Mengjun Li

28 papers receiving 293 citations

Peers

Mengjun Li
Comparison fields: 5 of 69
  • Software 16
  • Statistics, Probability and Uncertainty 26
  • Management Science and Operations Research 43
  • Artificial Intelligence 102
  • Signal Processing 30
Replace Ahmad W. Al-Dabbagh with:
Ahmad W. Al-Dabbagh Canada
Kaikai Pan China
Hongsheng Su China
Chunli Xie China
Ryôichi Sasaki Japan
Ji Wu China
Maha Elarbi Tunisia
Lev Kazakovtsev Russia
Jörg Gebhardt Germany
Mingjie Lin United States
Mengjun Li relative to Ahmad W. Al-Dabbagh Canada Ahmad W. Al-Dabbagh's profile →
Citations per field
00.5×4.3×
Ahmad W. Al-Dabbagh · 1×
Citations per year

Countries citing papers authored by Mengjun Li

Since Specialization
Citations

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

Fields of papers citing papers by Mengjun Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Mengjun Li, 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 Mengjun Li Line = papers co-authored together Mengjun Li links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 33 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201298
2 202350
3 201927
4 202125
5 201316
6 202312
7 201611
8 20249
9 20229
10
Support vector data description for weed/corn image recognition
20107
11 20136
12
Structure learning for belief rule base using principal component analysis
20145
13 20125
14 20225
15 20173
16 20243
17 20162
18 20082
19 20182
20 20172

About Mengjun Li

Mengjun Li is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems, Computational Theory and Mathematics and Software, having authored 33 papers that have together received 307 indexed citations. Recurring topics across this work include Software Reliability and Analysis Research (4 papers), Advanced Image Processing Techniques (4 papers), Image Processing Techniques and Applications (3 papers), Advanced Decision-Making Techniques (3 papers), Advanced Vision and Imaging (3 papers), Military Defense Systems Analysis (3 papers), Orbital Angular Momentum in Optics (2 papers) and Plasmonic and Surface Plasmon Research (2 papers). The work is most often cited by research in Software (16 citations), Statistics, Probability and Uncertainty (26 citations), Management Science and Operations Research (43 citations), Artificial Intelligence (102 citations) and Signal Processing (30 citations). Mengjun Li has collaborated with scholars based in China, Sweden and United Kingdom. Frequent co-authors include Leilei Chang, Jiang Jiang, Xiaohang Zhang, Yu Zhou, Chaojing Tang, Junwei Ou, Guoting Zhang, Jian Wu, Lining Xing and Jiang Jiang. Their work appears in journals such as Information Sciences, Energies, International Journal of Machine Learning and Cybernetics, Complex & Intelligent Systems and Knowledge-Based Systems.

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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