… Different machine learning-based techniques are developed and their accuracy is … In
this study, hence, machine learning (ML)-based models are proposed for predicting fragility …

… Municipal solid waste Optimized ensemble methods based on decision tree (DT), extreme
gradient boosting (XGB), random forest (RF), multilayer perceptron (MLP) and support vector …

… limit machine learning real-time … machine learning domain and discusses its complexities
for more comprehensive applications. Followed by an outline of how relevant machine learning …

… of three ensemble machine learning algorithms for predicting … mine, including gradient
boosting machine (GBM), random … to predict AOp and compared with those of the ensemble …

… , and classification accuracy. For this purpose, we used nine classifiers of machine learning
to the final dataset before and after the hyperparameter tuning of the machine learning …

… Over the past two decades, the use of machine learning (ML) methods to … approaches and
findings have yet to be systematically reviewed. In this work, the machine learning methods …

… learning approaches, specifically the random forest algorithm based on classification and …
The machine learning models are trained on the training data-set, tested on the test data-set…

… approaches [4]. However, to ensure realistic optimization of IES, accurate prediction of the
… criteria for optimal decision making, and machine learning (ML) techniques are recognized …

… The emergence of machine learning (… , approaches, and issues connected with ML in
disease diagnosis. We begin by outlining several methods to machine learning and deep learning …

… In this work a powerful Machine Learning-based approach to identify the most appropriate
manufacturing conditions to enhance LIB electrode homogeneity is presented. Indeed, it was …