Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …

Multi-modal fusion combines multiple modal information to overcome the limitation of
incomplete information expressed by a single modality, so as to realize the complementarity …

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

Frequent epileptic seizures cause damage to the human brain, resulting in memory
impairment, mental decline, and so on. Therefore, it is important to detect epileptic seizures …

In this paper, we base our research by dealing with a real‐world problem in an enterprise. A
RFM (recency, frequency, and monetary) model and K‐means clustering algorithm are …

In unsupervised domain adaptation (UDA), a classifier for a target domain is trained with
labeled source data and unlabeled target data. Existing UDA methods assume that the …

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 …

Among the currently proposed brain segmentation methods, brain tumor segmentation
methods based on traditional image processing and machine learning are not ideal enough …

Targeted drugs have been applied to the treatment of cancer on a large scale, and some
patients have certain therapeutic effects. It is a time-consuming task to detect drug–target …

Epilepsy is a neurological illness caused by abnormal discharge of brain neurons, where
epileptic seizure can lead to life-threatening emergencies. By analyzing the encephalogram …