Michael A. Schuh

20 papers receiving 231 citations

Peers

Michael A. Schuh
Comparison fields: 5 of 50
  • Signal Processing 83
  • Astronomy and Astrophysics 82
  • Artificial Intelligence 104
  • Computer Vision and Pattern Recognition 55
  • Software 8
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Countries citing papers authored by Michael A. Schuh

Since Specialization
Citations

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

Fields of papers citing papers by Michael A. Schuh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside Michael A. Schuh, 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 A. Schuh Line = papers co-authored together Michael A. Schuh links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202099
2 201333
3 201225
4 201813
5 201112
6 201410
7
Mitigating the Curse of Dimensionality for Exact kNN Retrieval
20148
8
Evolving Kernel Functions with Particle Swarms and Genetic Programming
20127
9 20167
10 20116
11 20166
12 20174
13 20183
14 20132
15 20142
16 20152
17 20201
18
Cluster Analysis for Optimal Indexing
20131
19
A Region-Based Retrieval System for Heliophysics Imagery.
20171
20 20161

About Michael A. Schuh

Michael A. Schuh is a scholar working on Artificial Intelligence, Astronomy and Astrophysics, Computer Vision and Pattern Recognition, Signal Processing and Computer Networks and Communications, having authored 21 papers that have together received 243 indexed citations. Recurring topics across this work include Solar and Space Plasma Dynamics (8 papers), Data Management and Algorithms (6 papers), Advanced Image and Video Retrieval Techniques (6 papers), Solar Radiation and Photovoltaics (5 papers), Geomagnetism and Paleomagnetism Studies (4 papers), Advanced Database Systems and Queries (3 papers), Data Mining Algorithms and Applications (2 papers) and Data Quality and Management (2 papers). The work is most often cited by research in Signal Processing (83 citations), Astronomy and Astrophysics (82 citations), Artificial Intelligence (104 citations), Computer Vision and Pattern Recognition (55 citations) and Software (8 citations). Michael A. Schuh has collaborated with scholars based in United States, Canada and Greece. Frequent co-authors include Rafal A. Angryk, P. C. H. Martens, Dustin Kempton, Berkay Aydin, Juan M. Banda, Soukaïna Filali Boubrahimi, Azim Ahmadzadeh, Shah Muhammad Hamdi, Sushant S. Mahajan and Sunitha Basodi. Their work appears in journals such as IEEE Aerospace and Electronic Systems Magazine, The Astrophysical Journal Supplement Series, Journal of Space Weather and Space Climate, Scientific Data and The Astrophysical 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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