Shifei Ding

10.6k total citations · 6 hit papers
288 papers, 7.8k citations indexed

About

Shifei Ding is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Shifei Ding has authored 288 papers receiving a total of 7.8k indexed citations (citations by other indexed papers that have themselves been cited), including 185 papers in Artificial Intelligence, 146 papers in Computer Vision and Pattern Recognition and 52 papers in Control and Systems Engineering. Recurrent topics in Shifei Ding's work include Face and Expression Recognition (94 papers), Advanced Clustering Algorithms Research (48 papers) and Neural Networks and Applications (47 papers). Shifei Ding is often cited by papers focused on Face and Expression Recognition (94 papers), Advanced Clustering Algorithms Research (48 papers) and Neural Networks and Applications (47 papers). Shifei Ding collaborates with scholars based in China, Singapore and Taiwan. Shifei Ding's co-authors include Junzhao Yu, Chunyang Su, Xinzheng Xu, Hongjie Jia, Mingjing Du, Weikuan Jia, Yu Xue, Ru Nie, Nan Zhang and Zhongzhi Shi and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Chemical Engineering Journal and Expert Systems with Applications.

In The Last Decade

Shifei Ding

271 papers receiving 7.5k citations

Hit Papers

An optimizing BP neural network algorithm based on geneti... 2011 2026 2016 2021 2011 2013 2016 2021 2021 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shifei Ding China 45 4.1k 2.4k 1.1k 948 640 288 7.8k
Jacek M. Żurada United States 36 3.7k 0.9× 1.7k 0.7× 1000 1.0× 943 1.0× 503 0.8× 208 7.4k
Yingjie Tian China 40 3.7k 0.9× 2.8k 1.2× 927 0.9× 577 0.6× 597 0.9× 330 7.8k
Ah Chung Tsoi Australia 31 5.1k 1.3× 2.8k 1.2× 1.1k 1.1× 1.1k 1.1× 1.2k 1.8× 184 11.0k
Michel Verleysen Belgium 44 4.1k 1.0× 2.5k 1.0× 522 0.5× 649 0.7× 1.0k 1.6× 287 8.7k
Simon Osindero United Kingdom 13 5.0k 1.2× 4.3k 1.8× 1.0k 1.0× 1.1k 1.2× 1.6k 2.5× 27 12.0k
Tommy W. S. Chow Hong Kong 49 2.8k 0.7× 2.0k 0.8× 2.3k 2.2× 917 1.0× 574 0.9× 275 7.5k
Qing He China 38 5.0k 1.2× 1.9k 0.8× 750 0.7× 813 0.9× 600 0.9× 297 10.6k
Ryszard Tadeusiewicz Poland 27 2.8k 0.7× 1.2k 0.5× 1.1k 1.0× 1.1k 1.1× 647 1.0× 241 8.0k
Yee‐Whye Teh Singapore 7 4.7k 1.2× 4.0k 1.7× 1.0k 1.0× 1.1k 1.2× 1.6k 2.4× 7 11.5k
Adam Krzyżak Canada 35 3.4k 0.8× 2.7k 1.1× 1.2k 1.1× 391 0.4× 722 1.1× 213 7.3k

Countries citing papers authored by Shifei Ding

Since Specialization
Citations

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

Fields of papers citing papers by Shifei Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shifei Ding

