Bangjun Wang

1.3k citations
72 papers · 1.0k · h-index 19

Impact in

Papers in

Bangjun Wang

66 papers receiving 1.0k citations

Peers

Bangjun Wang
Comparison fields: 5 of 129
  • General Energy 9
  • Computer Vision and Pattern Recognition 175
  • Pollution 82
  • Occupational Therapy 22
  • Artificial Intelligence 181
Replace Peter Chapman with:
Peter Chapman United Kingdom
Elżbieta Jasińska Poland
Ming Yang China
Li Yang China
Bartłomiej Kizielewicz Poland
B. Eswara Reddy India
Nilay Khare India
Ana de las Heras Spain
Yi Liang China
Sulaiman Khan Pakistan
Bangjun Wang relative to Peter Chapman United Kingdom Peter Chapman's profile →
Citations per field
00.5×11×
Peter Chapman · 1×
Citations per year

Countries citing papers authored by Bangjun Wang

Since Specialization
Citations

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

Fields of papers citing papers by Bangjun Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 72 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017111
2 201880
3 201973
4 202170
5 201862
6 201754
7 201450
8 201838
9 202232
10 202232
11 202030
12 202227
13 201625
14 201922
15 200420
16 201720
17 202119
18 201818
19 202218
20 201717

About Bangjun Wang

Bangjun Wang is a scholar working on Computer Vision and Pattern Recognition, Economics and Econometrics, Artificial Intelligence, Molecular Biology and Media Technology, having authored 72 papers that have together received 1.0k indexed citations. Recurring topics across this work include Face and Expression Recognition (14 papers), Energy, Environment, Economic Growth (9 papers), Sparse and Compressive Sensing Techniques (6 papers), Environmental Impact and Sustainability (6 papers), Gene expression and cancer classification (5 papers), Climate Change Policy and Economics (5 papers), Image Retrieval and Classification Techniques (5 papers) and Remote-Sensing Image Classification (4 papers). The work is most often cited by research in General Energy (9 citations), Computer Vision and Pattern Recognition (175 citations), Pollution (82 citations), Occupational Therapy (22 citations) and Artificial Intelligence (181 citations). Bangjun Wang has collaborated with scholars based in China and Bangladesh. Frequent co-authors include Li Zhang, Fanzhang Li, Zhao Zhang, Xiaojuan Huang, Kejia Xie, Feng Ji, Jie Zheng, Huihong Wang, Liping Sun and Jin Cao. Their work appears in journals such as Applied Intelligence, Energy, Energy Economics, Sustainability and Energies.

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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