Anhui Tan

37 papers receiving 659 citations

Peers

Anhui Tan
Comparison fields: 5 of 52
  • Computational Theory and Mathematics 523
  • Management Science and Operations Research 204
  • Information Systems 267
  • Artificial Intelligence 299
  • Signal Processing 73
Replace Caihui Liu with:
Caihui Liu China
Xiaoyan Zhang China
Gangqiang Zhang China
Yanting Guo China
Yanhui Zhai China
Wei-Zhi Wu China
Wojciech Kotłowski Poland
Jingqian Wang China
Anhui Tan relative to Caihui Liu China Caihui Liu's profile →
Citations per field
00.5×1.5×2.0×
Caihui Liu · 1×
Citations per year

Countries citing papers authored by Anhui Tan

Since Specialization
Citations

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

Fields of papers citing papers by Anhui Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 40 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018116
2 202059
3 201544
4 201543
5 201940
6 201437
7 202226
8 201623
9 202123
10 201722
11 201522
12 202220
13 201819
14 201819
15 201817
16 201917
17 202217
18 202114
19 202211
20 201910

About Anhui Tan

Anhui Tan is a scholar working on Computational Theory and Mathematics, Artificial Intelligence, Information Systems, Management Science and Operations Research and Computer Vision and Pattern Recognition, having authored 40 papers that have together received 669 indexed citations. Recurring topics across this work include Rough Sets and Fuzzy Logic (28 papers), Multi-Criteria Decision Making (13 papers), Data Mining Algorithms and Applications (13 papers), Text and Document Classification Technologies (9 papers), Image Retrieval and Classification Techniques (5 papers), Advanced Algebra and Logic (4 papers), Image Enhancement Techniques (3 papers) and Machine Learning in Bioinformatics (2 papers). The work is most often cited by research in Computational Theory and Mathematics (523 citations), Management Science and Operations Research (204 citations), Information Systems (267 citations), Artificial Intelligence (299 citations) and Signal Processing (73 citations). Anhui Tan has collaborated with scholars based in China, Canada and Saudi Arabia. Frequent co-authors include Wei-Zhi Wu, Jinjin Li, Guoping Lin, Jiye Liang, Jinkun Chen, Yaojin Lin, Yuhua Qian, Tong-Jun Li, Witold Pedrycz and Xiaoping Yang. Their work appears in journals such as International Journal of Machine Learning and Cybernetics, Information Sciences, International Journal of Approximate Reasoning, Knowledge-Based Systems and Fuzzy Sets and 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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