Ran Wang
Impact in
- Artificial Intelligence top 0.5%
- Metaheuristic Optimization Algorithms Research
- Machine Learning and ELM
- Text and Document Classification Technologies
- Domain Adaptation and Few-Shot Learning
- Evolutionary Algorithms and Applications
- Neural Networks and Applications
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- Advanced Multi-Objective Optimization Algorithms
Papers in
-
- Machine Learning and ELM 25
- Text and Document Classification Technologies 14
- Machine Learning and Algorithms 12
- Domain Adaptation and Few-Shot Learning 10
- Machine Learning and Data Classification 10
- Neural Networks and Applications 10
- Metaheuristic Optimization Algorithms Research 10
Ran Wang
84 papers receiving 2.4k citations
Hit Papers
Peers
Comparison fields: 5 of 140
- Artificial Intelligence 1.5k
- Computational Theory and Mathematics 485
- Computer Vision and Pattern Recognition 614
- Management Science and Operations Research 234
- Signal Processing 200
Countries citing papers authored by Ran Wang
This map shows the geographic impact of Ran Wang'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 Ran Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ran Wang more than expected).
Fields of papers citing papers by Ran Wang
This network shows the impact of papers produced by Ran Wang. 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 Ran Wang. The network helps show where Ran Wang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ran Wang, 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 | 2024 | 2 | |
| 2 | 2024 | 3 | |
| 3 | 2024 | 1 | |
| 4 | 2023 | 8 | |
| 5 | 2023 | 3 | |
| 6 | 2023 | 2 | |
| 7 | 2022 | 37 | |
| 8 | 2022 | 0 | |
| 9 | 2022 | 5 | |
| 10 | A Review of Generalized Zero-Shot Learning Methods Hit paper breakdown → | 2022 | 239 |
| 11 | 2022 | 5 | |
| 12 | 2020 | 25 | |
| 13 | 2020 | 18 | |
| 14 | 2019 | 26 | |
| 15 | 2018 | 4 | |
| 16 | 2017 | 52 | |
| 17 | 2015 | 8 | |
| 18 | 2014 | 26 | |
| 19 | 2013 | 7 | |
| 20 | 2013 | 38 |
About Ran Wang
Ran Wang is a scholar working on Artificial Intelligence, Computational Mathematics, Computer Vision and Pattern Recognition, Media Technology and Computational Theory and Mathematics, having authored 88 papers that have together received 2.4k indexed citations. Recurring topics across this work include Machine Learning and ELM (25 papers), Face and Expression Recognition (20 papers), Text and Document Classification Technologies (14 papers), Machine Learning and Algorithms (12 papers), Domain Adaptation and Few-Shot Learning (10 papers), Machine Learning and Data Classification (10 papers), Neural Networks and Applications (10 papers) and Metaheuristic Optimization Algorithms Research (10 papers). The work is most often cited by research in Artificial Intelligence (1.5k citations), Computational Theory and Mathematics (485 citations), Computer Vision and Pattern Recognition (614 citations), Management Science and Operations Research (234 citations) and Signal Processing (200 citations). Ran Wang has collaborated with scholars based in China, Hong Kong and Australia. Frequent co-authors include Sam Kwong, Xizhao Wang, Ke Li, Farhad Pourpanah, Chee Peng Lim, Qingfu Zhang, Miqing Li, Yuheng Jia, Xinlei Zhou and Jingjing Cao. Their work appears in journals such as Information Sciences, International Journal of Machine Learning and Cybernetics, Pattern Recognition, Neurocomputing and IEEE Transactions on Cybernetics.
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.