José Ranilla

1.4k total citations
87 papers, 859 citations indexed

About

José Ranilla is a scholar working on Artificial Intelligence, Information Systems and Computational Theory and Mathematics. According to data from OpenAlex, José Ranilla has authored 87 papers receiving a total of 859 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Artificial Intelligence, 19 papers in Information Systems and 17 papers in Computational Theory and Mathematics. Recurrent topics in José Ranilla's work include Speech and Audio Processing (12 papers), Music and Audio Processing (10 papers) and Quantum Computing Algorithms and Architecture (9 papers). José Ranilla is often cited by papers focused on Speech and Audio Processing (12 papers), Music and Audio Processing (10 papers) and Quantum Computing Algorithms and Architecture (9 papers). José Ranilla collaborates with scholars based in Spain, United States and Poland. José Ranilla's co-authors include Luciano Sánchez, Elías F. Combarro, Jakub Nalepa, Pablo Ribalta Lorenzo, Irene Dı́az, Michał Kawulok, Elena Montañés, Pedro Alonso, J. Vigo‐Aguiar and Javier Fernández and has published in prestigious journals such as Trends in Food Science & Technology, IEEE Access and IEEE Transactions on Biomedical Engineering.

In The Last Decade

José Ranilla

79 papers receiving 818 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
José Ranilla Spain 13 384 163 120 118 108 87 859
Dominic Grenier Canada 12 621 1.6× 121 0.7× 112 0.9× 128 1.1× 255 2.4× 54 1.3k
Yong Xiao China 18 247 0.6× 104 0.6× 79 0.7× 303 2.6× 22 0.2× 83 823
Cong Wang China 18 420 1.1× 180 1.1× 159 1.3× 227 1.9× 28 0.3× 99 894
Shubham Mahajan India 17 242 0.6× 50 0.3× 128 1.1× 136 1.2× 73 0.7× 88 851
Gongde Guo China 19 604 1.6× 84 0.5× 151 1.3× 27 0.2× 54 0.5× 81 852
Antonio Mucherino France 17 383 1.0× 47 0.3× 146 1.2× 120 1.0× 320 3.0× 55 1.1k
Lei Ju China 19 472 1.2× 155 1.0× 66 0.6× 740 6.3× 86 0.8× 127 1.5k
Om Prakash Verma India 23 617 1.6× 464 2.8× 1.1k 8.8× 162 1.4× 103 1.0× 125 1.9k
Rik Sarkar United Kingdom 16 342 0.9× 61 0.4× 133 1.1× 383 3.2× 82 0.8× 33 1.1k
Cong Liu China 16 704 1.8× 94 0.6× 296 2.5× 50 0.4× 17 0.2× 58 1.2k

Countries citing papers authored by José Ranilla

Since Specialization
Citations

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

Fields of papers citing papers by José Ranilla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of José Ranilla

This figure shows the co-authorship network connecting the top 25 collaborators of José Ranilla. A scholar is included among the top collaborators of José Ranilla 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 José Ranilla. José Ranilla 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.
Alonso, Pedro, et al.. (2025). The evolution of high-performance computing: how AI and quantum computing are reshaping supercomputing. The Journal of Supercomputing. 81(5). 1 indexed citations
2.
Cañadas-Quesada, F.J., et al.. (2024). Noise-tolerant NMF-based parallel algorithm for respiratory rate estimation. The Journal of Supercomputing. 80(19). 26922–26941. 1 indexed citations
3.
Combarro, Elías F., Raúl Pérez‐Fernández, José Ranilla, & Bernard De Baets. (2023). Solving the Kemeny ranking aggregation problem with quantum optimization algorithms. Mathematical Methods in the Applied Sciences. 46(16). 17065–17081.
4.
Cañadas-Quesada, F.J., et al.. (2023). Cochleogram-based adventitious sounds classification using convolutional neural networks. Biomedical Signal Processing and Control. 82. 104555–104555. 19 indexed citations
5.
Cañadas-Quesada, F.J., N. Ruiz-Reyes, P. Vera‐Candeas, et al.. (2023). Detection of valvular heart diseases combining orthogonal non-negative matrix factorization and convolutional neural networks in PCG signals. Journal of Biomedical Informatics. 145. 104475–104475. 5 indexed citations
6.
Vera‐Candeas, P., et al.. (2023). The music demixing machine: toward real-time remixing of classical music. The Journal of Supercomputing. 79(13). 14342–14357.
7.
Múñiz, Rubén, et al.. (2022). A system for biomedical audio signal processing based on high performance computing techniques. Integrated Computer-Aided Engineering. 30(1). 1–18. 5 indexed citations
8.
Combarro, Elías F., et al.. (2020). Quantum abstract detecting systems. Quantum Information Processing. 19(8). 2 indexed citations
9.
Sánchez, Luciano, et al.. (2018). Improving the energy efficiency of virtual data centers in an IT service provider through proactive fuzzy rules-based multicriteria decision making. The Journal of Supercomputing. 75(3). 1078–1093. 6 indexed citations
10.
Sánchez, Luciano, et al.. (2015). Leveraging a predictive model of the workload for intelligent slot allocation schemes in energy-efficient HPC clusters. Engineering Applications of Artificial Intelligence. 48. 95–105. 10 indexed citations
11.
Baumes, Laurent A. & José Ranilla. (2013). A Study on Factors Affecting the Reproducibility of a Chemical Tongue Analysis Responding to Amino Acids. Combinatorial Chemistry & High Throughput Screening. 16(7). 572–583. 2 indexed citations
12.
López‐Fernández, Jesús A., et al.. (2012). Parallelization of the FMM on distributed-memory GPGPU systems for acoustic-scattering prediction. The Journal of Supercomputing. 64(1). 17–27. 6 indexed citations
13.
Alonso, Pedro, et al.. (2012). A multicore solution to Block–Toeplitz linear systems of equations. The Journal of Supercomputing. 65(3). 999–1009. 2 indexed citations
14.
Dı́az, Irene, Elena Montañés, José Ranilla, & Montserrat Espuña‐Pons. (2010). A framework for diagnosis of urinary incontinence disease based on scoring measures and automatic classifiers. Computers in Biology and Medicine. 41(1). 11–17. 1 indexed citations
15.
Montañés, Elena, José Ramón Quevedo, Irene Dı́az, & José Ranilla. (2009). Collaborative tag recommendation system based on logistic regression. 173–188. 6 indexed citations
16.
Combarro, Elías F., et al.. (2009). Classification of semifields of order 64. Journal of Algebra. 322(11). 4011–4029. 20 indexed citations
17.
Alonso, Pedro, et al.. (2004). Neville elimination: a study of the efficiency using checkerboard partitioning. Linear Algebra and its Applications. 393. 3–14. 6 indexed citations
18.
Ranilla, José, Oscar Luaces, & A. Bahamonde. (2003). A heuristic for learning decision trees and pruning them into classification rules. AI Communications. 16(2). 71–87. 6 indexed citations
19.
Alonso, Pedro, et al.. (2001). A study of the performance of Neville elimination using two kinds of partitioning techniques. Linear Algebra and its Applications. 332-334. 111–117. 4 indexed citations
20.
Ranilla, José. (1998). Nivel de Impureza de una regla de clasificación aprendida a partir de ejemplos,El. 37. 1 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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