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Improving real-time emotion recognition system in assistive communication technologies for disabled persons using

Turki Ali Alghamdi1, Saud S Alotaibi2, Reem M Alharthi3,4

  • 1Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia. taghamdi@uqu.edu.sa.

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Summary

This study introduces a novel system for emotion recognition in text, enhancing communication for disabled individuals. The Sustainable Emotion Recognition System for Disabled Persons Using Deep Learning and Equilibrium Optimiser for Real-Time Communication Enhancement (SERDP-DLEOCE) achieved 95.15% accuracy.

Keywords:
Disabled personElman neural networkEmotion recognitionEquilibrium optimizerText pre-processing

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Assistive Technology

Background:

  • Disability presents significant challenges, often leading to frustration and dependence.
  • Effective communication is crucial for the inclusion and growth of individuals with disabilities.
  • Machine learning offers potential for creating inclusive smart cities and improving accessibility.

Purpose of the Study:

  • To introduce a novel Sustainable Emotion Recognition System for Disabled Persons Using Deep Learning and Equilibrium Optimiser for Real-Time Communication Enhancement (SERDP-DLEOCE).
  • To enhance real-time communication for people with disabilities through advanced emotion recognition in text.
  • To improve the quality of life and independence for disabled individuals.

Main Methods:

  • Text pre-processing to prepare raw data for analysis.
  • Word2Vec for word embedding to capture semantic meaning.
  • Elman neural network (ENN) for emotion recognition, optimized by the Equilibrium Optimizer (EO) for hyperparameter tuning.

Main Results:

  • The SERDP-DLEOCE approach demonstrated superior performance in emotion recognition.
  • Achieved a high classification accuracy of 95.15% on the Emotion detection from text dataset.
  • Outperformed existing techniques in accuracy for emotion detection.

Conclusions:

  • The SERDP-DLEOCE system effectively enhances communication for disabled individuals.
  • Deep learning and optimization techniques can significantly improve emotion recognition accuracy.
  • This approach contributes to creating more inclusive and supportive smart city environments for people with disabilities.