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相关概念视频

Modeling in Therapy01:26

Modeling in Therapy

116
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
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Autism Spectrum Disorder01:19

Autism Spectrum Disorder

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Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
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基于使用深度学习和机器学习模型的移动应用程序用户反分析的自闭症友好环境.

Mariem Haoues1,2, Raouia Mokni3,4

  • 1Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia.

PeerJ. Computer science
|September 14, 2023
PubMed
概括

对自闭症谱系障碍 (ASD) 应用程序的用户反的分析揭示了如何提高他们的服务. 机器学习模型准确地分类评论,指导自闭症学生和员工的改进.

关键词:
自闭症谱系障碍 自闭症谱系障碍适合自闭症的环境深度学习模型深度学习模型这是LSTM的LSTM.移动应用程序 移动应用程序一个RNN RNN情绪分析是一种情绪分析.用户反是用户反.

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相关实验视频

Last Updated: Jul 16, 2025

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科学领域:

  • 神经发育障碍 神经发育障碍
  • 人与计算机的交互
  • 应用机器学习应用机器学习

背景情况:

  • 自闭症患者经常面临就业和教育方面的挑战.
  • 针对自闭症谱系障碍的移动应用程序 (ASD应用程序) 旨在支持日常生活和条件管理.
  • 用户反分析对于改善ASD应用程序功能和用户体验至关重要.

研究的目的:

  • 调查ASD应用程序在改善自闭症学生和员工的生活质量的有效性.
  • 分析高排名的ASD应用程序的用户评论,以确定服务增强的领域.
  • 为ASD应用程序开发人员提供数据驱动的建议.

主要方法:

  • 在Google Play和Apple App商店收集了13个ASD应用程序中的97,051条用户评论.
  • 将评论分为负面,积极和中立的情绪类别.
  • 采用机器学习和深度学习模型,特别是结合反复神经网络 (RNN) 和长期短期记忆 (LSTM) 模型,进行情绪分析.

主要成果:

  • 使用RNN和LSTM模型进行综述分类,实现了高精度 (96.58%) 和曲线下面面积 (AUC) 99.41%.
  • 识别了用户反中的关键主题和情绪,以确定当前ASD应用程序的优缺点.
  • 证明了先进的机器学习技术在分析大规模用户生成数据方面的有效性.

结论:

  • 用户反分析是增强ASD应用程序的宝贵工具.
  • 结合RNN-LSTM模型在ASD应用程序评论的情绪分析中提供了卓越的性能.
  • 从此分析中得出的建议可以指导开发人员升级ASD应用程序服务,以更好地支持自闭症患者.