Alberto Cano is an Associate Professor in the Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia, United States, where he heads the High-Performance Data Mining laboratory. His research is focused on machine learning, big data, data streams, concept drift, continual learning, GPUs and distributed computing.

401 W. Main St, ERB2314, Richmond, Virginia, 23284, United States

+1 (804) 827-4002

acano@vcu.edu

Curriculum Vitae (CV) [View Image]

Education

Ph.D. in Computer Science, University of Granada, Spain, 2014. [View Image]

M.Sc. in Intelligent Systems, University of Córdoba, Spain, 2013. [View Image]

M.Sc. in Soft Computing and Intelligent Systems, University of Granada, Spain, 2011. [View Image]

B.Sc. in Computer Science, University of Córdoba, Spain, 2010. [View Image]

B.Sc. in Computer Engineering, University of Córdoba, Spain, 2008. [View Image]

Research

Director of the High-Performance Data Mining laboratory at the Virginia Commonwalth University

Graduate, summer, and visiting research positions are available in the areas of machine learning, data mining, and high performance computing. Please reach me by email at acano@vcu.edu.

Research projects (Principal Investigator)

Research projects (Participant)

Ph.D. adviser (Graduated)

Ph.D. adviser (Current)

Publications

Journal articles

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    M. Roseberry, B. Krawczyk, Y. Djenouri, and A. Cano. Self-Adjusting k Nearest Neighbors for Continual Learning from Multi-Label Drifting Data Streams. Neurocomputing, 442, 10-25, 2021.
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    A. Belhadi, Y. Djenouri, G. Srivastava, A. Cano, and J. Chun-Wei Lin. Hybrid Group Anomaly Detection for Sequence Data: Application to Trajectory Data Analytics. IEEE Intelligent Transportation Systems Transactions, In Press, 2021.
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    Y. Djenouri, H. Belhadi, K. Akli-Astouati, A. Cano, and J. Chun-Wei Lin. An Ontology Matching Approach for Semantic Modeling: A Case Study in Smart Cities. Computational Intelligence, In Press, 2021.
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    A. Cano and B. Krawczyk. Kappa Updated Ensemble for Drifting Data Stream Mining. Machine Learning, 109(1), 175-218, 2020.
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    J. Gonzalez-Lopez, S. Ventura, and A. Cano. Distributed multi-label feature selection using individual mutual information measures. Knowledge-Based Systems, vol. 188, 105052, 2020.
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    Y. Djenouri, D. Djenouri, Z. Habbas, J. Lin, T. Michalak, and A. Cano. When the Decomposition Meets the Constraint Satisfaction Problem. IEEE Access, vol. 8, 207034-207043, 2020.
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    A. Belhadi, Y. Djenouri, G. Srivastava, D. Djenouri, A. Cano, and J. Lin. A Two-Phase Anomaly Detection Model for Secure Intelligent Transportation Ride-Hailing Trajectories. IEEE Transactions on Intelligent Transportation Systems, 22(7), 4496-4506, 2020.
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    A. Belhadi, Y. Djenouri, J. Lin, and A. Cano. A Data-Driven Approach for Twitter Hashtag Recommendation. IEEE Access, vol. 8, 79182-79191, 2020.
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    A. Belhadi, Y. Djenouri, J. Lin, and A. Cano. Trajectory Outlier Detection: Algorithms, Taxonomies,Evaluation and Open Challenges. ACM Transactions on Management Information Systems, 11(30), art. 16, 2020.
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    A. Belhadi, Y. Djenouri, J. Lin, C. Zhang, and A. Cano. Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem. IEEE Access, vol. 8, 10569-10583, 2020.
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    A. Belhadi, Y. Djenouri, J. Lin, and A. Cano. A General-Purpose Distributed Pattern Mining System. Applied Intelligence, vol. 50, 2647-2662, 2020.
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    H.T. Nguyen, A. Cano, V. Tam, and T.N. Dinh. Blocking Self-avoiding Walks Stops Cyber-epidemics: A Scalable GPU-based Approach. IEEE Transactions on Knowledge and Data Engineering, 32(7), 1263-1275, 2020.
