Jin Ma

462 citations
34 papers · 262 · h-index 10

Impact in

Papers in

Jin Ma

30 papers receiving 252 citations

Peers

Jin Ma
Comparison fields: 5 of 101
  • Computer Graphics and Computer-Aided Design 19
  • Human-Computer Interaction 17
  • Health Informatics 3
  • Management of Technology and Innovation 15
  • Computer Vision and Pattern Recognition 43
Replace Koh Kakusho with:
Koh Kakusho Japan
Scarlett Herring United States
Liuqing Chen China
Cheng‐Hung Lo Taiwan
Kyung Hoon Hyun South Korea
Yunyi Zhu China
Pornchai Mongkolnam Thailand
Vandana Dixit Kaushik India
Matthew Yee-King United Kingdom
Janin Koch Finland
Jin Ma relative to Koh Kakusho Japan Koh Kakusho's profile →
Citations per field
00.5×10×15×18.3×
Koh Kakusho · 1×
Citations per year

Countries citing papers authored by Jin Ma

Since Specialization
Citations

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

Fields of papers citing papers by Jin Ma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jin Ma, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jin Ma Line = papers co-authored together Jin Ma links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201560
2 202023
3 202320
4 200820
5 202420
6 202319
7 202314
8 202212
9 202211
10 202310
11
Differential vascular cell adhesion molecule-1 expression and superoxide production in simulated microgravity rat vasculature.
20108
12 20238
13 20224
14 20224
15 20174
16
Dynamic analysis of a train-bridge system to vessel collision and running safety of high-speed trains
20153
17 20233
18 20233
19 20203
20 20232

About Jin Ma

Jin Ma is a scholar working on Artificial Intelligence, Mechanical Engineering, Biomedical Engineering, Computer Vision and Pattern Recognition and Cognitive Neuroscience, having authored 34 papers that have together received 262 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (3 papers), Topic Modeling (3 papers), Design Education and Practice (2 papers), Anomaly Detection Techniques and Applications (2 papers), Multimodal Machine Learning Applications (2 papers), Spaceflight effects on biology (2 papers), Creativity in Education and Neuroscience (2 papers) and Anesthesia and Neurotoxicity Research (2 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (19 citations), Human-Computer Interaction (17 citations), Health Informatics (3 citations), Management of Technology and Innovation (15 citations) and Computer Vision and Pattern Recognition (43 citations). Jin Ma has collaborated with scholars based in China, United States and Philippines. Frequent co-authors include Ken Friedman, Ying Shan, Yungang Bai, Ming Xu, Taihong Liu, Xinguo Liu, Edmond Q. Wu, Bin Zhang, Yizhen Chen and Muyang Zhang. Their work appears in journals such as She ji, IEEE Transactions on Automation Science and Engineering, The FASEB Journal, Laser Physics Letters and IEEE Sensors Journal.

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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