Feature selection is becoming a relevant problem within the field of machine learning. The
feature selection problem focuses on the selection of the small, necessary, and sufficient …

Existing facial expression recognition methods have some drawbacks. For example, it
becomes difficult for network learning on cross-dataset facial expressions, multi-region …

Feature selection, which aims to improve the classification accuracy and reduce the size of
the selected feature subset, is an important but challenging optimization problem in data …

The paper studies the problems of developing interconnected models of aggregates of
complex chemical-technological systems (CTS) in conditions of scarcity and fuzziness of the …

Due to the small data and unbalanced sample distribution in the existing facial emotion
datasets, the effect of facial expression recognition is not ideal. Traditional data …

The T-Spherical fuzzy set (TSFS) is the most generalized form among the introduced fuzzy
frameworks. It obtains maximum information from real-life phenomena due to its maximum …

Evolutionary algorithms (EAs) require extensive fitness evaluations, which constitutes a
barrier to solving computationally complex problems. In contrast, surrogate-assisted …

Drug-drug interaction (DDI) extraction has seen growing usage of deep models, but their
effectiveness has been restrained by limited domain-labeled data, a weak representation of …

The idea of linear Diophantine fuzzy set (LDFS) theory with its control parameters is a strong
model for machine learning and optimization under uncertainty. The activity times in the …

Feature selection is a crucial process in data science that involves selecting the most
effective subset of features. Evolutionary computation (EC) is one of the most commonly …