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This article discusses the use of machine learning techniques in predicting and optimizing the composting process, with a focus on reducing greenhouse gas emissions. It also highlights the potential of ML in accurately forecasting composting outcomes and its role in enhancing precision and efficiency in the composting process. The article outlines the stages involved in developing data-driven models for organic waste treatment using ML and emphasizes the importance of selecting the right model for optimal results.