This figure shows the co-authorship network connecting the top 25 collaborators of Shifei Ding. A scholar is included among the top collaborators of Shifei 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 Shifei Ding. Shifei Ding 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.
Ding, Shifei, et al.. (2025). A novel robust semi-supervised stochastic configuration network for regression tasks with noise. Information Sciences. 703. 121933–121933. 1 indexed citations
2.
Guo, Lili, Jiakui Li, Shifei Ding, & Jianwu Dang. (2025). APIN: Amplitude- and phase-aware interaction network for speech emotion recognition. Speech Communication. 169. 103201–103201. 1 indexed citations
3.
Ding, Shifei, et al.. (2025). Semi-supervised classification model with stochastic configuration networks. Knowledge and Information Systems. 67(11). 10427–10449.
4.
Ding, Shifei, Jie Li, & Lili Guo. (2025). A Cross-Modal Shared Attention Fusion Framework for Emotion Recognition in Multi-Party Conversations. IEEE Transactions on Multimedia. 1–8.
5.
Zheng, Xuan, Shifei Ding, Yi Lu, et al.. (2025). Neighboring clusters as additional adsorption sites to regulate the selectivity of single-atom catalysis. Chemical Engineering Journal. 512. 162508–162508. 2 indexed citations
6.
Guo, Lili, et al.. (2024). Speaker-aware cognitive network with cross-modal attention for multimodal emotion recognition in conversation. Knowledge-Based Systems. 296. 111969–111969. 5 indexed citations
7.
Ding, Ling, et al.. (2024). Survey of spectral clustering based on graph theory. Pattern Recognition. 151. 110366–110366. 49 indexed citations breakdown →
8.
Ding, Shifei, et al.. (2024). Towards Faster Deep Graph Clustering via Efficient Graph Auto-Encoder. ACM Transactions on Knowledge Discovery from Data. 18(8). 1–23.
9.
Du, Wei, et al.. (2024). Expressive Multi-Agent Communication via Identity-Aware Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 38(16). 17354–17361. 1 indexed citations
10.
Ding, Shifei, et al.. (2024). Graph-Based Semi-Supervised Deep Image Clustering With Adaptive Adjacency Matrix. IEEE Transactions on Neural Networks and Learning Systems. 35(12). 18828–18837. 4 indexed citations
11.
Ding, Shifei, et al.. (2024). Wavelet and Adaptive Coordinate Attention Guided Fine-Grained Residual Network for Image Denoising. IEEE Transactions on Circuits and Systems for Video Technology. 34(7). 6156–6166. 14 indexed citations
12.
Ding, Shifei, et al.. (2023). A novel image denoising algorithm combining attention mechanism and residual UNet network. Knowledge and Information Systems. 66(1). 581–611. 5 indexed citations
13.
Ding, Ling, Wei Du, Jian Zhang, et al.. (2023). Better value estimation in Q-learning-based multi-agent reinforcement learning. Soft Computing. 28(6). 5625–5638. 3 indexed citations
14.
Ding, Ling, et al.. (2023). Botnet DGA Domain Name Classification Using Transformer Network with Hybrid Embedding. Big Data Research. 33. 100395–100395. 7 indexed citations
15.
Ding, Shifei, et al.. (2023). Graph clustering network with structure embedding enhanced. Pattern Recognition. 144. 109833–109833. 22 indexed citations
16.
Ding, Shifei, et al.. (2023). Fast density peaks clustering algorithm based on improved mutual K-nearest-neighbor and sub-cluster merging. Information Sciences. 647. 119470–119470. 26 indexed citations
17.
Ding, Shifei, et al.. (2016). Self-adaptation NSCT-PCNN Image Fusion Based GA Optimization. 37(7). 1587. 2 indexed citations
18.
Xu, Xinzheng, Guanying Wang, Shifei Ding, Xiangying Jiang, & Zuopeng Zhao. (2015). A new method for constructing granular neural networks based on rule extraction and extreme learning machine. Pattern Recognition Letters. 67. 138–144. 9 indexed citations
19.
Ding, Shifei, Nan Zhang, Xinzheng Xu, Lili Guo, & Jian Zhang. (2015). Deep Extreme Learning Machine and Its Application in EEG Classification. Mathematical Problems in Engineering. 2015. 1–11. 131 indexed citations
20.
Ding, Shifei. (2007). PCP Comprehensive Decision-Making Model Based on Information Entropy. Mini-micro Systems. 2 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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