… safe operation of electric machines. This article proposes a novel meta-learning-enabled
method for the detection of fault in rolling bearings of electric machines under varying working …

… learner is 2,633 for the residential dataset and 265 for the non-residential dataset. For meta
learning, we propose a meta learning … In the deep learning algorithm society, meta learning …

… the use of meta-learning as an approach to electric load forecasting; this … for electric load
forecasting model selection. I choose meta-learning because it is rooted in the idea of learning …

… machine learning-based meta-modeling for the accelerated numerical optimization of electrical
machines … For example, the study in [1] shows how trained data-driven deep learning (DL…

… In this article, a meta-learning-based driving information prediction (DIP) method is … a
dual-electric machine (EM) driving system in each wheel. The proposed metalearning-based DIP …

… Machine learning (ML) and deep learning (DL) have enabled algorithms to autonomously …
ML and DL models have found extensive application within the domain of electric vehicle (EV) …

… a novel meta-learning strategy … electricity price is included in the feature pool for the
data-driven models. (2) This study proposes a new meta-learning strategy based on a machine …

… the extension to meta learning. … of (meta-)reinforcement learning in the domain of drive
control and temperature estimation in electric power systems using supervised machine learning. …

… However, a major drawback of existing work is that the machine learning models are trained
… -based OPF predictor that is trained using a meta-learning (MTL) approach. The key idea …

… machine learning ensemble approach for predicting the soil shear strength. The ensemble
includes the two capable individual machine learning … different types of machine learning; thus…