Shengwei An

623 citations
20 papers · 284 · h-index 8

Impact in

    • Advanced Malware Detection Techniques
    • Adversarial Robustness in Machine Learning
    • Anomaly Detection Techniques and Applications
    • Topic Modeling

Papers in

Shengwei An

18 papers receiving 283 citations

Peers

Shengwei An
Comparison fields: 5 of 57
  • Signal Processing 54
  • Artificial Intelligence 156
  • Water Science and Technology 41
  • Computer Vision and Pattern Recognition 44
  • Software 8
Replace Zhongru Wang with:
Zhongru Wang China
Jielong Xu United States
B. Uma Maheswari India
Inseop Lee South Korea
Yiwen Zhu China
Luyao Ren China
Eui‐Suk Jung South Korea
Mu Li China
Peiya Li China
Chi‐Shi Chen Taiwan
Shengwei An relative to Zhongru Wang China Zhongru Wang's profile →
Citations per field
00.5×10×
Zhongru Wang · 1×
Citations per year

Countries citing papers authored by Shengwei An

Since Specialization
Citations

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

Fields of papers citing papers by Shengwei An

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Shengwei An, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Shengwei An Line = papers co-authored together Shengwei An links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 202278
2 202233
3 202232
4 202226
5 202225
6
Backdoor Scanning for Deep Neural Networks through K-Arm Optimization
202121
7 202221
8 20238
9 20247
10 20156
11 20245
12 20235
13 20194
14 20243
15 20243
16 20233
17 20242
18 20252
19 20240
20 20230

About Shengwei An

Shengwei An is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Biomedical Engineering and Computer Networks and Communications, having authored 20 papers that have together received 284 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (10 papers), Advanced Malware Detection Techniques (6 papers), Anomaly Detection Techniques and Applications (5 papers), Topic Modeling (2 papers), Digital Media Forensic Detection (2 papers), Advanced Neural Network Applications (2 papers), Software System Performance and Reliability (1 paper) and Lignin and Wood Chemistry (1 paper). The work is most often cited by research in Signal Processing (54 citations), Artificial Intelligence (156 citations), Water Science and Technology (41 citations), Computer Vision and Pattern Recognition (44 citations) and Software (8 citations). Shengwei An has collaborated with scholars based in United States, Netherlands and China. Frequent co-authors include Guanhong Tao, Yingqi Liu, Shiqing Ma, Boxiong Shen, Zhiqiang Wang, Honghong Lyu, Xiangyu Zhang, Jian Zhao, Yichen Yan and Peng Sun. Their work appears in journals such as Frontiers in Ecology and Evolution, Proceedings of the ACM on Programming Languages, Journal of Environmental Management, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and Proceedings of the AAAI Conference on Artificial Intelligence.

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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