Sha Fu

736 citations
48 papers · 511 · h-index 13

Impact in

Papers in

Sha Fu

45 papers receiving 476 citations

Peers

Sha Fu
Comparison fields: 5 of 79
  • Management Science and Operations Research 125
  • Accounting 52
  • Artificial Intelligence 166
  • Management Information Systems 40
  • Information Systems 92
Replace Hangjun Zhou with:
Hangjun Zhou China
Zebin Yang United States
Antoni Wibowo Indonesia
Changzheng He China
Iwona Skalna Poland
Guoxun Wang China
Bing Zhu China
Dharmaraja Selvamuthu India
Arash Ghanbari Iran
C. Robert Kenley United States
Sha Fu relative to Hangjun Zhou China Hangjun Zhou's profile →
Citations per field
00.5×2×3×3.9×
Hangjun Zhou · 1×
Citations per year

Countries citing papers authored by Sha Fu

Since Specialization
Citations

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

Fields of papers citing papers by Sha Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 48 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201976
2 202164
3 202040
4 202237
5 202336
6 201624
7 201120
8 201920
9 200418
10 202218
11 202517
12 202116
13 202214
14 202113
15 20169
16 20209
17 20188
18 20217
19 20206
20 20166

About Sha Fu

Sha Fu is a scholar working on Management Science and Operations Research, Information Systems, Ecological Modeling, Artificial Intelligence and Computational Theory and Mathematics, having authored 48 papers that have together received 511 indexed citations. Recurring topics across this work include Multi-Criteria Decision Making (22 papers), Advanced Decision-Making Techniques (10 papers), Grey System Theory Applications (6 papers), Rough Sets and Fuzzy Logic (5 papers), Evaluation Methods in Various Fields (4 papers), Research studies in Vietnam (4 papers), Imbalanced Data Classification Techniques (4 papers) and Information and Cyber Security (3 papers). The work is most often cited by research in Management Science and Operations Research (125 citations), Accounting (52 citations), Artificial Intelligence (166 citations), Management Information Systems (40 citations) and Information Systems (92 citations). Sha Fu has collaborated with scholars based in China, United States and Ireland. Frequent co-authors include Hangjun Zhou, Guang Sun, Jing Liu, Xingxing Zhou, Jieyu Zhou, Ding Li, Ke Wang, Yanhua Wang, Ying Gao and Linli Wang. Their work appears in journals such as Mathematical and Computational Applications, IEEE Access, Journal of Applied Sciences, Computers, materials & continua/Computers, materials & continua (Print) and Scientific Reports.

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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