Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

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

Designing and printing metamaterials with customizable architectures enables the
realization of unprecedented mechanical behaviors that transcend those of their constituent …

Class imbalance occurs when the class distribution is not equal. Namely, one class is under-
represented (minority class), and the other class has significantly more samples in the data …

Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …

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 …

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

The advent of the Industry 4.0 initiative has made it so that manufacturing environments are
becoming more and more dynamic, connected but also inherently more complex, with …

The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …