Sheng Ding

670 total citations · 1 hit paper
17 papers, 450 citations indexed

About

Sheng Ding is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Sheng Ding has authored 17 papers receiving a total of 450 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 5 papers in Control and Systems Engineering. Recurrent topics in Sheng Ding's work include Advanced Neural Network Applications (4 papers), Metaheuristic Optimization Algorithms Research (4 papers) and Advanced Algorithms and Applications (3 papers). Sheng Ding is often cited by papers focused on Advanced Neural Network Applications (4 papers), Metaheuristic Optimization Algorithms Research (4 papers) and Advanced Algorithms and Applications (3 papers). Sheng Ding collaborates with scholars based in China, Germany and Japan. Sheng Ding's co-authors include Hui Li, Chen Li, Jin Cao, Kai Fan, Li Chen, Shunxin Li, Andrey Morozov, Hui Li, Klaus Janschek and Wenjing Zhang and has published in prestigious journals such as IEEE Access, Sensors and Journal of Inflammation Research.

In The Last Decade

Sheng Ding

15 papers receiving 422 citations

Hit Papers

A Novel Attribute-Based Access Control Scheme Using Block... 2019 2026 2021 2023 2019 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sheng Ding China 7 263 258 155 55 46 17 450
Jamil Ahmad Pakistan 9 139 0.5× 106 0.4× 106 0.7× 46 0.8× 39 0.8× 18 357
Hongyi Wu United States 12 252 1.0× 107 0.4× 80 0.5× 48 0.9× 24 0.5× 55 449
Kai Zhou United States 12 204 0.8× 132 0.5× 97 0.6× 88 1.6× 29 0.6× 53 412
Longfei Li China 9 323 1.2× 99 0.4× 73 0.5× 70 1.3× 14 0.3× 28 432
Héctor A. Durán-Limón Mexico 11 169 0.6× 220 0.9× 294 1.9× 70 1.3× 17 0.4× 40 425
Maxim Deryabin Russia 10 292 1.1× 167 0.6× 93 0.6× 101 1.8× 11 0.2× 31 406
Sahar F. Sabbeh Egypt 9 125 0.5× 70 0.3× 63 0.4× 41 0.7× 39 0.8× 27 274
Yanwei Xu China 8 120 0.5× 119 0.5× 185 1.2× 84 1.5× 16 0.3× 25 383

Countries citing papers authored by Sheng Ding

Since Specialization
Citations

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

Fields of papers citing papers by Sheng Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sheng Ding

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

All Works

17 of 17 papers shown
1.
Ding, Sheng, et al.. (2024). ZLN005 Reduces Neuroinflammation and Improves Mitochondrial Function in Mice with Perioperative Neurocognitive Disorders. Journal of Inflammation Research. Volume 17. 8135–8146. 2 indexed citations
4.
Zhang, Xinyu & Sheng Ding. (2021). Research on Traffic Target Detection Method Based on Improved YOLOv3. 39–43. 2 indexed citations
5.
Morozov, Andrey, et al.. (2021). Anomaly Detection for Cyber-Physical Systems Using Transformers. 1 indexed citations
7.
Liu, Qiao, Wenjing Zhang, Sheng Ding, Hui Li, & Yong Wang. (2020). Novel Secure Group Data Exchange Protocol in Smart Home with Physical Layer Network Coding. Sensors. 20(4). 1138–1138. 6 indexed citations
8.
Liu, Qiao, Hui Li, Sheng Ding, & Yong Wang. (2020). Lightweight Secure Data Exchange in Decentralized VANETs with Physical Layer Security. 1–5. 1 indexed citations
9.
Ding, Sheng, Jin Cao, Chen Li, Kai Fan, & Hui Li. (2019). A Novel Attribute-Based Access Control Scheme Using Blockchain for IoT. IEEE Access. 7. 38431–38441. 251 indexed citations breakdown →
11.
Ding, Sheng, et al.. (2018). Research on Daily Objects Detection Based on Deep Neural Network. IOP Conference Series Materials Science and Engineering. 322. 62024–62024. 12 indexed citations
12.
Ding, Sheng, Chen Li, & Hui Li. (2018). A Novel Efficient Pairing-Free CP-ABE Based on Elliptic Curve Cryptography for IoT. IEEE Access. 6. 27336–27345. 79 indexed citations
13.
Chen, Xiying, et al.. (2013). Research on the DC access grid security and stability analysis. 30. 942–946. 1 indexed citations
14.
Ding, Sheng & Li Chen. (2010). Intelligent Optimization Methods for High-Dimensional Data Classification for Support Vector Machines. Intelligent Information Management. 2(6). 354–364. 26 indexed citations
15.
Ding, Sheng, et al.. (2009). Evolutionary Computing Optimization for Parameter Determination and Feature Selection of Support Vector Machines. Zenodo (CERN European Organization for Nuclear Research). 46. 1–5. 8 indexed citations
16.
17.
Ding, Sheng. (2009). Feature Selection Based F-Score and ACO Algorithm in Support Vector Machine. 19–23. 40 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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