Enhancing Smart City Waste Management through LBBOA based RIAN Classification

Abstract

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.