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Title	Enhanced Cnn Architecture For Accurate Waste Classification In Smart Cities
ID_Doc	23601
Authors	Kabilan B.; Sairam R.; Praveen M.
Year	2024
Published	Proceedings of 5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024
DOI	http://dx.doi.org/10.1109/ICICNIS64247.2024.10823383
Abstract	Waste management is one of the biggest challenges of the present world as the generation of waste has risen tremendously. In this paper we introduce an automated waste classification system that uses Convolutional Neural Networks to effectively sort waste into four categories which are biodegradable, non-biodegradable, biomedical and electronic waste. The framework that has been suggested in this paper incorporates several new preprocessing techniques including data augmentation as well as noise removal for enhancing the robustness of the model. The CNN architecture has the convolutional, pooling and dense layers that are well tuned to identify essential features and enhance the accuracy of classification. The model was trained and validated using a large data set and the accuracy of the model was 91.87%, while the F1 score, recall and precision were also high for all the waste categories. In contrast to other methods for machine learning like SVM, RF, CNN was found to be better in dealing with high dimensional image data, owing to its flexibility and efficiency. The system can be implemented on edge devices which makes it possible to sort waste in real time and thus do not require much reliance on central systems. This approach has potential for application in cities, smart industries and healthcare units with a view of environmental conservation and optimal resource management. In the future, the work will be extended to improve the dataset, use transfer learning approaches and develop new applications including smart waste bins and hazardous material separation systems to enhance the field of automated waste management. © 2024 IEEE.
Author Keywords	Bio-degradable waste; Computer vision; Convolutional neural networks; Deep learning; Image recognition; non-Biodegradable waste; Waste segregation

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