… year in advance (long-term) forecast for MSW generation in the City of Johannesburg (CoJ).
To achieve this, we employed the machine learning approach in which two of its algorithms (…

… It is often difficult for a machine learning model trained based on a … learning-based capacity
estimation method that incorporates the concepts of transfer learning and ensemble learning. …

… machine learning and deep learning, while deep learning is a subset of machine learning. …
These models are to be incorporated with data processing and analytics of machine learning, …

… , machine learning (ML) tools were employed for bridging the inputs and outputs, which
can realize the prediction of hydrochar properties, and development of ML-based optimization …

… Ensemble method In supervised learning of machine learning, our goal is to learn a
stable … The ensemble method combines multiple models in order to get a better and more …

… in municipal solid waste production, and researchers have sought to use advanced technology
to solve this problem. Machine learning (… to promote municipal solid waste management (…

… This study leveraged on the power of ensemble machine learning by combining the …
datasets were used in training five different machine learning models. With cement, limestone …

… IOT and machine learning based household waste management system for Green smart
society … The machine learning technique such as KNN is used to generate an alert message for …

… of BKNN-ELM predicting the influent indicators based on activated carbon and … prediction.
This research focuses on introducing carbon prediction technique with machine learning …

… To conclude, considering that machine learning techniques are widespread for the … the
proposed ensemble learner, we can be able to overcome existing machine learning based state-…