Qingjun Wang

15 papers receiving 264 citations

Peers

Qingjun Wang
Comparison fields: 5 of 87
  • Business and International Management 8
  • Cognitive Neuroscience 74
  • Experimental and Cognitive Psychology 43
  • Human-Computer Interaction 17
  • Computer Vision and Pattern Recognition 61
Replace Kaushik Sekaran with:
Kaushik Sekaran India
Laiali Almazaydeh Jordan
J. Deny India
Seral Özşen Türkiye
J. Anitha India
Emad-ul-Haq Qazi Saudi Arabia
C. Jyotsna India
Fardin Abdali-Mohammadi Iran
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Citations per field
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Citations per year

Countries citing papers authored by Qingjun Wang

Since Specialization
Citations

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

Fields of papers citing papers by Qingjun Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

18 of 18 papers shown
#Work
1 202073
2 201861
3 201838
4 201825
5 201821
6 201313
7 20218
8 20228
9 20217
10 20215
11 20214
12 20234
13
Supervised Laplacian Discriminant Analysis for Small Sample Size Problem with Its Application to Face Recognition
20123
14 20252
15 20112
16 20250
17 20250
18 20240

About Qingjun Wang

Qingjun Wang is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience, Artificial Intelligence, Control and Systems Engineering and Cardiology and Cardiovascular Medicine, having authored 18 papers that have together received 274 indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (4 papers), Neural Networks and Applications (3 papers), Advanced Algorithms and Applications (2 papers), Neural dynamics and brain function (2 papers), Heart Rate Variability and Autonomic Control (2 papers), Video Surveillance and Tracking Methods (2 papers), Plant Water Relations and Carbon Dynamics (1 paper) and Autonomous Vehicle Technology and Safety (1 paper). The work is most often cited by research in Business and International Management (8 citations), Cognitive Neuroscience (74 citations), Experimental and Cognitive Psychology (43 citations), Human-Computer Interaction (17 citations) and Computer Vision and Pattern Recognition (61 citations). Qingjun Wang has collaborated with scholars based in China, Italy and India. Frequent co-authors include Yibo Li, Zhihan Lv, Yibo Li, Liang Qiao, Francesco Piccialli, Guangming Li, Zhendong Mu, Ke Lü, Mingquan Zhou and Zhongke Wu. Their work appears in journals such as ACM Transactions on Internet Technology, Neural Computing and Applications, iScience, Journal of Forecasting and IEEE/ACM Transactions on Computational Biology and Bioinformatics.

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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