Deep learning models have revolutionized numerous fields, yet their decision-making
processes often remain opaque, earning them the characterization of “black-box” models …

Anomaly detection of gas turbines faces the significant challenges of data imbalance and
inter-class overlap. In this paper, we develop a novel data augmentation method, namely …

This paper presents a comprehensive survey on interpretable intelligent fault diagnosis for
rotating machinery, addressing the challenge of the “black box” nature of machine learning …

Surveillance systems regularly create massive video data in the modern technological era,
making their analysis challenging for security specialists. Finding anomalous activities …

In this study, an internet of things (IoT)-enabled fuzzy intelligent system is introduced for the
remote monitoring, diagnosis, and prescription of treatment for patients with COVID-19. The …

COVID-19 disease causes serious respiratory illnesses. Therefore, accurate identification of
the viral infection cycle plays a key role in designing appropriate vaccines. The risk of this …

In this paper, we develop a novel ensemble model for seismic building damage prediction
that leverages machine learning algorithms of two completely different mechanisms, tree …

This research addresses a critical challenge in aerospace engineering: enhancing the
interpretability of machine learning models for predictive maintenance. By integrating …

The application of Artificial Intelligence (AI) and Machine Learning (ML) models is
increasingly leveraged to automate and optimize Data Centre (DC) operations. However …

In the present technological world, surveillance cameras generate an immense amount of
video data from various sources, making its scrutiny tough for computer vision specialists. It …