The concept of smart grid (SG) was made real to give the power grid the functions and
features it needs to make a smooth transition towards renewable energy integration and …

In supervised learning model development, domain experts are often used to provide the
class labels (annotations). Annotation inconsistencies commonly occur when even highly …

Abstract Machine Learning (ML) is integrated into a growing number of systems for various
applications. Because the performance of an ML model is highly dependent on the quality of …

Federated Learning (FL) is a distributed machine learning paradigm where clients
collaboratively train a model using their local (human-generated) datasets. While existing …

The cyber-threat landscape has evolved tremendously in recent years, with new threat
variants emerging daily, and large-scale coordinated campaigns becoming more prevalent …

Many real-world datasets encounter the issue of label noise (LN), which significantly
degrades the learning performances of classification models. While ensemble learning (EL) …

Class imbalance in data sets used to train classifiers can negatively affect the performance
of the resulting models. A commonly used solution to address this issue is to oversample the …

Federated Learning (FL) is a distributed machine learning paradigm where clients
collaboratively train a model using their local datasets. While existing studies focus on FL …

Real world datasets contain incorrectly labeled instances that hamper the performance of
the model and, in particular, the ability to generalize out of distribution. Also, each example …

Missing data and class imbalance hinder the accurate prediction of rare events such as
dairy mastitis. Resampling and imputation are employed to handle these problems. These …