Hong Peng
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Molecular Biology
- Signal Processing top 10%
- Computational Mechanics top 10%
- Co-authors
- Hongmin CaiZhao LiXindong WuQianli MaJiazhou ChenJia WeiGuoqiang HanHaiyan Wang
- Topics
- Face and Expression Recognition (14 papers)Video Surveillance and Tracking Methods (6 papers)Remote-Sensing Image Classification (6 papers)
- Partner nations
- ChinaUnited KingdomUnited States
In The Last Decade
Hong Peng
49 papers receiving 651 citations
Peers
Comparison fields: 5 of 96
- Artificial Intelligence 255
- Computer Vision and Pattern Recognition 246
- Molecular Biology 141
- Signal Processing 74
- Computational Mechanics 67
Countries citing papers authored by Hong Peng
This map shows the geographic impact of Hong Peng'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 Hong Peng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hong Peng more than expected).
Fields of papers citing papers by Hong Peng
This network shows the impact of papers produced by Hong Peng. 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 Hong Peng. The network helps show where Hong Peng may publish in the future.
Co-authorship network of co-authors of Hong Peng
This figure shows the co-authorship network connecting the top 25 collaborators of Hong Peng. A scholar is included among the top collaborators of Hong Peng 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 Hong Peng. Hong Peng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 4 | |
| 4 | 8 | |
| 5 | 3 | |
| 6 | 4 | |
| 7 | 21 | |
| 8 | 8 | |
| 9 | 43 | |
| 10 | 0 | |
| 11 | 3 | |
| 12 | Structured plans and observation reduction for plans with contexts | 2 |
| 13 | Structured plan and its execution for extended goals | 0 |
| 14 | Question categorization system based on vector space model | 0 |
| 15 | 33 | |
| 16 | 26 | |
| 17 | 14 | |
| 18 | Particle swarm optimization based on permutation | 1 |
| 19 | 9 | |
| 20 | An Adaptive Genetic Algorithm for Multiple Sequence Alignment | 1 |
About Hong Peng
Hong Peng is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Media Technology, having authored 55 papers that have together received 678 indexed citations. Recurring topics across this work include Face and Expression Recognition (14 papers), Video Surveillance and Tracking Methods (6 papers) and Remote-Sensing Image Classification (6 papers). The work is most often cited by research in Computational Mathematics (28 citations), Computer Vision and Pattern Recognition (246 citations) and Artificial Intelligence (255 citations). Hong Peng has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Hongmin Cai, Zhao Li, Xindong Wu, Qianli Ma, Jiazhou Chen, Jia Wei, Guoqiang Han, Haiyan Wang, Peijie Huang and Tingting Dan. Their work appears in journals such as Bioinformatics, Pattern Recognition and Information Sciences.
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.