Li Cui

511 citations
38 papers · 369 · h-index 10

Impact in

Papers in

Li Cui

37 papers receiving 356 citations

Peers

Li Cui
Comparison fields: 5 of 86
  • Computer Vision and Pattern Recognition 103
  • Applied Psychology 18
  • Computational Mathematics 2
  • Artificial Intelligence 100
  • Computer Networks and Communications 63
Replace Abdullah Alshehri with:
Abdullah Alshehri Saudi Arabia
Ji-Won Baek South Korea
Syed Thouheed Ahmed India
Samuli Hemminki Finland
Ryan McConville United Kingdom
Lauri Lovén Finland
Zhenglin Li China
Theodor Panagiotakopoulos Greece
Vesa Kyllönen Finland
Aws A. Magableh Jordan
Li Cui relative to Abdullah Alshehri Saudi Arabia Abdullah Alshehri's profile →
Citations per field
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Abdullah Alshehri · 1×
Citations per year

Countries citing papers authored by Li Cui

Since Specialization
Citations

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

Fields of papers citing papers by Li Cui

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201472
2 202042
3
Real-time Traffic Monitoring with Magnetic Sensor Networks *
201138
4 202133
5 201326
6 201921
7 202315
8
Forecast-based temporal data aggregation in wireless sensor networks
200712
9 200812
10 20149
11 20208
12 20067
13 20217
14 20187
15 20216
16 20126
17 20226
18 20194
19 20224
20 20204

About Li Cui

Li Cui is a scholar working on Computer Networks and Communications, Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering and Electrical and Electronic Engineering, having authored 38 papers that have together received 369 indexed citations. Recurring topics across this work include Energy Efficient Wireless Sensor Networks (9 papers), Indoor and Outdoor Localization Technologies (5 papers), Advanced Neural Network Applications (4 papers), Water Quality Monitoring Technologies (4 papers), Stroke Rehabilitation and Recovery (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Musculoskeletal pain and rehabilitation (3 papers) and EEG and Brain-Computer Interfaces (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (103 citations), Applied Psychology (18 citations), Computational Mathematics (2 citations), Artificial Intelligence (100 citations) and Computer Networks and Communications (63 citations). Li Cui has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Zhenhe Zhou, Jun Wang, Fang Yu, Lei Zhang, Rui Wang, Yongqiang Qin, Kejiang Xiao, Ye Zheng, Lei Zhang and Xueyuan Zhang. Their work appears in journals such as Scientific Reports, IEEE Transactions on Intelligent Transportation Systems, Journal of Coastal Research, Frontiers in Behavioral Neuroscience and IEEE Internet of Things Journal.

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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