Y. Ding

676 total citations
13 papers, 427 citations indexed

About

Y. Ding is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Y. Ding has authored 13 papers receiving a total of 427 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 2 papers in Information Systems. Recurrent topics in Y. Ding's work include Advanced Neural Network Applications (3 papers), Machine Learning and ELM (2 papers) and Topic Modeling (2 papers). Y. Ding is often cited by papers focused on Advanced Neural Network Applications (3 papers), Machine Learning and ELM (2 papers) and Topic Modeling (2 papers). Y. Ding collaborates with scholars based in China and Hong Kong. Y. Ding's co-authors include Qing Liao, Xuan Wang, Lionel M. Ni, Qiong Luo, Zoe L. Jiang, Wenyi Zhang, Wen Xia, Xiang Zhang, Hao Han and Siyuan Liu and has published in prestigious journals such as IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems and Neurocomputing.

In The Last Decade

Y. Ding

13 papers receiving 411 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Y. Ding China	6	218	133	77	70	63	13	427
Mohammed Kaosar Australia	9	269 1.2×	103 0.8×	111 1.4×	56 0.8×	37 0.6×	38	440
A. Razia Sulthana India	10	214 1.0×	95 0.7×	75 1.0×	22 0.3×	45 0.7×	26	405
Vahida Attar India	12	247 1.1×	96 0.7×	120 1.6×	94 1.3×	118 1.9×	51	556
Michele Ianni Italy	9	279 1.3×	66 0.5×	100 1.3×	32 0.5×	51 0.8×	36	420
Ala Mughaid Jordan	15	196 0.9×	199 1.5×	146 1.9×	37 0.5×	80 1.3×	47	582
Zhiqiang Yang China	10	242 1.1×	168 1.3×	76 1.0×	50 0.7×	44 0.7×	47	497
Dharmveer Singh Rajpoot India	9	343 1.6×	144 1.1×	48 0.6×	53 0.8×	72 1.1×	23	459
Deepti Saraswat India	10	178 0.8×	123 0.9×	107 1.4×	20 0.3×	52 0.8×	16	524

Countries citing papers authored by Y. Ding

Since Specialization

Citations

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

Fields of papers citing papers by Y. Ding

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Y. Ding

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

All Works

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13 of 13 papers shown

1.

Yin, Zhe, et al.. (2025). Dynamic Knowledge Path Learning for Few-Shot Learning. Big Data Mining and Analytics. 8(2). 479–495. 1 indexed citations

2.

Zhang, Jiexin, et al.. (2025). On the Passive Virtual Viscous Element Injection Method for Elastic Joint Robots. IEEE Transactions on Robotics. 41. 4078–4099. 1 indexed citations

3.

Tang, Siyu, et al.. (2023). MG-SIN: Multigraph Sparse Interaction Network for Multitask Stance Detection. IEEE Transactions on Neural Networks and Learning Systems. 36(2). 3111–3125. 1 indexed citations

4.

Zhang, Zhihang, Y. Ding, & Chenlin Huang. (2023). Automatic Front-end Code Generation from image Via Multi-Head Attention. 869–872. 3 indexed citations

5.

Su, Weijun, et al.. (2022). Improving Anomaly Detection with a Self-Supervised Task Based on Generative Adversarial Network. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 3563–3567. 2 indexed citations

6.

Ding, Y., et al.. (2022). MultiShadow: Shadow Synthesis for Multiple Virtual Objects. 27. 242–247. 1 indexed citations

7.

Yin, Zhe, et al.. (2022). A Model-Agnostic Approach to Mitigate Gradient Interference for Multi-Task Learning. IEEE Transactions on Cybernetics. 53(12). 7810–7823. 14 indexed citations

8.

Liao, Qing, Hao Han, Xiang Zhang, et al.. (2021). An Integrated Multi-Task Model for Fake News Detection. IEEE Transactions on Knowledge and Data Engineering. 34(11). 5154–5165. 111 indexed citations

9.

Zhang, Dian, et al.. (2021). Bitcoin price forecasting: A perspective of underlying blockchain transactions. Decision Support Systems. 151. 113650–113650. 66 indexed citations

10.

Ding, Y., et al.. (2021). iMatching: An interactive map-matching system. Neurocomputing. 444. 126–135. 4 indexed citations

11.

Huang, Xixi, Y. Ding, Zoe L. Jiang, et al.. (2020). DP-FL: a novel differentially private federated learning framework for the unbalanced data. World Wide Web. 23(4). 2529–2545. 52 indexed citations

12.

Ding, Y., et al.. (2020). FraudTrip: Taxi Fraudulent Trip Detection From Corresponding Trajectories. IEEE Internet of Things Journal. 8(16). 12505–12517. 110 indexed citations

13.

Liao, Qing, Y. Ding, Zoe L. Jiang, et al.. (2018). Multi-task deep convolutional neural network for cancer diagnosis. Neurocomputing. 348. 66–73. 61 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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