Mohamed Ragab

1.9k total citations · 2 hit papers
45 papers, 1.2k citations indexed

About

Mohamed Ragab is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Mohamed Ragab has authored 45 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 9 papers in Computer Vision and Pattern Recognition and 8 papers in Signal Processing. Recurrent topics in Mohamed Ragab's work include Anomaly Detection Techniques and Applications (10 papers), Domain Adaptation and Few-Shot Learning (10 papers) and Time Series Analysis and Forecasting (6 papers). Mohamed Ragab is often cited by papers focused on Anomaly Detection Techniques and Applications (10 papers), Domain Adaptation and Few-Shot Learning (10 papers) and Time Series Analysis and Forecasting (6 papers). Mohamed Ragab collaborates with scholars based in Singapore, Egypt and China. Mohamed Ragab's co-authors include Zhenghua Chen, Xiaoli Li, Min Wu, Chee Keong Kwoh, Emadeldeen Eldele, Cuntai Guan, Ruqiang Yan, Chuan-Sheng Foo, Wenyu Zhang and Moncef L. Nehdi and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Access and IEEE Transactions on Industrial Informatics.

In The Last Decade

Mohamed Ragab

36 papers receiving 1.1k citations

Hit Papers

Time-Series Representatio... 2021 2026 2022 2024 2021 2023 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Time-Series Representation Learning via Temporal and Contextual Contrasting
    2021 289 citations Emadeldeen Eldele, Mohamed Ragab et al. profile →
  2. Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification
    2023 89 citations Emadeldeen Eldele, Mohamed Ragab et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mohamed Ragab Singapore 17 438 333 251 157 133 45 1.2k
Orhan Yaman Türkiye 19 227 0.5× 208 0.6× 81 0.3× 270 1.7× 163 1.2× 76 1.2k
Xiaodong Li China 20 367 0.8× 469 1.4× 757 3.0× 269 1.7× 92 0.7× 88 1.6k
He Wang China 17 158 0.4× 245 0.7× 65 0.3× 111 0.7× 292 2.2× 105 955
J. Dayan Israel 12 693 1.6× 357 1.1× 53 0.2× 114 0.7× 110 0.8× 41 1.3k
Dimitrios Kosmopoulos Greece 22 385 0.9× 100 0.3× 113 0.5× 163 1.0× 579 4.4× 81 1.2k
Baochen Sun United States 6 942 2.2× 145 0.4× 86 0.3× 90 0.6× 812 6.1× 8 1.6k
Xuyun Fu China 13 377 0.9× 932 2.8× 82 0.3× 534 3.4× 139 1.0× 35 1.6k
Ahmad Kalhor Iran 19 270 0.6× 663 2.0× 45 0.2× 186 1.2× 221 1.7× 149 1.4k
Meng Cai China 18 312 0.7× 125 0.4× 179 0.7× 98 0.6× 431 3.2× 137 1.3k
Siti Nurmaini Indonesia 21 523 1.2× 143 0.4× 201 0.8× 62 0.4× 261 2.0× 190 1.7k

Countries citing papers authored by Mohamed Ragab

Since Specialization
Citations

This map shows the geographic impact of Mohamed Ragab's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mohamed Ragab with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohamed Ragab more than expected).

Fields of papers citing papers by Mohamed Ragab

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mohamed Ragab. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mohamed Ragab. The network helps show where Mohamed Ragab may publish in the future.

Co-authorship network of co-authors of Mohamed Ragab

This figure shows the co-authorship network connecting the top 25 collaborators of Mohamed Ragab. A scholar is included among the top collaborators of Mohamed Ragab based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mohamed Ragab. Mohamed Ragab is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ragab, Mohamed, Emadeldeen Eldele, Wenyu Zhang, et al.. (2025). Evidentially Calibrated Source-Free Time-Series Domain Adaptation With Temporal Imputation. IEEE Transactions on Knowledge and Data Engineering. 38(1). 290–306.
2.
Edward, Edward, Mohamed Ragab, Min Wu, et al.. (2025). EverAdapt: Continuous adaptation for dynamic machine fault diagnosis environments. Mechanical Systems and Signal Processing. 226. 112317–112317. 2 indexed citations
3.
Abdelraouf, Omar A. M., Sahar M. Mousa, & Mohamed Ragab. (2025). NanoPhotoNet: AI-enhanced design tool for reconfigurable and high-performance multi-layer metasurfaces. Photonics and Nanostructures - Fundamentals and Applications. 64. 101379–101379. 4 indexed citations
4.
Ragab, Mohamed, et al.. (2025). Evidential Domain Adaptation for Remaining Useful Life Prediction With Incomplete Degradation. IEEE Transactions on Instrumentation and Measurement. 74. 1–11.
5.
Eldele, Emadeldeen, Mohamed Ragab, Zhenghua Chen, et al.. (2024). Label-Efficient Time Series Representation Learning: A Review. IEEE Transactions on Artificial Intelligence. 5(12). 6027–6042. 9 indexed citations
6.
Yang, Wenmian, et al.. (2024). A Virtual-Label-Based Hierarchical Domain Adaptation Method for Time-Series Classification. IEEE Transactions on Neural Networks and Learning Systems. 36(6). 11456–11465.
7.
Li, Hailin, Raghavendra Ramachandra, Mohamed Ragab, et al.. (2024). Unsupervised Fingerphoto Presentation Attack Detection With Diffusion Models. 1–10.
8.
Nguyen, Manh Cuong, Arun Raja, Le Zhang, et al.. (2023). Diverse and consistent multi-view networks for semi-supervised regression. Machine Learning. 112(7). 2359–2395.
9.
Ragab, Mohamed, et al.. (2023). AA-DL: AoI-Aware Deep Learning Approach for D2D-Assisted Industrial IoT. VBN Forskningsportal (Aalborg Universitet). 127–133.
10.
11.
Ragab, Mohamed, et al.. (2022). Molecular characterization of some pathogenic bacterial strains and hematobiochemical profile in Barki sheep with diarrhea in Siwa Oasis. Mansoura Veterinary Medical Journal. 23(4). 59–69. 1 indexed citations
12.
13.
Eldele, Emadeldeen, Mohamed Ragab, Zhenghua Chen, et al.. (2022). ADAST: Attentive Cross-Domain EEG-Based Sleep Staging Framework With Iterative Self-Training. IEEE Transactions on Emerging Topics in Computational Intelligence. 7(1). 210–221. 37 indexed citations
14.
Ragab, Mohamed, Zhenghua Chen, Min Wu, et al.. (2021). Attention-based sequence to sequence model for machine remaining useful life prediction. Neurocomputing. 466. 58–68. 61 indexed citations
15.
Ragab, Mohamed, Zhenghua Chen, Min Wu, Chee Keong Kwoh, & Xiaoli Li. (2020). Adversarial Transfer Learning for Machine Remaining Useful Life Prediction. 1–7. 21 indexed citations
16.
17.
Ragab, Mohamed, et al.. (2016). Performance Efficient Launch Vehicle Recovery and Reuse. NASA STI Repository (National Aeronautics and Space Administration). 7 indexed citations
18.
Ragab, Mohamed & F. McNeil Cheatwood. (2015). Launch Vehicle Recovery and Reuse. AIAA SPACE 2015 Conference and Exposition. 12 indexed citations
19.
20.
Farag, Hoda Fahmy, et al.. (1980). A focus of human fascioliasis in the Nile Delta, Egypt.. PubMed. 82(9-10). 188–90. 35 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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