在使用鱼和鱼群优化随机森林模型的先进运输系统中进行驾驶员识别
Akshat Gaurav1, Brij B Gupta2,3,4,5,6, Razaz Waheeb Attar7
1Ronin Institute, Montclair, NJ, USA.
Scientific reports
|January 19, 2025
概括
一个新的随机森林模型使用优化功能选择和超参数调整精确识别驾驶员. 这种先进的驾驶员识别方法提高了运输系统的安全性和安全性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 运输工程 运输工程
背景情况:
- 在先进的运输系统中,驾驶员身份识别对于提高安全性,个性化和安全性至关重要.
- 现有的方法可能无法为现实应用提供足够的准确性或效率.
研究的目的:
- 提出一种新且高度准确的驾驶员识别方法.
- 用先进的算法优化一个随机森林模型用于驾驶员行为分析.
主要方法:
- 开发了一个随机森林模型,用于基于驾驶行为识别驾驶员.
- 用Osprey优化算法 (OOA) 进行了最佳特征选择.
- 使用Salp Swarm优化 (SSO) 来进行超参数调整.
主要成果:
- 拟议的模型实现了高性能指标:92%的准确性,91%的精度,93%的回忆和92%的F1得分.
- 超过了传统的机器学习模型,如XGBoost,CatBoost和支持矢量机器.
结论:
- 开发的驾驶员识别方法显示出显著的有效性和准确性.
- 这种方法为提高运输系统的安全性和效率提供了有价值的工具.
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