… lack of important predictive features. In this research, we developed an ensemble learning
… model to accurately predict the weekly waste generation of households within urban cities. …

… In this study, a stacking ensemble machine learning model was employed against traditional
models to extract clinical knowledge for better understanding patients’ characteristics; …

… The main aim of this work was to compare six machine learning (ML) - based models to
predict the municipal solid waste (MSW) generation from selected residential areas of Vietnam. …

… The accurate and effective classification of household solid waste (HSW) is an … In this paper,
a novel ensemble learning model called EnCNN-UPMWS, which is based on convolutional …

… Our findings indicate that these algorithms can correctly design an engineering method to
eliminate or control municipal solid wastes, thereby reducing the environmental, economic, …

… According to the US Environmental Protection Agency [1], landfill wastes are municipal
solid wastes composed of over nine types of materials: paper and paperboard, glass, metals, …

… Machine Learning (ML) is a classification of process which enables software application to
turn into more precise in predicting results [8]. The fundamental concept of ML is to construct …

… It is critical to predict MSW generation so that sustainable waste management can be
achieved. This study applies machine learning method (XGBoost) to estimate national and …

… on work utilizing ensemble learning in liquefaction prediction. To this extent, this work
examines useful ensemble learning approaches for seismic-induced liquefaction prediction. A …

… the accumulation of municipal solid waste in urban … machine learning algorithms (random
forest (RF) and logistic regression (LR)). The primary information that fed the machine learning …