised Autoencoder for better performance.
The integration of NLP tools can significantly benefit accessibility by aiding those with disabilities in emotional interaction.

Cite This Study

Al-Hagery et al. (2025) studied this question.

synapsesocial.com/papers/692b9d831d383f2b2a379717 https://doi.org/10.1142/s0219843625400201

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Implication

This approach demonstrates improved robustness and feature extraction in emotion recognition systems for supporting people with disabilities, suggesting promising advancements in NLP.

Enhancing Textual Emotion Recognition to Aid People with Disabilities Using Brown-Bear Optimization with Stacked Supervised Autoencoder Model

Discussion

Member takes

Implication

Key Points

Cite This Study

Enhancing Textual Emotion Recognition to Aid People with Disabilities Using Brown-Bear Optimization with Stacked Supervised Autoencoder Model

Discussion

Member takes

Implication

Key Points

Cite This Study

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