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Publications

2024

  • Cuesta, M., Lancho, C., Fernández-Isabel, A., Cano, E. L., & Martín De Diego, I. (2024). CSViz: Class Separability Visualization for high-dimensional datasets. Applied Intelligence, 54(1), 924-946. Springer US. https://www.doi.org/10.1007/s10489-023-05149-4
  • Aceña, V., Lancho, C., Martín de Diego, I., Moguerza, J. M., & Lee, D.-J. (2024). Dynamic Disagreeing Neighbors: A complexity measure for instance selection.
  • Martín de Diego, I., Prieto, J. C., Fernández-Isabel, A., Gomez, J., & Alfaro, C. (2024). Framework for scoring the scientific reputation of researchers. Knowledge and Information Systems, 1-23. Springer London. https://www.doi.org/10.1007/s10115-024-02071-0
  • Navarro, J., Fernández, R. R., Aceña, V., Fernández-Isabel, A., Lancho, C., & Martín de Diego, I. (2024, April). Real-time classification of cattle behavior using Wireless Sensor Networks. Internet of Things, 25, 101008. Elsevier.https://doi.org/10.1016/j.iot.2023.101008

2023

  • Ortega, F., Algar, M. J., Martín de Diego, I., & Moguerza, J. M. (2023). Unconventional application of k-means for distributed approximate similarity search. Information Sciences, 619, 208-234. Elsevier.
  • Fernández-Isabel, A., Martín de Diego, I., Cuesta, M., Lancho, C., & Moguerza, J.M. (2023). DSGAME KIDS: Learning Data Science Projects Through a Storytelling Board Game. In INTED2023 Proceedings (pp. 908-917). IATED.
  • López Cano, E., Cuesta, M., Lancho, C., Alfaro, C., Algar, M.J., Alonso-Ayuso, A., Fernández-Isabel, A., Gomez, J., Martin de Diego, I., Moguerza, J., Ortega, F., & Udias, A. (2023). DSEXAMS: Massive and Automated Generation of Randomized Multipurpose Questionnaires. In EDULEARN23 Proceedings (pp. 1620-1627). IATED.
  • Cuesta, M., Lancho, C., Martín de Diego, I., Fernández-Isabel, A., Cano, E. L., & Moguerza, J. M. (2023). Empowering Academic Performance: Data-Driven Mentoring for Personalized Education Through Learning Analytics. In ICERI2023 Proceedings (pp. 1833-1840). IATED.
  • Lancho, C., Martín de Diego, I., Cuesta, M., Aceña, V., & Moguerza, J. M. (2023, July). Hostility measure for multi-level study of data complexity. Applied Intelligence, 53(7), 8073-8096. Springer US. https://doi.org/10.1007/s10489-022-03793-w
  • Aceña Gil, V. C. (2023, May). Métodos de muestreo para la mejora de rendimiento en clasificadores de aprendizaje automático. Universidad Rey Juan Carlos. https://hdl.handle.net/10115/30399
  • Fernández-Isabel, A., Cabezas, J., Moctezuma, D., & Martín de Diego, I. (2023). Improving sentiment classification performance through coaching architectures. Cognitive Computation, 15(3), 1065-1081. Springer US. https://www.doi.org/10.1007/s12559-022-10018-2
  • Peribáñez, A. D., Fernández-Isabel, A., Martín de Diego, I., Condado, A., & Moguerza, J. M. (2023). Extracting Knowledge from Incompletely Known Models. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 257-268). Springer Nature Switzerland.
  • Lancho, C., de Souto, M. C. P., Lorena, A. C., & Martín de Diego, I. (2023). Complexity-Driven Sampling for Bagging. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 15-21). Springer Nature Switzerland.
  • González Fernández, C., Martín De Diego, I., Ferández-Isabel, A., Viseu Pinheiro, J. F. J., & Alonso, A. (2023). Detecting Low-Credibility Medical Websites through Semi-Supervised Learning Techniques. IEEE Access. IEEE. https://www.doi.org/10.1109/ACCESS.2023.3341756
  • Castro-Olmo, F. J., Morales-Fernández, P., Alcaide-Martín, M. J., & otros. (2023, March). Is minimising waste volume for drawing blood samples in critically ill patients feasible?. Enfermería Intensiva (English ed.), 34(1), 19-26. https://doi.org/10.1016/j.enfie.2022.06.002
  • Castro-Olmo, F. J., Morales-Fernández, P., Alcaide-Martín, M. J., & otros. (2023, March). Minimizar el volumen de descarte para la extracción de muestras sanguíneas en los pacientes críticos: ¿es factible?. Enfermería Intensiva, 34(1), 19-26. https://doi.org/10.1016/j.enfi.2022.06.004
  • Aceña, V., Martín de Diego, I., Fernández, R. R., & Moguerza, J. M. (2023, February). Support subsets estimation for support vector machines retraining. Pattern Recognition, 134, 109117. Pergamon. https://doi.org/10.1016/j.patcog.2022.109117

