Xiangjun Dong

101 papers receiving 1.2k citations

Peers

Xiangjun Dong
Comparison fields: 5 of 103
  • Information Systems 695
  • Computational Theory and Mathematics 398
  • Signal Processing 225
  • Artificial Intelligence 584
  • Computer Vision and Pattern Recognition 143
Replace Rosa Meo with:
Rosa Meo Italy
Feng Jiang China
Yun Sing Koh New Zealand
Jochen Hipp Germany
Jitender Kumar Chhabra India
Mohammad A. Hassonah Jordan
Stephen D. Bay United States
Pramod Kumar Singh India
Mehdi Hosseinzadeh Aghdam Iran
Andrei Petrovski United Kingdom
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Citations per field
00.5×4.5×
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Citations per year

Countries citing papers authored by Xiangjun Dong

Since Specialization
Citations

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

Fields of papers citing papers by Xiangjun Dong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 111 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202379
2 201373
3 201858
4 201455
5 201654
6 202053
7 201943
8 200738
9 201834
10 202430
11 201829
12 201726
13 200625
14 202024
15 201722
16 201622
17 201822
18 202421
19 200721
20 201820

About Xiangjun Dong

Xiangjun Dong is a scholar working on Information Systems, Computational Theory and Mathematics, Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 111 papers that have together received 1.3k indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (58 papers), Rough Sets and Fuzzy Logic (43 papers), Imbalanced Data Classification Techniques (25 papers), Data Management and Algorithms (14 papers), Advanced Database Systems and Queries (12 papers), Remote-Sensing Image Classification (7 papers), Image Retrieval and Classification Techniques (6 papers) and Text and Document Classification Technologies (5 papers). The work is most often cited by research in Information Systems (695 citations), Computational Theory and Mathematics (398 citations), Signal Processing (225 citations), Artificial Intelligence (584 citations) and Computer Vision and Pattern Recognition (143 citations). Xiangjun Dong has collaborated with scholars based in China, Australia and Hong Kong. Frequent co-authors include Longbing Cao, Yongshun Gong, Tiantian Xu, Long Zhao, Yuhai Zhao, Zhigang Zheng, Min Xing, Yan Sun, Zhendong Niu and Guohua Lv. Their work appears in journals such as IEEE Access, IEEE Transactions on Geoscience and Remote Sensing, Knowledge-Based Systems, IEEE Transactions on Neural Networks and Learning Systems and Symmetry.

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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