Xinxin Chen

19 papers receiving 632 citations

Peers

Xinxin Chen
Comparison fields: 5 of 89
  • Environmental Engineering 193
  • Media Technology 117
  • Computer Vision and Pattern Recognition 190
  • Geology 46
  • Nature and Landscape Conservation 77
Replace Shaohua Wang with:
Shaohua Wang China
Everton Castel�ão Tetila Brazil
Diogo Nunes Gonçalves Brazil
Fangming Wu China
Anderson Santos Brazil
Mingzhu Wan China
Dong Liang China
Lirong Xiang United States
Panče Panov Slovenia
Henrik Skov Midtiby Denmark
Xinxin Chen relative to Shaohua Wang China Shaohua Wang's profile →
Citations per field
00.5×5.9×
Shaohua Wang · 1×
Citations per year

Countries citing papers authored by Xinxin Chen

Since Specialization
Citations

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

Fields of papers citing papers by Xinxin Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2021180
2 2021130
3 2016119
4 201787
5 201967
6 202021
7 202314
8 20225
9 20234
10 20223
11 20233
12 20233
13 20222
14 20211
15 20251
16 20241
17 20251
18 20141
19 20061
20 20250

About Xinxin Chen

Xinxin Chen is a scholar working on Computer Vision and Pattern Recognition, Environmental Engineering, Molecular Biology, Nature and Landscape Conservation and Artificial Intelligence, having authored 23 papers that have together received 644 indexed citations. Recurring topics across this work include Remote Sensing and LiDAR Applications (3 papers), Forest ecology and management (3 papers), Stock Market Forecasting Methods (2 papers), Advanced Vision and Imaging (2 papers), Machine Learning in Bioinformatics (2 papers), Cancer Research and Treatments (2 papers), Complex Systems and Time Series Analysis (2 papers) and Nanoplatforms for cancer theranostics (2 papers). The work is most often cited by research in Environmental Engineering (193 citations), Media Technology (117 citations), Computer Vision and Pattern Recognition (190 citations), Geology (46 citations) and Nature and Landscape Conservation (77 citations). Xinxin Chen has collaborated with scholars based in China and United States. Frequent co-authors include Ting Yun, Wei Chen, Hao Lin, Hua Tang, Xiangjun Wang, Zhu Yushi, Kang Jiang, Yi Yuan, Xiaoyang Lyu and Mengmeng Wang. Their work appears in journals such as The North American Journal of Economics and Finance, Forests, Pharmaceutics, BioMed Research International and IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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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