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Smart city article details

Title	Enhancing Smart City Waste Management Through Lbboa Based Rian Classification
ID_Doc	23971
Authors	Sankar K.; Krishnan V.G.; Lakshmi S.V.; Kaviarasan S.; Abins A.A.
Year	2023
Published	International Research Journal of Multidisciplinary Technovation, 5, 6
DOI	http://dx.doi.org/10.54392/irjmt2365
Abstract	Effective trash management has become a top environmental priority, especially in urban areas with significant population growth where waste output is on the rise. As cities work to manage waste 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 waste classification model 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, the Rectified Spatial Attention (RSpaA) module is also introduced to obtain rotation-invariant spectral-spatial data contained in SCM patches. The suggested RIAN for SCM classification is built on integrating the CSpeA, 1×1 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 waste classification dataset. © The Author(s) 2023.
Author Keywords	Center spectral attention; Ladybug beetle optimization algorithm; Rectified spatial attention; Rotation-invariant attention network; Smart city management

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