Peter Malík
Impact in
- Health Informatics top 10%
- Artificial Intelligence top 10%
- Anomaly Detection Techniques and Applications
Papers in
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- Advanced Data Compression Techniques 7
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- Digital Filter Design and Implementation 11
- Co-authors
- Viet Tran (1 shared paper)Ladislav Hluchý (1 shared paper)Martin Bobák (1 shared paper)Štefan Dlugolinský (1 shared paper)Giang Nguyen (1 shared paper)Álvaro López García (1 shared paper)Ignacio Heredia (1 shared paper)Andrej Halabuk (1 shared paper)
In The Last Decade
Peter Malík
29 papers receiving 686 citations
Peter Malík's Hit Papers
Peers
Comparison fields: 5 of 131
- Health Informatics 13
- Artificial Intelligence 233
- Signal Processing 75
- Hardware and Architecture 40
- Computer Vision and Pattern Recognition 106
Countries citing papers authored by Peter Malík
This map shows the geographic impact of Peter Malík'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 Peter Malík with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Malík more than expected).
Fields of papers citing papers by Peter Malík
This network shows the impact of papers produced by Peter Malík. 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 Peter Malík. The network helps show where Peter Malík may publish in the future.
Co-authors
The 12 scholars most cited alongside Peter Malík, 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 31 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey Hit paper breakdown → | 2019 | 598 |
| 2 | 2015 | 20 | |
| 3 | 2015 | 16 | |
| 4 | 2023 | 14 | |
| 5 | 2020 | 11 | |
| 6 | 2024 | 10 | |
| 7 | 2019 | 6 | |
| 8 | 1984 | 5 | |
| 9 | 2006 | 4 | |
| 10 | 2013 | 3 | |
| 11 | 2007 | 3 | |
| 12 | 2007 | 3 | |
| 13 | 2009 | 3 | |
| 14 | 2007 | 2 | |
| 15 | 2014 | 2 | |
| 16 | 2018 | 2 | |
| 17 | 2011 | 2 | |
| 18 | 2021 | 2 | |
| 19 | 2020 | 1 | |
| 20 | 2020 | 1 |
About Peter Malík
Peter Malík is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Hardware and Architecture, Computer Networks and Communications and Artificial Intelligence, having authored 31 papers that have together received 717 indexed citations. Recurring topics across this work include Digital Filter Design and Implementation (11 papers), Embedded Systems Design Techniques (7 papers), Advanced Data Compression Techniques (7 papers), Numerical Methods and Algorithms (5 papers), Parallel Computing and Optimization Techniques (3 papers), Artificial Intelligence in Games (2 papers), Digital Games and Media (2 papers) and Computational Physics and Python Applications (2 papers). The work is most often cited by research in Health Informatics (13 citations), Artificial Intelligence (233 citations), Signal Processing (75 citations), Hardware and Architecture (40 citations) and Computer Vision and Pattern Recognition (106 citations). Peter Malík has collaborated with scholars based in Slovakia, Canada and Poland. Frequent co-authors include Viet Tran, Ladislav Hluchý, Martin Bobák, Štefan Dlugolinský, Giang Nguyen, Álvaro López García, Ignacio Heredia, Andrej Halabuk, Hung‐Yin Tsai and Ivan Kuric. Their work appears in journals such as Artificial Intelligence Review, Remote Sensing, International Journal of Control, Computer Communications and Internet of things.
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.