Oscar Luaces

1.2k total citations
40 papers, 713 citations indexed

About

Oscar Luaces is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Oscar Luaces has authored 40 papers receiving a total of 713 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 14 papers in Information Systems and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Oscar Luaces's work include Sensory Analysis and Statistical Methods (5 papers), Recommender Systems and Techniques (5 papers) and Image Retrieval and Classification Techniques (5 papers). Oscar Luaces is often cited by papers focused on Sensory Analysis and Statistical Methods (5 papers), Recommender Systems and Techniques (5 papers) and Image Retrieval and Classification Techniques (5 papers). Oscar Luaces collaborates with scholars based in Spain, United States and China. Oscar Luaces's co-authors include Antonio Bahamonde, Jorge Díez, Juan José del Coz, José Ramón Quevedo, José Barranquero Tolosa, Jaime Alonso, F. Goyache, José Ranilla, Zhezhou Yu and Shuchao Pang and has published in prestigious journals such as Trends in Food Science & Technology, Expert Systems with Applications and Computers & Education.

In The Last Decade

Oscar Luaces

38 papers receiving 680 citations

Peers

Oscar Luaces

CVPR

Antonio Bahamonde Spain

José Ramón Quevedo Spain

Yun Xu China

Minghui Li China

Jorge Eduardo Pérez Pérez Chile

Xiaoyan Zhang China

Mohammed Hassan Nigeria

James R. Foulds United States

Parteek Kumar India

Ali Ahmadi Iran

Antonio Bahamonde Spain

Oscar Luaces

380 ×1.2

165 ×1.0

130 ×0.9

CVPR

74 ×1.1

72 ×1.3

Citations per year, relative to Oscar Luaces Oscar Luaces (= 1×) peers Antonio Bahamonde

Countries citing papers authored by Oscar Luaces

Since Specialization

Citations

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

Fields of papers citing papers by Oscar Luaces

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Oscar Luaces

This figure shows the co-authorship network connecting the top 25 collaborators of Oscar Luaces. A scholar is included among the top collaborators of Oscar Luaces 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 Oscar Luaces. Oscar Luaces is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Díez, Jorge, et al.. (2023). Users' photos of items can reveal their tastes in a recommender system. Information Sciences. 642. 119227–119227. 4 indexed citations

Díez, Jorge, et al.. (2023). All-in-one picture: visual summary of items in a recommender system. Neural Computing and Applications. 35(27). 20339–20349.

Lira, Madalena, et al.. (2022). Eye-LRCN: A Long-Term Recurrent Convolutional Network for Eye Blink Completeness Detection. IEEE Transactions on Neural Networks and Learning Systems. 35(4). 5130–5140. 11 indexed citations

Díez, Jorge, et al.. (2020). Towards explainable personalized recommendations by learning from users’ photos. Information Sciences. 520. 416–430. 22 indexed citations

Luaces, Oscar, Jorge Díez, & Antonio Bahamonde. (2018). A peer assessment method to provide feedback, consistent grading and reduce students' burden in massive teaching settings. Computers & Education. 126. 283–295. 27 indexed citations

Díez, Jorge, David Martínez‐Rego, Amparo Alonso‐Betanzos, Oscar Luaces, & Antonio Bahamonde. (2018). Optimizing novelty and diversity in recommendations. Progress in Artificial Intelligence. 8(1). 101–109. 13 indexed citations

deAndrés‐Galiana, Enrique J., Juan Luis Fernández‐Martínez, Oscar Luaces, et al.. (2016). Analysis of clinical prognostic variables for Chronic Lymphocytic Leukemia decision-making problems. Journal of Biomedical Informatics. 60. 342–351. 12 indexed citations

Luaces, Oscar, Jorge Díez, José Barranquero Tolosa, Juan José del Coz, & Antonio Bahamonde. (2012). Binary relevance efficacy for multilabel classification. Progress in Artificial Intelligence. 1(4). 303–313. 152 indexed citations

Quevedo, José Ramón, Antonio Bahamonde, Miguel Pérez‐Enciso, & Oscar Luaces. (2011). Disease Liability Prediction from Large Scale Genotyping Data Using Classifiers with a Reject Option. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9(1). 88–97. 6 indexed citations

10.

Luaces, Oscar, José Ramón Quevedo, Miguel Pérez‐Enciso, et al.. (2010). Explaining the Genetic Basis of Complex Quantitative Traits through Prediction Models. Journal of Computational Biology. 17(12). 1711–1723. 1 indexed citations

11.

Luaces, Oscar, et al.. (2009). Predicting the probability of survival in intensive care unit patients from a small number of variables and training examples. Artificial Intelligence in Medicine. 45(1). 63–76. 12 indexed citations

12.

Luaces, Oscar, José Ramón Quevedo, Francisco Taboada, Guillermo M. Albaiceta, & Antonio Bahamonde. (2007). Prediction of probability of survival in critically ill patients optimizing the area under the ROC curve. International Joint Conference on Artificial Intelligence. 956–961. 5 indexed citations

13.

Coz, Juan José del, Gustavo F. Bayón, Jorge Díez, et al.. (2004). Trait Selection for Assessing Beef Meat Quality Using Non-linear SVM. Neural Information Processing Systems. 17. 321–328. 13 indexed citations

14.

Bahamonde, Antonio, Gustavo F. Bayón, Jorge Díez, et al.. (2004). Feature subset selection for learning preferences. Consultation of the Doctoral Thesis Database (TESEO) (Ministerio de Educación, Cultura y Deporte). 7–7. 33 indexed citations

15.

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

16.

Luaces, Oscar & Antonio Bahamonde. (2003). Inflating examples to obtain rules. International Journal of Intelligent Systems. 18(11). 1113–1143. 10 indexed citations

17.

Díez, Jorge, Antonio Bahamonde, Jaime Alonso, et al.. (2003). Artificial intelligence techniques point out differences in classification performance between light and standard bovine carcasses. Meat Science. 64(3). 249–258. 19 indexed citations

18.

Goyache, F., Juan José del Coz, José Ramón Quevedo, et al.. (2001). Using artificial intelligence to design and implement a morphological assessment system in beef cattle. Animal Science. 73(1). 49–60. 25 indexed citations

19.

Goyache, F., Antonio Bahamonde, Jaime Alonso, et al.. (2001). The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry. Trends in Food Science & Technology. 12(10). 370–381. 62 indexed citations

20.

Goyache, F., et al.. (2000). Un sistema inteligente para calificar morfológicamente a bovinos de la raza Asturiana de los Valles. INTELIGENCIA ARTIFICIAL. 4(10). 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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