Bing He

773 citations
36 papers · 597 · h-index 10

Impact in

Papers in

Bing He

32 papers receiving 584 citations

Peers

Bing He
Comparison fields: 5 of 107
  • Computer Vision and Pattern Recognition 260
  • Media Technology 78
  • Automotive Engineering 78
  • Statistics, Probability and Uncertainty 27
  • Health Informatics 4
Replace Yafang Wang with:
Yafang Wang China
Xinyue Wang China
Aditya Prakash India
Yihong Dong China
Chunfang Liu China
Jiazhang Wang United States
Jinwen Liang China
Guangquan Lu China
Liping Yang China
Bing He relative to Yafang Wang China Yafang Wang's profile →
Citations per field
00.5×10×13.5×
Yafang Wang · 1×
Citations per year

Countries citing papers authored by Bing He

Since Specialization
Citations

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

Fields of papers citing papers by Bing He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019237
2 201174
3 202061
4 202144
5 201132
6 201225
7 201122
8 201917
9 201113
10 20169
11 20098
12 20238
13 20137
14 20095
15 20184
16 20133
17 20113
18 20253
19 20242
20 20202

About Bing He

Bing He is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications, Molecular Biology, Control and Systems Engineering and Artificial Intelligence, having authored 36 papers that have together received 597 indexed citations. Recurring topics across this work include Computer Graphics and Visualization Techniques (4 papers), Medical Image Segmentation Techniques (3 papers), Bioinformatics and Genomic Networks (3 papers), Complex Network Analysis Techniques (3 papers), Data Management and Algorithms (2 papers), Advanced Neural Network Applications (2 papers), Image Processing and 3D Reconstruction (2 papers) and scientometrics and bibliometrics research (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (260 citations), Media Technology (78 citations), Automotive Engineering (78 citations), Statistics, Probability and Uncertainty (27 citations) and Health Informatics (4 citations). Bing He has collaborated with scholars based in China, United States and Sweden. Frequent co-authors include Yonghong Tian, Jia Li, Yifan Zhao, Ying Ding, Jie Tang, Judy Qiu, David Wild, Huijun Wang, Xiao Dong and Chaoqun Ni. Their work appears in journals such as PLoS ONE, Materials Today Energy, Plant Communications, Coordination Chemistry Reviews and IEEE Transactions on Systems Man and Cybernetics 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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