Lida Zhou
Impact in
- Signal Processing top 10%
- Data Management and Algorithms
- Artificial Intelligence top 5%
- Advanced Clustering Algorithms Research
- Anomaly Detection Techniques and Applications
Papers in
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- Advanced Image and Video Retrieval Techniques 4
- Face and Expression Recognition 1
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- Data Management and Algorithms 5
- Co-authors
- Yewang Chen (6 shared papers)Ji‐Xiang Du (5 shared papers)Yi Chen (2 shared papers)Nizar Bouguila (4 shared papers)Songwen Pei (2 shared papers)Cheng Wang (1 shared paper)Zhiwen Yu (1 shared paper)Xin Liu (1 shared paper)
- Journals
- Information Sciences (2 papers)International Journal of Medical Sciences (1 paper)IEEE Transactions on Systems Man and Cybernetics Systems (1 paper)Pattern Recognition (1 paper)
- Partner nations
- ChinaCanadaUnited States
In The Last Decade
Lida Zhou
7 papers receiving 385 citations
Peers
Comparison fields: 5 of 79
- Signal Processing 80
- Artificial Intelligence 218
- Computer Vision and Pattern Recognition 132
- Computational Mathematics 3
- Statistical and Nonlinear Physics 58
Countries citing papers authored by Lida Zhou
This map shows the geographic impact of Lida Zhou'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 Lida Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lida Zhou more than expected).
Fields of papers citing papers by Lida Zhou
This network shows the impact of papers produced by Lida Zhou. 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 Lida Zhou. The network helps show where Lida Zhou may publish in the future.
Co-authors
The 25 scholars most cited alongside Lida Zhou, 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 | 137 | |
| 2 | 2020 | 114 | |
| 3 | 2018 | 66 | |
| 4 | 2016 | 57 | |
| 5 | 2018 | 13 | |
| 6 | 2017 | 3 | |
| 7 | 2023 | 1 |
About Lida Zhou
Lida Zhou is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence, Oncology and Aerospace Engineering, having authored 7 papers that have together received 391 indexed citations. Recurring topics across this work include Data Management and Algorithms (5 papers), Advanced Image and Video Retrieval Techniques (4 papers), Algorithms and Data Compression (3 papers), Advanced Clustering Algorithms Research (3 papers), Face and Expression Recognition (1 paper), Robotics and Sensor-Based Localization (1 paper), T-cell and B-cell Immunology (1 paper) and Immune Cell Function and Interaction (1 paper). The work is most often cited by research in Signal Processing (80 citations), Artificial Intelligence (218 citations), Computer Vision and Pattern Recognition (132 citations), Computational Mathematics (3 citations) and Statistical and Nonlinear Physics (58 citations). Lida Zhou has collaborated with scholars based in China, Canada and United States. Frequent co-authors include Yewang Chen, Ji‐Xiang Du, Yi Chen, Nizar Bouguila, Songwen Pei, Cheng Wang, Zhiwen Yu, Xin Liu, Naixue Xiong and Huazhen Wang. Their work appears in journals such as Information Sciences, International Journal of Medical Sciences, IEEE Transactions on Systems Man and Cybernetics Systems and Pattern Recognition.
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.