Neil Mac Parthaláin

1.4k total citations
39 papers, 950 citations indexed

About

Neil Mac Parthaláin is a scholar working on Computational Theory and Mathematics, Information Systems and Artificial Intelligence. According to data from OpenAlex, Neil Mac Parthaláin has authored 39 papers receiving a total of 950 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Computational Theory and Mathematics, 21 papers in Information Systems and 16 papers in Artificial Intelligence. Recurrent topics in Neil Mac Parthaláin's work include Rough Sets and Fuzzy Logic (28 papers), Data Mining Algorithms and Applications (21 papers) and Fuzzy Logic and Control Systems (6 papers). Neil Mac Parthaláin is often cited by papers focused on Rough Sets and Fuzzy Logic (28 papers), Data Mining Algorithms and Applications (21 papers) and Fuzzy Logic and Control Systems (6 papers). Neil Mac Parthaláin collaborates with scholars based in United Kingdom, China and Spain. Neil Mac Parthaláin's co-authors include Richard Jensen, Qiang Shen, Reyer Zwiggelaar, P. D. Jones, Keir Lewis, Yasir Syed, Chris Phillips, Tim Claypole, Yanpeng Qu and Changjing Shang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Dairy Science and Pattern Recognition.

In The Last Decade

Neil Mac Parthaláin

38 papers receiving 912 citations

Peers

Neil Mac Parthaláin
Comparison fields: 5 of 105
  • Computational Theory and Mathematics 433
  • Artificial Intelligence 407
  • Information Systems 287
  • Computer Vision and Pattern Recognition 175
  • Management Science and Operations Research 119
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Citations per field, relative to Neil Mac Parthaláin
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Citations per year, relative to Neil Mac Parthaláin
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Countries citing papers authored by Neil Mac Parthaláin

Since Specialization
Citations

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

Fields of papers citing papers by Neil Mac Parthaláin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Neil Mac Parthaláin. 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 Neil Mac Parthaláin. The network helps show where Neil Mac Parthaláin may publish in the future.

Co-authorship network of co-authors of Neil Mac Parthaláin

This figure shows the co-authorship network connecting the top 25 collaborators of Neil Mac Parthaláin. A scholar is included among the top collaborators of Neil Mac Parthaláin 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 Neil Mac Parthaláin. Neil Mac Parthaláin 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
# Work Indexed citations
1 0
2 4
3 91
4 24
5 23
6 35
7 56
8 42
9 1
10 4
11 13
12 30
13 1
14 19
15 75
16 11
17 3
18 8
19 105
20
Rough and Fuzzy-rough methods for mammographic data analysis
1

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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