一个智能模糊神经框架用于自闭症感官评估,使用层次语言建模和基于风险的时间决策
Nabilah Abughazalah1, Majid Khan2
1Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O.Box 84428, 11671, Riyadh, Saudi Arabia.
Scientific reports
|October 1, 2025
概括
这项研究引入了一个新的混合框架,使用模糊逻辑和决策理论来改善自闭症诊断和感官分类. 该方法涉及主观性和时间模式,以获得更好的护理工具.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 模糊的逻辑 模糊的逻辑
背景情况:
- 自闭症诊断和感官测试面临的主观性,时间动态和模糊性等挑战.
- 经常性神经网络 (RNN) 和长期短期记忆 (LSTM) 网络被探索用于学习自闭症的时间感官反应模式.
研究的目的:
- 开发一个原创的混合框架,将双层次犹的语言术语集 (DHHLTS) 和时间三向决策 (TWD) 结合起来,以应对自闭症感官分类的挑战.
- 通过整合模糊逻辑和决策理论,为自闭症护理创造一个可执行的工具.
主要方法:
- 使用双模糊等级映射来处理精确的专家评估.
- 开发了一个时间意识的RNN架构,灵感来自Hamacher t-norm/t-conorm聚合.
- 实施可解释的概率TWD用于风险分类.
主要成果:
- 混合框架有效地处理自闭症感官数据的主观和时间方面.
- 拟议的模型提供了精确的专家评估和风险分类.
- 证明了在自闭症护理中的可执行工具的潜力.
结论:
- 新的混合框架通过整合DHHLTS和TWD,为自闭症感官分类提供了强大的解决方案.
- 这项研究在将模糊逻辑和决策理论应用于神经发育障碍评估方面取得了进展.
- 开发的工具增强了改善自闭症患者护理和支持的潜力.
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