Qun Song

2.3k total citations · 1 hit paper
49 papers, 1.5k citations indexed

About

Qun Song is a scholar working on Artificial Intelligence, Signal Processing and Statistical and Nonlinear Physics. According to data from OpenAlex, Qun Song has authored 49 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Artificial Intelligence, 10 papers in Signal Processing and 8 papers in Statistical and Nonlinear Physics. Recurrent topics in Qun Song's work include Neural Networks and Applications (12 papers), Fuzzy Logic and Control Systems (10 papers) and Speech and Audio Processing (6 papers). Qun Song is often cited by papers focused on Neural Networks and Applications (12 papers), Fuzzy Logic and Control Systems (10 papers) and Speech and Audio Processing (6 papers). Qun Song collaborates with scholars based in China, Singapore and New Zealand. Qun Song's co-authors include Nikola Kasabov, Rui Tan, Zhenghong Deng, Liang Kee Goh, Zhenyu Yan, Li Gao, Yan Xu, Chao Ren, Tao Wu and Mingyu Gao and has published in prestigious journals such as IEEE Transactions on Smart Grid, IEEE Transactions on Fuzzy Systems and Neurocomputing.

In The Last Decade

Qun Song

47 papers receiving 1.4k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction

2002 913 citations Nikola Kasabov, Qun Song profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Qun Song China	17	983	283	153	150	140	49	1.5k
Xiaowei Gu United Kingdom	27	930 0.9×	259 0.9×	118 0.8×	195 1.3×	112 0.8×	94	1.8k
Junhai Zhai China	20	943 1.0×	131 0.5×	83 0.5×	128 0.9×	203 1.4×	98	1.4k
Kenneth W. Bauer United States	19	553 0.6×	110 0.4×	175 1.1×	149 1.0×	94 0.7×	119	1.3k
Michael G. Madden Ireland	16	611 0.6×	134 0.5×	100 0.7×	135 0.9×	104 0.7×	65	1.3k
Chen‐Chia Chuang Taiwan	14	639 0.7×	384 1.4×	52 0.3×	95 0.6×	110 0.8×	63	1.1k
Claus Weihs Germany	20	437 0.4×	199 0.7×	118 0.8×	105 0.7×	79 0.6×	110	1.3k
Takashi Onoda Japan	12	811 0.8×	252 0.9×	101 0.7×	42 0.3×	218 1.6×	73	1.4k
Shie-Jue Lee Taiwan	21	1.0k 1.0×	197 0.7×	193 1.3×	333 2.2×	196 1.4×	120	1.8k
Anna Maria Fanelli Italy	18	788 0.8×	133 0.5×	79 0.5×	164 1.1×	64 0.5×	101	1.2k
Olga Valenzuela Spain	18	462 0.5×	131 0.5×	88 0.6×	177 1.2×	157 1.1×	84	1.1k

Countries citing papers authored by Qun Song

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Song, Qun, et al.. (2024). Analysis of payoff expectation in evolutionary game based on Bush–Mosteller model. Chaos Solitons & Fractals. 185. 115161–115161. 1 indexed citations

Cao, Chengtai, Xinhong Chen, Jianping Wang, et al.. (2024). CCTR: Calibrating Trajectory Prediction for Uncertainty-Aware Motion Planning in Autonomous Driving. Proceedings of the AAAI Conference on Artificial Intelligence. 38(19). 20949–20957. 2 indexed citations

Zhang, Yang, et al.. (2024). Enhancing adversarial transferability with local transformation. Complex & Intelligent Systems. 11(1). 1 indexed citations

Yan, Zhenyu, et al.. (2023). VI-Map: Infrastructure-Assisted Real-Time HD Mapping for Autonomous Driving. 1–15. 12 indexed citations

Song, Qun, et al.. (2023). Indoor Smartphone SLAM With Acoustic Echoes. IEEE Transactions on Mobile Computing. 23(6). 6634–6649. 4 indexed citations

Song, Qun, Rui Tan, & Jianping Wang. (2023). Towards Efficient Personalized Driver Behavior Modeling with Machine Unlearning. CityU Scholars. 31–36. 2 indexed citations

Song, Qun, Rui Tan, Chao Ren, et al.. (2022). On Credibility of Adversarial Examples Against Learning-Based Grid Voltage Stability Assessment. IEEE Transactions on Dependable and Secure Computing. 21(2). 585–599. 9 indexed citations

Song, Qun, et al.. (2022). Indoor Smartphone SLAM with Learned Echoic Location Features. 489–503. 6 indexed citations

Ren, Chao, Xiaoning Du, Yan Xu, et al.. (2021). Vulnerability Analysis, Robustness Verification, and Mitigation Strategy for Machine Learning-Based Power System Stability Assessment Model Under Adversarial Examples. IEEE Transactions on Smart Grid. 13(2). 1622–1632. 29 indexed citations

10.

Song, Qun, et al.. (2019). The evolution of cooperation in multi-games with aspiration-driven updating rule. Chaos Solitons & Fractals. 128. 313–317. 20 indexed citations

11.

Deng, Zhenghong, Bo Gao, Qun Song, et al.. (2019). Popularity-driven strategy updating rule promotes cooperation in the spatial prisoner's dilemma game. Applied Mathematics and Computation. 353. 82–87. 12 indexed citations

12.

Song, Qun, Chaojie Gu, & Rui Tan. (2018). Deep Room Recognition Using Inaudible Echos. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 2(3). 1–28. 21 indexed citations

13.

Verma, Anju, Nikola Kasabov, Elaine Rush, & Qun Song. (2009). Ontology Based Personalized Modeling for Chronic Disease Risk Analysis: An Integrated Approach. 10(3). 3 indexed citations

14.

Song, Qun. (2007). Extracting association classification rules from RBF kernel. Journal of Northwest University. 2 indexed citations

15.

Song, Qun & Nikola Kasabov. (2006). TWNFI — a transductive neuro-fuzzy inference system with weighted data normalization for personalized modeling. Neural Networks. 19(10). 1591–1596. 33 indexed citations

16.

Song, Qun & Nikola Kasabov. (2005). WDN-RBF: weighted data normalization for radial basic function type neural networks. 3. 2095–2098. 1 indexed citations

17.

Song, Qun, et al.. (2005). Integrating regression formulas and kernel functions into locally adaptive knowledge-based neural networks: A case study on renal function evaluation. Artificial Intelligence in Medicine. 36(3). 235–244. 4 indexed citations

18.

Goh, Liang Kee, Qun Song, & Nikola Kasabov. (2004). A novel feature selection method to improve classification of gene expression data. 161–166. 49 indexed citations

19.

Song, Qun & Nikola Kasabov. (2001). ECM — A Novel On-line, Evolving Clustering Method and Its Applications. 37 indexed citations

20.

Song, Qun & Nikola Kasabov. (2000). Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS): On-line learning and Application for Time-Series Prediction. 33 indexed citations

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.

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