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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- Stock Market Forecasting Methods 20
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- Imbalanced Data Classification Techniques 11
- Neural Networks and Applications 11
- Sentiment Analysis and Opinion Mining 8
- Co-authors
- Pradip Kumar SharmaJong Hyuk ParkHsiu‐Sen ChiangMu‐Jung HuangAn‐Pin ChenYu ShuShu-Hsuan ChangKuan-Cheng Lin
In The Last Decade
Mu‐Yen Chen
157 papers receiving 3.7k citations
Hit Papers
Peers
Comparison fields: 5 of 173
- Management Science and Operations Research 712
- Information Systems 819
- Artificial Intelligence 1.1k
- Computer Networks and Communications 623
- Accounting 310
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-authorship network
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
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 1 | |
| 4 | 2025 | 1 | |
| 5 | 2025 | 0 | |
| 6 | 2025 | 0 | |
| 7 | 2024 | 17 | |
| 8 | 2024 | 9 | |
| 9 | 2024 | 1 | |
| 10 | 2022 | 33 | |
| 11 | 2022 | 4 | |
| 12 | 2021 | 3 | |
| 13 | 2020 | 8 | |
| 14 | Do Learning Styles Matter? Motivating Learners in an Augmented Geopark | 2019 | 29 |
| 15 | 2017 | 2 | |
| 16 | 2016 | 2 | |
| 17 | 2014 | 3 | |
| 18 | An Empirical Study on the Relationship between R&D and Financial Performance | 2013 | 27 |
| 19 | Why do Individuals Use e-Portfolios? | 2012 | 30 |
| 20 | 2011 | 98 |
About Mu‐Yen Chen
Mu‐Yen Chen is a scholar working on Management Science and Operations Research, Artificial Intelligence, Computer Vision and Pattern Recognition, Accounting and Signal Processing, having authored 167 papers that have together received 3.9k 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), Sentiment Analysis and Opinion Mining (8 papers), Digital Marketing and Social Media (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 (712 citations), Information Systems (819 citations), Artificial Intelligence (1.1k citations), Computer Networks and Communications (623 citations) and Accounting (310 citations). Mu‐Yen Chen has collaborated with scholars based in Taiwan, Türkiye and Austria. Frequent co-authors include Pradip Kumar Sharma, Jong Hyuk Park, Hsiu‐Sen Chiang, Mu‐Jung Huang, 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 Computers in Human Behavior.
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.