2022

  • Lancho, C., Cuesta, M., Martín de Diego, I., Cano, E. L., Fernández-Isabel, A., & Gómez Miguel, J. (2022). Early student detection for improvement of academic results using machine learning models. In EDULEARN22 Proceedings (pp. 6177-6185).
  • Navarro, J., Martín de Diego, I., & Redondo, A. R. (2022). Time series representation for information gathering via low resolution wireless sensor networks. International Journal of Sensor Networks, 40(1), 57-66. Inderscience Publishers (IEL).
  • Martín de Diego, I., Fernández-Isabel, A., San Román, I., Conde, C., & Cabello, E. (2022). Novel context-aware methodology for risk assessment in intelligent video-surveillance systems. International Journal of Sensor Networks, 40(3), 145-159. https://www.doi.org/10.1504/IJSNET.2022.127121
  • Cuenca, W., González-Fernández, C., Fernández-Isabel, A., Martín de Diego, I., & Martín, A. G. (2022). Combining conceptual graphs and sentiment analysis for fake news detection. In Computational Intelligence and Mathematics for Tackling Complex Problems 2 (pp. 129-138). Springer International Publishing.
  • Navarro, J., Martín de Diego, I., Fernández, R. R., & Moguerza, J. M. (2022). Triangle-based outlier detection. Pattern Recognition Letters, 156, 152-159.
  • Martín, A. G., Martín de Diego, I., Fernández-Isabel, A., Beltrán, M., & Fernández, R. R. (2022). Combining user behavioural information at the feature level to enhance continuous authentication systems. Knowledge-Based Systems, 244, 108544. https://www.doi.org/10.1016/j.knosys.2022.108544
  • Martín De Diego, I., Redondo, A. R., Fernández, R. R., Navarro, J., & Moguerza, J. M. (2022). General Performance Score for classification problems. Applied Intelligence, 52, 12049-12063.
  • Moreno, R., Viajes, M., Fernández-Isabel, A., Martín de Diego, I., Moguerza, J. M., Lancho, C., & Cuesta, M. (2022). Automatic detection of potential customers by opinion mining and intelligent agents. In 2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS) (pp. 93-101). IEEE.
  • Martín de Diego, I., Riera-Arnau, J., Bots, S., Belitser, S., Messina, D., Schultze, A., Douglas, I., Duran, C., Poza, P. G., Gini, R., Herings, R. M. C., Sisay, M. M., Martin, I., Villalobos, F., Klungel, O. H., & Sturkenboom, M. (2022). Association between SARS-CoV-2 Vaccines and Myoand Pericarditis; a Large Observational Study Using Electronic Healthcare Data from Four European Countries. Drug Safety, 45(10), 1305-1306. Springer Nature BV.
  • Aceña, V., Martín de Diego, I., Fernández, R. R., & Moguerza, J. M. (2022, October). Minimally overfitted learners: a general framework for ensemble learning. Knowledge-Based Systems, 254, 109669. https://doi.org/10.1016/j.knosys.2022.109669
  • Fernández, R. R., Martín de Diego, I., Moguerza, J. M., & Herrera, F. (2022). Explanation sets: A general framework for machine learning explainability. Information Sciences, 617, 464-481.

