通过基于 LBBOA 的 RIAN 分类加强智慧城市垃圾管理

Sankar K, Gokula Krishnan V, V. S, Kaviarasan S, Arockia Abins A
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引用次数: 0

摘要

有效的垃圾管理已成为环境问题的重中之重,尤其是在人口大幅增长、垃圾产量不断增加的城市地区。在城市努力妥善管理垃圾的过程中,创新的垃圾管理方案有可能提高效率、降低成本并改善公共场所的美观。本文将介绍基于物联网(IoT)和深度学习(DL)技术的功能强大的 "智慧城市管理和分类系统 "SCM-RIAN。卷积神经网络用于垃圾分类模型,该模型是在这个智慧城市管理和分类框架内实现的。该垃圾分类系统旨在将垃圾收集站的垃圾分为几类,鼓励回收利用。旋转不变注意网络(RIAN)是一种独特的分类方法,用于解决智慧城市管理(SCM)中普遍存在的问题。RIAN 内建的中心光谱关注(CSpeA)模块可将光谱带与其他类别像素的影响隔离开来,从而减少冗余。作为传统 3 3 卷积的替代方案,为了获得单片机补丁中包含的旋转不变光谱空间数据,还引入了整流空间关注(RSpaA)模块。建议用于单片机分类的 RIAN 建立在 CSpeA、11 卷积和 RSpaA 模块的基础上。瓢虫优化算法(LBBOA)用于优化超参数。与其他现有模型相比,SCM-RIAN 的结果有所改进,在使用垃圾分类数据集时,其准确率(ACC)达到了 98.12%,灵敏度(SEN)、特异度(SPEC)和卡帕指数(KI)都很高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Smart City Waste Management through LBBOA based RIAN Classification
Effective trash management has become a top environmental priority, especially in urban areas with significant population growth where garbage output is on the rise. As cities work to manage garbage properly, innovative waste management programmes have the potential to increase effectiveness, cut costs, and improve the aesthetic appeal of public places. This article introduces SCM-RIAN, a powerful "Smart City Management and Classification System" built on the Internet of Things (IoT) and deep learning (DL) technologies. Convolutional neural networks are used in the garbage classification model that is implemented within this smart city management and classification framework. This system for classifying waste is intended to categorise rubbish into several classes at waste collection sites, encouraging recycling. The Rotation-Invariant Attention Network (RIAN) is a unique approach presented for the categorization process to address a prevalent problem in smart city management (SCM). A Centre Spectral Attention (CSpeA) module built within RIAN isolates spectral bands from other categories of pixels' influence, reducing redundancy. As an alternative to the conventional 3 3 convolution, to obtain rotation-invariant spectral-spatial data contained in SCM patches, the Rectified Spatial Attention (RSpaA) module is also introduced. The suggested RIAN for SCM classification is built on the integration of the CSpeA, 11 convolution, and RSpaA modules. The Ladybird Beetle Optimisation Algorithm (LBBOA) is used to optimise hyperparameters. With improved results compared to other current models, this suggested SCM-RIAN achieved 98.12% accuracy (ACC) with high sensitivity (SEN), specificity (SPEC), and kappa index (KI) using the garbage classification dataset.
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