Cun Ji
Impact in
- Signal Processing top 2%
- Time Series Analysis and Forecasting
- Music and Audio Processing
- Artificial Intelligence top 5%
- Anomaly Detection Techniques and Applications
- Neural Networks and Applications
Papers in
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- Time Series Analysis and Forecasting 27
- Music and Audio Processing 5
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- Anomaly Detection Techniques and Applications 20
- Advanced Text Analysis Techniques 4
- Co-authors
- Xiangwei Zheng (26 shared papers)Shijun Liu (15 shared papers)Li Pan (10 shared papers)Chenglei Yang (9 shared papers)Yupeng Hu (11 shared papers)Xiangxu Meng (4 shared papers)Chao Zhao (4 shared papers)Bin Hu (4 shared papers)
In The Last Decade
Cun Ji
45 papers receiving 491 citations
Peers
Comparison fields: 5 of 71
- Signal Processing 266
- Artificial Intelligence 263
- Industrial and Manufacturing Engineering 31
- Management Science and Operations Research 37
- Computer Vision and Pattern Recognition 55
Countries citing papers authored by Cun Ji
This map shows the geographic impact of Cun Ji'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 Cun Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cun Ji more than expected).
Fields of papers citing papers by Cun Ji
This network shows the impact of papers produced by Cun Ji. 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 Cun Ji. The network helps show where Cun Ji may publish in the future.
Co-authors
The 25 scholars most cited alongside Cun Ji, 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 48 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 63 | |
| 2 | 2016 | 36 | |
| 3 | 2023 | 32 | |
| 4 | 2022 | 30 | |
| 5 | 2022 | 29 | |
| 6 | 2019 | 21 | |
| 7 | 2016 | 19 | |
| 8 | 2017 | 18 | |
| 9 | 2018 | 17 | |
| 10 | 2024 | 16 | |
| 11 | 2019 | 16 | |
| 12 | 2021 | 15 | |
| 13 | 2018 | 15 | |
| 14 | 2022 | 15 | |
| 15 | 2019 | 13 | |
| 16 | 2019 | 13 | |
| 17 | 2019 | 12 | |
| 18 | 2015 | 12 | |
| 19 | 2022 | 9 | |
| 20 | 2021 | 9 |
About Cun Ji
Cun Ji is a scholar working on Signal Processing, Artificial Intelligence, Economics and Econometrics, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 48 papers that have together received 497 indexed citations. Recurring topics across this work include Time Series Analysis and Forecasting (27 papers), Anomaly Detection Techniques and Applications (20 papers), Complex Systems and Time Series Analysis (9 papers), Music and Audio Processing (5 papers), EEG and Brain-Computer Interfaces (5 papers), Emotion and Mood Recognition (5 papers), Advanced Text Analysis Techniques (4 papers) and Advanced Chemical Sensor Technologies (4 papers). The work is most often cited by research in Signal Processing (266 citations), Artificial Intelligence (263 citations), Industrial and Manufacturing Engineering (31 citations), Management Science and Operations Research (37 citations) and Computer Vision and Pattern Recognition (55 citations). Cun Ji has collaborated with scholars based in China and Australia. Frequent co-authors include Xiangwei Zheng, Shijun Liu, Li Pan, Chenglei Yang, Yupeng Hu, Xiangxu Meng, Chao Zhao, Bin Hu, Lei Wu and Bo Li. Their work appears in journals such as Information Sciences, Neural Processing Letters, Computer Networks, Journal of King Saud University - Computer and Information Sciences and Applied 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.