Fei Zheng
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Information Systems
- Management Science and Operations Research
- Molecular Biology
- Co-authors
- Geoffrey I. WebbKai HuLiguo WengJunlan JinPramuditha SuraweeraJiasheng WuYanwen ZhangMin Xia
- Topics
- Bayesian Modeling and Causal Inference (3 papers)Human Pose and Action Recognition (3 papers)Gait Recognition and Analysis (3 papers)
In The Last Decade
Fei Zheng
19 papers receiving 240 citations
Peers
Comparison fields: 5 of 75
- Artificial Intelligence 162
- Computer Vision and Pattern Recognition 61
- Information Systems 28
- Management Science and Operations Research 23
- Molecular Biology 19
Countries citing papers authored by Fei Zheng
This map shows the geographic impact of Fei Zheng'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 Fei Zheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fei Zheng more than expected).
Fields of papers citing papers by Fei Zheng
This network shows the impact of papers produced by Fei Zheng. 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 Fei Zheng. The network helps show where Fei Zheng may publish in the future.
Co-authorship network of co-authors of Fei Zheng
This figure shows the co-authorship network connecting the top 25 collaborators of Fei Zheng. A scholar is included among the top collaborators of Fei Zheng 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 Fei Zheng. Fei Zheng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 2 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 2 | |
| 7 | 0 | |
| 8 | 7 | |
| 9 | 56 | |
| 10 | 12 | |
| 11 | 19 | |
| 12 | Optimal wireless sensor network using cloud adaptive particle-swarm-optimization algorithm | 2 |
| 13 | 2 | |
| 14 | 48 | |
| 15 | Decreasingly naive Bayes: Aggregating n-dependence estimators | 1 |
| 16 | 2 | |
| 17 | 1 | |
| 18 | 1 | |
| 19 | 44 | |
| 20 | A comparative study of Semi-naive Bayes methods in classification learning | 43 |
About Fei Zheng
Fei Zheng is a scholar working on Energy Engineering and Power Technology, Artificial Intelligence and Management Science and Operations Research, having authored 20 papers that have together received 248 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (3 papers), Human Pose and Action Recognition (3 papers) and Gait Recognition and Analysis (3 papers). The work is most often cited by research in Artificial Intelligence (162 citations), Computer Vision and Pattern Recognition (61 citations) and Management Science and Operations Research (23 citations). Fei Zheng has collaborated with scholars based in China, Australia and India. Frequent co-authors include Geoffrey I. Webb, Kai Hu, Liguo Weng, Junlan Jin, Pramuditha Suraweera, Jiasheng Wu, Yanwen Zhang, Min Xia, Ling Shi and Zheming Zhang. Their work appears in journals such as Renewable and Sustainable Energy Reviews, Building and Environment and Machine Learning.
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.