216 citations · 540 across the 21 of their papers we have counts for
5 papers · 1 filter
EverAdapt: Continuous Adaptation for Dynamic Machine Fault Diagnosis Environments
Edward, Mohamed Ragab, Yuecong Xu +4
Unsupervised Domain Adaptation (UDA) has emerged as a key solution in data-driven fault diagnosis, addressing domain shift where models underperform in changing environments. Howev…
Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation
Mohamed Ragab, Peiliang Gong, Emadeldeen Eldele +6
Source-free domain adaptation (SFDA) aims to adapt a model pre-trained on a labeled source domain to an unlabeled target domain without access to source data, preserving the source…
Overcoming Negative Transfer by Online Selection: Distant Domain Adaptation for Fault Diagnosis
Ziyan Wang, Mohamed Ragab, Wenmian Yang +4
Unsupervised domain adaptation (UDA) has achieved remarkable success in fault diagnosis, bringing significant benefits to diverse industrial applications. While most UDA methods fo…
TSLANet: Rethinking Transformers for Time Series Representation Learning
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +2
Time series data, characterized by its intrinsic long and short-range dependencies, poses a unique challenge across analytical applications. While Transformer-based models excel at…
Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias
Wenyu Zhang, Qingmu Liu, Felix Ong Wei Cong +2
Domain adaptation is a critical task in machine learning that aims to improve model performance on a target domain by leveraging knowledge from a related source domain. In this wor…