Junhai Zhai
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 5%
- Computational Theory and Mathematics top 2%
- Information Systems top 5%
- Electrical and Electronic Engineering
- Topics
- Rough Sets and Fuzzy Logic (33 papers)Data Mining Algorithms and Applications (22 papers)Machine Learning and ELM (21 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionComputational Theory and Mathematics
- Journals
- IEEE AccessEnergyPattern Recognition
In The Last Decade
Junhai Zhai
90 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 120
- Artificial Intelligence 943
- Computer Vision and Pattern Recognition 331
- Computational Theory and Mathematics 234
- Information Systems 205
- Electrical and Electronic Engineering 203
Countries citing papers authored by Junhai Zhai
This map shows the geographic impact of Junhai Zhai'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 Junhai Zhai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junhai Zhai more than expected).
Fields of papers citing papers by Junhai Zhai
This network shows the impact of papers produced by Junhai Zhai. 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 Junhai Zhai. The network helps show where Junhai Zhai may publish in the future.
Co-authorship network of co-authors of Junhai Zhai
This figure shows the co-authorship network connecting the top 25 collaborators of Junhai Zhai. A scholar is included among the top collaborators of Junhai Zhai based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Junhai Zhai. Junhai Zhai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 2 | |
| 7 | 2 | |
| 8 | 2 | |
| 9 | 3 | |
| 10 | 5 | |
| 11 | 44 | |
| 12 | 7 | |
| 13 | 8 | |
| 14 | 43 | |
| 15 | 25 | |
| 16 | 2 | |
| 17 | Information granularity,information entropy and decision tree | 0 |
| 18 | 138 | |
| 19 | Theoretical foundation of ID3 algorithm | 1 |
| 20 | Review of the relationship between wheat roots and water stress | 0 |
About Junhai Zhai
Junhai Zhai is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition, having authored 98 papers that have together received 1.4k indexed citations. Recurring topics across this work include Rough Sets and Fuzzy Logic (33 papers), Data Mining Algorithms and Applications (22 papers) and Machine Learning and ELM (21 papers). The work is most often cited by research in Artificial Intelligence (943 citations), Computer Vision and Pattern Recognition (331 citations) and Computational Theory and Mathematics (234 citations). Junhai Zhai has collaborated with scholars based in China, Hong Kong and Canada. Frequent co-authors include Xizhao Wang, Sufang Zhang, Shuxia Lu, Junfen Chen, Hongyu Xu, Qiang He, Chenxi Wang, Mengyao Zhai, Xiaomeng Liu and Mingyang Zhang. Their work appears in journals such as IEEE Access, Energy and Pattern Recognition.
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.