Marián Mach

34 papers receiving 187 citations

Peers

Marián Mach
Comparison fields: 5 of 66
  • Artificial Intelligence 89
  • Computer Vision and Pattern Recognition 38
  • Information Systems 36
  • Computer Networks and Communications 30
  • Sociology and Political Science 22
Replace Ahmad Zainul Fanani with:
Ahmad Zainul Fanani Indonesia
Tahani Jaser Alahmadi Saudi Arabia
Daniel Hládek Slovakia
K. Anandakumar India
Anil Audumbar Pise South Africa
Sebastiano Pizzutilo Italy
Elliott Wen New Zealand
Sattam Almatarneh Jordan
Oleksiy Khriyenko Finland
Karol Kurach United States
Marián Mach relative to Ahmad Zainul Fanani Indonesia Ahmad Zainul Fanani's profile →
Citations per field
00.5×1.5×1.8×
Ahmad Zainul Fanani · 1×
Citations per year

Countries citing papers authored by Marián Mach

Since Specialization
Citations

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

Fields of papers citing papers by Marián Mach

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marián Mach

This figure shows the co-authorship network connecting the top 25 collaborators of Marián Mach. A scholar is included among the top collaborators of Marián Mach 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 Marián Mach. Marián Mach 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 1
2 0
3 4
4 1
5 0
6 1
7 6
8 16
9 0
10 53
11 5
12 5
13 6
14 2
15
USE OF E-PARTICIPATION TOOLS FOR SUPPORT OF POLICY MODELLING AT REGIONAL LEVEL
1
16 2
17 1
18 5
19
A KNOWLEDGE-BASED SYSTEM FOR SUPPORT OF PUBLIC ADMINISTRATION
1
20
Support of Knowledge Management in Distributed Environment
1

About Marián Mach

Marián Mach is a scholar working on Artificial Intelligence, Management Information Systems and Information Systems, having authored 39 papers that have together received 194 indexed citations. Recurring topics across this work include Service-Oriented Architecture and Web Services (6 papers), Spam and Phishing Detection (5 papers) and Business Process Modeling and Analysis (5 papers). The work is most often cited by research in Artificial Intelligence (89 citations), Computer Vision and Pattern Recognition (38 citations) and Human-Computer Interaction (10 citations). Marián Mach has collaborated with scholars based in Slovakia, Germany and Chile. Frequent co-authors include Kristína Machová, Peter Sinčák, Xiaoying Gao, Tomáš Sabol, Evangelos Papapetrou, Jeroen Famaey, Albert Cabellos‐Aparicio, Christoph Grimm, Maria Torres Vega and Kaori Yoshida. Their work appears in journals such as Sensors, Journal of the Association for Information Systems and Toxicology Letters.

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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