Qiangfu Zhao
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Computational Theory and Mathematics top 5%
- Signal Processing top 5%
- Electrical and Electronic Engineering
- Co-authors
- Hazem M. El‐BakryXin YaoT. HiguchiCheng‐Hsiung HsiehTakahiro HiguchiJia HaoRung-Ching ChenYong Liu
- Topics
- Neural Networks and Applications (47 papers)Face and Expression Recognition (41 papers)Advanced Neural Network Applications (16 papers)
In The Last Decade
Qiangfu Zhao
171 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 103
- Artificial Intelligence 634
- Computer Vision and Pattern Recognition 435
- Computational Theory and Mathematics 181
- Signal Processing 153
- Electrical and Electronic Engineering 134
Countries citing papers authored by Qiangfu Zhao
This map shows the geographic impact of Qiangfu Zhao'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 Qiangfu Zhao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qiangfu Zhao more than expected).
Fields of papers citing papers by Qiangfu Zhao
This network shows the impact of papers produced by Qiangfu Zhao. 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 Qiangfu Zhao. The network helps show where Qiangfu Zhao may publish in the future.
Co-authorship network of co-authors of Qiangfu Zhao
This figure shows the co-authorship network connecting the top 25 collaborators of Qiangfu Zhao. A scholar is included among the top collaborators of Qiangfu Zhao 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 Qiangfu Zhao. Qiangfu Zhao 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 | 7 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 9 | |
| 8 | 3 | |
| 9 | 2 | |
| 10 | 1 | |
| 11 | 4 | |
| 12 | 7 | |
| 13 | 2 | |
| 14 | 3 | |
| 15 | 3 | |
| 16 | 32 | |
| 17 | An Efficient Method for Evolving Important Training Data | 0 |
| 18 | Growing neural network trees efficiently and effectively | 6 |
| 19 | A study on efficient generation of decision trees using genetic programming | 14 |
| 20 | 34 |
About Qiangfu Zhao
Qiangfu Zhao is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology, having authored 191 papers that have together received 1.2k indexed citations. Recurring topics across this work include Neural Networks and Applications (47 papers), Face and Expression Recognition (41 papers) and Advanced Neural Network Applications (16 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (435 citations), Artificial Intelligence (634 citations) and Signal Processing (153 citations). Qiangfu Zhao has collaborated with scholars based in Japan, Taiwan and China. Frequent co-authors include Hazem M. El‐Bakry, Xin Yao, T. Higuchi, Cheng‐Hsiung Hsieh, Takahiro Higuchi, Jia Hao, Rung-Ching Chen, Yong Liu, Yan Yan and Yoichi Tomioka. Their work appears in journals such as IEEE Access, Sensors and International Journal of Biological Macromolecules.
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.