As network techniques rapidly evolve, attacks are becoming increasingly sophisticated and
threatening. Network intrusion detection has been widely accepted as an effective method to …

Current research on imbalanced data recognises that class imbalance is aggravated by
other data intrinsic characteristics, among which class overlap stands out as one of the most …

Despite over two decades of progress, imbalanced data is still considered a significant
challenge for contemporary machine learning models. Modern advances in deep learning …

Although the existing generative adversarial networks (GAN) have the potential for data
augmentation and intelligent fault diagnosis of planetary gearbox, it remains difficult to deal …

Objective Methods to correct class imbalance (imbalance between the frequency of outcome
events and nonevents) are receiving increasing interest for developing prediction models …

Abstract The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known
resampling strategy that has been successfully used for dealing with the class-imbalance …

We have developed capAI, a conformity assessment procedure for AI systems, to provide an
independent, comparable, quantifiable, and accountable assessment of AI systems that …

Online media has an increasing presence on the restaurants' activities through social media
websites, coinciding with an increase in customers' reviews of these restaurants. These …

In recent years, class imbalance learning (CIL) has become an important branch of machine
learning. The Synthetic Minority Oversampling TEchnique (SMOTE) is considered to be a …

Credit card fraud is one of the biggest cybercrimes faced by users. Intelligent machine
learning based fraudulent transaction detection systems are very effective in real-world …