s                         s

Picture Leonardo Franco
                  Computational Neuroscience Oxford


Prof Dr  Leonardo  Franco
(Catedrático de Universidad)

IEEE Senior Member
IBIMA Member
IEEE TNNLS Associate Editor
Comp. Intell. & Neuroscience Editorial Board Member

Universidad de Málaga
Depto. de Lenguajes y Ciencias de la Computación

Campus de Teatinos S/N
Málaga 29071

email: lfranco@lcc.uma.es
Web:  http://www.lcc.uma.es/~lfranco/

Tel:  +34 - 952 - 133304
Fax: +34 - 952 - 131397


International Conference on Computational Science

Málaga, 2-4 July 2024



Special Issue: Machine Learning Applications in Metabolomics Analysis

SS metabolites


RESEARCH TOPIC:  AI and Multi-Omics for Rare diseases: Challenges, Advances and Perspectives. Volume III



Curso on-line: Deep Learning: Introducción

Este curso brinda una introducción práctica al aprendizaje profundo (Deep Learning), una técnica de la inteligencia artificial que ha revolucionado la escena tecnológica para posicionarse de la mano del Big Data como un elemento central en la estrategia de innovación de las principales empresas del sector. Se introducirán primero los conceptos generales necesarios sobre reconocimiento de patrones, redes neuronales, aprendizaje supervisado, etc., que permitan entender el funcionamiento y la utilidad de los sistemas de reconocimiento y predicción de patrones basados en aprendizaje profundo, para poder luego realizar una implementación práctica de los mismos, primero utilizando ejemplos clásicos y posteriormente poder realizar un proyecto individual.

Inscripciones: aquí

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Dr.  L. Franco  grew up in Argentina, completing his master and doctoral degree in the theoretical physics group at FaMAF, University of Córdoba. He obtained a PhD under the mentorship of Dr. Sergio A. Cannas, studying the generalization properties of feedforward neural networks. He did a postdoctoral fellowship for two years at SISSA, Trieste-Italy in Prof. Alessandro Treves laboratory. From 2002 to 2005 he worked as a post-doctoral fellow with Dr. Edmund Rolls at the University of Oxford, United Kingdom. He joined Málaga University as a Ramón y Cajal fellow in 2005. In June 2010 he became Associate Professor, to then got the Full Professorship post in July 2021 in the Department of Computer Science, Malaga University  School of  Engineering in Informatics . He belongs to the Computational Intelligence and Bioinformatics group (ICB), that is also integrated within IBIMA(Institute for Biomedical Research Málaga).

L. Franco does research on the three following areas described in detail below. He is senior member of the IEEE, belonging to the Computational Intelligence Society, and associate editor for the Journal IEEE Transactions on Neural Networks and Learning Systems. He regularly lectures undergraduates courses on "Computational Models" and "Automata Theory and Formal Languages", and master courses on  "Artificial Neural Networks" and "New technologies for Marketing". 