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    J. Gonzalez-Lopez, S. Ventura, and A. Cano. Distributed selection of continuous features in multi-label classification using mutual information. IEEE Transactions on Neural Networks and Learning Systems, 31(7), 2280-2293, 2020.
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    J. Gao, H. Wei, A. Cano, and L. Kurgan. PSIONplusm Server for Accurate Multi-Label Prediction of Ion Channels and Their Types. Biomolecules, 10(6), art. 876, 2020.
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    M. Roseberry, B. Krawczyk, and A. Cano. Multi-label Punitive kNN with Self-Adjusting Memory for Drifting Data Streams. ACM Transactions on Knowledge Discovery from Data, 13(6), art. 60, 2019.
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    A. Cano and B. Krawczyk. Evolving Rule-Based Classifiers with Genetic Programming on GPUs for Drifting Data Streams. Pattern Recognition, vol. 87, 248-268, 2019.
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    A.C. Fuentes-Fayos, M.L. Gandía-González, A. Cano, et al. Metabolomics and molecular profiling in glioma patients: an interactomic approach. Neuro-Oncology, 21(3), 64-65, 2019.
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    A. Cano and J.D. Leonard. Interpretable Multi-view Early Warning System adapted to Underrepresented Student Populations. IEEE Transactions on Learning Technologies, 12(2), 198-211, 2019.
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    Y. Djenouri, A. Belhadi, J. Lin, D. Djenouri, and A. Cano. A Survey on Urban Traffic Anomalies Detection Algorithms. IEEE Access, vol. 7, 12192-12205, 2019.
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    Y. Djenouri, A. Belhadi, J. Lin, and A. Cano. Adapted k Nearest Neighbors for Detecting Anomalies on Spatio-Temporal Traffic Flow. IEEE Access, vol. 7, 10015-10027, 2019.
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    P. Skryjomski, B. Krawczyk, and A. Cano. Speeding up k-Nearest Neighbors Classifier for Large-Scale Multi-Label Learning on GPUs. Neurocomputing, vol. 354, 10-19, 2019.
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    Y. Djenouri, D. Djenouri, A. Belhadi, and A. Cano. Exploiting GPU and Cluster Parallelism in Single Scan Frequent Itemset Mining. Information Sciences, vol. 496, 363-377, 2019.
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    A. Cano. A survey on graphic processing unit computing for large-scale data mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(1), e1232, 2018.
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    J. Gonzalez-Lopez, S. Ventura, and A. Cano. Distributed Nearest Neighbor Classification for Large-Scale Multi-label Data on Spark. Future Generation Computer Systems, vol. 87, 66-82, 2018.
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    G. Melki, V. Kecman, S. Ventura, and A. Cano. OLLAWV: OnLine Learning Algorithm using Worst-Violators. Applied Soft Computing, vol. 66, 384-393, 2018.
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    G. Melki, A. Cano, and S. Ventura. MIRSVM: Multi-Instance Support Vector Machine with Bag Representatives. Pattern Recognition, vol. 79, 228-241, 2018.
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    B. Krawczyk and A. Cano. Online Ensemble Learning with Abstaining Classifiers for Drifting and Noisy Data Streams. Applied Soft Computing, vol. 68, 677-692, 2018.
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    A. Cano, E. Yeguas-Bolivar, R. Muñoz-Salinas, R. Medina-Carnicer, and S. Ventura. Parallelization Strategies for Markerless Human Motion Capture. Journal of Real-Time Image Processing, 14(2), 453-467, 2018.
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    O. Reyes, A. Cano, H. Fardoun, and S. Ventura. A locally weighted learning method based on a data gravitation model for multi-target regression. International Journal of Computational Intelligence Systems, 11(1), 282-295, 2018.
