Michael Mayo

70 papers receiving 781 citations

Peers

Michael Mayo
Comparison fields: 5 of 133
  • Health Informatics 10
  • Ecological Modeling 28
  • Computer Vision and Pattern Recognition 137
  • Artificial Intelligence 181
  • Health Information Management 18
Replace Tsubasa Hirakawa with:
Tsubasa Hirakawa Japan
Yang Han China
C. David Page United States
Mohammed Elanbari Qatar
Sylvain Gugger United States
Jingyang Gao China
Xiaoling Xia China
Abu Sufian India
Simon Bernard France
Michael Mayo relative to Tsubasa Hirakawa Japan Tsubasa Hirakawa's profile →
Citations per field
00.5×4.7×
Tsubasa Hirakawa · 1×
Citations per year

Countries citing papers authored by Michael Mayo

Since Specialization
Citations

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

Fields of papers citing papers by Michael Mayo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Michael Mayo, 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 Michael Mayo Line = papers co-authored together Michael Mayo links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 77 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200697
2 201996
3 201279
4 201141
5 201534
6 200928
7 201927
8 202226
9 202222
10 200822
11 201020
12 201918
13 202018
14 200517
15 201717
16 201515
17 202214
18 201314
19
Experiments with multi-view multi-instance learning for supervised image classification
201112
20 201210

About Michael Mayo

Michael Mayo is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Aerospace Engineering, Endocrinology, Diabetes and Metabolism and Computer Networks and Communications, having authored 77 papers that have together received 811 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (9 papers), Wind Energy Research and Development (6 papers), Machine Learning and Data Classification (6 papers), Diabetes Management and Research (6 papers), Image Retrieval and Classification Techniques (5 papers), Face and Expression Recognition (4 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Gene expression and cancer classification (4 papers). The work is most often cited by research in Health Informatics (10 citations), Ecological Modeling (28 citations), Computer Vision and Pattern Recognition (137 citations), Artificial Intelligence (181 citations) and Health Information Management (18 citations). Michael Mayo has collaborated with scholars based in New Zealand, United States and United Kingdom. Frequent co-authors include Eibe Frank, Panos Patros, Bernhard Pfahringer, Stefan Krämer, Sarah Wakes, Lynne Chepulis, Ryan Paul, Robert A. Holt, Brad H. Nelson and John R. Webb. Their work appears in journals such as Journal of the Royal Society of New Zealand, Applied Artificial Intelligence, Lecture notes in computer science, Current Diabetes Reviews and Clinical and Experimental Ophthalmology.

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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