Fang Mei
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 5%
- Computer Networks and Communications top 10%
- Electrical and Electronic Engineering
- Biomedical Engineering
- Co-authors
- Dan ZengWei ShenYuanwang WeiZhijiang ZhangRadovan KovacevicYanheng LiuXuanjing ShenGuangxue Yue
- Topics
- Vehicular Ad Hoc Networks (VANETs) (4 papers)Mobile Ad Hoc Networks (4 papers)Metaheuristic Optimization Algorithms Research (4 papers)
- Partner nations
- ChinaUnited StatesSingapore
In The Last Decade
Fang Mei
33 papers receiving 485 citations
Hit Papers
Peers
Comparison fields: 5 of 82
- Computer Vision and Pattern Recognition 245
- Artificial Intelligence 224
- Computer Networks and Communications 129
- Electrical and Electronic Engineering 58
- Biomedical Engineering 57
Countries citing papers authored by Fang Mei
This map shows the geographic impact of Fang Mei'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 Fang Mei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fang Mei more than expected).
Fields of papers citing papers by Fang Mei
This network shows the impact of papers produced by Fang Mei. 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 Fang Mei. The network helps show where Fang Mei may publish in the future.
Co-authorship network of co-authors of Fang Mei
This figure shows the co-authorship network connecting the top 25 collaborators of Fang Mei. A scholar is included among the top collaborators of Fang Mei 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 Fang Mei. Fang Mei is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Casesbreakdown → | 15 |
| 3 | 5 | |
| 4 | 13 | |
| 5 | 11 | |
| 6 | 13 | |
| 7 | 12 | |
| 8 | 1 | |
| 9 | 5 | |
| 10 | 5 | |
| 11 | 40 | |
| 12 | 0 | |
| 13 | The Study on An Application of Otsu Method in Canny Operator | 30 |
| 14 | 11 | |
| 15 | 6 | |
| 16 | Application of Otsu thresholding method on Canny operator | 10 |
| 17 | 2 | |
| 18 | 1 | |
| 19 | Algorithms for B-spline Surface Skinning | 2 |
| 20 | 37 |
About Fang Mei
Fang Mei is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Computer Networks and Communications, having authored 36 papers that have together received 509 indexed citations. Recurring topics across this work include Vehicular Ad Hoc Networks (VANETs) (4 papers), Mobile Ad Hoc Networks (4 papers) and Metaheuristic Optimization Algorithms Research (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (245 citations), Ecological Modeling (36 citations) and Artificial Intelligence (224 citations). Fang Mei has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Dan Zeng, Wei Shen, Yuanwang Wei, Zhijiang Zhang, Radovan Kovacevic, Yanheng Liu, Xuanjing Shen, Guangxue Yue, Geng Sun and Jun Qin. Their work appears in journals such as Expert Systems with Applications, Sensors and IEEE Transactions on Vehicular Technology.
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.