Yilin Yan
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Computer Networks and Communications top 10%
- Information Systems top 5%
- Electrical and Electronic Engineering
- Co-authors
- Mei‐Ling ShyuShu‐Ching ChenSaad SadiqYudong TaoHaiman TianSamira PouyanfarS. S. IyengarMaria Presa Reyes
- Topics
- Anomaly Detection Techniques and Applications (7 papers)Imbalanced Data Classification Techniques (5 papers)Data Stream Mining Techniques (3 papers)
- Journals
- ACM Computing SurveysJournal of ChemometricsJournal of Visual Communication and Image Representation
- Partner nations
- United StatesChinaTaiwan
In The Last Decade
Yilin Yan
23 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 159
- Artificial Intelligence 635
- Computer Vision and Pattern Recognition 352
- Computer Networks and Communications 129
- Information Systems 122
- Electrical and Electronic Engineering 110
Countries citing papers authored by Yilin Yan
This map shows the geographic impact of Yilin Yan'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 Yilin Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yilin Yan more than expected).
Fields of papers citing papers by Yilin Yan
This network shows the impact of papers produced by Yilin Yan. 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 Yilin Yan. The network helps show where Yilin Yan may publish in the future.
Co-authorship network of co-authors of Yilin Yan
This figure shows the co-authorship network connecting the top 25 collaborators of Yilin Yan. A scholar is included among the top collaborators of Yilin Yan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yilin Yan. Yilin Yan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 12 | |
| 2 | Florida International University - University of Miami Trecvid 2014 | 2 |
| 3 | A Survey on Deep Learningbreakdown → | 915 |
| 4 | 55 | |
| 5 | 19 | |
| 6 | 13 | |
| 7 | 25 | |
| 8 | 8 | |
| 9 | 3 | |
| 10 | 3 | |
| 11 | 7 | |
| 12 | 9 | |
| 13 | 6 | |
| 14 | 116 | |
| 15 | 1 | |
| 16 | 30 | |
| 17 | 1 | |
| 18 | 9 | |
| 19 | 17 | |
| 20 | 2 |
About Yilin Yan
Yilin Yan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 23 papers that have together received 1.3k indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (7 papers), Imbalanced Data Classification Techniques (5 papers) and Data Stream Mining Techniques (3 papers). The work is most often cited by research in Artificial Intelligence (635 citations), Computer Vision and Pattern Recognition (352 citations) and Health Informatics (17 citations). Yilin Yan has collaborated with scholars based in United States, China and Taiwan. Frequent co-authors include Mei‐Ling Shyu, Shu‐Ching Chen, Saad Sadiq, Yudong Tao, Haiman Tian, Samira Pouyanfar, S. S. Iyengar, Maria Presa Reyes, Min Chen and Long Yu. Their work appears in journals such as ACM Computing Surveys, Journal of Chemometrics and Journal of Visual Communication and Image Representation.
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.