Martin Macaš
Impact in
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- Robotic Path Planning Algorithms
- Health Information Management top 10%
Papers in
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- Neural Networks and Applications 6
- Machine Learning and Algorithms 4
- Metaheuristic Optimization Algorithms Research 3
- Machine Learning and Data Classification 3
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- EEG and Brain-Computer Interfaces 8
- Neural dynamics and brain function 3
- Co-authors
- Lenka Lhotská (24 shared papers)Martin Saska (1 shared paper)Libor Přeučil (1 shared paper)Fabio Moretti (3 shared papers)Stefano Pizzuti (2 shared papers)Kateřina Štechová (3 shared papers)Alessandro Fonti (1 shared paper)Andrea Giantomassi (1 shared paper)
In The Last Decade
Martin Macaš
34 papers receiving 408 citations
Peers
Comparison fields: 5 of 87
- Computer Vision and Pattern Recognition 124
- Health Information Management 19
- Building and Construction 58
- Artificial Intelligence 114
- Cognitive Neuroscience 45
Countries citing papers authored by Martin Macaš
This map shows the geographic impact of Martin Macaš'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 Martin Macaš with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martin Macaš more than expected).
Fields of papers citing papers by Martin Macaš
This network shows the impact of papers produced by Martin Macaš. 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 Martin Macaš. The network helps show where Martin Macaš may publish in the future.
Co-authors
The 25 scholars most cited alongside Martin Macaš, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2006 | 107 | |
| 2 | 2015 | 54 | |
| 3 | 2015 | 25 | |
| 4 | 2020 | 25 | |
| 5 | 2008 | 22 | |
| 6 | 2014 | 22 | |
| 7 | 2009 | 14 | |
| 8 | 2012 | 13 | |
| 9 | 2017 | 13 | |
| 10 | 2008 | 12 | |
| 11 | 2013 | 12 | |
| 12 | 2019 | 11 | |
| 13 | 2018 | 10 | |
| 14 | 2015 | 9 | |
| 15 | 2018 | 8 | |
| 16 | 2005 | 7 | |
| 17 | 2018 | 6 | |
| 18 | 2007 | 6 | |
| 19 | 2018 | 5 | |
| 20 | 2013 | 5 |
About Martin Macaš
Martin Macaš is a scholar working on Artificial Intelligence, Cognitive Neuroscience, Computer Vision and Pattern Recognition, Control and Systems Engineering and Endocrinology, Diabetes and Metabolism, having authored 34 papers that have together received 425 indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (8 papers), Neural Networks and Applications (6 papers), Machine Learning and Algorithms (4 papers), Blind Source Separation Techniques (3 papers), Diabetes Management and Research (3 papers), Neural dynamics and brain function (3 papers), Metaheuristic Optimization Algorithms Research (3 papers) and Machine Learning and Data Classification (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (124 citations), Health Information Management (19 citations), Building and Construction (58 citations), Artificial Intelligence (114 citations) and Cognitive Neuroscience (45 citations). Martin Macaš has collaborated with scholars based in Czechia, Italy and India. Frequent co-authors include Lenka Lhotská, Martin Saska, Libor Přeučil, Fabio Moretti, Stefano Pizzuti, Kateřina Štechová, Alessandro Fonti, Andrea Giantomassi, Gabriele Comodi and Mauro Annunziato. Their work appears in journals such as Computer Methods and Programs in Biomedicine, Energy and Buildings, Neurocomputing, Physiological Measurement and Clinical Neurophysiology.
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.