Yan Pei
Impact in
- Artificial Intelligence top 2%
- AI in cancer detection
- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
- Neurology top 5%
- Brain Tumor Detection and Classification
Papers in
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- Evolutionary Algorithms and Applications 41
- Metaheuristic Optimization Algorithms Research 40
- AI in cancer detection 17
- Neural Networks and Applications 16
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- Face and Expression Recognition 11
- Co-authors
- Jianqiang Li (53 shared papers)Pengzhi Li (4 shared papers)Hideyuki Takagi (22 shared papers)Tariq Mahmood (9 shared papers)Faheem Akhtar (19 shared papers)Azhar Imran (9 shared papers)Khalil Ur Rehman (9 shared papers)Ji‐Jiang Yang (8 shared papers)
In The Last Decade
Yan Pei
140 papers receiving 2.1k citations
Yan Pei's Hit Papers
Peers
Comparison fields: 5 of 156
- Artificial Intelligence 964
- Neurology 198
- Radiology, Nuclear Medicine and Imaging 479
- Health Informatics 32
- Computer Vision and Pattern Recognition 469
Countries citing papers authored by Yan Pei
This map shows the geographic impact of Yan Pei'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 Yan Pei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yan Pei more than expected).
Fields of papers citing papers by Yan Pei
This network shows the impact of papers produced by Yan Pei. 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 Yan Pei. The network helps show where Yan Pei may publish in the future.
Co-authors
The 25 scholars most cited alongside Yan Pei, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 157 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | A comprehensive survey on design and application of autoencoder in deep learning Hit paper breakdown → | 2023 | 336 |
| 2 | 2020 | 127 | |
| 3 | A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis Hit paper breakdown → | 2024 | 112 |
| 4 | 2019 | 90 | |
| 5 | 2022 | 83 | |
| 6 | 2022 | 75 | |
| 7 | 2021 | 70 | |
| 8 | 2021 | 69 | |
| 9 | 2019 | 68 | |
| 10 | 2012 | 58 | |
| 11 | 2021 | 57 | |
| 12 | 2023 | 53 | |
| 13 | 2020 | 44 | |
| 14 | 2014 | 36 | |
| 15 | 2020 | 32 | |
| 16 | 2019 | 31 | |
| 17 | 2020 | 29 | |
| 18 | 2021 | 28 | |
| 19 | 2024 | 26 | |
| 20 | 2012 | 25 |
About Yan Pei
Yan Pei is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Computational Theory and Mathematics and Radiology, Nuclear Medicine and Imaging, having authored 157 papers that have together received 2.1k indexed citations. Recurring topics across this work include Evolutionary Algorithms and Applications (41 papers), Metaheuristic Optimization Algorithms Research (40 papers), Advanced Multi-Objective Optimization Algorithms (19 papers), AI in cancer detection (17 papers), Neural Networks and Applications (16 papers), Radiomics and Machine Learning in Medical Imaging (13 papers), Music and Audio Processing (12 papers) and Face and Expression Recognition (11 papers). The work is most often cited by research in Artificial Intelligence (964 citations), Neurology (198 citations), Radiology, Nuclear Medicine and Imaging (479 citations), Health Informatics (32 citations) and Computer Vision and Pattern Recognition (469 citations). Yan Pei has collaborated with scholars based in Japan, China and Pakistan. Frequent co-authors include Jianqiang Li, Pengzhi Li, Hideyuki Takagi, Tariq Mahmood, Faheem Akhtar, Azhar Imran, Khalil Ur Rehman, Ji‐Jiang Yang, Saqib Ali and Keshav K. Pingali. Their work appears in journals such as IEEE Access, Applied Soft Computing, International Journal of Web and Grid Services, International Journal of Machine Learning and Cybernetics and Cancers.
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.