High-durability concrete is required in extremely cold or ocean environments, making the
design of concrete mixes highly important and complicated. In this study, a hybrid intelligent …

Recently, deep learning models have made great breakthroughs in the field of computer
vision, relying on large-scale class-balanced datasets. However, most of them do not …

Class overlap in imbalanced datasets is the most common challenging situation for
researchers in the fields of deep learning (DL) machine learning (ML), and big data (BD) …

In recent years, the frenetic advances of blockchain techniques have promoted the large-
scale application of cryptocurrency and attracted significant attention in the mushrooming …

For class-imbalance problems, traditional supervised learning algorithms tend to favor
majority instances (also called negative instances). Therefore, it is difficult for them to …

Oversampling is a popular yet useful method to fulfill the binary classification of imbalanced
data, however many existing results of oversampling are very likely to generate …

Many real‐world application datasets such as medical diagnostics, fraud detection,
biological classification, risk analysis and so forth are facing class imbalance and …

Among the existing bearing fault diagnosis algorithms, the diagnosis time of algorithms with
high accuracy is relatively long, and the accuracy of some lightweight methods is low …

Imbalanced data classification remains a research hotspot and a challenging problem in the
field of machine learning. The challenge of imbalanced learning lies not only in class …

Spontaneous combustion of coal increases the temperature in adjoining overburden strata
of coal seams and poses a challenge when loading blastholes. This condition, known as hot …