Xuewen Chen
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Molecular Biology
- Information Systems top 10%
- Computational Theory and Mathematics top 10%
- Co-authors
- Xiaotong LinIshan JindalMatthew NoklebyDavid CasasentBo LuoChanglin MaJinlong LiHaixun Wang
- Topics
- Topic Modeling (3 papers)Advanced Text Analysis Techniques (3 papers)Machine Learning and Data Classification (3 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionHealth Information Management
- Journals
- SmallIEEE AccessPattern Recognition
- Partner nations
- United StatesChina
In The Last Decade
Xuewen Chen
19 papers receiving 483 citations
Peers
Comparison fields: 5 of 84
- Artificial Intelligence 357
- Computer Vision and Pattern Recognition 99
- Molecular Biology 71
- Information Systems 65
- Computational Theory and Mathematics 37
Countries citing papers authored by Xuewen Chen
This map shows the geographic impact of Xuewen 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 Xuewen Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xuewen Chen more than expected).
Fields of papers citing papers by Xuewen Chen
This network shows the impact of papers produced by Xuewen 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 Xuewen Chen. The network helps show where Xuewen Chen may publish in the future.
Co-authorship network of co-authors of Xuewen Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Xuewen Chen. A scholar is included among the top collaborators of Xuewen 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 Xuewen Chen. Xuewen 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 | 3 | |
| 2 | 5 | |
| 3 | 6 | |
| 4 | 4 | |
| 5 | 3 | |
| 6 | 1 | |
| 7 | 5 | |
| 8 | 97 | |
| 9 | 7 | |
| 10 | 17 | |
| 11 | 1 | |
| 12 | 3 | |
| 13 | [Prediction of soil organic carbon in different soil fractions of black soils in Northeast China using near-infrared reflectance spectroscopy]. | 6 |
| 14 | 12 | |
| 15 | 7 | |
| 16 | 163 | |
| 17 | 114 | |
| 18 | 1 | |
| 19 | 52 |
About Xuewen Chen
Xuewen Chen is a scholar working on Artificial Intelligence, Toxicology and Computer Vision and Pattern Recognition, having authored 19 papers that have together received 507 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Advanced Text Analysis Techniques (3 papers) and Machine Learning and Data Classification (3 papers). The work is most often cited by research in Artificial Intelligence (357 citations), Computer Vision and Pattern Recognition (99 citations) and Health Information Management (15 citations). Xuewen Chen has collaborated with scholars based in United States and China. Frequent co-authors include Xiaotong Lin, Ishan Jindal, Matthew Nokleby, David Casasent, Bo Luo, Changlin Ma, Jinlong Li, Haixun Wang, Wei Zhao and Zhizheng Liang. Their work appears in journals such as Small, IEEE Access and Pattern Recognition.
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.