用于认知行为分析的机器学习:数据集,方法,范式和研究方向
Priya Bhatt1, Amanrose Sethi1, Vaibhav Tasgaonkar1
1Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, India.
Brain informatics
|July 31, 2023
概括
这项研究审查了认知行为分析,探索人工智能和机器学习 (AI/ML) 如何推断人类情绪和欺骗法医和心理健康应用. 它检查了各种方法和数据集,以推进行为科学研究.
科学领域:
- 行为科学 行为科学
- 认知科学 认知科学
- 法医科学 法医科学 法医科学
背景情况:
- 人类的行为与认知能力和情感经验密切相关.
- 区分思想,感受和行为对于情绪调节和有效反应至关重要.
- 欺骗检测是连接人类行为分析的关键应用,特别是在法医环境中.
研究的目的:
- 提供关于认知行为分析研究的广泛审查.
- 探索人工智能和机器学习 (AI/ML) 在推断人类行为的应用.
- 关于认知行为分析各方面的参数研究.
主要方法:
- 审查关于认知行为分析现有研究.
- 参数研究考虑身体特征,情绪行为和感知机制.
- 使用AI/ML模型分析单模和多模数据集.
主要成果:
- 人工智能/ML显著有助于模式识别,数据提取和行为分析的解释.
- 该研究评估了用于认知行为分析的不同AI/ML建模方法.
- 确定该领域的关键挑战和未来研究方向.
结论:
- 由AI/ML增强的认知行为分析对心理健康和法医调查具有重大潜力.
- 需要进一步的研究来应对挑战,并完善AI / ML应用程序来理解复杂的人类行为.
- 该评论强调了认知行为分析的跨学科性质,整合了心理学,计算机科学和法医学.
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