Mu‐Yen Chen
Impact in
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- Stock Market Forecasting Methods
- Information Systems top 0.5%
- Blockchain Technology Applications and Security
Papers in
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- Imbalanced Data Classification Techniques 11
- Neural Networks and Applications 11
- Sentiment Analysis and Opinion Mining 8
- Co-authors
- Pradip Kumar Sharma (2 shared papers)Jong Hyuk Park (1 shared paper)Mu-Jung Huang (5 shared papers)Hsiu‐Sen Chiang (14 shared papers)An‐Pin Chen (5 shared papers)Yu Shu (5 shared papers)Shu-Hsuan Chang (2 shared papers)Kuan-Cheng Lin (3 shared papers)
In The Last Decade
Mu‐Yen Chen
160 papers receiving 3.8k citations
Mu‐Yen Chen's Hit Papers
Peers
Comparison fields: 5 of 171
- Management Science and Operations Research 721
- Information Systems 829
- Artificial Intelligence 1.2k
- Accounting 316
- Signal Processing 296
Countries citing papers authored by Mu‐Yen Chen
This map shows the geographic impact of Mu‐Yen Chen'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 Mu‐Yen Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mu‐Yen Chen more than expected).
Fields of papers citing papers by Mu‐Yen Chen
This network shows the impact of papers produced by Mu‐Yen Chen. 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 Mu‐Yen Chen. The network helps show where Mu‐Yen Chen may publish in the future.
Co-authors
The 25 scholars most cited alongside Mu‐Yen Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 168 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | A Software Defined Fog Node Based Distributed Blockchain Cloud Architecture for IoT Hit paper breakdown → | 2017 | 494 |
| 2 | 2011 | 170 | |
| 3 | 2014 | 168 | |
| 4 | 2011 | 159 | |
| 5 | 2018 | 145 | |
| 6 | 2006 | 133 | |
| 7 | 2019 | 132 | |
| 8 | 2020 | 131 | |
| 9 | 2008 | 127 | |
| 10 | 2006 | 119 | |
| 11 | 2011 | 100 | |
| 12 | 2019 | 89 | |
| 13 | 2013 | 82 | |
| 14 | 2013 | 82 | |
| 15 | 2020 | 77 | |
| 16 | 2021 | 75 | |
| 17 | 2021 | 63 | |
| 18 | 2019 | 57 | |
| 19 | 2019 | 53 | |
| 20 | 2019 | 52 |
About Mu‐Yen Chen
Mu‐Yen Chen is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research, Information Systems and Computer Networks and Communications, having authored 168 papers that have together received 4.0k indexed citations. Recurring topics across this work include Stock Market Forecasting Methods (20 papers), Imbalanced Data Classification Techniques (11 papers), Neural Networks and Applications (11 papers), Financial Distress and Bankruptcy Prediction (10 papers), Energy Load and Power Forecasting (9 papers), Digital Marketing and Social Media (8 papers), Sentiment Analysis and Opinion Mining (8 papers) and Complex Systems and Time Series Analysis (7 papers). The work is most often cited by research in Management Science and Operations Research (721 citations), Information Systems (829 citations), Artificial Intelligence (1.2k citations), Accounting (316 citations) and Signal Processing (296 citations). Mu‐Yen Chen has collaborated with scholars based in Taiwan, Türkiye and India. Frequent co-authors include Pradip Kumar Sharma, Jong Hyuk Park, Mu-Jung Huang, Hsiu‐Sen Chiang, An‐Pin Chen, Yu Shu, Shu-Hsuan Chang, Kuan-Cheng Lin, G. J. Y. Hsu and Erol Eğrioğlu. Their work appears in journals such as Applied Soft Computing, IEEE Access, Expert Systems with Applications, Granular Computing and Soft Computing.
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.