Xin He

5.4k total citations · 1 hit paper
62 papers, 2.6k citations indexed

About

Xin He is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Xin He has authored 62 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 14 papers in Electrical and Electronic Engineering and 11 papers in Artificial Intelligence. Recurrent topics in Xin He's work include Advanced Data and IoT Technologies (5 papers), Advanced Neural Network Applications (5 papers) and Telecommunications and Broadcasting Technologies (4 papers). Xin He is often cited by papers focused on Advanced Data and IoT Technologies (5 papers), Advanced Neural Network Applications (5 papers) and Telecommunications and Broadcasting Technologies (4 papers). Xin He collaborates with scholars based in China, United States and Hong Kong. Xin He's co-authors include Xiaowen Chu, Kaiyong Zhao, Cong Yao, Shangbang Long, Meng Liu, Hanliang Shao, Dong Yuanhua, Ling Li, Jiafeng Jiang and Jiangang Li and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and European Journal of Operational Research.

In The Last Decade

Xin He

55 papers receiving 2.4k citations

Hit Papers

AutoML: A survey of the state-of-the-art 2020 2026 2022 2024 2020 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xin He China 19 682 610 411 283 231 62 2.6k
Mauro Castelli Portugal 31 1.5k 2.2× 457 0.7× 398 1.0× 210 0.7× 121 0.5× 205 4.3k
Mamta Mittal India 28 1.0k 1.5× 709 1.2× 360 0.9× 199 0.7× 224 1.0× 109 3.2k
Adnan Mohsin Abdulazeez Iraq 24 1.5k 2.3× 769 1.3× 495 1.2× 371 1.3× 129 0.6× 115 4.5k
Marko Robnik‐Šikonja Slovenia 17 1.9k 2.7× 835 1.4× 209 0.5× 208 0.7× 94 0.4× 86 4.2k
Hossein Azizpour Sweden 16 1.6k 2.4× 2.4k 3.9× 526 1.3× 277 1.0× 601 2.6× 30 5.7k
Chris Chatwin United Kingdom 23 191 0.3× 299 0.5× 401 1.0× 398 1.4× 249 1.1× 227 2.1k
Daji Ergu China 21 534 0.8× 655 1.1× 79 0.2× 164 0.6× 164 0.7× 69 3.6k
Flavio Villanustre United States 9 711 1.0× 296 0.5× 129 0.3× 192 0.7× 56 0.2× 20 2.0k
Muhammad Fazal Ijaz South Korea 34 1.2k 1.7× 783 1.3× 341 0.8× 308 1.1× 204 0.9× 95 4.0k
Siddhartha Bhattacharyya India 33 2.0k 3.0× 895 1.5× 185 0.5× 287 1.0× 368 1.6× 208 4.1k

Countries citing papers authored by Xin He

Since Specialization
Citations

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

Fields of papers citing papers by Xin He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xin He

This figure shows the co-authorship network connecting the top 25 collaborators of Xin He. A scholar is included among the top collaborators of Xin He 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 Xin He. Xin He 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.
Chen, Yuhan, Zeyu Li, X. L. Kang, et al.. (2025). BurstGPT: A Real-World Workload Dataset to Optimize LLM Serving Systems. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 5831–5841. 1 indexed citations
2.
Xu, Xi, et al.. (2025). Normalized Intrinsic Deep Features Based Zero-Watermarking Scheme for Remote Sensing Images Using U-Net and K-Means. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18. 18224–18239.
4.
Yao, Fanglong, Youzhi Liu, Wenyi Zhang, et al.. (2025). AeroVerse-Review: Comprehensive survey on aerial embodied vision-and-language navigation. 1(1). 100015–100015.
5.
Shen, Mengyi, et al.. (2023). Radiomics Nomogram Based on Dual‐Sequence MRI for Assessing Ki‐67 Expression in Breast Cancer. Journal of Magnetic Resonance Imaging. 60(3). 1203–1212. 4 indexed citations
6.
Zhang, Qingqing, Tao Zhang, Qiang Zhang, et al.. (2022). Big Data based Potential Fixed-Mobile Convergence User Mining. 1193–1198.
7.
Zhang, Chi, Wenqian Huang, Xin He, et al.. (2022). Slight crack identification of cottonseed using air-coupled ultrasound with sound to image encoding. Frontiers in Plant Science. 13. 956636–956636. 9 indexed citations
8.
He, Yi, Yingqian Zhang, Xin He, & Xingyuan Wang. (2021). A new image encryption algorithm based on the OF-LSTMS and chaotic sequences. Scientific Reports. 11(1). 6398–6398. 42 indexed citations
9.
Zhao, Xin, Xin He, Tao Feng, & Zhipeng Qiu. (2020). A stochastic switched SIRS epidemic model with nonlinear incidence and vaccination: Stationary distribution and extinction. International Journal of Biomathematics. 13(3). 2050020–2050020. 14 indexed citations
10.
Guo, Lei, et al.. (2020). [Effects of glyphosate and paraquat on root morphology and aboveground growth of Prunus persica seedlings].. PubMed. 31(2). 524–532. 3 indexed citations
11.
He, Xin, Shihao Wang, Shaohuai Shi, et al.. (2019). Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 4839–4844. 15 indexed citations
12.
Wang, Yuxin, Qiang Wang, Shaohuai Shi, et al.. (2019). Benchmarking the Performance and Power of AI Accelerators for AI Training. arXiv (Cornell University). 7 indexed citations
13.
Wang, Yuxin, Qiang Wang, Shaohuai Shi, et al.. (2019). Performance and Power Evaluation of AI Accelerators for Training Deep Learning Models. arXiv (Cornell University). 1 indexed citations
14.
Cui, Wei, Dongyou Zhang, Xin He, et al.. (2019). Multi-Scale Remote Sensing Semantic Analysis Based on a Global Perspective. ISPRS International Journal of Geo-Information. 8(9). 417–417. 9 indexed citations
15.
Yang, You, Qiong Liu, Xin He, & Zhen Liu. (2018). Cross-View Multi-Lateral Filter for Compressed Multi-View Depth Video. IEEE Transactions on Image Processing. 28(1). 302–315. 34 indexed citations
16.
Zhang, Bin, Xin He, Fusheng Ouyang, et al.. (2017). Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Letters. 403. 21–27. 204 indexed citations
17.
He, Xin. (2014). The Effect of Supply Chain Strategy on Quality and U. S. Competitiveness. Communications of the IIMA. 13(2). 1 indexed citations
18.
Xu, Xiaobo & Xin He. (2014). Impact of Team Attitude and Behavior on IS Project Success. Communications of the IIMA. 8(4). 5 indexed citations
19.
He, Xin, Xiaobo Xu, & Jack C. Hayya. (2011). THE EFFECT OF LEAD-TIME ON THE SUPPLY CHAIN: THE MEAN VERSUS THE VARIANCE. International Journal of Information Technology & Decision Making. 10(1). 175–185. 7 indexed citations
20.
He, Xin, Yaqin Zhao, & Xianzhong Zhou. (2005). Hierarchical Support Vector Machines for Audio Classification *. 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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