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    A. Cano. An ensemble approach to multi-view multi-instance learning. Knowledge-Based Systems, vol. 136, 46-57, 2017.
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    A. Cano, C. Garcia, and S. Ventura. Extremely High-dimensional Optimization with MapReduce: Scaling Functions and Algorithm. Information Sciences, vol. 415-416, 110-127, 2017.
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    G. Melki, A. Cano, V. Kecman, and S. Ventura. Multi-Target Support Vector Regression Via Correlation Regressor Chains. Information Sciences, vol. 415-416, 53-69, 2017.
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    A. Cano, S. Ventura, and K.J. Cios. Multi-Objective Genetic Programming for Feature Extraction and Data Visualization. Soft Computing, 21(8), 2069-2089, 2017.
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    J.M. Luna, A. Cano, V. Sakalauskas, and S. Ventura. Discovering Useful Patterns from Multiple Instance Data. Information Sciences, vol. 357, 23-38, 2016.
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    A. Cano, J.M. Luna, E.L. Gibaja, and S. Ventura. LAIM discretization for multi-label data. Information Sciences, vol. 330, 370-384, 2016.
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    A. Cano, D.T. Nguyen, S. Ventura and K.J. Cios. ur-CAIM: improved CAIM discretization for unbalanced and balanced data. Soft Computing, 20(1), 173-188, 2016.
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    J.M. Luna, A. Cano, M. Pecheniskiy, and S. Ventura. Speeding-up Association Rule Mining with Inverted Index Compression. IEEE Transactions on Cybernetics, 46(12), 3059-3072, 2016.
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    C. Márquez-Vera, A. Cano, C. Romero, A. Yousef Mohammad, H. Mousa Fardoun, and S. Ventura. Early Dropout Prediction using Data Mining: A Case Study with High School Students. Expert Systems, 33(1), 107-124, 2016.
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    A. Cano, J.M. Luna, A. Zafra, and S. Ventura. A Classification Module for Genetic Programming Algorithms in JCLEC. Journal of Machine Learning Research, vol. 16, 491-494, 2015.
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    A. Cano, A. Zafra, and S. Ventura. Speeding up multiple instance learning classification rules on GPUs. Knowledge and Information Systems, 44(1), 127-145, 2015.
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    A. Cano, S. Ventura, and K.J. Cios. Scalable CAIM discretization on multiple GPUs using concurrent kernels. Journal of Supercomputing, 69(1), 273-292, 2014.
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    A. Cano, A. Zafra, and S. Ventura. Parallel evaluation of Pittsburgh rule-based classifiers on GPUs. Neurocomputing, vol. 126, 45-57, 2014.
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    C. Márquez-Vera, A. Cano, C. Romero, and S. Ventura. Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data. Applied Intelligence, 38 (3), 315-330, 2013.
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    A. Cano, J.M. Luna, and S. Ventura. High Performance Evaluation of Evolutionary-Mined Association Rules on GPUs. Journal of Supercomputing, 66(3), 1438-1461, 2013.
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    A. Cano, A. Zafra, and S. Ventura. An Interpretable Classification Rule Mining Algorithm. Information Sciences, vol. 240, 1-20, 2013.
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    A. Cano, J.L. Olmo, and S. Ventura. Parallel Multi-Objective Ant Programming for Classification Using GPUs. Journal of Parallel and Distributed Computing, 73 (6), 713-728, 2013.
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    A. Cano, A. Zafra, and S. Ventura. Weighted Data Gravitation Classification for Standard and Imbalanced Data. IEEE Transactions on Cybernetics, 43 (6) pages 1672-1687, 2013.
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    A. Cano, A. Zafra, and S. Ventura. Speeding up the evaluation phase of GP classification algorithms on GPUs. Soft Computing, 16 (2), 187-202, 2012.

