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基于无线传感器网络的机器学习框架,用于智能城市的智能废物管理.

Karan Belsare1, Manwinder Singh1, Anudeep Gandam2

  • 1School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, 144411, India.

Heliyon
|September 10, 2024
PubMed
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此摘要是机器生成的。

本研究介绍了使用机器学习和物联网 (IoT) 进行高效垃圾分类和监控的智能废物管理系统. 该系统通过实时数据分析和自主废物分类来提高回收安全性和环境可持续性.

科学领域:

  • 环境科学与工程环境科学与工程
  • 计算机科学和人工智能 人工智能
  • 可持续发展 可持续发展 可持续发展

背景情况:

  • 有效的废物管理和回收对于可持续的经济和环境安全至关重要.
  • 传统的废物管理方法缺乏效率和安全性,需要智能解决方案.
  • 智能设备和物联网的整合为优化废物收集和分类流程提供了一条途径.

研究的目的:

  • 使用物联网和远程 (LoRa) 技术开发一个自主,智能废物参数监测系统.
  • 创建一个高效和智能废物管理系统,以提高回收的安全性和有效性.
  • 为实时垃圾跟踪和分类设计基于机器学习的架构.

主要方法:

  • 利用物联网 (IoT) 和远程 (LoRa) 技术从垃圾桶中实时收集数据.
  • 实施了四层智能废物分类框架:输入,特征,分类和输出.
  • 采用Resnet-101用于特征提取和多核支持矢量机 (SVM) 和Adaboost组合分类器用于使用Thrash Box数据集进行废物分类.

主要成果:

  • 拟议的系统成功地跟踪了废物参数,如体积,恶臭,空气质量,重量和烟雾水平.
  • 将废物精确分类为家庭,医疗和电子垃圾等类别.
关键词:
物联网的物联网,就是物联网.机器学习是机器学习.智能废物管理系统是一个智能废物管理系统.无线传感器网络无线传感器网络

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  • 与实验试验中的现有最先进模型相比,在废物分类和预测方面表现出卓越的性能.

    • 结论:

    • 开发的基于机器学习的架构为废物管理提供了高效和智能化的解决方案.
    • 物联网和先进的机器学习模型的整合提高了回收过程的安全性和有效性.
    • 这种新的系统通过优化废物处理,为环境安全和可持续的经济实践做出了贡献.