Power generation from renewable energy resources is on the increase in most countries,
and this trend is expected to continue in the foreseeable future. In an effort to enhance the …

Machine learning draws its power from various disciplines, including computer science,
cognitive science, and statistics. Although machine learning has achieved great …

Based on the interval type-2 fuzzy (IT2F) approach, this article investigates the fault
detection filter design problem for a class of nonhomogeneous higher level Markov jump …

Regular machine learning and data mining techniques study the training data for future
inferences under a major assumption that the future data are within the same feature space …

In this paper, we address the problem of unsupervised domain transfer learning in which no
labels are available in the target domain. We use a transformation matrix to transfer both the …

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 …

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 …

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

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 …

Data science is a research field concerned with processes and systems that extract
knowledge from massive amounts of data. In some situations, however, data shortage …