2021

  • Lancho, C., Martín de Diego, I., Cuesta, M., Aceña, V., & Moguerza, J. M. (2021, November). A complexity measure for binary classification problems based on lost points. In Intelligent Data Engineering and Automated Learning―IDEAL 2021: 22nd (pp. 137-146). Springer. https://doi.org/10.1007/978-3-030-91608-4_14
  • Cuesta, M., Martín de Diego, I., Lancho, C., Aceña, V., & Moguerza, J. M. (2021, November). From Classification to Visualization: A Two Way Trip. In Intelligent Data Engineering and Automated Learning―IDEAL 2021: 22nd (pp. 289-299). Springer. https://doi.org/10.1007/978-3-030-91608-4_29
  • González-Fernández, C., Fernández-Isabel, A., Martín de Diego, I., Fernández, R. R., & Viseu Pinheiro, J. F. J. (2021). Experts perception-based system to detect misinformation in health websites. Pattern Recognition Letters, 152, 333-339. https://www.doi.org/10.1016/j.patrec.2021.11.008
  • Navarro, J., Martín de Diego, I., Carballo Pérez, P., & Ortega, F. (2021). Outlier detection in animal multivariate trajectories. Computers and Electronics in Agriculture, 190, 106401.
  • González de Villaumbrosia, C., Sáez López, P., Martín de Diego, I., Lancho Martín, C., Cuesta Santa Teresa, M., Alarcón, T., ... & González-Montalvo, J. I. (2021). Predictive model of gait recovery at one month after hip fracture from a national cohort of 25,607 patients: the Hip Fracture Prognosis (HF-Prognosis) tool. International journal of environmental research and public health, 18(7), 3809.
  • Moguerza, J. M., Perelló Oliver, S., Martín de Diego, I., Aceña, V., Lancho, C., Cuesta, M., & González Fernández, C. (2021, May). Health Sufficiency Indicators for Pandemic Monitoring. International Journal of Environmental Research and Public Health, 18(10), 5358. https://doi.org/10.3390/ijerph18105358
  • Prieto, J. C., Fernández-Isabel, A., Martín de Diego, I., Ortega, F., & Moguerza, J. M. (2021). Knowledge-Based Approach to Detect Potentially Risky Websites. IEEE Access, 9, 11633-11643. https://www.doi.org/10.1109/ACCESS.2021.3051374
  • Martín, A. G., Fernández-Isabel, A., Martín de Diego, I., & Beltrán, M. (2021). A survey for user behavior analysis based on machine learning techniques: current models and applications. Applied Intelligence, 1-27. https://www.doi.org/10.1007/s10489-020-02160-x
  • Martín, A. G., Fernández-Isabel, A., González-Fernández, C., Lancho, C., Cuesta, M., & Martín de Diego, I. (2021). Suspicious news detection through semantic and sentiment measures. Engineering Applications of Artificial Intelligence, 101, 104230. https://www.doi.org/10.1016/j.engappai.2021.104230
  • Martín, A. G., Beltrán, M., Fernández-Isabel, A., & Martín de Diego, I. (2021). An approach to detect user behaviour anomalies within identity federations. Computers & Security, 102356. https://www.doi.org/10.1016/j.cose.2021.102356
  • Martín de Diego, I., González-Fernández, C., Fernández-Isabel, A., Fernández, R. R., & Cabezas, J. (2021). System for evaluating the reliability and novelty of medical scientific papers. Journal of Informetrics, 15(4), 101188. https://www.doi.org/10.1016/j.joi.2021.101188
  • Cabezas, J., Moctezuma, D., Fernández-Isabel, A., & Martín de Diego, I. (2021). Detecting Emotional Evolution on Twitter during the COVID-19 Pandemic Using Text Analysis. International Journal of Environmental Research and Public Health, 18(13), 6981. https://www.doi.org/10.3390/ijerph18136981
  • Cano, E. L., García-Camús, J. M., Garzás, J., Moguerza, J. M., & Sánchez, N. N. (2021). A Scrum-based framework for new product development in the non-software industry. Journal of Engineering and Technology Management, 61, 101634.
  • Alonso Ayuso, A., Laguna, M., Molina Ferragut, E., & García Heredia, D. (2020). A solution method for the shared Resource Constrained Multi-Shortest Path Problem (No. 30793). Universidad Carlos III de Madrid. Departamento de Estadística.
  • Gil-Borrás, S., Pardo, E. G., Alonso-Ayuso, A., & Duarte, A. (2021). A heuristic approach for the online order batching problem with multiple pickers. Computers & Industrial Engineering, 107517.
  • Escudero, L. F., Alonso-Ayuso, A., & Maculan, N. (2021). Essays on OR in ALIO country members (part 2). Top, 29(1), 1-4.
  • Sierra-Paradinas, M., Soto-Sánchez, Ó., Alonso-Ayuso, A., Martín-Campo, F. J., & Gallego, M. (2021). An exact model for a slitting problem in the steel industry. European Journal of Operational Research.
  • Escudero, L. F., Alonso-Ayuso, A., & Nelson Maculan, F. (Eds.). (2021). Special Issue on Essays in Operations Research from ALIO, the Latin Ibero-American Association of Operations Research (part 2). Springer.

