在支持物联网的智能城市中,智能废物分类使用VGG16和Cat Swarm 优化随机森林
Akshat Gaurav1, Brij Bhooshan Gupta2,3,4,5,6, Varsha Arya7,8
1Ronin Institute, Montclair, New Jersey, United States of America.
PloS one
|February 28, 2025
概括
本研究介绍了一种使用VGG16和Random Forest用于智慧城市的先进废物分类模型. 该模型显著提高了垃圾分类和回收效率,精度达85%.
科学领域:
- 计算机科学 计算机科学
- 环境科学 环境科学
- 人工智能的人工智能
背景情况:
- 可持续的城市发展越来越依赖于有效的废物管理.
- 智能城市利用物联网技术来增强城市服务,包括废物管理.
- 有效的垃圾分类和回收对于环境可持续性至关重要.
研究的目的:
- 为智能城市应用开发和评估一种新的废物分类模型.
- 通过智能技术增强垃圾分类和回收机制.
- 将拟议模型的性能与传统机器学习方法进行比较.
主要方法:
- 利用VGG16模型的转移学习从废弃图像中提取特征.
- 实现了一个通过猫群优化 (CSO) 优化的随机森林分类器.
- 在Kaggle垃圾分类数据集上训练和测试模型.
主要成果:
- VGG16-CSO-随机森林模型实现了85%的准确性和0.85.85的AUC.
- 超越了传统的模型,如支持矢量机 (SVM),XGBoost和后勤回归.
- 在精度,回忆和F1得分方面表现出卓越的性能.
结论:
- 拟议的废物分类模型显示了智能城市废物管理的巨大潜力.
- 转移学习与优化的随机森林相结合,为自动垃圾分类提供了有效的解决方案.
- 这种方法可以促进更有效的回收和可持续的城市环境.
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