Can Chen
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Computer Networks and Communications top 10%
- Information Systems top 10%
- Signal Processing top 10%
- Topics
- Face and Expression Recognition (4 papers)Advanced Image Processing Techniques (4 papers)IoT and Edge/Fog Computing (3 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputer Networks and CommunicationsMedia Technology
- Journals
- European Journal of Operational ResearchConstruction and Building MaterialsIEEE Communications Surveys & Tutorials
- Partner nations
- ChinaUnited StatesMacao
In The Last Decade
Can Chen
46 papers receiving 704 citations
Hit Papers
Peers
Comparison fields: 5 of 148
- Computer Vision and Pattern Recognition 197
- Artificial Intelligence 163
- Computer Networks and Communications 143
- Information Systems 105
- Signal Processing 51
Countries citing papers authored by Can Chen
This map shows the geographic impact of Can Chen'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 Can Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Can Chen more than expected).
Fields of papers citing papers by Can Chen
This network shows the impact of papers produced by Can Chen. 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 Can Chen. The network helps show where Can Chen may publish in the future.
Co-authorship network of co-authors of Can Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Can Chen. A scholar is included among the top collaborators of Can Chen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Can Chen. Can Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiesbreakdown → | 67 |
| 3 | 2 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 2 | |
| 7 | 1 | |
| 8 | 16 | |
| 9 | 13 | |
| 10 | 7 | |
| 11 | 2 | |
| 12 | 0 | |
| 13 | 9 | |
| 14 | 4 | |
| 15 | 3 | |
| 16 | 10 | |
| 17 | 21 | |
| 18 | 25 | |
| 19 | 15 | |
| 20 | To development a knowledge data base of the network expert system for rice production | 1 |
About Can Chen
Can Chen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Finance, having authored 55 papers that have together received 729 indexed citations. Recurring topics across this work include Face and Expression Recognition (4 papers), Advanced Image Processing Techniques (4 papers) and IoT and Edge/Fog Computing (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (197 citations), Computer Networks and Communications (143 citations) and Media Technology (49 citations). Can Chen has collaborated with scholars based in China, United States and Macao. Frequent co-authors include Yanjie Fu, Jingyi Yu, Hui Xiong, Sing Bing Kang, Haiting Lin, Xinjiang Lu, Zhan Yu, Jin Ho Yang, Jianjun Pan and Shu Kee Lam. Their work appears in journals such as European Journal of Operational Research, Construction and Building Materials and IEEE Communications Surveys & Tutorials.
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.