2020

  • Fernández-Isabel, A., Fuentes-Fernández, R., & de Diego, I. M. (2020). Modeling multi-agent systems to simulate sensor-based Smart Roads. Simulation Modelling Practice and Theory, 99, 101994. https://www.doi.org/10.1016/j.simpat.2019.101994
  • Moguerza, J. M., Oliver, S. P., Martín de Diego, I., Aceña, V., Cuesta, M., Lancho, C., & Fernández, C. G. (2020, April). Suficiencia sanitaria y COVID-19. methaodos. revista de ciencias sociales, 8(1), 140-168. https://doi.org/10.17502/m.rcs.v8i1.349
  • Ortega, D., Fernández-Isabel, A., Martín de Diego, I., Conde, C., & Cabello, E. (2020). Dynamic facial presentation attack detection for automated border control systems. Computers & Security, 92, 101744. https://www.doi.org/10.1016/j.cose.2020.101744
  • González-Fernández, C., Cabezas, J., Fernández-Isabel, A., & Martín de Diego, I. (2020, June). Combining Multi-Agent Systems and Subjective Logic to Develop Decision Support Systems. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 143-157). Springer, Cham.
  • Cabezas, J., Fernandez-Isabel, A., Fernández, R. R., González-Fernández, C., Alonso, A., & Martín de Diego, I. (2020, August). Bio-Inspired Agent-Based Architecture for Fraud Detection. In Proceedings of the 2020 3rd International Conference on Information Management and Management Science (pp. 67-71).
  • Fernández, R. R., Martín de Diego, I., Aceña, V., Fernández-Isabel, A., & Moguerza, J. M. (2020, November). Random forest explainability using counterfactual sets. Information Fusion, 63, 196-207. https://www.doi.org/10.1016/j.inffus.2020.07.001
  • Fernández-Isabel, A., Barriuso, A. A., Cabezas, J., Martín de Diego, I., & Pinheiro, J. V. (2020). Knowledge-based framework for estimating the relevance of scientific articles. Expert Systems with Applications, 161, 113692. https://www.doi.org/10.1016/j.eswa.2020.113692
  • Redondo, A. R., Navarro, J., Fernández, R. R., Martín de Diego, I., Moguerza, J. M., & Fernández-Muñoz, J. J. (2020, November). Unified Performance Measure for Binary Classification Problems. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 104-112). Springer, Cham.
  • Alfaro-Navarro, J. L., Cano, E. L., Alfaro-Cortés, E., García, N., Gámez, M., & Larraz, B. (2020). A fully automated adjustment of ensemble methods in machine learning for modeling complex real estate systems. Complexity, 2020.
  • Vara, J. A. D. L., Berruga, M. I., Serrano, M. P., Cano, E. L., García, A. J., Landete-Castillejos, T., ... & Molina, A. (2020). Red deer (Cervus elaphus) colostrum during its transition to milk.
  • Gil-Borrás, S., Pardo, E. G., Alonso-Ayuso, A., & Duarte, A. (2020). Fixed versus variable time window warehousing strategies in real time. Progress in Artificial Intelligence, 9(4), 315-324.
  • Sierra-Paradinas, M., Alonso-Ayuso, A., Martín-Campo, F. J., Rodríguez-Calo, F., & Lasso, E. (2020). Facilities Delocation in the Retail Sector: A Mixed 0-1 Nonlinear Optimization Model and Its Linear Reformulation. Mathematics, 8(11), 1986.
  • Escudero, L. F., Alonso-Ayuso, A., & Maculan, N. (2020). Essays on OR in ALIO country members (part 1). Top, 28(3), 545-548.
  • Gil-Borrás, S., Pardo, E. G., Alonso-Ayuso, A., & Duarte, A. (2020). GRASP with Variable Neighborhood Descent for the online order batching problem. Journal of Global Optimization, 78(2).
  • García-Heredia, D., Molina, E., Laguna, M., & Alonso-Ayuso, A. (2021). A solution method for the shared resource-constrained multi-shortest path problem. Expert Systems with Applications, 182, 115193.
  • Alonso-Ayuso, A., Escudero, L. F., Guignard, M., & Weintraub, A. (2020). On dealing with strategic and tactical decision levels in forestry planning under uncertainty. Computers & Operations Research, 115, 104836.

