M. Yin
Impact in
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- Intellectual Property and Patents
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- Topic Modeling
- Advanced Text Analysis Techniques
- Semantic Web and Ontologies
- Advanced Graph Neural Networks
Papers in
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- Topic Modeling 2
- Advanced Text Analysis Techniques 1
- Adversarial Robustness in Machine Learning 1
- Anomaly Detection Techniques and Applications 1
- Co-authors
- Charles X. Ling (3 shared papers)Boyu Wang (1 shared paper)C. Wang (1 shared paper)S. H. Oh (1 shared paper)
- Journals
- Expert Systems with Applications (1 paper)Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment (1 paper)Highlights in Science Engineering and Technology (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- CanadaChinaUnited States
In The Last Decade
M. Yin
2 papers receiving 5 citations
Peers
Comparison fields: 5 of 7
- Management of Technology and Innovation 1
- Artificial Intelligence 4
- Statistical and Nonlinear Physics 1
- Information Systems 1
- Control and Systems Engineering 1
Countries citing papers authored by M. Yin
This map shows the geographic impact of M. Yin'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 M. Yin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Yin more than expected).
Fields of papers citing papers by M. Yin
This network shows the impact of papers produced by M. Yin. 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 M. Yin. The network helps show where M. Yin may publish in the future.
Co-authors
The 6 scholars most cited alongside M. Yin, 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 | 2023 | 4 | |
| 2 | 2024 | 1 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 1993 | 0 |
About M. Yin
M. Yin is a scholar working on Artificial Intelligence, Control and Systems Engineering, Information Systems, Radiation and Atmospheric Science, having authored 5 papers that have together received 5 indexed citations. Recurring topics across this work include Topic Modeling (2 papers), Advanced Text Analysis Techniques (1 paper), Radioactive Decay and Measurement Techniques (1 paper), Expert finding and Q&A systems (1 paper), Remote Sensing and Land Use (1 paper), Scientific Measurement and Uncertainty Evaluation (1 paper), Adversarial Robustness in Machine Learning (1 paper) and Anomaly Detection Techniques and Applications (1 paper). The work is most often cited by research in Management of Technology and Innovation (1 citation), Artificial Intelligence (4 citations), Statistical and Nonlinear Physics (1 citation), Information Systems (1 citation) and Control and Systems Engineering (1 citation). M. Yin has collaborated with scholars based in Canada, China and United States. Frequent co-authors include Charles X. Ling, Boyu Wang, Boyu Wang, C. Wang, Boyu Wang and S. H. Oh. Their work appears in journals such as Expert Systems with Applications, Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, Highlights in Science Engineering and Technology and Proceedings of the AAAI Conference on Artificial Intelligence.
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.