使用深度学习模型的新和高效的个性化压力检测技术
Ulligaddala Srinivasarao1, Gopisetty Rathnamma2, M Satish Kumar3
1Department of CSE, GITAM (Deemed to be) University, Rudraram Village, Hyderabad, India. ulligaddalasrinu@gmail.com.
Scientific reports
|August 21, 2025
概括
这项研究引入了一种有效的方法来检测社交媒体文字中的压力. 这种新的方法将高级文本表示与深度学习模型集成在一起,在压力检测中实现高精度.
科学领域:
- 计算语言学
- 人工智能
- 心理学
背景情况:
- 压力严重影响成人和老年人的健康,导致慢性疾病.
- 由于复杂性和计算需求, 从社交媒体文本中检测压力存在挑战.
- 现有的机器学习和深度学习模型面临长时间的培训时间和功能限制等局限性.
研究的目的:
- 开发一种高效准确的社交媒体文本压力检测技术.
- 在培训时间和功能利用方面克服现有方法的局限性.
- 使用新型模型整合和优化来提高应力检测的准确性.
主要方法:
- 集成先进的文本表示技术:FastText,字体表示的全球向量 (Glove),DeepMoji和XLNet.
- 使用深度可分离卷积与残余网络 (DSC-ResNet) 进行精确的应力检测.
- 使用混沌的Fennec Fox优化算法 (CFFO) 进行超参数调整.
主要成果:
- 这种技术的准确性高达98.42%.
- 精度,回忆力,特异性和F1分数分别为97. 58%,98. 12%,98. 28%和98. 38%.
- 与现有技术相比,该模型表现出更高的性能.
结论:
- 文本表示和DSC-ResNet的新型集成为压力检测提供了有效的解决方案.
- 提出的方法有效地解决了先前方法的局限性,提供了高精度和性能.
- 这项技术有望通过社交媒体分析在心理健康监测中应用.
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