Milan Čermák

688 citations
20 papers · 392 indexed · h-index 8
Topics
Network Security and Intrusion Detection (13 papers)Internet Traffic Analysis and Secure E-voting (7 papers)Anomaly Detection Techniques and Applications (3 papers)
Partner nations
Czechia

In The Last Decade

Milan Čermák

17 papers receiving 374 citations

Peers

Milan Čermák
Comparison fields: 5 of 31
  • Computer Networks and Communications 344
  • Artificial Intelligence 331
  • Signal Processing 158
  • Information Systems 80
  • Computer Vision and Pattern Recognition 51
Replace Thijs van Ede with:
Thijs van Ede Netherlands
Petr Velan Czechia
Zigang Cao China
Riccardo Bortolameotti Netherlands
Tal Shapira Israel
Z. Muda Malaysia
Martin Drašar Czechia
Fabian Lanze Luxembourg
Kevin P. Dyer United States
Gözde Karataş Türkiye
Milan Čermák relative to Thijs van Ede Netherlands Thijs van Ede's profile →
Citations per field
00.5×10×14.5×
Thijs van Ede · 1×
Citations per year

Countries citing papers authored by Milan Čermák

Since Specialization
Citations

This map shows the geographic impact of Milan Čermák'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 Milan Čermák with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Milan Čermák more than expected).

Fields of papers citing papers by Milan Čermák

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Milan Čermák. 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 Milan Čermák. The network helps show where Milan Čermák may publish in the future.

Co-authorship network of co-authors of Milan Čermák

This figure shows the co-authorship network connecting the top 25 collaborators of Milan Čermák. A scholar is included among the top collaborators of Milan Čermák based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Milan Čermák. Milan Čermák is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 2
2 11
3 1
4 0
5
Real-time Pattern Detection in IP Flow Data using Apache Spark
1
6
Využití indocyaninové zeleně při robotických výkonech v urologii
2
7 1
8 6
9 7
10 13
11 16
12
A Performance Benchmark of NetFlow Data Analysis on Distributed Stream Processing Systems
13
13
Selektivní klamping při roboticky asistované resekci ledviny
3
14 0
15 53
16 1
17 8
18 1
19 239
20 14

About Milan Čermák

Milan Čermák is a scholar working on Computer Networks and Communications, Artificial Intelligence and Signal Processing, having authored 20 papers that have together received 392 indexed citations. Recurring topics across this work include Network Security and Intrusion Detection (13 papers), Internet Traffic Analysis and Secure E-voting (7 papers) and Anomaly Detection Techniques and Applications (3 papers). The work is most often cited by research in Computer Networks and Communications (344 citations), Signal Processing (158 citations) and Artificial Intelligence (331 citations). Milan Čermák has collaborated with scholars based in Czechia. Frequent co-authors include Pavel Čeleda, Martin Drašar, Petr Velan, Martin Husák, Tomáš Jirsík, Daniel Tovarňák, Jan Vykopal and Jiří Heráček. Their work appears in journals such as IEEE Communications Magazine, Immunotechnology and International Journal of Network Management.

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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