Matej Gazda
Impact in
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- Voice and Speech Disorders
Papers in
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- Medical Image Segmentation Techniques 2
- Face and Expression Recognition 2
- Advanced Neural Network Applications 2
- Co-authors
- Peter Drotár (11 shared papers)Liberios Vokorokos (1 shared paper)Jakub Gazda (5 shared papers)Nemuel Daniel Pah (1 shared paper)Mohammod Abdul Motin (1 shared paper)Dinesh Kumar (1 shared paper)Ján Plavka (2 shared papers)K.K. Ang (1 shared paper)
- Journals
- Journal of Personalized Medicine (1 paper)IEEE Transactions on Systems Man and Cybernetics Systems (1 paper)Digestive and Liver Disease (1 paper)Frontiers in Neuroinformatics (1 paper)Medical Image Analysis (1 paper)
- Partner nations
- SlovakiaUnited StatesCanada
In The Last Decade
Matej Gazda
15 papers receiving 366 citations
Peers
Comparison fields: 5 of 73
- Health Informatics 7
- Physiology 114
- Neurology 56
- Hepatology 28
- Artificial Intelligence 124
Countries citing papers authored by Matej Gazda
This map shows the geographic impact of Matej Gazda'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 Matej Gazda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matej Gazda more than expected).
Fields of papers citing papers by Matej Gazda
This network shows the impact of papers produced by Matej Gazda. 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 Matej Gazda. The network helps show where Matej Gazda may publish in the future.
Co-authors
The 25 scholars most cited alongside Matej Gazda, 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 | 2021 | 94 | |
| 2 | 2018 | 74 | |
| 3 | 2021 | 61 | |
| 4 | 2021 | 47 | |
| 5 | 1992 | 27 | |
| 6 | 2022 | 22 | |
| 7 | 2022 | 10 | |
| 8 | 2022 | 10 | |
| 9 | 2022 | 10 | |
| 10 | 2017 | 7 | |
| 11 | 2021 | 6 | |
| 12 | 2021 | 4 | |
| 13 | 2022 | 2 | |
| 14 | 2020 | 1 | |
| 15 | 2023 | 1 | |
| 16 | 2025 | 0 |
About Matej Gazda
Matej Gazda is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Physiology, Hepatology and Neurology, having authored 16 papers that have together received 376 indexed citations. Recurring topics across this work include Voice and Speech Disorders (3 papers), Medical Image Segmentation Techniques (2 papers), Data Mining Algorithms and Applications (2 papers), Face and Expression Recognition (2 papers), COVID-19 diagnosis using AI (2 papers), Advanced Neural Network Applications (2 papers), Liver Diseases and Immunity (2 papers) and Music and Audio Processing (2 papers). The work is most often cited by research in Health Informatics (7 citations), Physiology (114 citations), Neurology (56 citations), Hepatology (28 citations) and Artificial Intelligence (124 citations). Matej Gazda has collaborated with scholars based in Slovakia, United States and Canada. Frequent co-authors include Peter Drotár, Liberios Vokorokos, Jakub Gazda, Nemuel Daniel Pah, Mohammod Abdul Motin, Dinesh Kumar, Ján Plavka, K.K. Ang, L. J. Peters and Timothy E. Schultheiss. Their work appears in journals such as Journal of Personalized Medicine, IEEE Transactions on Systems Man and Cybernetics Systems, Digestive and Liver Disease, Frontiers in Neuroinformatics and Medical Image Analysis.
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.