Qian Yan

473 total citations
32 papers, 309 citations indexed

About

Qian Yan is a scholar working on Plant Science, Animal Science and Zoology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Qian Yan has authored 32 papers receiving a total of 309 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Plant Science, 5 papers in Animal Science and Zoology and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Qian Yan's work include Smart Agriculture and AI (5 papers), Effects of Environmental Stressors on Livestock (3 papers) and Complex Network Analysis Techniques (3 papers). Qian Yan is often cited by papers focused on Smart Agriculture and AI (5 papers), Effects of Environmental Stressors on Livestock (3 papers) and Complex Network Analysis Techniques (3 papers). Qian Yan collaborates with scholars based in China, Singapore and Canada. Qian Yan's co-authors include Baohua Yang, Bing Wang, Jun Zhang, Wenyan Wang, Peng Chen, Wenqing Yin, Hao Huang, Peng Cao, Lin Qi and Fei Hu and has published in prestigious journals such as Sensors, IEEE Transactions on Knowledge and Data Engineering and Computers and Electronics in Agriculture.

In The Last Decade

Qian Yan

29 papers receiving 298 citations

Peers

Qian Yan
Jing Pang China
Qian Yan
Citations per year, relative to Qian Yan Qian Yan (= 1×) peers Jing Pang

Countries citing papers authored by Qian Yan

Since Specialization
Citations

This map shows the geographic impact of Qian Yan'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 Qian Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qian Yan more than expected).

Fields of papers citing papers by Qian Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Qian Yan. 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 Qian Yan. The network helps show where Qian Yan may publish in the future.

Co-authorship network of co-authors of Qian Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Qian Yan. A scholar is included among the top collaborators of Qian Yan 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 Qian Yan. Qian Yan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yan, Qian, et al.. (2024). Diffusion pattern mining. Knowledge and Information Systems. 67(2). 1101–1129.
2.
Wu, Xiaoyi, et al.. (2024). Optimizing Hierarchical Federated Learning: A Reinforcement Learning Approach. IEEE Transactions on Consumer Electronics. 71(2). 4076–4086. 3 indexed citations
3.
Xu, Jianyou, Qian Yan, Shuo Zhang, & Chin‐Chia Wu. (2023). Demand Prediction of Shared Bicycles Based on Graph Convolutional Network-Gated Recurrent Unit-Attention Mechanism. Mathematics. 11(24). 4994–4994. 3 indexed citations
4.
Yang, Dongjie, Xun Hou, Lele Zhang, et al.. (2022). Laparoscopy-endoscopy Cooperative Surgery for the Treatment of Gastric Gastrointestinal Stromal Tumors. Journal of Visualized Experiments.
5.
Yan, Qian, et al.. (2022). Judgment Model of Cock Reproductive Performance based on Vison Transformer. 37–42. 1 indexed citations
7.
Tian, Na, et al.. (2022). DISPOSAL CHARACTERISTICS OF SLUDGE RESULTED FROM THE TREATMENT OF MUNICIPAL WASTEWATER BY HETEROGENEOUS PHOTOCATALYTIC TECHNOLOGY. Environmental Engineering and Management Journal. 21(2). 247–254. 3 indexed citations
8.
Yan, Qian, et al.. (2021). Classification of rice seed variety using point cloud data combined with. International journal of agricultural and biological engineering. 14(5). 206–212. 1 indexed citations
9.
Yan, Qian, et al.. (2021). Classification of rice seed variety using point cloud data combined with deep learning. International journal of agricultural and biological engineering. 14(5). 206–212. 24 indexed citations
10.
Wu, Jia‐Hong, et al.. (2021). An Atmospheric Visibility Grading Method Based on Ensemble Learning and Stochastic Weight Average. Atmosphere. 12(7). 869–869. 4 indexed citations
12.
Yang, Baohua, et al.. (2020). Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery. Sensors. 20(18). 5021–5021. 33 indexed citations
13.
Zhang, Huaxi, et al.. (2019). Rice seeds identification based on back propagation neural network model. International journal of agricultural and biological engineering. 12(6). 122–128. 5 indexed citations
14.
Zhang, Xuhui, et al.. (2019). A NOVEL METHOD FOR THE GROUP CHARACTERISTICS ANALYSIS OF YELLOW FEATHER BROILERS UNDER THE HEAT STRESS BASED ON OBJECT DETECTION AND TRANSFER LEARNING. INMATEH Agricultural Engineering. 59(3). 49–58. 6 indexed citations
15.
Huang, Hao, et al.. (2019). LERI: Local Exploration for Rare-Category Identification. IEEE Transactions on Knowledge and Data Engineering. 1–1. 4 indexed citations
16.
Li, Shoushan, et al.. (2018). Domain-specific Named Entity Recognition with Document-Level Optimization. ACM Transactions on Asian and Low-Resource Language Information Processing. 17(4). 1–15. 4 indexed citations
17.
18.
Tao, Hong, et al.. (2015). The development and application of Nurse Job Performance Scale. ˜The œJournal of practical nursing. 31(1). 19–22. 1 indexed citations
20.
Yan, Qian, et al.. (2005). A Study On Teaching About Death For Elderly Education. 1 indexed citations

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.

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