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- W4379875209 abstract "To lessen the mounting burden on landfills, recycling household and industrial waste has been suggested as a viable solution. However, effective waste management requires proper segregation of waste types as each category requires different treatment. The current segregation process involves manual sorting which can be time-consuming and Workforce-intensive. In this study, a novel approach using deep learning techniques was utilized to automatically classify waste based on its image into six distinct types: paper, metal, plastic, glass, trash and cardboard. CNN model was employed for the waste classification task." @default.
- W4379875209 created "2023-06-09" @default.
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- W4379875209 date "2023-05-04" @default.
- W4379875209 modified "2023-09-27" @default.
- W4379875209 title "Automated Garbage Classification using Deep Learning" @default.
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- W4379875209 doi "https://doi.org/10.1109/icaaic56838.2023.10141483" @default.
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