Ce Ju
Impact in
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- Advanced Image and Video Retrieval Techniques
- Video Surveillance and Tracking Methods
- Image Retrieval and Classification Techniques
- Advanced Neural Network Applications
- Artificial Intelligence top 10%
- Privacy-Preserving Technologies in Data
- Cryptography and Data Security
- Domain Adaptation and Few-Shot Learning
Papers in ⓘ
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- EEG and Brain-Computer Interfaces 3
- Neural dynamics and brain function 2
- Functional Brain Connectivity Studies 1
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- Advanced Neural Network Applications 1
- Co-authors
- Cuntai Guan (4 shared papers)Dashan Gao (1 shared paper)Lixin Fan (1 shared paper)Tianyu Zhang (1 shared paper)Qiang Yang (1 shared paper)Han Yu (1 shared paper)Yang Liu (1 shared paper)Kam Woh Ng (1 shared paper)
- Journals
- IEEE Transactions on Neural Networks and Learning Systems (2 papers)Artificial Intelligence (1 paper)Rare & Special e-Zone (The Hong Kong University of Science and Technology) (1 paper)DR-NTU (Nanyang Technological University) (1 paper)PubMed (1 paper)
- Partner nations
- SingaporePolandSouth Korea
In The Last Decade
Ce Ju
8 papers receiving 246 citations
Peers
Comparison fields: 5 of 51
- Computer Vision and Pattern Recognition 83
- Artificial Intelligence 109
- Cognitive Neuroscience 58
- Signal Processing 28
- Computer Science Applications 11
Countries citing papers authored by Ce Ju
This map shows the geographic impact of Ce Ju'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 Ce Ju with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ce Ju more than expected).
Fields of papers citing papers by Ce Ju
This network shows the impact of papers produced by Ce Ju. 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 Ce Ju. The network helps show where Ce Ju may publish in the future.
Co-authors
The 17 scholars most cited alongside Ce Ju, 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 | 2019 | 69 | |
| 2 | 2020 | 59 | |
| 3 | 2022 | 58 | |
| 4 | 2019 | 29 | |
| 5 | 2023 | 18 | |
| 6 | 2019 | 10 | |
| 7 | 2023 | 3 | |
| 8 | 2025 | 1 |
About Ce Ju
Ce Ju is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence and Automotive Engineering, having authored 8 papers that have together received 247 indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (3 papers), Neural dynamics and brain function (2 papers), Gaze Tracking and Assistive Technology (1 paper), Domain Adaptation and Few-Shot Learning (1 paper), Functional Brain Connectivity Studies (1 paper), Advanced Neural Network Applications (1 paper), Robotics and Sensor-Based Localization (1 paper) and Traffic and Road Safety (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (83 citations), Artificial Intelligence (109 citations), Cognitive Neuroscience (58 citations), Signal Processing (28 citations) and Computer Science Applications (11 citations). Ce Ju has collaborated with scholars based in Singapore, Poland and South Korea. Frequent co-authors include Cuntai Guan, Dashan Gao, Lixin Fan, Tianyu Zhang, Qiang Yang, Han Yu, Yang Liu, Kam Woh Ng, Chee Seng Chan and Anbu Huang. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Artificial Intelligence, Rare & Special e-Zone (The Hong Kong University of Science and Technology), DR-NTU (Nanyang Technological University) and PubMed.
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.