(Aim) Multiple sclerosis is a neurological condition that may cause neurologic disability.
Convolutional neural network can achieve good results, but tuning hyperparameters of CNN …

Recognition of epileptic seizures from offline EEG signals is very important in clinical
diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine …

In the field of intelligent transportation, transfer learning (TL) is often used to recognize EEG-
based drowsy driving for a new subject with few subject-specific calibration data. However …

Recognition of epileptic electroencephalogram (EEG) signals using machine learning
techniques is becoming popular. In general, the construction of intelligent epileptic EEG …

Artificial intelligence algorithms have been used in a wide range of applications in clinical
aided diagnosis, such as automatic MR image segmentation and seizure EEG signal …

The infinite increase in population, the pressure of survival, and the pressure of learning
make the competition between people more and more fierce. Some college students have …

With excellent global approximation performance and interpretability, Takagi-Sugeno-Kang
(TSK) fuzzy systems have enjoyed a wide range of applications in various fields, such as …

Previous studies have shown that the performance of a classifier on imbalanced data
heavily relies on informative objects lying in borderline or overlapping areas. In this study …

Transfer learning techniques have been broadly applied in applications where labeled data
in a target domain are difficult to obtain while a lot of labeled data are available in related …

Although Takagi-Sugeno-Kang (TSK) fuzzy classifier has been applied to a wide range of
practical scenarios, how to enhance its classification accuracy and interpretability …