Juan Luis Castro

3.2k total citations
87 papers, 2.1k citations indexed

About

Juan Luis Castro is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Juan Luis Castro has authored 87 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 65 papers in Artificial Intelligence, 16 papers in Computational Theory and Mathematics and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Juan Luis Castro's work include Fuzzy Logic and Control Systems (26 papers), Neural Networks and Applications (18 papers) and Rough Sets and Fuzzy Logic (11 papers). Juan Luis Castro is often cited by papers focused on Fuzzy Logic and Control Systems (26 papers), Neural Networks and Applications (18 papers) and Rough Sets and Fuzzy Logic (11 papers). Juan Luis Castro collaborates with scholars based in Spain, France and Saudi Arabia. Juan Luis Castro's co-authors include José M. Benítez, J.M. Zurita, Ignacio Requena, Miguel Delgado‐Rodríguez, Frank Klawonn, Carlos J. Mantas, Antonio Araúzo-Azofra, Alejandro Moreo, J.J. Castro-Schez and Margarita Sánchez Romero and has published in prestigious journals such as Expert Systems with Applications, Information Sciences and IEEE Transactions on Fuzzy Systems.

In The Last Decade

Juan Luis Castro

79 papers receiving 2.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Juan Luis Castro Spain 19 1.4k 409 347 323 193 87 2.1k
Detlef Nauck United Kingdom 20 1.5k 1.1× 320 0.8× 260 0.7× 227 0.7× 163 0.8× 66 2.3k
László T. Kóczy Hungary 21 1.7k 1.3× 463 1.1× 508 1.5× 621 1.9× 303 1.6× 262 2.5k
Rafael Alcalá Spain 25 2.3k 1.7× 308 0.8× 515 1.5× 384 1.2× 247 1.3× 71 2.9k
Thomas A. Runkler Germany 24 1.4k 1.0× 307 0.8× 236 0.7× 339 1.0× 203 1.1× 132 2.6k
Alireza Sadeghian Canada 24 693 0.5× 624 1.5× 264 0.8× 230 0.7× 88 0.5× 135 2.1k
Marek Reformat Canada 23 1.2k 0.9× 240 0.6× 187 0.5× 474 1.5× 100 0.5× 152 2.2k
Xinyang Deng China 32 1.2k 0.9× 472 1.2× 515 1.5× 1.4k 4.2× 288 1.5× 108 3.1k
Mahardhika Pratama Singapore 30 2.0k 1.5× 581 1.4× 107 0.3× 256 0.8× 144 0.7× 157 3.0k
Tadahiko Murata Japan 20 2.2k 1.6× 586 1.4× 1.3k 3.8× 372 1.2× 131 0.7× 108 4.3k
Kagan Tumer United States 25 1.4k 1.1× 285 0.7× 174 0.5× 379 1.2× 35 0.2× 126 2.5k

Countries citing papers authored by Juan Luis Castro

Since Specialization
Citations

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

Fields of papers citing papers by Juan Luis Castro

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Juan Luis Castro. 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 Juan Luis Castro. The network helps show where Juan Luis Castro may publish in the future.

Co-authorship network of co-authors of Juan Luis Castro

This figure shows the co-authorship network connecting the top 25 collaborators of Juan Luis Castro. A scholar is included among the top collaborators of Juan Luis Castro 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 Juan Luis Castro. Juan Luis Castro 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
1.
Castro, Juan Luis, et al.. (2023). A Methodology to Quickly Perform Opinion Mining and Build Supervised Datasets Using Social Networks Mechanics. IEEE Transactions on Knowledge and Data Engineering. 35(9). 9797–9808. 1 indexed citations
2.
Castro, Juan Luis, et al.. (2019). Snomed2Vec: Representation of SNOMED CT Terms with Word2Vec. Institutional Repository of the University of Granada (University of Granada). 678–683. 9 indexed citations
3.
Castro, Juan Luis, et al.. (2017). DNER Clinical (Named Entity Recognition) from Free Clinical Text to Snomed-CT Concept. WSEAS Transactions on Computers archive. 16. 7 indexed citations
4.
Castro, Juan Luis, et al.. (2015). Visualization models for Virtual Learning Environments. 16–21. 3 indexed citations
5.
Romero, Margarita Sánchez, Alejandro Moreo, Juan Luis Castro, & J.M. Zurita. (2012). Using Wikipedia concepts and frequency in language to extract key terms from support documents. Expert Systems with Applications. 39(18). 13480–13491. 8 indexed citations
6.
Castro, Juan Luis, et al.. (2011). Intelligent surveillance system with integration of heterogeneous information for intrusion detection. Expert Systems with Applications. 38(9). 11182–11192. 31 indexed citations
7.
Castro, Juan Luis, et al.. (2006). Similarity local adjustment: Introducing attribute risk into the case.
8.
Castro-Schez, J.J., Juan Luis Castro, & J.M. Zurita. (2004). Fuzzy Repertory Table: A Method for Acquiring Knowledge About Input Variables to Machine Learning Algorithm. IEEE Transactions on Fuzzy Systems. 12(1). 123–139. 18 indexed citations
9.
Araúzo-Azofra, Antonio & Juan Luis Castro. (2004). A feature set measure based on Relief. 38 indexed citations
10.
Castro, Juan Luis, et al.. (2001). La función de los mitos fundacionales en la promoción de una identidad disciplinar para la psicología. Revista de historia de la psicología. 22(3). 297–310. 3 indexed citations
11.
Castro, Juan Luis, et al.. (2001). Similarity relations based on distances as fuzzy concepts.. European Society for Fuzzy Logic and Technology Conference. 187–190. 1 indexed citations
12.
Castro, Juan Luis, Miguel Delgado, & Carlos J. Mantas. (2000). SEPARATE: a machine learning method based on semi-global partitions. IEEE Transactions on Neural Networks. 11(3). 710–720. 7 indexed citations
13.
Castro, Juan Luis, J.J. Castro-Schez, & J.M. Zurita. (1999). Learning maximal structure rules in fuzzy logic for knowledge acquisition in expert systems. Fuzzy Sets and Systems. 101(3). 331–342. 59 indexed citations
14.
Castro, Juan Luis & J.M. Zurita. (1998). A heuristic in rules based systems for searching of inconsistencies. Information Sciences. 108(1-4). 135–148. 2 indexed citations
15.
Castro, Juan Luis & Miguel Delgado‐Rodríguez. (1996). Fuzzy systems with defuzzification are universal approximators. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 26(1). 149–152. 160 indexed citations
16.
Castro, Juan Luis & Enric Trillas. (1994). CONSTRAINTS AS INCOMPATIBILITY RELATIONS IN KBS. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems. 2(1). 121–126. 1 indexed citations
17.
Castro, Juan Luis & Enric Trillas. (1993). The management of the inconsistency in expert systems. Fuzzy Sets and Systems. 58(1). 51–57. 2 indexed citations
18.
Castro, Juan Luis. (1991). Contribución al estudio de modelos lógicos para la inteligencia artificial. Dialnet (Universidad de la Rioja). 3 indexed citations
19.
Jiménez‐Sáenz, Manuel, José L. Venero, Juan Luis Castro, J M Herrerías, & Marcelo Garrido. (1990). Hepatic Hydrothorax without Ascites: A Rare Form of a Common Complication. Journal of the Royal Society of Medicine. 83(11). 747–748. 8 indexed citations
20.
Castro, Juan Luis, et al.. (1989). Sobre preórdenes y operadores de consecuencias de Tarski. 4(11). 419–425. 2 indexed citations

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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