Transient stability assessment (TSA) is critical to the reliable operation of a power system
against severe fault conditions. In practice, TSA based on deep learning is preferable for its …

The integration of different learning paradigms has long been a focus of machine learning
research, aimed at overcoming the inherent limitations of individual methods. Fuzzy rule …

It is not uncommon that real-world data are distributed with a long tail. For such data, the
learning of deep neural networks becomes challenging because it is hard to classify tail …

Credit risk assessment is significantly hindered by the problem of class imbalance, and cost-
sensitive methods represent an effective strategy to address this issue. However, most …

The advancement of machine learning in industrial applications has necessitated the
development of tailored solutions to address specific challenges, particularly in multi-class …

Low visibility is a major cause of flight delays and airport cancellations. Hence, providing
accurate prediction of airport visibility is vital to prevent significant losses for airlines …

Reliable and interpretable traffic crash modeling is essential for understanding causality and
improving road safety. This study introduces a novel approach to predicting collision types …

Federated learning (FL) over wireless networks offers a promising approach to enable
decentralized machine learning among massive mobile edge nodes while ensuring privacy …

Discovering the Higgs boson, a fundamental particle in particle physics, was a remarkable
achievement. However, distinguishing between the signal process that produces Higgs …