Shaojun Wang

162 papers receiving 2.7k citations

Peers

Shaojun Wang
Comparison fields: 5 of 163
  • Management Information Systems 573
  • Industrial and Manufacturing Engineering 407
  • Strategy and Management 477
  • Artificial Intelligence 673
  • Inorganic Chemistry 262
Replace Juanjuan Li with:
Juanjuan Li China
Vicente Botti Spain
Minqiang Li China
Jingjing Chen China
Sang Chan Park South Korea
Junliang Chen China
Xiaotong Zhang China
Morten Lind Denmark
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Shaojun Wang relative to Juanjuan Li China Juanjuan Li's profile →
Citations per field
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Juanjuan Li · 1×
Citations per year

Countries citing papers authored by Shaojun Wang

Since Specialization
Citations

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

Fields of papers citing papers by Shaojun Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 173 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004253
2 2000208
3 2000198
4 2004169
5 2006164
6 2000118
7 2003100
8 201874
9 202267
10 202363
11 200863
12 200462
13 201561
14 200351
15 200244
16 201839
17 201538
18 201737
19 202135
20 201135

About Shaojun Wang

Shaojun Wang is a scholar working on Artificial Intelligence, Mechanical Engineering, Control and Systems Engineering, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition, having authored 173 papers that have together received 2.9k indexed citations. Recurring topics across this work include Topic Modeling (24 papers), Natural Language Processing Techniques (22 papers), Remote Sensing and Land Use (9 papers), Speech Recognition and Synthesis (9 papers), Remote-Sensing Image Classification (9 papers), Fault Detection and Control Systems (7 papers), Advanced machining processes and optimization (7 papers) and Manufacturing Process and Optimization (7 papers). The work is most often cited by research in Management Information Systems (573 citations), Industrial and Manufacturing Engineering (407 citations), Strategy and Management (477 citations), Artificial Intelligence (673 citations) and Inorganic Chemistry (262 citations). Shaojun Wang has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Bhaba R. Sarker, A. M. M. Jamal, Dale Schuurmans, Fuchun Peng, Yu Peng, Ning Ma, Datong Liu, Philip H. W. Leong, Vlado Kešelj and Fuxia Sun. Their work appears in journals such as The International Journal of Advanced Manufacturing Technology, IEEE Transactions on Instrumentation and Measurement, European Journal of Operational Research, Microelectronics Reliability and The Journal of Engineering.

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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