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改进实时情绪识别系统,用于残疾人的辅助通信技术,使用深度学习与平衡算法进行深度学习.

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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概括

这项研究引入了在文本中识别情绪的新系统,增强了残疾人的沟通能力. 使用深度学习和平衡优化器用于实时通信增强的残疾人可持续情绪识别系统 (SERDP-DLEOCE) 实现了95.15%的准确性.

关键词:
残疾人残疾人残疾人残疾人埃尔曼神经网络是一个神经网络.情绪识别 情绪识别平衡优化器的平衡优化器文字预处理 文字预处理

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科学领域:

  • 人工智能的人工智能
  • 自然语言处理自然语言处理.
  • 辅助技术 辅助技术 辅助技术

背景情况:

  • 残疾带来了重大挑战,往往导致挫折和依赖.
  • 有效的沟通对于残疾人的包容和成长至关重要.
  • 机器学习为创造包容性智能城市和改善可访问性提供了潜力.

研究的目的:

  • 为残疾人推出一种新的可持续情绪识别系统,使用深度学习和平衡优化器进行实时通信增强 (SERDP-DLEOCE).
  • 通过在文本中先进的情感识别来增强残疾人的实时沟通.
  • 改善残疾人的生活质量和独立性.

主要方法:

  • 文字预处理以准备原始数据进行分析.
  • Word2Vec用于词嵌入以捕捉语义意义.
  • 埃尔曼神经网络 (ENN) 用于情绪识别,通过平衡优化器 (EO) 优化,用于超参数调整.

主要成果:

  • 该SERDP-DLEOCE方法在情绪识别方面表现出卓越的表现.
  • 在从文本数据集中检测情绪时,获得了95.15%的高分类准确度.
  • 在情绪检测的准确性方面超越了现有的技术.

结论:

  • 该SERDP-DLEOCE系统有效地提高了残疾人的沟通.
  • 深度学习和优化技术可以显著提高情绪识别准确度.
  • 这种方法有助于为残疾人创造更具包容性和支持性的智能城市环境.