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Smart city article details

Title	Optimized Parallel Depthwise Separable Convolutional Neural Network-Enabled Smart Waste Management Iot In Smart Cities
ID_Doc	40744
Authors	Vincy V.G.A.G.; Nisha M.G.
Year	2024
Published	Environmental Engineering and Management Journal, 23, 9
DOI	http://dx.doi.org/10.30638/eemj.2024.158
Abstract	The real-time application of tracking and managing smart cities is sophisticatedly performed with the Internet of Things paradigm. The predominant issue of the smart cities is solid waste management which explicitly impacts the health of the society and environment. Managing waste is a challenging task worldwide and several works have been presented by the researchers to ensure the minimization of waste by proper disposal of waste in the respective way. The proposed work detects the objects using the Attentive Single Shot Multibox detector (ASSD) followed by the Intuitionistic Fuzzy Local Binary Pattern (IFLBP) based feature extraction and convolutional neural network (PDSCNN) technique for the classification of the feature vectors that are extracted from the detected objects. The overfitting issues that occur during the training process can be tackled with the introduction of the IKO algorithm. For the validation of the effectiveness, we conducted the simulation in MATLAB simulator and analysed the robustness by comparing it with the state-of-art works.
Author Keywords	Attentive Single Shot Multibox detector; parallel depthwise separable convolutional neural network; smart cities; solid waste management

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