S. Shan

1.9k total citations · 1 hit paper
9 papers, 1.5k citations indexed

About

S. Shan is a scholar working on Computational Theory and Mathematics, Industrial and Manufacturing Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, S. Shan has authored 9 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Computational Theory and Mathematics, 3 papers in Industrial and Manufacturing Engineering and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in S. Shan's work include Manufacturing Process and Optimization (3 papers), BIM and Construction Integration (2 papers) and Advanced Multi-Objective Optimization Algorithms (2 papers). S. Shan is often cited by papers focused on Manufacturing Process and Optimization (3 papers), BIM and Construction Integration (2 papers) and Advanced Multi-Objective Optimization Algorithms (2 papers). S. Shan collaborates with scholars based in Canada, China and Poland. S. Shan's co-authors include G. Gary Wang, Zbigniew Suraj, Nick J. Pizzi, Witold Pedrycz, Sheela Ramanna, James F. Peters, Xinchun Cui, Xiangwei Zheng, Xiaoli Liu and Jin‐Xing Liu and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, Pattern Recognition Letters and IEEE Transactions on Instrumentation and Measurement.

In The Last Decade

S. Shan

7 papers receiving 1.4k citations

Hit Papers

Review of Metamodeling Techniques in Support of Engineeri... 2006 2026 2012 2019 2006 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
S. Shan Canada 6 774 479 346 330 234 9 1.5k
Jiexiang Hu China 22 543 0.7× 437 0.9× 277 0.8× 404 1.2× 138 0.6× 61 1.3k
Shinkyu Jeong Japan 21 759 1.0× 401 0.8× 253 0.7× 292 0.9× 121 0.5× 105 1.7k
Joseph Morlier France 22 462 0.6× 296 0.6× 162 0.5× 295 0.9× 515 2.2× 110 1.7k
Debiao Meng China 27 501 0.6× 989 2.1× 229 0.7× 405 1.2× 585 2.5× 70 2.0k
Stephen M. Batill United States 24 561 0.7× 536 1.1× 237 0.7× 239 0.7× 257 1.1× 101 1.9k
Samy Missoum United States 19 486 0.6× 714 1.5× 177 0.5× 159 0.5× 572 2.4× 87 1.4k
Srinivas Kodiyalam United States 18 432 0.6× 406 0.8× 151 0.4× 271 0.8× 423 1.8× 57 1.3k
Nielen Stander United States 19 352 0.5× 273 0.6× 132 0.4× 337 1.0× 357 1.5× 48 1.3k
Ruichen Jin United States 11 1.6k 2.0× 1.3k 2.6× 802 2.3× 494 1.5× 557 2.4× 20 2.8k
Steven F. Wojtkiewicz United States 11 286 0.4× 536 1.1× 176 0.5× 119 0.4× 310 1.3× 42 1.0k

Countries citing papers authored by S. Shan

Since Specialization
Citations

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

Fields of papers citing papers by S. Shan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S. Shan

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

All Works

9 of 9 papers shown
1.
Shan, S., et al.. (2024). DMA-HPCNet: Dual Multi-Level Attention Hybrid Pyramid Convolution Neural Network for Alzheimer’s Disease Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 32. 1955–1964. 3 indexed citations
2.
Shan, S., et al.. (2024). Multifeature Dynamic Weighting Moments Estimation for Boundary Layer Radar Wind Profiler. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–14.
4.
Cui, Xinchun, Chao Zhao, Xiangwei Zheng, et al.. (2023). A Multiscale Hybrid Attention Networks Based on Multiview Images for the Diagnosis of Parkinson’s Disease. IEEE Transactions on Instrumentation and Measurement. 73. 1–11. 10 indexed citations
5.
Wang, G. Gary & S. Shan. (2011). REVIEW OF METAMODELING TECHNIQUES FOR PRODUCT DESIGN WITH COMPUTATION-INTENSIVE PROCESSES. Proceedings of the Canadian Engineering Education Association (CEEA). 8 indexed citations
6.
Wang, G. Gary & S. Shan. (2006). Review of Metamodeling Techniques in Support of Engineering Design Optimization. 415–426. 73 indexed citations
7.
Wang, G. Gary & S. Shan. (2006). Review of Metamodeling Techniques in Support of Engineering Design Optimization. Journal of Mechanical Design. 129(4). 370–380. 1347 indexed citations breakdown →
8.
Shan, S., et al.. (2004). Space exploration and global optimization for computationally intensive design problems: a rough set based approach. Structural and Multidisciplinary Optimization. 28(6). 427–441. 37 indexed citations
9.
Peters, James F., Zbigniew Suraj, S. Shan, et al.. (2002). Classification of meteorological volumetric radar data using rough set methods. Pattern Recognition Letters. 24(6). 911–920. 23 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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