Objective In recent years, we have increasingly observed issues concerning quality of online
information due to misinformation and disinformation. Aside from social media, there is …

Due to the rapidly increasing number of Internet-connected objects, a huge amount of data
is created, stored, and shared. Depending on the use case, this data is visualized, cleaned …

Bagging and boosting are two popular ensemble methods in machine learning (ML) that
produce many individual decision trees. Due to the inherent ensemble characteristic of …

This pioneering research introduces a novel approach for decision-makers in the heavy
machinery industry, focusing on production management. The study integrates machine …

Despite the tremendous advances in machine learning (ML), training with imbalanced data
still poses challenges in many real‐world applications. Among a series of diverse …

As the complexity of machine learning (ML) models increases and their application in
different (and critical) domains grows, there is a strong demand for more interpretable and …

In Explainable Artificial Intelligence (XAI) research, various local model-agnostic methods
have been proposed to explain individual predictions to users in order to increase the …

This pioneering research introduces a novel approach for decision-makers in the heavy
machinery industry, specifically focusing on production management. The study integrates …

Explainability is increasingly considered a critical component of artificial intelligence (AI)
systems, especially in high-stake domains where AI systems' decisions can significantly …