Neural networks and learning models: One of the most interesting properties of artificial neural networks is its ability to generalize to novel inputs. Few years ago I proposed a measure for the complexity of the Boolean functions related to the generalization ability that can be obtained when the functions are implemented on neural networks. In collaboration with Dr. J.M. Jerez (Málaga) , Dr. M. Anthony (LSE) and Dr. S.A. Cannas (Córdoba) we have analyzed the properties of this measure and also explored interesting extensions and links with physics.  In general, we analyze the computational capabilities of different Neural Networks architectures, developing methods that will help to improve and understand the process of generalization.  We are actually working on constructive learning algorithms that permit to build almost optimal neural architectures, avoiding the problem of selecting the proper architecture for a given problem. Also these constructive algorithms facilitate the knowledge (or rule) extraction procedure that helps to understand how the network is solving the problem. In collaboration with David Elizondo, Leicester, we have recently edited a book about Constructive Neural Networks, published by Springer in the  Computational Intelligence Series Collection. Constructive Neural Networks are an alternative to the problem of selecting a proper neural architecture for a given problem and we have recently developed two algorithms: DASG & C-Mantec. DASG is a decomposition algorithm that gets as input a Boolean function (or data) and decompose it according to the influence of its variables to obtain a set of linearly separable functions.This work was published in IEEE TCAS-I in 2008 [A23]. C-Mantec, a very powerful constructive neural network algorithm that utilizes competition between neurons to create very compact architectures with good generalization capabilities has been published in the Neural Networks journal
[A29],  (test-version of C-Mantec  are available upon request). In relationship to this algorithm, we have also applied it to Wireless Sensor Networks [A30]. In collaboration with Francisco Ortega we have implemented the C-Mantec and Back-propagation algorithms in FPGA, programmable hardware devices that permit to execute very fast the algorithm. We are currently working on the application of C-Mantec  in Sensor Networks for monitoring tasks and more recently we have managed to implement in FPGA a deep version of the Back-Propagation algorithm, so we are entering the Deep Learning area [A44]. Deep Learning can be considered an extension of traditional back-propagation networks but consisting in architectures with several hidden layers. The results obtained so far with Deep Neural Networks have been astonishing as for several problems superhuman performances were achieved.

Biomedicine: Another line of research is the application of neural networks, statistics  and artificial intelligence techniques to the problem of prognosis in Medicine. In collaboration with Dr. J.M. Jerez (Málaga), we organized a special session entitled  "ANN and Prognosis in Medicine" at the European Symposium on Artificial Neural Networks (ESANN, 2005). Recently, one of our works in collaboration with Dr. E. Alba has been accepted for publication in  the journal Breast Cancer Research and Treatment. In this line of research, We are also collaborating with Dr. V. Hadjianastiassiou (Oxford, London) applying neural networks to the problem of survival prediction in Abdominal Aortic Aneurism. In a recent work (2010) we have analyzed the imputation of missing data using several machine learning and statistical techniques. We are mainly focused on the analysis of -omic data as new advances on cancer treatment nad understanding can be obtained from them.

Neuroscience: My area of research lies within the field of Computational Neuroscience, where  my  interest is in neuronal coding, i.e., how the information is encoded and transmitted in the brain. I analyze recordings using information theory, developing new methods and performing simulations to test for different hypothesis. We analyze the coding of information under natural conditions to analyze whether most of the information is in the "rate code" or whether correlations play an important role.  We have also analyzed the role of correlation between the responses of the cells and also quantified the sparseness of the representation in the inferior temporal visual cortex cells [A20]. A more recent work, [A24], analyzes how the information is encoded in the voxels recorded from fMRI signals in a decision task, and a comparison to  neurophysiological data is carried out. Our more recent work in this area analyzes the relationship between Neuroscience and Deep Learning approaches.


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[B2] CNN BOOK COVERConstructive neural networks. L.Franco, D. Elizondo and J.M. Jerez (Eds.), Springer (2010). Series: Studies in Computational   Intelligence, Vol. 258, ISBN:  978-3-642-04511-0. [Springer site]  [More Info]

[B1]  LIBRO OMAU  Escenario de Emisiones de Gases de Efecto Invernadero en la Ciudad de Málaga. I. Molina Conde, J.M.  Jerez Aragonés, L. Franco, E.M. Sánchez Teba and P. Marín Cots. Ediciones del Ayuntamiento de Málaga, España. ISBN: 978-84-692-0913-4. (2009).

