Machine learning and its subfield deep learning techniques provide opportunities for the
development of operator mental state monitoring, especially for cognitive workload …

Feature selection (FS), as one of the most significant preprocessing techniques in the fields
of machine learning and pattern recognition, has received great attention. In recent years …

Stochastic optimization has been found in many applications, especially for several local
optima problems, because of their ability to explore and exploit various zones of the feature …

Renewable energy is becoming more popular due to environmental concerns about the
previous energy source. Accurate solar photovoltaic system model parameters substantially …

In this paper, an adaptive Harris hawk optimization with persistent trigonometric (sine–
cosine)-differences (ADHHO) is proposed for parameter identification of Photovoltaic (PV) …

Subspace learning decreases the dimensions for high-dimensional data by projecting the
original data into a low-dimensional subspace, as well as preserving the similarity among …

Nowadays, multi-label learning is ubiquitous in practical applications, in which multi-label
data is always confronted with the curse of high-dimensional features. Feature selection has …

Multi-threshold image segmentation (MIS) is now a well known image segmentation
technique, and many researchers have applied intelligent algorithms to it, but these methods …

Noisy data is ubiquitous in quantum computer, greatly affecting the performance of various
algorithms. However, existing quantum support vector machine models are not equipped …

Because of its simplicity and effectiveness, fuzzy K-nearest neighbors (FKNN) is widely used
in literature. The parameters have an essential impact on the performance of FKNN. Hence …