2019

  • Martín de Diego, I., Siordia, O. S., Fernández-Isabel, A., Conde, C., & Cabello, E. (2019). Subjective data arrangement using clustering techniques for training expert systems. Expert Systems with Applications, 115, 1-15. https://www.doi.org/10.1016/j.eswa.2018.07.058
  • Algar, M. J., Martín de Diego, I., Fernández-Isabel, A., Monjas, M. Á., Ortega, F., Moguerza, J. M., & Jacynycz, H. (2019). A quality of experience management framework for mobile users. Wireless Communications and Mobile Computing, 2019. https://www.doi.org/10.1155/2019/2352941
  • Navarro, J., Martín de Diego, I., Fernández-Isabel, A., & Ortega, F. (2019, January). Fusion of GPS and accelerometer information for anomalous trajectories detection. In Proceedings of the 2019 the 5th International Conference on e-Society, e-Learning and e-Technologies (pp. 52-57).
  • Pérez, P. C., Ortega, F., García, J. N., & Martín de Diego, I. (2019, January). Combining Machine Learning and Symbolic Representation of Time Series for Classification of Behavioural Patterns. In Proceedings of the 2019 the 5th International Conference on e-Society, e-Learning and e-Technologies (pp. 93-97).
  • Martín de Diego, I., San Román, I., Montero, J. C., Conde, C., & Cabello, E. (2019). Scalable and flexible wireless distributed architecture for intelligent video surveillance systems. Multimedia Tools and Applications, 78(13), 17437-17459.
  • San Román, I., Martín de Diego, I., Conde, C., & Cabello, E. (2019). Outlier trajectory detection through a context-aware distance. Pattern Analysis and Applications, 22(3), 831-839.
  • Fernández-Isabel, A., Peixoto, P., Martín de Diego, I., Conde, C., & Cabello, E. (2019). Combining dynamic finite state machines and text-based similarities to represent human behavior. Engineering Applications of Artificial Intelligence, 85, 504-516. https://www.doi.org/10.1016/j.engappai.2019.07.006
  • Aceña, V., Moguerza, J. M., Martín de Diego, I., & Fernández, R. R. (2019, October). Weighted Nearest Centroid Neighbourhood. In Intelligent Data Engineering and Automated Learning―IDEAL 2019: 20th (pp. 94-101). Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_11
  • Fernández, R. R., Martín de Diego, I., Aceña, V., Moguerza, J. M., & Fernández-Isabel, A. (2019, October). Relevance metric for counterfactuals selection in decision trees. In Intelligent Data Engineering and Automated Learning―IDEAL 2019: 20th (pp. 85-93). Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_10
  • Muñoz-Tébar, N., De la Vara, J. A., de Elguea-Culebras, G. O., Cano, E. L., Molina, A., Carmona, M., & Berruga, M. I. (2019). Enrichment of sheep cheese with chia (Salvia hispanica L.) oil as a source of omega-3. LWT, 108, 407-415.
  • Garcia-Heredia, D., Alonso-Ayuso, A., & Molina, E. (2019). A Combinatorial model to optimize air traffic flow management problems. Computers & Operations Research, 112, 104768.
  • Gil-Borrás, S., Pardo, E. G., Alonso-Ayuso, A., & Duarte, A. (2019, October). Basic VNS for a variant of the online order batching problem. In International Conference on Variable Neighborhood Search (pp. 17-36). Springer, Cham.
  • Nadal-Roig, E., Plà-Aragonès, L. M., & Alonso-Ayuso, A. (2019). Production planning of supply chains in the pig industry. Computers and Electronics in Agriculture, 161, 72-78.

