Zhi Liu

5.4k total citations · 2 hit papers
180 papers, 3.6k citations indexed

About

Zhi Liu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Zhi Liu has authored 180 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Computer Vision and Pattern Recognition, 33 papers in Artificial Intelligence and 30 papers in Biomedical Engineering. Recurrent topics in Zhi Liu's work include Network Security and Intrusion Detection (13 papers), Remote-Sensing Image Classification (11 papers) and Anomaly Detection Techniques and Applications (11 papers). Zhi Liu is often cited by papers focused on Network Security and Intrusion Detection (13 papers), Remote-Sensing Image Classification (11 papers) and Anomaly Detection Techniques and Applications (11 papers). Zhi Liu collaborates with scholars based in China, United States and Australia. Zhi Liu's co-authors include Yang Xin, Haixia Hou, Yanmiao Li, Qingli Li, Hongliang Zhu, Chunhua Wang, Yuling Chen, Lizhen Cui, Yuefeng Zhao and Xiaoyan Xiao and has published in prestigious journals such as Journal of the American Chemical Society, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Zhi Liu

159 papers receiving 3.5k citations

Hit Papers

Machine Learning and Deep Learning Methods for Cybersecurity 2018 2026 2020 2023 2018 2019 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zhi Liu China 28 900 830 662 504 440 180 3.6k
Kaushik Roy United States 31 1.1k 1.3× 534 0.6× 779 1.2× 1.2k 2.3× 302 0.7× 284 3.7k
Yun Lin China 41 2.1k 2.3× 911 1.1× 492 0.7× 687 1.4× 223 0.5× 281 6.9k
Sen Wang China 35 1.2k 1.4× 541 0.7× 331 0.5× 1.1k 2.1× 296 0.7× 236 4.7k
N. Arunkumar India 37 993 1.1× 741 0.9× 493 0.7× 933 1.9× 504 1.1× 109 5.0k
Tai-hoon Kim South Korea 34 982 1.1× 1.4k 1.6× 437 0.7× 762 1.5× 185 0.4× 297 4.1k
Yun Yang China 30 1.6k 1.8× 338 0.4× 254 0.4× 907 1.8× 324 0.7× 210 3.4k
Jiayu Zhou United States 35 2.4k 2.7× 306 0.4× 313 0.5× 905 1.8× 155 0.4× 237 5.2k
Weishan Zhang China 27 1.2k 1.3× 776 0.9× 202 0.3× 579 1.1× 134 0.3× 234 3.1k
Ping Chen China 26 456 0.5× 390 0.5× 149 0.2× 543 1.1× 471 1.1× 289 3.1k
Jianyong Chen China 47 1.3k 1.5× 476 0.6× 163 0.2× 489 1.0× 743 1.7× 256 6.7k

Countries citing papers authored by Zhi Liu

Since Specialization
Citations

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

Fields of papers citing papers by Zhi Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhi Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Zhi Liu. A scholar is included among the top collaborators of Zhi Liu 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 Zhi Liu. Zhi Liu 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.
Li, Kan, Zhi Liu, Xiaonan Hu, et al.. (2025). Morphable 3D architectures enabled by shear-guided approach. Materials Today. 86. 28–41. 1 indexed citations
2.
Zhao, Wei, et al.. (2024). Understanding world models through multi-step pruning policy via reinforcement learning. Information Sciences. 686. 121361–121361. 2 indexed citations
3.
Mi, Jia, et al.. (2024). A semi-automatic cardiovascular annotation and quantification toolbox utilizing prior knowledge-guided feature learning. Biomedical Signal Processing and Control. 102. 107201–107201. 1 indexed citations
5.
Liu, Xiaojuan, et al.. (2024). Lesion region inpainting: an approach for pseudo-healthy image synthesis in intracranial infection imaging. Frontiers in Microbiology. 15. 1453870–1453870. 3 indexed citations
7.
Wang, Fengdan, Jia Li, Ling Yuan, et al.. (2023). Correlation analysis of quantitative MRI measurements of thigh muscles with histopathology in patients with idiopathic inflammatory myopathy. European Radiology Experimental. 7(1). 51–51. 10 indexed citations
8.
Schoepf, U. Joseph, Rock H. Savage, Kunlin Cao, et al.. (2023). A deep learning-based fully automatic and clinical-ready framework for regional myocardial segmentation and myocardial ischemia evaluation. Medical & Biological Engineering & Computing. 61(6). 1507–1520. 3 indexed citations
9.
Jayasundera, Krishanthi Padmarani, et al.. (2023). Development and Field Installation of Smart Sensor Nodes for Quantification of Missing Water in Soil. IEEE Sensors Journal. 23(21). 26495–26502. 7 indexed citations
10.
Zhang, Pengfei, et al.. (2022). AIVUS: Guidewire Artifacts Inpainting for Intravascular Ultrasound Imaging With United Spatiotemporal Aggregation Learning. IEEE Transactions on Computational Imaging. 8. 679–692. 3 indexed citations
11.
Cui, Xiaoxiao, Zhi Liu, Xiaoyu Sui, et al.. (2022). TRSA-Net: Task Relation Spatial Co-Attention for Joint Segmentation, Quantification and Uncertainty Estimation on Paired 2D Echocardiography. IEEE Journal of Biomedical and Health Informatics. 26(8). 4067–4078. 7 indexed citations
12.
Xiao, Xiaoyan, Zhi Liu, Longkun Guo, et al.. (2020). Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification. Scientific Programming. 2020. 1–6. 17 indexed citations
13.
Liu, Zhi, Yujun Li, Xiaoyan Xiao, et al.. (2020). Multiparameter Synchronous Measurement With IVUS Images for Intelligently Diagnosing Coronary Cardiac Disease. IEEE Transactions on Instrumentation and Measurement. 70. 1–10. 14 indexed citations
14.
Zhang, Ranran, Xiaoyan Xiao, Zhi Liu, et al.. (2018). A New Motor Imagery EEG Classification Method FB-TRCSP+RF Based on CSP and Random Forest. IEEE Access. 6. 44944–44950. 26 indexed citations
15.
Xin, Yang, et al.. (2018). Multimodal Feature-Level Fusion for Biometrics Identification System on IoMT Platform. IEEE Access. 6. 21418–21426. 67 indexed citations
16.
Wang, Yuanlong, Wanzhong Zhao, Huaming Wang, & Zhi Liu. (2018). A bio-inspired novel active elastic component based on negative Poisson’s ratio structure and dielectric elastomer. Smart Materials and Structures. 28(1). 15011–15011. 10 indexed citations
17.
Jing, Bing‐Yi, Zhi Liu, & Xinbing Kong. (2016). ESTIMATING VOLATILITY FUNCTIONALS WITH MULTIPLE TRANSACTIONS. Econometric Theory. 33(2). 331–365. 6 indexed citations
18.
Wang, Zongliang, Jun Chang, Sasa Zhang, et al.. (2014). Application of wavelet transform modulus maxima in raman distributed temperature sensors. Photonic Sensors. 4(2). 142–146. 16 indexed citations
19.
Chang, Jun, Guangping Lv, Zongliang Wang, et al.. (2013). Wavelength dispersion analysis on fiber-optic Raman distributed temperature sensor system. Photonic Sensors. 3(3). 256–261. 22 indexed citations
20.
Zhao, Wei, Zhi Liu, & Cai Chang. (2009). Three-dimensional power Doppler imaging diagnosis of endometrial polyps. Zhongguo yixue yingxiang jishu. 25(9). 1648–1650. 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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