Zebin Yang
Impact in
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- Stock Market Forecasting Methods
- Forecasting Techniques and Applications
Papers in
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- Neural Networks and Applications 5
- Machine Learning and ELM 4
- Adversarial Robustness in Machine Learning 2
- Explainable Artificial Intelligence (XAI) 2
- Data Stream Mining Techniques 1
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- Energy Load and Power Forecasting 4
- Co-authors
- Lean Yu (4 shared papers)Ling Tang (4 shared papers)Aijun Zhang (7 shared papers)Agus Sudjianto (6 shared papers)Yaqing Zhao (1 shared paper)Dennis K. J. Lin (1 shared paper)Dan A. Ralescu (1 shared paper)Hengtao Zhang (1 shared paper)
- Journals
- International Journal of Information Technology & Decision Making (1 paper)IEEE Transactions on Knowledge and Data Engineering (1 paper)Neurocomputing (1 paper)Flexible Services and Manufacturing Journal (1 paper)Journal of Forecasting (1 paper)
- Partner nations
- United StatesHong KongChina
In The Last Decade
Zebin Yang
11 papers receiving 429 citations
Peers
Comparison fields: 5 of 93
- Management Science and Operations Research 128
- Health Informatics 9
- Artificial Intelligence 154
- Economics and Econometrics 106
- Accounting 42
Countries citing papers authored by Zebin Yang
This map shows the geographic impact of Zebin Yang'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 Zebin Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zebin Yang more than expected).
Fields of papers citing papers by Zebin Yang
This network shows the impact of papers produced by Zebin Yang. 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 Zebin Yang. The network helps show where Zebin Yang may publish in the future.
Co-authors
The 16 scholars most cited alongside Zebin Yang, 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 | 2018 | 172 | |
| 2 | 2021 | 77 | |
| 3 | 2020 | 61 | |
| 4 | 2015 | 45 | |
| 5 | 2018 | 35 | |
| 6 | 2016 | 20 | |
| 7 | 2015 | 18 | |
| 8 | 2018 | 10 | |
| 9 | 2021 | 3 | |
| 10 | 2021 | 1 | |
| 11 | 2023 | 1 | |
| 12 | 2025 | 0 | |
| 13 | 2018 | 0 |
About Zebin Yang
Zebin Yang is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Economics and Econometrics, Statistics and Probability and Automotive Engineering, having authored 13 papers that have together received 443 indexed citations. Recurring topics across this work include Neural Networks and Applications (5 papers), Energy Load and Power Forecasting (4 papers), Machine Learning and ELM (4 papers), Statistical Methods and Inference (2 papers), Adversarial Robustness in Machine Learning (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Data Stream Mining Techniques (1 paper) and Complex Systems and Time Series Analysis (1 paper). The work is most often cited by research in Management Science and Operations Research (128 citations), Health Informatics (9 citations), Artificial Intelligence (154 citations), Economics and Econometrics (106 citations) and Accounting (42 citations). Zebin Yang has collaborated with scholars based in United States, Hong Kong and China. Frequent co-authors include Lean Yu, Ling Tang, Aijun Zhang, Agus Sudjianto, Yaqing Zhao, Dennis K. J. Lin, Dan A. Ralescu, Hengtao Zhang, Lingling Hu and Vijayan N. Nair. Their work appears in journals such as International Journal of Information Technology & Decision Making, IEEE Transactions on Knowledge and Data Engineering, Neurocomputing, Flexible Services and Manufacturing Journal and Journal of Forecasting.
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.