Qinxue Meng
Impact in
- Artificial Intelligence top 5%
- Imbalanced Data Classification Techniques
- Anomaly Detection Techniques and Applications
- Metaheuristic Optimization Algorithms Research
- AI in cancer detection
- Evolutionary Algorithms and Applications
Papers in
-
- Complex Network Analysis Techniques 6
- Opinion Dynamics and Social Influence 2
-
- Advanced Clustering Algorithms Research 2
- Imbalanced Data Classification Techniques 1
- Cryptography and Data Security 1
- Co-authors
- Paul Kennedy (7 shared papers)Wei Liu (1 shared paper)Shoujin Wang (1 shared paper)Jia Wu (1 shared paper)Longbing Cao (1 shared paper)Xinxin Wang (1 shared paper)Yanchun Zhang (1 shared paper)Fengqi Li (1 shared paper)
- Journals
- Information Sciences (1 paper)BMC Bioinformatics (1 paper)Annals of Data Science (1 paper)International Journal of Parallel Emergent and Distributed Systems (1 paper)UTS ePRESS (University of Technology Sydney) (5 papers)
In The Last Decade
Qinxue Meng
9 papers receiving 416 citations
Qinxue Meng's Hit Papers
Peers
Comparison fields: 5 of 101
- Artificial Intelligence 248
- Health Informatics 5
- Computer Vision and Pattern Recognition 64
- Signal Processing 32
- Health Information Management 11
Countries citing papers authored by Qinxue Meng
This map shows the geographic impact of Qinxue Meng'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 Qinxue Meng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qinxue Meng more than expected).
Fields of papers citing papers by Qinxue Meng
This network shows the impact of papers produced by Qinxue Meng. 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 Qinxue Meng. The network helps show where Qinxue Meng may publish in the future.
Co-authors
The 12 scholars most cited alongside Qinxue Meng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Training deep neural networks on imbalanced data sets Hit paper breakdown → | 2016 | 313 |
| 2 | 2020 | 39 | |
| 3 | 2023 | 29 | |
| 4 | 2014 | 21 | |
| 5 | 2013 | 15 | |
| 6 | 2017 | 8 | |
| 7 | 2012 | 4 | |
| 8 | 2012 | 3 | |
| 9 | 2014 | 2 | |
| 10 | Using network evolution theory and singular value decomposition method to improve accuracy of link prediction in social networks | 2012 | 0 |
About Qinxue Meng
Qinxue Meng is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence, Molecular Biology, Sociology and Political Science and Information Systems, having authored 10 papers that have together received 434 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (6 papers), Advanced Clustering Algorithms Research (2 papers), Recommender Systems and Techniques (2 papers), Opinion Dynamics and Social Influence (2 papers), Imbalanced Data Classification Techniques (1 paper), Electricity Theft Detection Techniques (1 paper), Gene expression and cancer classification (1 paper) and Cryptography and Data Security (1 paper). The work is most often cited by research in Artificial Intelligence (248 citations), Health Informatics (5 citations), Computer Vision and Pattern Recognition (64 citations), Signal Processing (32 citations) and Health Information Management (11 citations). Qinxue Meng has collaborated with scholars based in Australia, China and Canada. Frequent co-authors include Paul Kennedy, Wei Liu, Shoujin Wang, Jia Wu, Longbing Cao, Xinxin Wang, Yanchun Zhang, Fengqi Li, Nana Yaw Asabere and Haifeng Liu. Their work appears in journals such as Information Sciences, BMC Bioinformatics, Annals of Data Science, International Journal of Parallel Emergent and Distributed Systems and UTS ePRESS (University of Technology Sydney).
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.