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 …

Purpose Inscrutable machine learning (ML) models are part of increasingly many
information systems. Understanding how these models behave, and what their output is …

Abstract Machine learning and artificial intelligence (ML/AI), previously considered black box
approaches, are becoming more interpretable, as a result of the recent advances in …

Deep learning models have achieved high performance across different domains, such as
medical decision-making, autonomous vehicles, decision support systems, among many …

Interpretability and explainability are crucial for machine learning (ML) and statistical
applications in medicine, economics, law, and natural sciences and form an essential …

Explainability has become one of the most discussed topics in machine learning research in
recent years, and although a lot of methodologies that try to provide explanations to black …

Vulnerable road users (VRUs) are exposed to the highest risk in the road traffic environment.
Analyzing contributing factors that affect injury severity facilitates injury severity prediction …

The heat increase caused by climate change has worsened the urban heat environment and
damaged human health, which has led to heat-related mortality. One of the most important …

Accurately modelling crashes, and predicting crash occurrence and associated severities
are a prerequisite for devising countermeasures and develo** effective road safety …