2018

  • Fernández-Isabel, A., Prieto, J. C., Ortega, F., Martín de Diego, I., Moguerza, J. M., Mena, J., ... & Napalkova, L. (2018). A unified knowledge compiler to provide support the scientific community. Knowledge-Based Systems, 161, 157-171. https://www.doi.org/10.1016/j.knosys.2018.07.044
  • Martín de Diego, I., Fernández-Isabel, A., Ortega, F., & Moguerza, J. M. (2018). A visual framework for dynamic emotional web analysis. Knowledge-Based Systems, 145, 264-273. https://www.doi.org/10.1016/j.knosys.2018.01.023
  • Prieto, J. C., Fernández-Isabel, A., Martín de Diego, I., & Ortega, F. (2018, January). A supervised learning approach to detect copyright infrigments. In Information Management and Processing (ICIMP), 2018 International Conference on (pp. 100-106). IEEE.
  • Martos, G., Hernández, N., Muñoz, A., & Moguerza, J. M. (2018). Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection. Entropy, 20(1), 33.
  • Alonso-Ayuso, A., Escudero, L. F., Guignard, M., & Weintraub, A. (2018). Risk management for forestry planning under uncertainty in demand and prices. European Journal of Operational Research, 267(3), 1051-1074.
  • San Román, I., Martín de Diego, I., Conde, C., & Cabello, E. Outlier trajectory detection through a context-aware distance. Pattern Analysis and Applications, 1-9.

2017

  • Cano, J., Moguerza, J. M., & Prieto, F. J. (2017). Using Improved Directions of Negative Curvature for the Solution of Bound-Constrained Nonconvex Problems. Journal of Optimization Theory and Applications, 174(2), 474-499.
  • Menéndez, B., Pardo, E. G., Alonso-Ayuso, A., Molina, E., & Duarte, A. (2017). Variable neighborhood search strategies for the order batching problem. Computers & Operations Research, 78, 500-512.
  • Convertino, G., Zancanaro, M., Piccardi, T., & Ortega, F. (2017). Toward a mixed-initiative QA system: from studying predictors in Stack Exchange to building a mixed-initiative tool. International Journal of Human-Computer Studies, 99, 1-20.

2016

  • Alonso-Ayuso, A., Escudero, L. F., & Martín-Campo, F. J. (2016). Multiobjective optimization for aircraft conflict resolution. A metaheuristic approach. European Journal of Operational Research, 248(2), 691-702.
  • Gomez, J., Insua, D. R., & Alfaro, C. (2016). A participatory budget model under uncertainty. European Journal of Operational Research, 249(1), 351-358.
  • Alfaro, C., Cano-Montero, J., Gómez, J., Moguerza, J. M., & Ortega, F. (2016). A multi-stage method for content classification and opinion mining on weblog comments. Annals of Operations Research, 236(1), 197-213.
  • del Rio, J. S., Moctezuma, D., Conde, C., Martín de Diego, I., & Cabello, E. (2016). Automated border control e-gates and facial recognition systems. computers & security, 62, 49-72.
  • Baidyk, T., Kussul, E., Monterrosas, Z. C., Gallardo, A. I., Serrato, K. R., Conde, C., ... & Cabello, E. (2016). Face recognition using a permutation coding neural classifier. Neural Computing and Applications, 27(4), 973-987.
  • Salazar, A. M., Martín de Diego, I., Conde, C., & Pardos, E. C. (2016). Evaluation of Keypoint Descriptors Applied in the Pedestrian Detection in Low Quality Images. IEEE Latin America Transactions, 14(3), 1401-1407.

2015

  • Alonso-Ayuso, A., Escudero, L. F., Martín-Campo, F. J., & Mladenović, N. (2015). A VNS metaheuristic for solving the aircraft conflict detection and resolution problem by performing turn changes. Journal of Global Optimization, 63(3), 583-596.
  • Moctezuma, D., Conde, C., Martín de Diego, I., & Cabello, E. (2015). Soft-biometrics evaluation for people re-identification in uncontrolled multi-camera environments. EURASIP Journal on Image and Video Processing, 2015(1), 28.