The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification.

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[1] Sam Single, Saeid Iranmanesh, Raad Raad, "RealWaste", IEEE Dataport, 2023. [Online]. Available: http://dx.doi.org/10.21227/65m5-rr72. Accessed: Dec. 09, 2024.
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doi = {10.21227/65m5-rr72},
url = {http://dx.doi.org/10.21227/65m5-rr72},
author = {Sam Single; Saeid Iranmanesh; Raad Raad },
publisher = {IEEE Dataport},
title = {RealWaste},
year = {2023} }
TY - DATA
T1 - RealWaste
AU - Sam Single; Saeid Iranmanesh; Raad Raad
PY - 2023
PB - IEEE Dataport
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Sam Single, Saeid Iranmanesh, Raad Raad. (2023). RealWaste. IEEE Dataport. http://dx.doi.org/10.21227/65m5-rr72
Sam Single, Saeid Iranmanesh, Raad Raad, 2023. RealWaste. Available at: http://dx.doi.org/10.21227/65m5-rr72.
Sam Single, Saeid Iranmanesh, Raad Raad. (2023). "RealWaste." Web.
1. Sam Single, Saeid Iranmanesh, Raad Raad. RealWaste [Internet]. IEEE Dataport; 2023. Available from : http://dx.doi.org/10.21227/65m5-rr72
Sam Single, Saeid Iranmanesh, Raad Raad. "RealWaste." doi: 10.21227/65m5-rr72