Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Distances between intuitionistic fuzzy sets
20001.2k citationsEulalia Szmidt, Janusz Kacprzykprofile →
Entropy for intuitionistic fuzzy sets
2001676 citationsEulalia Szmidt, Janusz Kacprzykprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Eulalia Szmidt
Since
Specialization
Citations
This map shows the geographic impact of Eulalia Szmidt'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 Eulalia Szmidt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eulalia Szmidt more than expected).
This network shows the impact of papers produced by Eulalia Szmidt. 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 Eulalia Szmidt. The network helps show where Eulalia Szmidt may publish in the future.
Co-authorship network of co-authors of Eulalia Szmidt
This figure shows the co-authorship network connecting the top 25 collaborators of Eulalia Szmidt.
A scholar is included among the top collaborators of Eulalia Szmidt 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 Eulalia Szmidt. Eulalia Szmidt is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kacprzyk, Janusz, et al.. (2018). A Heuristic Algorithm of Possibilistic Clustering with Partial Supervision for Classification of the Intuitionistic Fuzzy Data. 31. 399–423.
Szmidt, Eulalia & Janusz Kacprzyk. (2009). Analysis of Similarity Measures for Atanassov's Intuitionistic Fuzzy Sets.. European Society for Fuzzy Logic and Technology Conference. 1416–1421.22 indexed citations
Szmidt, Eulalia, et al.. (2004). Entropy for Intuitionistic Fuzzy, Set Theory and Mass Assignment Theory.26 indexed citations
16.
Szmidt, Eulalia & Janusz Kacprzyk. (2003). A measure of similarity for intuitionistic fuzzy sets.. European Society for Fuzzy Logic and Technology Conference. 206–209.3 indexed citations
17.
Szmidt, Eulalia & Janusz Kacprzyk. (2002). Using intuitionistic fuzzy sets in group decision making. Control and Cybernetics. 31(4). 1055–1057.231 indexed citations
18.
Szmidt, Eulalia & Janusz Kacprzyk. (2001). Analysis of consensus under intuitionistic fuzzy preferences.. European Society for Fuzzy Logic and Technology Conference. 79–82.28 indexed citations
Szmidt, Eulalia & Janusz Kacprzyk. (1998). Group decision making under intuitionistic fuzzy preference relations. 172–178.66 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
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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.