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Title	Solid Waste Identification And Classification Method Based On Feature Selection And Hybrid Resnet Cnn Models In Smart Environment
ID_Doc	52263
Authors	Ramya R.; Vinitha Shree S.; Yogeshwari S.; Venkatesan S.
Year	2023
Published	Lecture Notes in Networks and Systems, 730 LNNS
DOI	http://dx.doi.org/10.1007/978-981-99-3963-3_24
Abstract	Garbage disposal is a significant environmental problem that presents a serious obstacle in practically all industrialized and developing nations. It is the biggest obstacle to ecological harmony and sustainable growth. The idea of a smart city is essential for raising living standards. The Indian government has decided to establish 100 smart cities. An innovative sensor network, cameras, wireless devices, fast networks like 5G, IT infrastructure, and certification of their efficient management are all included in a smart city's technological arsenal. When it comes to rethinking cities for environmental sustainability, waste management is an essential component of city administration. This paper mainly focuses on applying convolutional neural networks (CNNs) models to analyze and classify (segregate) solid waste to provide a problem-solving procedure for solid waste materials and improve the separation process of waste. ResNet-152 and MobileNet V2 models were trained using transfer learning to create a convolutional neural network (CNN) and CNN algorithm from scratch for image categorization. The model achieves an accuracy of 0.97 and a loss of 0.03. Therefore, here the proposed work using CNN models to classify garbage produce better outcomes and effectively minimize losses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Author Keywords	Classification; CNN; Deep learning; Features; ResNet-152; Solid waste

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