… of machine learning (ML) methods. A new methodological approach is here presented, based
on the ensemble … Such approach has been tested in the Monterosso al Mare area, Cinque …

… The machine learning classifiers have aided in minimizing human … machine learning
classifiers for data modeling and prediction; in our work, we employed support vector machine (SVM…

… in recent years, which is also studied as remaining useful life (RUL) prediction in much …
-based methods and machine learning (ML) based methods. Model-based methods typically …

… machine learning technology in computational materials and computational mechanics, the
machine learning-based … art review on the machine learning-based multiscale modeling and …

… Machine learning (ML) has gained popularity in many research fields. The foundations of
ML were already laid in 1959, when Arthur Samuel, a pioneer in AI, coined the term (Samuel, …

… machine learning are extensively researched, there is a lack of study that exclusively focus
on the evolution of ML-based … on the application of machine learning techniques for big data …

… predict the stability of a decentralized power grid. The simulated data obtained from the online
machine learning … all other machine learning algorithms based on performance. XGBoost …

… is considered as a supervised learning task of inferring a function from labeled … is a machine
learning technique that assembles weak prediction models to establish a robust prediction …

… This paper proposes the ensemble learning-based EEG emotion recognition system.
Firstly, the differential entropy was extracted from different frequency bands of EEG signals. …

… In this section, we show how our machine learning algorithm can be used as a predictive
tool for an entire economic system, focusing on Italian SMEs. In more detail, we considered all …