Jingda Guo
Impact in
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- Advanced Neural Network Applications
- Video Surveillance and Tracking Methods
- Visual Attention and Saliency Detection
- Automotive Engineering top 5%
- Autonomous Vehicle Technology and Safety
Papers in
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- Advanced Neural Network Applications 11
- Video Surveillance and Tracking Methods 4
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- Domain Adaptation and Few-Shot Learning 5
- Privacy-Preserving Technologies in Data 2
- Co-authors
- Qing Yang (6 shared papers)Song Fu (10 shared papers)Sihai Tang (7 shared papers)Qi Chen (7 shared papers)Xu Ma (6 shared papers)Jinbo Xiong (2 shared papers)Renwan Bi (1 shared paper)Qing Yang (4 shared papers)
- Journals
- IEEE Internet of Things Journal (2 papers)IEEE Transactions on Multimedia (1 paper)Ceramics International (1 paper)IEEE Wireless Communications (1 paper)
- Partner nations
- United StatesChina
In The Last Decade
Jingda Guo
13 papers receiving 531 citations
Peers
Comparison fields: 5 of 58
- Computer Vision and Pattern Recognition 276
- Automotive Engineering 125
- Artificial Intelligence 156
- Computer Science Applications 24
- Media Technology 36
Countries citing papers authored by Jingda Guo
This map shows the geographic impact of Jingda Guo'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 Jingda Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jingda Guo more than expected).
Fields of papers citing papers by Jingda Guo
This network shows the impact of papers produced by Jingda Guo. 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 Jingda Guo. The network helps show where Jingda Guo may publish in the future.
Co-authors
The 22 scholars most cited alongside Jingda Guo, 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 | 244 | |
| 2 | 2020 | 99 | |
| 3 | 2020 | 61 | |
| 4 | 2021 | 42 | |
| 5 | 2021 | 32 | |
| 6 | 2022 | 19 | |
| 7 | 2021 | 18 | |
| 8 | 2019 | 9 | |
| 9 | 2019 | 5 | |
| 10 | 2021 | 3 | |
| 11 | 2020 | 3 | |
| 12 | 2020 | 2 | |
| 13 | 2020 | 1 |
About Jingda Guo
Jingda Guo is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Electrical and Electronic Engineering, Automotive Engineering and Neurology, having authored 13 papers that have together received 538 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (11 papers), Domain Adaptation and Few-Shot Learning (5 papers), Video Surveillance and Tracking Methods (4 papers), Vehicular Ad Hoc Networks (VANETs) (2 papers), Brain Tumor Detection and Classification (2 papers), Privacy-Preserving Technologies in Data (2 papers), Autonomous Vehicle Technology and Safety (2 papers) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (276 citations), Automotive Engineering (125 citations), Artificial Intelligence (156 citations), Computer Science Applications (24 citations) and Media Technology (36 citations). Jingda Guo has collaborated with scholars based in United States and China. Frequent co-authors include Qing Yang, Song Fu, Sihai Tang, Qi Chen, Xu Ma, Jinbo Xiong, Renwan Bi, Qing Yang, Qi Chen and Paparao Palacharla. Their work appears in journals such as IEEE Internet of Things Journal, IEEE Transactions on Multimedia, Ceramics International and IEEE Wireless Communications.
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.