人工智能作为一个工具来减少组织性问卷中的项目
Ivan Mihajlović1, Nikola Petrović1, Vesna Spasojević Brkić1
1Faculty of Mechanical Engineering, University of Belgrade, Serbia.
概括
人工智能 (AI) 可以通过确定评估组织弹性最相关的问题来减少冗长的安全问卷. 这种机器学习方法提高了安全气候研究中的数据收集效率和准确性.
科学领域:
- 职业健康和安全问题 职业健康和安全问题
- 研究中的人工智能.
- 心理测量 心理测量 心理测量
背景情况:
- 缺乏安全气候和弹性标准化的问卷.
- 目前的方法涉及漫长的问卷,增加了受访者负担.
- 在这种情况下,人工智能并没有被用来减少项目.
研究的目的:
- 探索人工智能的使用,以减少安全气候和弹性评估中的问卷单项.
- 开发一个用于集群受访者评级和预测组织性的模型.
- 以足够的准确性确定最相关和有信息性的问题.
主要方法:
- 使用的人工智能工具:多重线性回归分析 (MLRA) 和多层感知器人工神经网络 (MLP ANN).
- 开发了一个模型来聚合受访者评级.
- 基于特定问题评级预测的组织弹性.
主要成果:
- 人工智能展示了作为减少项目的一个有价值的工具的潜力.
- MLRA实现了70.4-71.5%的预测准确度.
- MLP ANN的预测准确率达到了76.4%.
结论:
- 机器学习算法可以有效地构建用于调查项目选择的预测模型.
- 这些模型使用安全气候因素识别了最能预测组织性的问题.
- 人工智能提供了一种优化安全和弹性研究问卷设计的方法.
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