Edited books

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    A. Cano, Social Media and Machine Learning, InTech, ISBN 978-1-78984-028-5, 2020.
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    S. Ventura, J. M. Luna, and A. Cano, Big Data on Real-World Applications, InTech, ISBN 978-953-51-2490-0, 2016.

Book chapters

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    J.M. Luna, A. Cano and S. Ventura. Genetic Programming for Mining Association Rules in Relational Database Environments. In Handbook of Genetic Programming Applications, Springer, 2015. ISBN 978-3-319-20882-4.
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    J.M. Luna, A. Cano and S. Ventura. An Evolutionary Self-Adaptive Algorithm for Mining Association Rules. In Data Mining: Principles, Applications and Emerging Challenges, Nova Publishers, 2015. ISBN 978-1-63463-770-1.

International conference contributions

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    B. Krawczyk and A. Cano. Locally Linear Support Vector Machines for Imbalanced Data Classification. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 616-628, 2021.
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    J. Perez, [...], A. Cano, et. al. An Endocrine and metabolic interactomic approach to identify novel diagnostic/prognostic biomarkers and therapeutic targets in gliomas. In 22nd European Congress of Endocrinology, 2020.
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    L. Korycki, A. Cano, and B. Krawczyk. Active Learning with Abstaining Classifiers for Imbalanced Drifting Data Streams. In IEEE BigData, 2334-2343, 2019.
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    B. Krawczyk and A. Cano. Adaptive ensemble active learning for drifting data stream mining. In International Joint Conference on Artificial Intelligence, 2763-2771, 2019.
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    J. Gonzalez-Lopez, S. Ventura, and A. Cano. ARFF data source library for distributed single/multiple instance, single/multiple output learning on Apache Spark. In International Conference on Computational Science, 173-179, 2019.
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    J.M. Moyano, E. Gibaja, S. Ventura, and A. Cano. Speeding up Classifier Chains in Multi-Label Classification. In International Conference on Internet of Things, Big Data and Security, 29-37, 2019.
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    M. Roseberry and A. Cano. Multi-label kNN Classifier with Self Adjusting Memory for Drifting Data Streams. In Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, LIDTA@PKDD/ECML, PMLR 94:23-37, 2018.
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    A. Cano and B. Krawczyk. Learning classification rules with differential evolution for high-speed data stream mining on GPUs. In IEEE Congress on Evolutionary Computation, 197-204, 2018.
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    B. Krawczyk, A. Cano, and M. Wozniak. Selecting local ensembles for multi-class imbalanced data classification. In International Joint Conference on Neural Networks, 1848-1855, 2018.
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    J. Gonzalez-Lopez, A. Cano, and S. Ventura. Large-scale multi-label ensemble learning on Spark. In IEEE Trustcom/BigDataSE/ICESS, 893-900, 2017.
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    A. Olex, B. McInnes, and A. Cano. Parsing MetaMap Files in Hadoop. In American Medical Informatics Association Symposium, 2017.
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    B. Krawczyk, B. McInnes, and A. Cano. Sentiment Classification from Multi-Class Imbalanced Twitter Data using Binarization. In 12th International Conference on Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science, vol 10334, 26-37, 2017.
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    A. Cano and C. Garcia-Martinez. 100 Million Dimensions Large-Scale Global Optimization Using Distributed GPU Computing. In IEEE Congress on Evolutionary Computation, 3566-3573, 2016.
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    F. Padillo, J.M. Luna, A. Cano, and S. Ventura. A Data Structure to Speed-Up Machine Learning Algorithms on Massive Datasets. In 11th International Conference on Hybrid Artificial Intelligent Systems. Lecture Notes in Computer Science, vol 9648, 365-376, 2016.
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    D. Pinheiro, A. Cano and S. Ventura. Synthesis of In-Place Iterative Sorting Algorithms Using GP: A Comparison Between STGP, SFGP, G3P and GE. In 17th Portuguese Conference on Artificial Intelligence. Lecture Notes in Computer Science, vol 9273, 305-310, 2015.
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    A. Cano and S. Ventura. GPU-parallel subtree interpreter for genetic programming. In Conference on Genetic and Evolutionary Computation, 887-894, 2014.
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    J.A. Pedraza, C. Garcia-Martinez, A. Cano, and S. Ventura. Classification Rule Mining with Iterated Greedy. In 9th International Conference on Hybrid Artificial Intelligent Systems (HAIS). Lecture Notes in Computer Science, 8480 LNCS:585-596, 2014.
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    A. Cano, A. Zafra, E.L. Gibaja, and S. Ventura. A Grammar-Guided Genetic Programming Algorithm for Multi-Label Classification. In 16th European Conference on Genetic Programming, EuroGP'13. Lecture Notes in Computer Science, vol 7831, 217-228, 2013.
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    J.L. Olmo, A. Cano, J.R. Romero, and S. Ventura. Binary and Multiclass Imbalanced Classification Using Multi-Objective Ant Programming. In 12th International Conference on Intelligent Systems Design and Applications, ISDA'12, 70-76, 2012.
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    A. Cano, A. Zafra, and S. Ventura. An EP algorithm for learning highly interpretable classifiers. In 11th International Conference on Intelligent Systems Design and Applications, ISDA'11, 325-330, 2011.
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    A. Cano, A. Zafra, and S. Ventura. A parallel genetic programming algorithm for classification. In 6th International Conference on Hybrid Artificial Intelligent Systems (HAIS). Lecture Notes in Computer Science, 6678 LNAI(PART 1):172-181, 2011.
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    A. Cano, J.M. Luna, J.L. Olmo, and S. Ventura. JCLEC meets WEKA! In 6th International Conference on Hybrid Artificial Intelligent Systems (HAIS). Lecture Notes in Computer Science, 6678 LNAI(PART 1):388-395, 2011.
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    A. Cano, A. Zafra, and S. Ventura. Solving classification problems using genetic programming algorithms on GPUs. In 5th International Conference on Hybrid Artificial Intelligent Systems (HAIS). Lecture Notes in Computer Science, 6077 LNAI(PART 2):17-26, 2010.
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    J. Fernández-Berni, R. Carmona-Galán, L. Carranza-González, A. Cano-Rojas, J. F. Martínez-Carmona, Á. Rodríguez-Vázquez, and S. Morillas-Castillo. On-site forest fire smoke detection by low-power autonomous vision sensor. In VI International Conference on Forest Fire Research, page 94, 2010.

