Ming Dong

724 total citations
38 papers, 494 citations indexed

About

Ming Dong is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Ming Dong has authored 38 papers receiving a total of 494 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Artificial Intelligence, 10 papers in Computer Vision and Pattern Recognition and 6 papers in Information Systems. Recurrent topics in Ming Dong's work include Advanced Clustering Algorithms Research (4 papers), Machine Learning in Healthcare (4 papers) and Bayesian Methods and Mixture Models (3 papers). Ming Dong is often cited by papers focused on Advanced Clustering Algorithms Research (4 papers), Machine Learning in Healthcare (4 papers) and Bayesian Methods and Mixture Models (3 papers). Ming Dong collaborates with scholars based in United States, China and Singapore. Ming Dong's co-authors include Rohit Kothari, Lijun Wang, Manjeet Rege, Ravi Kothari, Farshad Fotouhi, Yanhua Chen, Chuanling Qiao, Chao Yang, Hamid Soltanian‐Zadeh and Mostafa Ghannad‐Rezaie and has published in prestigious journals such as IEEE Transactions on Biomedical Engineering, Pattern Recognition and Biotechnology and Bioengineering.

In The Last Decade

Ming Dong

36 papers receiving 467 citations

Peers

Ming Dong
Comparison fields: 5 of 98
  • Artificial Intelligence 275
  • Computer Vision and Pattern Recognition 144
  • Information Systems 73
  • Signal Processing 48
  • Computational Theory and Mathematics 46
Replace Matteo Cristani with:
Matteo Cristani Italy
Erkki Mäkinen Finland
Hongyun Bao China
Margareta Ackerman United States
Haofen Wang China
Weiming Liu China
Xiaohai Sun Germany
Rajesh Prasad India
Wajdi Dhifli France
Madalina Croitoru France
Matteo Cristani Italy View profile →
Citations per field, relative to Ming Dong
Ming Dong · 1×
Citations per year, relative to Ming Dong
Ming Dong · 1×

Countries citing papers authored by Ming Dong

Since Specialization
Citations

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

Fields of papers citing papers by Ming Dong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming Dong

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Dong. A scholar is included among the top collaborators of Ming Dong 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 Ming Dong. Ming Dong 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
# Work Indexed citations
1 2
2 0
3 1
4 9
5 1
6 9
7
Predicting the Outcome of Patient-Provider Communication Sequences using Recurrent Neural Networks and Probabilistic Models.
7
8 7
9
Hidden Semi-Markov Model-Based Reputation Management System for Online to Offline (O2O) E-Commerce Markets
4
10 20
11 30
12 52
13 19
14 44
15 1
16
Can Fuzzy Logic Make Technical Analysis 20/20?
4
17
Language engineering for the Semantic Web: a digital library for endangered languages.
6
18 3
19 50
20
Exploring the Fuzzy Nature of Technical Patterns of U.S. Market.
1

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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