Michal Munk
Impact in
-
- Online Learning and Analytics
- Information Systems top 2%
- Spam and Phishing Detection
- Data Mining Algorithms and Applications
- Blockchain Technology Applications and Security
Papers in
-
- Natural Language Processing Techniques 19
- Topic Modeling 18
- Text Readability and Simplification 11
-
- Data Mining Algorithms and Applications 18
- Recommender Systems and Techniques 10
- Web Data Mining and Analysis 10
- Co-authors
- Petr Hájek (13 shared papers)Jozef Kapusta (25 shared papers)Martin Drlík (23 shared papers)Aliaksandr Barushka (3 shared papers)Alena Hašková (15 shared papers)Anna Pilková (20 shared papers)Md. Shahriare Satu (1 shared paper)Md. Jahidul Islam (1 shared paper)
In The Last Decade
Michal Munk
120 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 115
- Computer Science Applications 144
- Information Systems 449
- Artificial Intelligence 509
- Developmental Biology 24
- Signal Processing 74
Countries citing papers authored by Michal Munk
This map shows the geographic impact of Michal Munk'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 Michal Munk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michal Munk more than expected).
Fields of papers citing papers by Michal Munk
This network shows the impact of papers produced by Michal Munk. 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 Michal Munk. The network helps show where Michal Munk may publish in the future.
Co-authors
The 25 scholars most cited alongside Michal Munk, 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 136 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 129 | |
| 2 | 2010 | 84 | |
| 3 | 2022 | 80 | |
| 4 | 2013 | 51 | |
| 5 | 2011 | 37 | |
| 6 | 2018 | 31 | |
| 7 | 2014 | 28 | |
| 8 | 2023 | 27 | |
| 9 | 2013 | 27 | |
| 10 | Data advance preparation factors affecting results of sequence rule analysis in web log mining | 2010 | 23 |
| 11 | 2011 | 22 | |
| 12 | 2021 | 20 | |
| 13 | 2020 | 20 | |
| 14 | 2024 | 19 | |
| 15 | 2021 | 17 | |
| 16 | 2010 | 17 | |
| 17 | 2017 | 16 | |
| 18 | 2011 | 16 | |
| 19 | 2013 | 15 | |
| 20 | 2012 | 14 |
About Michal Munk
Michal Munk is a scholar working on Artificial Intelligence, Information Systems, Education, Computer Science Applications and Computer Networks and Communications, having authored 136 papers that have together received 1.2k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (19 papers), Topic Modeling (18 papers), Data Mining Algorithms and Applications (18 papers), Online Learning and Analytics (13 papers), Text Readability and Simplification (11 papers), Recommender Systems and Techniques (10 papers), Web Data Mining and Analysis (10 papers) and Online and Blended Learning (8 papers). The work is most often cited by research in Computer Science Applications (144 citations), Information Systems (449 citations), Artificial Intelligence (509 citations), Developmental Biology (24 citations) and Signal Processing (74 citations). Michal Munk has collaborated with scholars based in Slovakia, Czechia and Poland. Frequent co-authors include Petr Hájek, Jozef Kapusta, Martin Drlík, Aliaksandr Barushka, Alena Hašková, Anna Pilková, Md. Shahriare Satu, Md. Jahidul Islam, Mohammad Zoynul Abedin and Jozef Hvorecký. Their work appears in journals such as IEEE Access, Informatics in Education, Neural Computing and Applications, Sustainability and Scientific Reports.
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.