Lijin Wang
- Artificial Intelligence
- Electrical and Electronic Engineering
- Atomic and Molecular Physics, and Optics
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
- Topics
- Magnetic properties of thin films (6 papers)Advanced Neural Network Applications (6 papers)Metaheuristic Optimization Algorithms Research (5 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionElectronic, Optical and Magnetic Materials
- Partner nations
- ChinaUnited StatesSingapore
In The Last Decade
Lijin Wang
32 papers receiving 271 citations
Hit Papers
Peers
Comparison fields: 5 of 71
- Artificial Intelligence 74
- Electrical and Electronic Engineering 64
- Atomic and Molecular Physics, and Optics 44
- Computer Vision and Pattern Recognition 38
- Control and Systems Engineering 35
Countries citing papers authored by Lijin Wang
This map shows the geographic impact of Lijin Wang'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 Lijin Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lijin Wang more than expected).
Fields of papers citing papers by Lijin Wang
This network shows the impact of papers produced by Lijin Wang. 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 Lijin Wang. The network helps show where Lijin Wang may publish in the future.
Co-authorship network of co-authors of Lijin Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Lijin Wang. A scholar is included among the top collaborators of Lijin Wang 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 Lijin Wang. Lijin Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 3 | |
| 3 | PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detectionbreakdown → | 21 |
| 4 | 8 | |
| 5 | 1 | |
| 6 | 20 | |
| 7 | 24 | |
| 8 | 6 | |
| 9 | 3 | |
| 10 | 3 | |
| 11 | 4 | |
| 12 | 4 | |
| 13 | 2 | |
| 14 | 4 | |
| 15 | 7 | |
| 16 | 4 | |
| 17 | 17 | |
| 18 | 3 | |
| 19 | A study on forest landscape evaluation based on projection pursuit. | 1 |
| 20 | 8 |
About Lijin Wang
Lijin Wang is a scholar working on Computer Vision and Pattern Recognition, Atomic and Molecular Physics, and Optics and Electronic, Optical and Magnetic Materials, having authored 34 papers that have together received 281 indexed citations. Recurring topics across this work include Magnetic properties of thin films (6 papers), Advanced Neural Network Applications (6 papers) and Metaheuristic Optimization Algorithms Research (5 papers). The work is most often cited by research in Artificial Intelligence (74 citations), Computer Vision and Pattern Recognition (38 citations) and Electronic, Optical and Magnetic Materials (33 citations). Lijin Wang has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Yiwen Zhong, Yilong Yin, Ping Xie, Guoqian Jiang, Xiaoli Li, Jiao Teng, Zhiling Cai, Haicheng Wang, Qun He and Wenyue Li. Their work appears in journals such as Applied Physics Letters, The Journal of Physical Chemistry C and Energy.
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.