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Learning Disabilities01:25

Learning Disabilities

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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
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A novel hybrid attention based deep learning framework for textual emotion recognition using natural language

Mohammed Abdullah Al-Hagery1, Abeer A K Alharbi2, Abdulwhab Alkharashi3

  • 1Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia. hajry@qu.edu.sa.

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Summary

This study introduces a novel deep learning technique for recognizing emotions in text, enhancing assistive technologies for individuals with disabilities. The method achieved 98.86% accuracy, improving understanding and support for disabled persons.

Keywords:
Deep learningDisabled personsEmotion recognitionNatural language processingWord embedding

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Disability presents significant challenges, often limiting individual involvement and growth.
  • Assistive technologies are crucial for enhancing the independence and quality of life for people with disabilities.
  • Emotion detection from text is vital for developing more responsive and empathetic technological solutions.

Purpose of the Study:

  • To propose a Novel Hybrid Attention-Based Deep Learning for Textual Emotion Recognition Using Natural Language Processing Technologies (HADLTER-NLPT).
  • To improve assistive technologies and emotional understanding for disabled persons through accurate text-based emotion recognition.
  • To enhance the capabilities of machine learning in creating supportive environments for individuals with disabilities.

Main Methods:

  • Text pre-processing for data cleaning and normalization.
  • Word2Vec for semantic word embedding.
  • Hybrid Attention-based Long Short-Term Memory (HA-LSTM) for emotion classification.
  • Oscillating Chaotic Sunflower Optimization (OCSFO) for hyperparameter tuning.

Main Results:

  • The HADLTER-NLPT technique demonstrated superior performance in textual emotion recognition.
  • Achieved a high accuracy of 98.86% on the Emotion detection from text dataset.
  • Outperformed existing models in recognizing emotional expressions from textual data.

Conclusions:

  • The HADLTER-NLPT model offers a significant advancement in emotion detection from text.
  • This technology can substantially benefit the development of assistive tools for disabled individuals.
  • The proposed method highlights the potential of deep learning and NLP in addressing challenges faced by people with disabilities.