Peter Malík

28 papers receiving 619 citations

Peter Malík's Hit Papers

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey 2019 · 534 citations
5340+2+4Years since publication100200300400500

Peers

Peter Malík
Comparison fields: 5 of 125
  • Health Informatics 11
  • Signal Processing 68
  • Artificial Intelligence 195
  • Hardware and Architecture 36
  • Computer Vision and Pattern Recognition 92
Replace Viet Tran with:
Viet Tran Slovakia
Martin Bobák Slovakia
Štefan Dlugolinský Slovakia
Ignacio Heredia Spain
Maria Presa Reyes United States
Giang Nguyen Slovakia
Tai-hoon Kim South Korea
Stefan Rüping Germany
Suhel Hammoud United Kingdom
Yilin Yan United States
Peter Malík relative to Viet Tran Slovakia Viet Tran's profile →
Citations per field
00.5×1.5×
Viet Tran · 1×
Citations per year

Countries citing papers authored by Peter Malík

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

Border = papers with Peter Malík Line = papers co-authored together Peter Malík links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 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 →
2019534
2 201520
3 202314
4 201514
5 20249
6 20209
7 20196
8 20064
9 19843
10 20133
11 20073
12 20093
13 20072
14 20142
15 20072
16 20182
17 20112
18 20212
19 20201
20 20211

About Peter Malík

Peter Malík is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Hardware and Architecture, Artificial Intelligence and Computer Networks and Communications, having authored 30 papers that have together received 644 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 (11 citations), Signal Processing (68 citations), Artificial Intelligence (195 citations), Hardware and Architecture (36 citations) and Computer Vision and Pattern Recognition (92 citations). Peter Malík has collaborated with scholars based in Slovakia, Canada and Poland. Frequent co-authors include Giang Nguyen, Ladislav Hluchý, Ignacio Heredia, Štefan Dlugolinský, Martin Bobák, Viet Tran, Álvaro López García, Andrej Halabuk, Witold A. Pleskacz and Viera Stopjaková. Their work appears in journals such as International Journal of Control, Remote Sensing, Microelectronics Reliability, Sensors and Artificial Intelligence Review.

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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