Mohamed Ragab

About Mohamed Ragab

Mohamed Ragab, With an exceptional h-index of 13 and a recent h-index of 13 (since 2020), a distinguished researcher at Nanyang Technological University, specializes in the field of Self-supervised Learning, Transfer Learning, Time Series Data, Predictive Maintenance.

His recent articles reflect a diverse array of research interests and contributions to the field:

TSLANet: Rethinking Transformers for Time Series Representation Learning

Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias

AA-DL: AoI-Aware Deep Learning Approach for D2D-Assisted Industrial IoT

Source-free domain adaptation with temporal imputation for time series data

Self-supervised contrastive representation learning for semi-supervised time-series classification

Contrastive domain adaptation for time-series via temporal mixup

Adatime: A benchmarking suite for domain adaptation on time series data

Self-supervised learning for label-efficient sleep stage classification: A comprehensive evaluation

Mohamed Ragab Information

University

Position

PhD Student at School of Computer Science and Engineering

Citations(all)

693

Citations(since 2020)

692

Cited By

14

hIndex(all)

13

hIndex(since 2020)

13

i10Index(all)

14

i10Index(since 2020)

14

Email

University Profile Page

Google Scholar

Mohamed Ragab Skills & Research Interests

Self-supervised Learning

Transfer Learning

Time Series Data

Predictive Maintenance

Top articles of Mohamed Ragab

TSLANet: Rethinking Transformers for Time Series Representation Learning

arXiv preprint arXiv:2404.08472

2024/4/12

Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias

arXiv preprint arXiv:2403.11234

2024/3/17

Wenyu Zhang
Wenyu Zhang

H-Index: 12

Mohamed Ragab
Mohamed Ragab

H-Index: 2

AA-DL: AoI-Aware Deep Learning Approach for D2D-Assisted Industrial IoT

2023/12/10

Mohamed Ragab
Mohamed Ragab

H-Index: 2

Source-free domain adaptation with temporal imputation for time series data

2023/8/6

Self-supervised contrastive representation learning for semi-supervised time-series classification

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

2023/8

Contrastive domain adaptation for time-series via temporal mixup

IEEE Transactions on Artificial Intelligence

2023/7/10

Adatime: A benchmarking suite for domain adaptation on time series data

ACM Transactions on Knowledge Discovery from Data

2023/5/12

Self-supervised learning for label-efficient sleep stage classification: A comprehensive evaluation

IEEE Transactions on Neural Systems and Rehabilitation Engineering

2023/2/14

Label-efficient time series representation learning: A review

2023/2/13

ADAST: Attentive cross-domain EEG-based sleep staging framework with iterative self-training

IEEE Transactions on Emerging Topics in Computational Intelligence

2022/8/10

Self-supervised autoregressive domain adaptation for time series data

IEEE Transactions on Neural Networks and Learning Systems

2022/6/23

Beat-based PPG-ABP cleaning technique for blood pressure estimation

IEEE Access

2022/5/16

Contrastive adversarial knowledge distillation for deep model compression in time-series regression tasks

Neurocomputing

2022/5/7

Conditional contrastive domain generalization for fault diagnosis

IEEE Transactions on Instrumentation and Measurement

2022/2/24

Robust domain-free domain generalization with class-aware alignment

2021/6/6

Wenyu Zhang
Wenyu Zhang

H-Index: 12

Mohamed Ragab
Mohamed Ragab

H-Index: 2

Attention-based sequence to sequence model for machine remaining useful life prediction

Neurocomputing

2021/11/27

Predicting antimicrobial activity of conjugated oligoelectrolyte molecules via machine learning

Journal of the American Chemical Society

2021/11/5

Time-Series Representation Learning via Temporal and Contextual Contrasting

2021/6/26

Secure transfer learning for machine fault diagnosis under different operating conditions

2020/11/20

Chao Jin
Chao Jin

H-Index: 10

Mohamed Ragab
Mohamed Ragab

H-Index: 2

Contrastive adversarial domain adaptation for machine remaining useful life prediction

IEEE Transactions on Industrial Informatics

2020/10/21

See List of Professors in Mohamed Ragab University(Nanyang Technological University)

Co-Authors

academic-engine