Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based
methodological development in a broad range of domains. In this rapidly evolving field …

Artificial intelligence (AI) applications have transformed healthcare. This study is based on a
general literature review uncovering the role of AI in healthcare and focuses on the following …

This review synthesizes the literature on explaining machine-learning models for digital
health data in precision medicine. As healthcare increasingly tailors treatments to individual …

Currently, artificial intelligence is facing several problems with its practical implementation in
various application domains. The explainability of advanced artificial intelligence algorithms …

Machine learning, a subfield of artificial intelligence, emphasizes the creation of algorithms
capable of learning from data and generating predictions. However, in actuarial science, the …

Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers
because they can capture intricate associations among variables and generate precise …

The feature attribution-based explanation (FAE) methods, which indicate how much each
input feature contributes to the model's output for a given data point, are one of the most …

Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address
the interpretability challenges posed by complex machine learning models. In this survey …

This study introduces an efficient methodology for addressing fault detection, classification,
and severity estimation in rolling element bearings. The methodology is structured into three …

Artificial intelligence technologies such as machine learning and deep learning employ
techniques to anticipate results more effectively without human involvement. Since AI …