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Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
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使用Lévy飞行和交互式交叉式爬行动物搜索算法进行超参数调整,用于对眼动事件的分类.

V Pradeep1, Ananda Babu Jayachandra2, S S Askar3

  • 1Department of Information Science and Engineering, Alva's Institute of Engineering and Technology, Mangaluru, India.

Frontiers in physiology
|May 30, 2024
PubMed
概括

这项研究引入了一种新的方法,用于检测眼动事件,使用双向长期短期记忆 (BILSTM) 网络,由爬行动物搜索算法 (LICRSA) 优化. 这种方法显著提高了辅助技术的分类准确性.

关键词:
在F1得分中,得分为F1.莱维飞行和交互式交叉飞行准确度 准确度 准确度 准确度 准确度双向长期短期记忆 双向长期短期记忆眼睛运动事件的分类,眼动事件的分类.模糊的数据增强.爬行动物搜索算法 爬行动物搜索算法

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科学领域:

  • 计算机科学 计算机科学
  • 生物医学工程 生物医学工程
  • 人工智能的人工智能

背景情况:

  • 眼动分析对于人机界面和辅助技术至关重要.
  • 为眼动事件开发准确的分类器仍然是一个重大挑战.

研究的目的:

  • 提出一种有效的眼动事件分类方法.
  • 通过超参数优化,提高双向长短期存储 (BILSTM) 网络的性能.

主要方法:

  • 使用双向长短期记忆 (BILSTM) 网络进行分类.
  • 采用莱维飞行和交互交叉式爬行动物搜索算法 (LICRSA) 来进行超参数优化.
  • 集成的模糊数据增强 (FDA) 来减轻过度拟合和VGG-19用于特征提取.

主要成果:

  • 拟议的BILSTM-LICRSA模型在四个数据集 (Lund2013,收集数据集,GazeBaseR,UTMultiView) 中实现了高性能.
  • 证明了卓越的性能,在GazeBaseR数据集上F1得分为98.99%,超过了多源信息嵌入式方法 (MSIEA).

结论:

  • BILSTM-LICRSA模型为眼动事件分类提供了强大而准确的解决方案.
  • 这种方法有望促进患者的辅助技术的发展.