Qun Song
- Artificial Intelligence top 1%
- Control and Systems Engineering top 5%
- Signal Processing top 5%
- Management Science and Operations Research top 5%
- Electrical and Electronic Engineering
- Topics
- Neural Networks and Applications (12 papers)Fuzzy Logic and Control Systems (10 papers)Speech and Audio Processing (6 papers)
- Partner nations
- ChinaSingaporeNew Zealand
In The Last Decade
Qun Song
47 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 109
- Artificial Intelligence 983
- Control and Systems Engineering 283
- Signal Processing 153
- Management Science and Operations Research 150
- Electrical and Electronic Engineering 140
Countries citing papers authored by Qun Song
This map shows the geographic impact of Qun Song'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 Qun Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qun Song more than expected).
Fields of papers citing papers by Qun Song
This network shows the impact of papers produced by Qun Song. 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 Qun Song. The network helps show where Qun Song may publish in the future.
Co-authorship network of co-authors of Qun Song
This figure shows the co-authorship network connecting the top 25 collaborators of Qun Song. A scholar is included among the top collaborators of Qun Song 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 Qun Song. Qun Song 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 | 2 | |
| 3 | 1 | |
| 4 | 12 | |
| 5 | 4 | |
| 6 | 9 | |
| 7 | 6 | |
| 8 | 1 | |
| 9 | 29 | |
| 10 | 20 | |
| 11 | 12 | |
| 12 | 21 | |
| 13 | Ontology Based Personalized Modeling for Chronic Disease Risk Analysis: An Integrated Approach | 3 |
| 14 | Extracting association classification rules from RBF kernel | 2 |
| 15 | 33 | |
| 16 | 4 | |
| 17 | 1 | |
| 18 | A novel feature selection method to improve classification of gene expression data | 49 |
| 19 | ECM — A Novel On-line, Evolving Clustering Method and Its Applications | 37 |
| 20 | Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS): On-line learning and Application for Time-Series Prediction | 33 |
About Qun Song
Qun Song is a scholar working on Signal Processing, Artificial Intelligence and Statistical and Nonlinear Physics, having authored 49 papers that have together received 1.5k indexed citations. Recurring topics across this work include Neural Networks and Applications (12 papers), Fuzzy Logic and Control Systems (10 papers) and Speech and Audio Processing (6 papers). The work is most often cited by research in Artificial Intelligence (983 citations), Signal Processing (153 citations) and Control and Systems Engineering (283 citations). Qun Song has collaborated with scholars based in China, Singapore and New Zealand. Frequent co-authors include Nikola Kasabov, Rui Tan, Zhenghong Deng, Liang Kee Goh, Zhenyu Yan, Li Gao, Yan Xu, Chao Ren, Tao Wu and Chaojie Gu. Their work appears in journals such as IEEE Transactions on Smart Grid, IEEE Transactions on Fuzzy Systems and Neurocomputing.
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.