Class imbalance (CI) is a well-known problem in data science. Nowadays, it is affecting the
data modeling of many of the real-world processes that are being digitized. The …

Artificial intelligence (AI) in machine tools offers diverse advantages, including learning and
optimizing machining processes, compensating errors, saving energy, and preventing …

Digital twin (DT) is a key technology for realizing the interconnection and intelligent
operation of the physical world and the world of information and provides a new paradigm …

Imbalanced dataset classification issue poses a major challenge on machine learning
domain. Traditional supervised learning algorithms usually bias towards the majority class …

Fault diagnosis plays an essential role in rotating machinery manufacturing systems to
reduce their maintenance costs. How to improve diagnosis accuracy remains an open issue …

Considering the difficulty of data acquisition in industry, especially for failure data of large-
scale equipment, classification with these class-imbalanced datasets can lead to the …

Learning from imbalanced datasets poses a major challenge in data mining community.
When dealing with imbalanced datasets, conventional classification algorithms generally …

Oversampling is a widely employed technique for addressing imbalanced datasets, facing
challenges like class overlaps, intra-class imbalance, and noise. In this paper, we introduce …

Imbalanced datasets classification remains an important domain in machine learning.
Conventional supervised learning algorithms tend to be biased towards the majority class …

In recent times, digital twins (DT) is becoming an emerging and key technology for smart
industrial control systems and Industrial Internet of things (IIoT) applications. The DT …