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Title	Efficient Future Waste Management: A Learning-Based Approach With Deep Neural Networks For Smart System (Lads)
ID_Doc	22313
Authors	Chauhan R.; Shighra S.; Madkhali H.; Nguyen L.; Prasad M.
Year	2023
Published	Applied Sciences (Switzerland), 13, 7
DOI	http://dx.doi.org/10.3390/app13074140
Abstract	Waste segregation, management, transportation, and disposal must be carefully managed to reduce the danger to patients, the public, and risks to the environment’s health and safety. The previous method of monitoring trash in strategically placed garbage bins is a time-consuming and inefficient method that wastes time, human effort, and money, and is also incompatible with smart city needs. So, the goal is to reduce individual decision-making and increase the productivity of the waste categorization process. Using a convolutional neural network (CNN), the study sought to create an image classifier that recognizes items and classifies trash material. This paper provides an overview of trash monitoring methods, garbage disposal strategies, and the technology used in establishing a waste management system. Finally, an efficient system and waste disposal approach is provided that may be employed in the future to improve performance and cost effectiveness. One of the most significant barriers to efficient waste management can now be overcome with the aid of a deep learning technique. The proposed method outperformed the alternative AlexNet, VGG16, and ResNet34 methods. © 2023 by the authors.
Author Keywords	artificial intelligence; convolutional neural network; deep learning; waste categorization

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