注意意识深度学习方法,以获得有效的压力分类模型
Muhammad Zulqarnain1, Habib Shah2, Rozaida Ghazali3
1Faculty of Computing, The Islamia University of Bahawalpur, Punjab, Pakistan.
Brain sciences
|July 29, 2023
概括
这项研究引入了一种增强的长期短期记忆 (E-LSTM) 模型,该模型具有特征注意力机制,用于准确的压力分类. 这种新的方法改善了监控系统中的压力检测和预测.
科学领域:
- 人工智能的人工智能
- 计算语言学 计算语言学
- 医疗信息学 医疗信息学
背景情况:
- 压力对现代社会产生重大影响,影响日常活动并导致各种疾病.
- 精确的压力测量对于公共卫生倡议和改善生活质量至关重要.
- 现有的压力监测系统需要先进的分类技术来解释生理和情绪反应.
研究的目的:
- 为准确的压力分类系统提出一种新的深度学习方法.
- 开发一个增强的长期短期记忆 (E-LSTM) 模型,与特征注意力机制集成,用于压力极性确定.
- 提高压力监测系统中的压力检测和预测能力.
主要方法:
- 使用了一种新的深度学习方法,增强长期短期记忆 (E-LSTM) 具有特征注意力机制.
- 采用了顺序建模和文字特征捕获来进行应力分类.
- 使用来自韩国国家健康和营养检查调查 (KNHANES VI) 的健康相关压力数据评估了该模型.
主要成果:
- 具有特征关注的E-LSTM模型实现了75.54%的精度,74.26%的精度,72.99%的回忆率和74.58%的F1得分.
- 在压力检测分类中表现优异,与像天真贝叶斯学,SVM,深信网络和标准LSTM这样的传统方法相比.
- 特征注意力机制有效地识别了复杂的关系,并提取了相关的关键词进行分类.
结论:
- 提出的基于特征注意力机制的E-LSTM方法对于准确分类压力是有效的.
- 这种方法显示了加强压力监测和预测系统的巨大潜力.
- 该研究强调了深度学习的有效性,特别是带有注意力的E-LSTM,在分析复杂的与健康有关的压力数据方面.
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