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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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Auditory Pathway01:15

Auditory Pathway

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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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相关实验视频

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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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改进手语识别系统,以协助聋人使用路径查找算法与表示学习模型.

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.

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概括

本研究介绍了一种基于Pathfinder算法的新型手语识别系统 (PASLR-DDFEM),以改善聋个体的沟通. 该系统实现了98.80%的准确性,显著提高了手语识别能力.

关键词:
聋的人.埃尔曼神经网络是一个神经网络.功能提取 功能提取路径寻找者算法的算法标志语言识别功能 标志语言识别功能

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 人与计算机的交互

背景情况:

  • 标语识别 (SLR) 对于有听力障碍的人的沟通可访问性至关重要.
  • 机器学习 (ML) 和深度学习 (DL) 已经推进了SLR,但在准确性和稳定性方面仍然存在挑战.
  • 现有系统需要进一步增强,以实现现实世界的有效性.

研究的目的:

  • 提出一个新的手语识别系统 (PASLR-DDFEM),集成高级优化和表示学习.
  • 为了提高手语识别的准确性,稳定性和现实应用性.
  • 为了促进听力障碍者有效沟通.

主要方法:

  • 使用高斯过 (GF) 进行图像预处理以减少噪声.
  • 使用SE-DenseNet模型进行特征提取.
  • 使用埃尔曼神经网络 (ENN) 模型进行分类,通过Pathfinder算法 (PFA) 进行优化.

主要成果:

  • 拟议的PASLR-DDFEM方法在美国手语 (ASL) 数据集上表现出卓越的性能.
  • 实现了98.80%的高精度.
  • 在模拟研究中表现优于现有的手语识别模型.

结论:

  • PASLR-DDFEM方法显著提高了手语识别的准确性和有效性.
  • 将PFA与ENN和SE-DenseNet集成,为通信可访问性提供了一个强大的解决方案.
  • 这种系统具有很大的潜力,可以帮助聋人士进行日常沟通.