分析员工的勤奋和挖掘基于肖像描绘的行为模式
1School of Business and Economics, Universiti Putra Malaysia, 43400, Kuala Lumpur, Selangor, Malaysia. puhai592@outlook.com.
Scientific reports
|May 24, 2024
概括
一个新的数据挖掘模型通过将勤奋分析准确度提高到80.39%,并预测软件使用率为0.82983的RMSE,从而提高了员工肖像的描述,从而提供了对员工行为更好的洞察力.
科学领域:
- 数据挖掘 数据挖掘
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 企业数字化建设需要先进的员工行为分析.
- 当前的员工肖像描绘方法往往是简单的,低效的,并产生片面的结果.
研究的目的:
- 提出一种基于数据挖掘的新型员工肖像描绘模型.
- 提高员工行为分析和预测的准确性和效率.
主要方法:
- 使用改进的生成对抗网络 (GAN) 开发了一种勤奋分析模型 (DAM).
- 使用局部异常因素 (LOF) 和隔离森林 (IF) 来检测异常行为.
- 利用基于注意力的长期短期记忆 (Att-LSTM) 模型来预测软件使用行为.
主要成果:
- 达姆在勤奋分析中实现了80.39%的准确性,超过了现有的方法.
- 达姆显示出高一致性,卡帕系数为0.7384.
- 阿特-LSTM表现出卓越的预测性能,其RMSE为0.82983和MAPE为0.80323.
结论:
- 拟议的数据挖掘方法有效地解决了当前员工行为分析的局限性.
- 该模型为构建企业级数字员工肖像提供了一个新的框架.
- 这些发现为分析和理解数字环境中的员工行为提供了有价值的见解.
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