Bei Yang

733 citations
34 papers · 495 · h-index 13

Impact in

Papers in

    • AI in cancer detection 4
    • Imbalanced Data Classification Techniques 3
    • Sentiment Analysis and Opinion Mining 3
    • Domain Adaptation and Few-Shot Learning 2
    • Data Mining Algorithms and Applications 3

Bei Yang

33 papers receiving 477 citations

Peers

Bei Yang
Comparison fields: 5 of 123
  • Computational Theory and Mathematics 81
  • Artificial Intelligence 145
  • Health Information Management 19
  • Health Informatics 5
  • Signal Processing 26
Replace Anthony Rios with:
Anthony Rios United States
M. Sadiq Ali Khan Pakistan
Bridget T. McInnes United States
Giovanna Maria Dimitri Italy
Chandrabose Aravindan India
Bob J.A. Schijvenaars Netherlands
Qinmin Hu Canada
Gregor Leban Slovenia
Mohamed Elaraby Egypt
Shan Chen China
Bei Yang relative to Anthony Rios United States Anthony Rios's profile →
Citations per field
00.5×1.5×2.4×
Anthony Rios · 1×
Citations per year

Countries citing papers authored by Bei Yang

Since Specialization
Citations

This map shows the geographic impact of Bei Yang'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 Bei Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bei Yang more than expected).

Fields of papers citing papers by Bei Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bei Yang. 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 Bei Yang. The network helps show where Bei Yang may publish in the future.

Co-authors

The 25 scholars most cited alongside Bei Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Bei Yang Line = papers co-authored together Bei Yang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017111
2 202057
3 202250
4 201849
5 201831
6 201320
7 202118
8 200917
9 202015
10 201015
11
An Improved Particle Swarm Optimization Algorithm
200714
12 201813
13 202112
14 201512
15 201210
16 20227
17 20236
18 20215
19 20235
20 20085

About Bei Yang

Bei Yang is a scholar working on Artificial Intelligence, Information Systems, Molecular Biology, Computer Vision and Pattern Recognition and Signal Processing, having authored 34 papers that have together received 495 indexed citations. Recurring topics across this work include AI in cancer detection (4 papers), Data Management and Algorithms (3 papers), Data Mining Algorithms and Applications (3 papers), Imbalanced Data Classification Techniques (3 papers), Sentiment Analysis and Opinion Mining (3 papers), Digital Imaging for Blood Diseases (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Electricity Theft Detection Techniques (2 papers). The work is most often cited by research in Computational Theory and Mathematics (81 citations), Artificial Intelligence (145 citations), Health Information Management (19 citations), Health Informatics (5 citations) and Signal Processing (26 citations). Bei Yang has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Zhaoxian Zhou, Chaoyang Zhang, Ping Gong, Huixiao Hong, Andrew Maxwell, Runzhi Li, Heng Weng, Yanbu Guo, Weihua Li and Bingyi Wang. Their work appears in journals such as Expert Systems with Applications, Medical Physics, Experimental Biology and Medicine, The Visual Computer and Theriogenology.

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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