Lian Wu

957 citations
32 papers · 689 · h-index 12

Impact in

Papers in

Lian Wu

30 papers receiving 664 citations

Peers

Lian Wu
Comparison fields: 5 of 109
  • Infectious Diseases 103
  • Computer Vision and Pattern Recognition 98
  • Nutrition and Dietetics 63
  • Pharmacy 18
  • Emergency Medical Services 24
Replace Jos van der Meer with:
Jos van der Meer Netherlands
Wenting Fan China
Asha Asha India
Jing Lu China
Aihua Yin China
Md Khairul Islam Bangladesh
Ahmed S. Sultan United States
Ningning Huang China
Shuichi Kawano Japan
Dong‐Hyeok Kim South Korea
Lian Wu relative to Jos van der Meer Netherlands Jos van der Meer's profile →
Citations per field
00.5×11.7×
Jos van der Meer · 1×
Citations per year

Countries citing papers authored by Lian Wu

Since Specialization
Citations

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

Fields of papers citing papers by Lian Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008150
2 2004106
3 200690
4 200572
5 202248
6 202330
7 202323
8 200421
9 200421
10 200220
11 202118
12 201611
13 20229
14 20219
15 20249
16 20158
17 20138
18 20137
19 20137
20 19927

About Lian Wu

Lian Wu is a scholar working on Computer Vision and Pattern Recognition, Biomedical Engineering, Artificial Intelligence, Materials Chemistry and Infectious Diseases, having authored 32 papers that have together received 689 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (3 papers), Anomaly Detection Techniques and Applications (3 papers), Video Surveillance and Tracking Methods (3 papers), Face and Expression Recognition (2 papers), Advanced Neural Network Applications (2 papers), Nanowire Synthesis and Applications (2 papers), Ga2O3 and related materials (2 papers) and Membrane Separation Technologies (2 papers). The work is most often cited by research in Infectious Diseases (103 citations), Computer Vision and Pattern Recognition (98 citations), Nutrition and Dietetics (63 citations), Pharmacy (18 citations) and Emergency Medical Services (24 citations). Lian Wu has collaborated with scholars based in China, New Zealand and Taiwan. Frequent co-authors include Yong Xu, Peter Black, Xiaoling Wang, Jean‐Marie Basset, F. Lefebvre, Xianliang Fu, Kristin Wickens, Susan L. Prescott, Edwin A. Mitchell and Julian Crane. Their work appears in journals such as Applied Intelligence, IEEE Transactions on Circuits and Systems for Video Technology, Neural Networks, Vaccine and Journal of Catalysis.

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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