Wei Ding
- Artificial Intelligence top 0.5%
- Topic Modeling 14
- Machine Learning and Data Classification 14
- Anomaly Detection Techniques and Applications 13
- Natural Language Processing Techniques 11
- Data Stream Mining Techniques 11
- Bayesian Modeling and Causal Inference 10
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- Face and Expression Recognition 14
- Information Systems top 0.5%
- Data Mining Algorithms and Applications 18
- Signal Processing top 2%
Wei Ding
155 papers receiving 4.2k citations
Hit Papers
Peers
Comparison fields: 5 of 174
- Artificial Intelligence 2.2k
- Computer Vision and Pattern Recognition 989
- Information Systems 1.1k
- Signal Processing 429
- Management Information Systems 298
Countries citing papers authored by Wei Ding
This map shows the geographic impact of Wei 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 Wei Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wei Ding more than expected).
Fields of papers citing papers by Wei Ding
This network shows the impact of papers produced by Wei 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 Wei Ding. The network helps show where Wei Ding may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Wei Ding, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 10 | |
| 3 | 2024 | 0 | |
| 4 | 2023 | 1 | |
| 5 | 2023 | 6 | |
| 6 | 2022 | 1 | |
| 7 | 2022 | 1 | |
| 8 | 2021 | 7 | |
| 9 | 2020 | 1 | |
| 10 | 2019 | 41 | |
| 11 | Crater Detection via Convolutional Neural Networks | 2016 | 3 |
| 12 | A Comprehensive Literature Review on Big Data in Healthcare | 2016 | 2 |
| 13 | Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence | 2012 | 1 |
| 14 | Mars Weekend: A Panel and Games at the Museum of Science Boston | 2012 | 1 |
| 15 | Semi-supervised based active class selection for automatic identification of sub-kilometer craters | 2011 | 5 |
| 16 | 2011 | 3 | |
| 17 | Automatic Detection of Sub-km Craters Using Shape and Texture Information | 2010 | 17 |
| 18 | Online Streaming Feature Selection | 2010 | 88 |
| 19 | Word Classification: An Experimental Approach with Naïve Bayes. | 2009 | 2 |
| 20 | Parsing tree matching based question answering | 2008 | 1 |
About Wei Ding
Wei Ding is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 166 papers that have together received 4.4k indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (18 papers), Topic Modeling (14 papers), Face and Expression Recognition (14 papers), Machine Learning and Data Classification (14 papers), Anomaly Detection Techniques and Applications (13 papers), Natural Language Processing Techniques (11 papers), Data Stream Mining Techniques (11 papers) and Bayesian Modeling and Causal Inference (10 papers). The work is most often cited by research in Artificial Intelligence (2.2k citations), Computer Vision and Pattern Recognition (989 citations) and Information Systems (1.1k citations). Wei Ding has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Xindong Wu, Xingquan Zhu, Gongqing Wu, Kui Yu, T. F. Stepinski, Melissa S. Morabito, Jian Pei, Yang Mu, Hao Wang and Ping Chen. Their work appears in journals such as Applied Intelligence, ACM Transactions on Knowledge Discovery from Data, IEEE Transactions on Knowledge and Data Engineering, Knowledge-Based Systems and Data Mining and Knowledge Discovery.
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.