Yan Qiang

431 total citations
20 papers, 316 citations indexed

About

Yan Qiang is a scholar working on Artificial Intelligence, Computer Networks and Communications and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Yan Qiang has authored 20 papers receiving a total of 316 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Computer Networks and Communications and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Yan Qiang's work include Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (4 papers) and Metaheuristic Optimization Algorithms Research (3 papers). Yan Qiang is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (4 papers) and Metaheuristic Optimization Algorithms Research (3 papers). Yan Qiang collaborates with scholars based in China and United States. Yan Qiang's co-authors include Juanjuan Zhao, Wenkai Yang, Qianqian Du, Xiaotang Yang, Xiaohong Han, Kun Wu, Xiaoyan Xiong, Yuan Lan, Jie Xiang and Juanjuan Zhao and has published in prestigious journals such as IEEE Access, Applied Soft Computing and Energies.

In The Last Decade

Yan Qiang

19 papers receiving 309 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Yan Qiang China	10	145	87	63	47	46	20	316
Jiawen Wang China	9	143 1.0×	53 0.6×	87 1.4×	109 2.3×	23 0.5×	45	363
Jamal Alhiyafi Saudi Arabia	9	220 1.5×	78 0.9×	52 0.8×	21 0.4×	18 0.4×	25	495
Karisma Trinanda Putra Indonesia	9	129 0.9×	29 0.3×	43 0.7×	28 0.6×	51 1.1×	49	325
Bader Fahad Alkhamees Saudi Arabia	9	129 0.9×	47 0.5×	34 0.5×	22 0.5×	19 0.4×	20	274
Mahsa Rezaei Iran	4	109 0.8×	60 0.7×	48 0.8×	24 0.5×	15 0.3×	6	309
Mahmudul Hasan Bangladesh	6	403 2.8×	114 1.3×	26 0.4×	47 1.0×	16 0.3×	10	625
Mohemmed Sha Saudi Arabia	9	137 0.9×	63 0.7×	28 0.4×	58 1.2×	11 0.2×	45	319
Ketan Gupta United States	12	99 0.7×	43 0.5×	71 1.1×	37 0.8×	65 1.4×	44	417
Nasmin Jiwani United States	14	100 0.7×	46 0.5×	74 1.2×	42 0.9×	71 1.5×	37	446
Raed Alazaidah Jordan	14	194 1.3×	67 0.8×	123 2.0×	60 1.3×	15 0.3×	51	448

Countries citing papers authored by Yan Qiang

Since Specialization

Citations

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

Fields of papers citing papers by Yan Qiang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yan Qiang

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

All Works

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20 of 20 papers shown

Qiang, Yan, et al.. (2025). Combating Medical Label Noise through more precise partition-correction and progressive hard-enhanced learning. Computer Methods and Programs in Biomedicine. 265. 108734–108734.

Qiang, Yan, et al.. (2023). Graph Neural Network for representation learning of lung cancer. BMC Cancer. 23(1). 1037–1037. 7 indexed citations

Wu, Wei, et al.. (2023). Residual neural network with mixed loss based on batch training technique for identification of EGFR mutation status in lung cancer. Multimedia Tools and Applications. 82(21). 33443–33463. 2 indexed citations

Xue, Chen, et al.. (2022). Classification of mild cognitive impairment based on handwriting dynamics and qEEG. Computers in Biology and Medicine. 152. 106418–106418. 28 indexed citations

Zhao, Juanjuan, et al.. (2022). A Fast and Secured Peer-to-Peer Energy Trading Using Blockchain Consensus. 1–8. 3 indexed citations

Zhang, Yanan, et al.. (2022). Adaptive mutation quantum-inspired squirrel search algorithm for global optimization problems. Alexandria Engineering Journal. 61(9). 7441–7476. 13 indexed citations

Zhao, Juanjuan, et al.. (2021). The Reform and Exploration of Intelligent PYTHON Language Teaching. 2. 883–888. 2 indexed citations

Shi, Guohua, Jiawen Wang, Yan Qiang, et al.. (2020). Knowledge-guided synthetic medical image adversarial augmentation for ultrasonography thyroid nodule classification. Computer Methods and Programs in Biomedicine. 196. 105611–105611. 53 indexed citations

Qiang, Yan, et al.. (2020). Segmentation of Liver Lesions Without Contrast Agents With Radiomics-Guided Densely UNet-Nested GAN. IEEE Access. 9. 2864–2878. 10 indexed citations

10.

Du, Qianqian, et al.. (2020). DRGAN: a deep residual generative adversarial network for PET image reconstruction. IET Image Processing. 14(9). 1690–1700. 7 indexed citations

11.

Yang, Wenkai, Yunyun Dong, Qianqian Du, et al.. (2020). Integrate domain knowledge in training multi-task cascade deep learning model for benign–malignant thyroid nodule classification on ultrasound images. Engineering Applications of Artificial Intelligence. 98. 104064–104064. 50 indexed citations

12.

Wu, Kun, Yan Qiang, Kai Song, et al.. (2019). Image synthesis in contrast MRI based on super resolution reconstruction with multi-refinement cycle-consistent generative adversarial networks. Journal of Intelligent Manufacturing. 31(5). 1215–1228. 10 indexed citations

13.

Lv, Jun, et al.. (2019). Consortium Blockchain-Based Microgrid Market Transaction Research. Energies. 12(20). 3812–3812. 29 indexed citations

14.

Wang, Yu, et al.. (2019). A Distributed Energy Trading Authentication Mechanism Based on a Consortium Blockchain. Energies. 12(15). 2878–2878. 31 indexed citations

15.

Han, Xiaohong, Yan Qiang, & Yuan Lan. (2017). A Bird Flock Gravitational Search Algorithm Based on the Collective Response of Birds. The Computer Journal. 60(11). 1687–1716. 1 indexed citations

16.

Xiang, Jie, et al.. (2015). A novel hybrid system for feature selection based on an improved gravitational search algorithm and k-NN method. Applied Soft Computing. 31. 293–307. 53 indexed citations

17.

Qiang, Yan, Bo Pei, Wei Wei, & Yue Li. (2015). An Efficient Cluster Head Selection Approach for Collaborative Data Processing in Wireless Sensor Networks. International Journal of Distributed Sensor Networks. 11(6). 794518–794518. 14 indexed citations

18.

Qiang, Yan, et al.. (2014). A Multi-Level Data Fusion Algorithm for Wireless Sensor Networks. Sensor Letters. 12(1). 123–128. 1 indexed citations

19.

Qiang, Yan, et al.. (2014). A New Energy Reduction Method Based on Fire Probability Threshold Switch for WSN. 12. 30–37. 1 indexed citations

20.

Qiang, Yan, et al.. (2013). Discourse Anaphora Resolution Strategy Based on Syntactic and Semantic Analysis. Information Technology Journal. 12(24). 8204–8211. 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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