In this study, we propose a long short-term memory autoencoder (LSTM-AE) based approach for anomaly identification in multivariate time-series measurement data of PTs. Generally, the LSTM-AE is ...

The purpose of this study is to investigate a novel end-to-end deep learning method of emotion recognition using EEG data, which prefaces a combination of two-dimensional (2D) convolutional network ...