National conference contributions

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    A. Cano and C. Garcia-Martinez. Optimización con 100 millones de variables reales sobre múltiples unidades de procesamiento gráfico. XI Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB), 377-386, 2016.
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    F. Ibáñez A. Cano, and S. Ventura. Evaluación distribuida transparente para algoritmos evolutivos en JCLEC. II Jornadas de Algoritmos Evolutivos y Metaheurísticas (XVI CAEPIA), 231-240, 2015.
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    J.M. Moyano, E.L. Gibaja, A. Cano, J.M. Luna, and S. Ventura. Diseño Automático de Multi-Clasificadores Basados en Proyecciones de Etiquetas. II Jornadas de Fusión de la Información y ensembles (XVI CAEPIA), 355-366, 2015.
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    J.M. Moyano, E.L. Gibaja, A. Cano, J.M. Luna, and S. Ventura. Algoritmo evolutivo para optimizar ensembles de clasificadores multi-etiqueta. X Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB), 219-225, 2015.
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    A. Cano, J.L. Olmo, and S. Ventura. Programación Automática con Colonias de Hormigas Multi-Objetivo en GPUs. IX Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB), 288-297, 2013.
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    A. Cano, A. Zafra, and S. Ventura. Parallel Data Mining Algorithms on GPUs. Doctoral Consortium de la Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA), 1603-1606, 2013.
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    A. Cano, J.M. Luna, A. Zafra, and S. Ventura. Modelo gravitacional para clasificación. VIII Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB), 63-70, 2012.
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    J.L. Olmo, A. Cano, J.R. Romero, and S. Ventura. Programación con Hormigas Multi-Objetivo para la Extracción de Reglas de Clasificación. VIII Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB), 219-226, 2012.
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    A. Cano, A. Zafra, and S. Ventura. Speeding up evolutionary learning algorithms using GPUs. In ESTYLF 2010 XV Congreso Español sobre Tecnologías y Lógica Fuzzy, 229-234, 2010.
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    R. Molina, J. Jiménez, C. Sánchez, and A. Cano. Adecuación de la red WiFi para cumplimiento de la normativa y permitir acceso a internet a los pacientes. In XI Congreso Nacional de Informática de la Salud, 2008.

