Michael Mayo
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- Advanced Image and Video Retrieval Techniques 6
- Face and Expression Recognition 4
- Image Retrieval and Classification Techniques 3
- Artificial Intelligence top 10%
- Machine Learning and Data Classification 4
- Neural Networks and Applications 4
- Health Information Management top 10%
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- Wind Energy Research and Development 6
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- Diabetes Management and Research 5
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- Advanced Multi-Objective Optimization Algorithms 4
- Co-authors
- Eibe FrankPanos PatrosStefan KrämerSarah WakesRyan PaulBernhard PfahringerLynne ChepulisThomas Zeng
- Journals
- Applied Artificial Intelligence (2 papers)Journal of the Royal Society of New Zealand (2 papers)Scientific Reports (1 paper)
- Partner nations
- New ZealandUnited StatesUnited Kingdom
In The Last Decade
Michael Mayo
52 papers receiving 648 citations
Peers
Comparison fields: 5 of 136
- Ecological Modeling 27
- Health Informatics 8
- Computer Vision and Pattern Recognition 119
- Artificial Intelligence 147
- Health Information Management 17
Countries citing papers authored by Michael Mayo
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
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-authorship network
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.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2024 | 3 | |
| 3 | 2023 | 3 | |
| 4 | 2023 | 6 | |
| 5 | 2022 | 3 | |
| 6 | 2022 | 25 | |
| 7 | 2022 | 16 | |
| 8 | 2022 | 8 | |
| 9 | 2022 | 8 | |
| 10 | 2021 | 9 | |
| 11 | 2021 | 3 | |
| 12 | 2020 | 9 | |
| 13 | 2019 | 84 | |
| 14 | 2019 | 25 | |
| 15 | Enhancing Regulatory Compliance by Using Artificial Intelligence Text Mining to Identify Penalty Clauses in Legislation | 2017 | 7 |
| 16 | Proceedings of the 29th International Conference on Image and Vision Computing New Zealand | 2014 | 1 |
| 17 | 2011 | 38 | |
| 18 | 2010 | 6 | |
| 19 | 2005 | 15 | |
| 20 | 2004 | 1 |
About Michael Mayo
Michael Mayo is a scholar working on Health Informatics, Computer Vision and Pattern Recognition, Artificial Intelligence, Health Information Management and Endocrinology, Diabetes and Metabolism, having authored 55 papers that have together received 676 indexed citations. Recurring topics across this work include Wind Energy Research and Development (6 papers), Advanced Image and Video Retrieval Techniques (6 papers), Diabetes Management and Research (5 papers), Face and Expression Recognition (4 papers), Advanced Multi-Objective Optimization Algorithms (4 papers), Machine Learning and Data Classification (4 papers), Neural Networks and Applications (4 papers) and Image Retrieval and Classification Techniques (3 papers). The work is most often cited by research in Ecological Modeling (27 citations), Health Informatics (8 citations), Computer Vision and Pattern Recognition (119 citations), Artificial Intelligence (147 citations) and Health Information Management (17 citations). Michael Mayo has collaborated with scholars based in New Zealand, United States and United Kingdom. Frequent co-authors include Eibe Frank, Panos Patros, Stefan Krämer, Sarah Wakes, Ryan Paul, Bernhard Pfahringer, Lynne Chepulis, Thomas Zeng, Brad H. Nelson and Mauro Castellarin. Their work appears in journals such as Applied Artificial Intelligence, Journal of the Royal Society of New Zealand, Scientific Reports, Knowledge-Based Systems and Machine Learning.
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.