Daniel Ruiz-Aguilera

1.6k citations
46 papers · 1.0k indexed · h-index 18

Daniel Ruiz-Aguilera

43 papers receiving 972 citations

Peers

Daniel Ruiz-Aguilera
Comparison fields: 5 of 52
  • Management Science and Operations Research 772
  • Computational Theory and Mathematics 722
  • Statistics and Probability 232
  • Numerical Analysis 46
  • Artificial Intelligence 254
Replace M. Mas with:
M. Mas Spain
M. Monserrat Spain
Siegfried Weber Germany
Junsheng Qiao China
Ana Pradera Spain
Debdas Ghosh India
Michał Baczyński Poland
Raúl Orduna-Urrutia Spain
Balasubramaniam Jayaram India
Ming-Hu Ha China
Daniel Ruiz-Aguilera relative to M. Mas Spain M. Mas's profile →
Citations per field
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M. Mas · 1×
Citations per year

Countries citing papers authored by Daniel Ruiz-Aguilera

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Ruiz-Aguilera

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Ruiz-Aguilera. 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 Daniel Ruiz-Aguilera. The network helps show where Daniel Ruiz-Aguilera may publish in the future.

Co-authorship network

The 16 scholars most cited alongside Daniel Ruiz-Aguilera, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Ruiz-Aguilera Line = papers co-authored together Daniel Ruiz-Aguilera links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20214
2 201816
3 20185
4 20186
5 20189
6 201714
7 201720
8 201614
9 20162
10 20163
11 201558
12 20159
13 20151
14 201512
15 20153
16 201417
17 201471
18
Distributivity of strong implications over conjunctive and disjunctive uninorms.
200646
19
Distributive residual implications from uninorms
20051
20
Residual implications and co-implications from idempotent uninorms
200463

About Daniel Ruiz-Aguilera

Daniel Ruiz-Aguilera is a scholar working on Management Science and Operations Research, Computational Theory and Mathematics and Statistics and Probability, having authored 46 papers that have together received 1.0k indexed citations. Recurring topics across this work include Multi-Criteria Decision Making (29 papers), Advanced Algebra and Logic (20 papers), Rough Sets and Fuzzy Logic (18 papers), Fuzzy Logic and Control Systems (9 papers), Fuzzy Systems and Optimization (7 papers), Image and Signal Denoising Methods (4 papers), Medical Image Segmentation Techniques (4 papers) and Advanced Optimization Algorithms Research (4 papers). The work is most often cited by research in Management Science and Operations Research (772 citations), Computational Theory and Mathematics (722 citations) and Statistics and Probability (232 citations). Daniel Ruiz-Aguilera has collaborated with scholars based in Spain, China and United States. Frequent co-authors include Joan Torrens, Sebastià Massanet, M. Mas, M. Monserrat, Manuel González-Hidalgo, Arnau Mir, Juan Vicente Riera, Yong Su, Paweł Drygaś and Huawen Liu. Their work appears in journals such as Fuzzy Sets and Systems, IEEE Transactions on Fuzzy Systems, Information Sciences, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems and Kybernetika.

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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