Zedong Tang

956 citations
28 papers · 696 · h-index 15

Impact in

Papers in

Zedong Tang

26 papers receiving 688 citations

Peers

Zedong Tang
Comparison fields: 5 of 77
  • Artificial Intelligence 462
  • Computational Theory and Mathematics 227
  • Computer Vision and Pattern Recognition 150
  • Media Technology 46
  • Statistical and Nonlinear Physics 54
Replace Eric Wiewiora with:
Eric Wiewiora United States
Mohd. Asyraf Mansor Malaysia
Zhenyu Wu China
Chun-Ru Dong China
Francisco Escolano Spain
Simon Lacoste-Julien Canada
Dinabandhu Bhandari India
Hsu‐Chun Yen Taiwan
Saratha Sathasivam Malaysia
Zedong Tang relative to Eric Wiewiora United States Eric Wiewiora's profile →
Citations per field
00.5×8.5×
Eric Wiewiora · 1×
Citations per year

Countries citing papers authored by Zedong Tang

Since Specialization
Citations

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

Fields of papers citing papers by Zedong Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019134
2 202093
3 202073
4 202253
5 202044
6 201935
7 202131
8 202328
9 201923
10 202122
11 202222
12 202017
13 201916
14 201716
15 202315
16 201614
17 201914
18 202112
19 202110
20 20206

About Zedong Tang

Zedong Tang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Statistical and Nonlinear Physics and Computational Mechanics, having authored 28 papers that have together received 696 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (9 papers), Advanced Graph Neural Networks (7 papers), Advanced Multi-Objective Optimization Algorithms (7 papers), Evolutionary Algorithms and Applications (5 papers), Complex Network Analysis Techniques (4 papers), Remote-Sensing Image Classification (4 papers), Advanced Neural Network Applications (3 papers) and Machine Learning and ELM (3 papers). The work is most often cited by research in Artificial Intelligence (462 citations), Computational Theory and Mathematics (227 citations), Computer Vision and Pattern Recognition (150 citations), Media Technology (46 citations) and Statistical and Nonlinear Physics (54 citations). Zedong Tang has collaborated with scholars based in China, Australia and Hong Kong. Frequent co-authors include Maoguo Gong, Hao Li, Yu Xie, Yue Wu, Jun Zhang, A. K. Qin, Chao Han, Wenping Ma, Qiguang Miao and Fenlong Jiang. Their work appears in journals such as Neural Networks, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Evolutionary Computation.

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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