Bi-Ru Dai
Impact in
- Signal Processing top 5%
- Data Management and Algorithms
- Time Series Analysis and Forecasting
- Artificial Intelligence top 5%
- Advanced Clustering Algorithms Research
- Data Stream Mining Techniques
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
Papers in
-
- Data Stream Mining Techniques 6
- Advanced Clustering Algorithms Research 6
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- Data Mining Algorithms and Applications 8
- Recommender Systems and Techniques 4
- Co-authors
- Ming-Syan Chen⋆ (7 shared papers)Mi-Yen Yeh (3 shared papers)Po‐Wei Liang (1 shared paper)Jen-Wei Huang (3 shared papers)Jianwei Liao (1 shared paper)Kai‐Lung Hua (1 shared paper)Cheng-Ru Lin (2 shared papers)Vishal Sharma (1 shared paper)
In The Last Decade
Bi-Ru Dai
24 papers receiving 320 citations
Peers
Comparison fields: 5 of 55
- Signal Processing 136
- Artificial Intelligence 232
- Information Systems 115
- Computer Vision and Pattern Recognition 68
- Statistical and Nonlinear Physics 19
Countries citing papers authored by Bi-Ru Dai
This map shows the geographic impact of Bi-Ru Dai'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 Bi-Ru Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bi-Ru Dai more than expected).
Fields of papers citing papers by Bi-Ru Dai
This network shows the impact of papers produced by Bi-Ru Dai. 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 Bi-Ru Dai. The network helps show where Bi-Ru Dai may publish in the future.
Co-authors
The 11 scholars most cited alongside Bi-Ru Dai, 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 26 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 64 | |
| 2 | 2006 | 63 | |
| 3 | 2013 | 60 | |
| 4 | 2007 | 31 | |
| 5 | 2005 | 20 | |
| 6 | 2017 | 17 | |
| 7 | 2015 | 14 | |
| 8 | 2007 | 14 | |
| 9 | 2021 | 9 | |
| 10 | 2013 | 8 | |
| 11 | 2014 | 7 | |
| 12 | 2011 | 7 | |
| 13 | 2011 | 6 | |
| 14 | 2010 | 6 | |
| 15 | 2003 | 5 | |
| 16 | 2007 | 5 | |
| 17 | 2020 | 3 | |
| 18 | 2023 | 2 | |
| 19 | 2011 | 2 | |
| 20 | 2015 | 1 |
About Bi-Ru Dai
Bi-Ru Dai is a scholar working on Artificial Intelligence, Information Systems, Signal Processing, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 26 papers that have together received 348 indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (8 papers), Data Management and Algorithms (6 papers), Data Stream Mining Techniques (6 papers), Advanced Clustering Algorithms Research (6 papers), Complex Network Analysis Techniques (4 papers), Recommender Systems and Techniques (4 papers), Caching and Content Delivery (3 papers) and Time Series Analysis and Forecasting (3 papers). The work is most often cited by research in Signal Processing (136 citations), Artificial Intelligence (232 citations), Information Systems (115 citations), Computer Vision and Pattern Recognition (68 citations) and Statistical and Nonlinear Physics (19 citations). Bi-Ru Dai has collaborated with scholars based in Taiwan and India. Frequent co-authors include Ming-Syan Chen⋆, Mi-Yen Yeh, Po‐Wei Liang, Jen-Wei Huang, Jianwei Liao, Kai‐Lung Hua, Cheng-Ru Lin, Vishal Sharma, Kathiravan Srinivasan and Yong-Huai Huang. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Knowledge Discovery from Data, Knowledge and Information Systems, The VLDB Journal and Expert Systems with Applications.
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.