Teaching

Teaching publications

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    A. Cano and A. Rojas. Autómatas celulares y aplicaciones. UNIÓN. Revista Iberoamericana de Educación Matemática, (46):33-48, 2016.
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    A. Cano, J.M. Luna, and A. Rojas. Cómo compartir un secreto usando sistemas de ecuaciones lineales. Suma, (79):33-39, 2015.
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    A. Rojas and A. Cano. Cifrado de imágenes y matemáticas. TE&ET. Revista Iberoamericana de Tecnología en Educación y Educación en Tecnología, (6):30-37, 2011.
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    A. Rojas and A. Cano. Una clase de aritmética modular, matrices y cifrado para ingeniería. UNIÓN. Revista Iberoamericana de Educación Matemática, 1(25):89-108, 2011.
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    A. Rojas and A. Cano. Trabajando con imágenes digitales en clase de matemáticas. La Gaceta de la Real Sociedad Matemática Española, 2(13):317-336, 2010.
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    A. Rojas and A. Cano. Interpolación polinómica y la división de secretos. In XIV Congreso de Enseñanza y Aprendizaje de las Matemáticas, 2012.
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    A. Rojas and A. Cano. Motivando el aprendizaje del Álgebra lineal a través de sus aplicaciones: la división de secretos. In XX Congreso universitario de innovación educativa en las enseñanzas técnicas, 2012.
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    E. Gibaja, A. Zafra, M. Luque, and A. Cano. Recursos didácticos en el grado en ingeniería informática para el aprendizaje de matemáticas a través de la programación de ordenadores. In II Jornadas Andaluzas de Informática, 90-95, 2011.
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    A. Rojas and A. Cano. Cifrado de imágenes y reparto de secretos en clase de matemáticas. In XV Jornadas para el Aprendizaje y Enseñanza de las Matemáticas, 2011.
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    A. Rojas and A. Cano. Motivando el aprendizaje del álgebra lineal a través de sus aplicaciones. In II Jornadas sobre Innovación Docente y Adaptación al EEES en las Titulaciones Técnicas, 2011.
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    A. Rojas and A. Cano. Álgebra lineal y cifrado de imágenes. In CEAM 2010 XIII Congreso de enseñanza y aprendizaje de las matemáticas, 2010.
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    A. Cano. Reparto de secretos usando un sudoku. In CEAM 2010 XIII Congreso de enseñanza y aprendizaje de las matemáticas, 2010.
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    A. Cano and A. Rojas. Coloreado de imágenes y sistemas de ecuaciones lineales. In CEAM 2010 XIII Congreso de enseñanza y aprendizaje de las matemáticas, 2010.
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    A. Cano and A. Rojas. Fotomontajes de imágenes digitales y sistemas de ecuaciones lineales. In CEAM 2010 XIII Congreso de enseñanza y aprendizaje de las matemáticas, 2010.
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    A. Rojas and A. Cano. Descomposición en valores singulares e imágenes. In CEAM 2010 XIII Congreso de enseñanza y aprendizaje de las matemáticas, 2010.
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    A. Rojas and A. Cano. Álgebra lineal, secretos e imágenes. In CUIEET 2010 XVIII Congreso universitario de innovación educativa en las enseñanzas técnicas, 2010.
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    A. Rojas and A. Cano. Innovación en clase de matemáticas. In CUIEET 2010 XVIII Congreso universitario de innovación educativa en las enseñanzas técnicas, 2010.
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    A. Cano and A. Rojas. Descomposición en valores singulares e imágenes. In I Jornadas Andaluzas de Informática, 2009.
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New Classification Models through Evolutionary Algorithms [View Image]
A. Cano, XPS: EXPL: Scalable distributed GPU computing for extremely high-dimensional optimization [View Image]
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