基于卷积神经网络的科学和技术领域的年轻人才的移动性变化的综合预测和控制
Lianfeng Xia1,2, Fanshuai Meng1,2
1Henan Polytechnic, Zhengzhou, 450046, China.
Heliyon
|March 4, 2024
概括
这项研究引入了一个卷积神经网络 (CNN) 模型来预测科学和技术领域的年轻人才流动. 与现有方法相比,CNN模型表现出卓越的准确性和速度,有助于人才管理策略.
科学领域:
- 科学与技术科学与技术
- 人力资本动力学 人力资本动力学
- 人才管理的人才管理.
背景情况:
- 科技领域年轻人才的重要性日益增大.
- 缺乏针对年轻专业人士流动性的全面预测模型.
- 需要有效的策略来管理和保留STEM领域的人才.
研究的目的:
- 开发和评估一个综合的方法,预测和管理科学和技术的年轻人才的流动.
- 评估卷积神经网络 (CNN) 在预测人才流动方面的有效性.
- 确定影响年轻专业运动的关键特征.
主要方法:
- 使用卷积神经网络 (CNN) 开发一个预测模型.
- 整合各种流动特征:个人,职业,组织和网络.
- 对反复预测错误 (RPE) 和反向传播 (BP) 算法的比较分析.
主要成果:
- 拟议的CNN模型实现了76.98%的预测准确度,超过了比较方法.
- 与基于RPE的模型相比,CNN模型的平均误差较低 (0.0285).
- 基于CNN的模型的处理时间明显更快,平均比基于BP的模型少41.6秒.
结论:
- 卷积神经网络 (CNN) 有效地利用多方面的特征预测年轻人才流动.
- 与传统方法相比,CNN模型提供了更高的准确性和处理速度.
- 这些发现为组织和政府机构提供了宝贵的见解,以提高人才管理策略.
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