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Related Concept Videos

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
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Auditory Pathway01:15

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Auditory pathways constitute the complex neural circuits responsible for transmitting and interpreting auditory information from the peripheral auditory system to the brain. Sound waves are initially captured by the outer ear, funneled through the ear canal, and reach the tympanic membrane (eardrum). These vibrations are transmitted via the middle ear's ossicles to the inner ear's cochlea.
When viewed cross-sectionally, the cochlea reveals the scala vestibuli and scala tympani flanking...
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Related Experiment Video

Updated: Jan 7, 2026

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Improving sign Language recognition system for assisting deaf and dumb people using pathfinder algorithm with

Nadhem Nemri1, Mohammed Yahya Alzahrani2, Wided Bouchelligua3

  • 1Department of Information Systems, Applied College at Mahayil, King Khalid University, Khalid , Saudi Arabia. nnemri@kku.edu.sa.

Scientific Reports
|December 30, 2025
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Summary
This summary is machine-generated.

This study introduces a novel Pathfinder Algorithm-based Sign Language Recognition System (PASLR-DDFEM) to improve communication for deaf and dumb individuals. The system achieved 98.80% accuracy, significantly enhancing sign language recognition capabilities.

Keywords:
Deaf and dumb peopleElman neural networkFeature extractionPathfinder algorithmSign language recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Sign Language Recognition (SLR) is crucial for communication accessibility for individuals with hearing impairments.
  • Machine learning (ML) and deep learning (DL) have advanced SLR, but challenges in accuracy and robustness remain.
  • Existing systems require further enhancement for real-world effectiveness.

Purpose of the Study:

  • To propose a novel Sign Language Recognition system (PASLR-DDFEM) integrating advanced optimization and representation learning.
  • To improve the accuracy, robustness, and real-world applicability of Sign Language Recognition.
  • To facilitate effective communication for individuals with hearing challenges.

Main Methods:

  • Image pre-processing using Gaussian filtering (GF) for noise reduction.
  • Feature extraction using the SE-DenseNet model.
  • Classification using the Elman neural network (ENN) model, optimized by the Pathfinder Algorithm (PFA).

Main Results:

  • The proposed PASLR-DDFEM method demonstrated superior performance on the American Sign Language (ASL) dataset.
  • Achieved a high accuracy of 98.80%.
  • Outperformed existing Sign Language Recognition models in simulation studies.

Conclusions:

  • The PASLR-DDFEM approach significantly enhances Sign Language Recognition accuracy and effectiveness.
  • The integration of PFA with ENN and SE-DenseNet offers a robust solution for communication accessibility.
  • This system holds great potential for assisting deaf and dumb individuals in daily communication.