Journals :

[A55] F.J. Moreno-Barea, L. Franco, D.  Elizondo  & M. Grootveld. Application of data augmentation techniques towards metabolomics. Computers in Biology and Medicine, 148, 105916 (2022). [pdf]

[A54] S. Bottini, F. Emmert-Streib & L. Franco  (2021). AI and Multi-Omics for Rare diseases: Challenges, Advances and Perspectives. FRONTIERS IN MOLECULAR BIOSCIENCES, 8:719978. [pdf]

[A53] N. Ribelles, J.M. Jerez, et al. Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients. European Journal of Cancer, 144, pp. 224-231 (2021). [doi]

[A52] F.J. Moreno Barea, J.M. Jerez, and L. Franco. Improving Classification Accuracy Using Data Augmentation on Small Data Sets.Experts Systems with Applications, 161:113696 (2020). [pdf]

[A51] G. López-García, J.M. Jerez, L. Franco, and F.J.Veredas. Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data. PLOS ONE, 15(3):e0230536 (2020). [pdf]

[A50] I. Gómez, H. Mesa, F. Ortega-Zamorano, J.M. Jerez and L. Franco. Improving learning and generalization capabilities of the C-Mantec constructive neural network algorithm. Neural Computing and Applications 32, pp. 8955–8963 (2020). [pdf]

[A49] J. González-Enrique,  I.J. Turias,  J.J. Ruiz-Aguilar; J.A.  Moscoso-López, J.M. Jerez and L. Franco. Estimation of NO2 concentration values in a monitoring sensor network using a fusion approach. Fresenius Environmental Bulletin, 28, pp. 681-686 (2019). [pdf]

[A48] J. González-Enrique,  I.J. Turias,  J.J. Ruiz-Aguilar; J.A. Moscoso-López, and L. Franco. Spatial and meteorological relevance in NO2 estimations. A case study in the bay of Algeciras. Stochastic Environmental Research and Risk Assessment, 33, pp. 801-815 (2019).  [pdf]

[A47] D. Urda, F. Aragón,  R. Bautista, L. Franco, F. Veredas, G. Claros, and J.M. Jerez. BLASSO: integration of biological knowledge into a regularized linear model. BMC Systems Biology, 12 (Suppl 5):94  (2018). [pdf] 

[A46] F. Ortega-Zamorano, J.M. Jerez, G.E. Juárez,  and L. Franco. FPGA implementation of neurocomputational models: comparison between standard Back-Propagation and C-Mantec constructive algorithm. Neural Processing Letters,  46, pp. 899-914 (2017). [pdf]

[A45] C. Rodríguez-Rivero, J. Pucheta, S. Laboret,  V. Sauchelli,  A.D. Orjuela-Cañon, and L. Franco.  Noisy Chaotic time series forecast approximated by combining Renyi's entropy with Energy associated to series method: application to rainfall series. IEEE Latin American Transactions, 15, pp. 1318-1325. (2017). [pdf]

[A44] F. Ortega-Zamorano, J.M. Jerez, I. Gómez,  and L. Franco. Layer Multiplexing FPGA implementation for Deep Back-Propagation Learning. Integrated Computer Aided-Engineering, 24, pp.171-185 (2017). [pdf]

[A43] J. Montes-Torres, J.L. Subirats, N. Ribelles, D. Urda, L. Franco, E. Alba and J.M. Jerez. Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science. PLOS ONE, 11(8): e0161135 (2016). [pdf] highly accessed

[A42] I. Gómez, N. Ribelles, L. Franco, E. Alba, and J.M. Jerez. Supervised discretization can discover risk groups in cancer survival analysis . Computer Methods and Programs in Biomedicine, 136, pp 11-19 (2016). [pdf]

[A41] F. Ortega-Zamorano, M. Montemurro, S.A. Cannas, J.M. Jerez,  and L. Franco. FPGA Hardware Acceleration of Monte Carlo Simulations for the Ising Model. IEEE Transactions on Parallel and Distributed Systems, 27, pp. 2618 - 2627 (2016). [pdf]

[A40] F. Ortega-Zamorano, J.M. Jerez, D. Urda, R.M. Luque-Baena and L. Franco. Efficient Implementation of the Backpropagation Algorithm in FPGAs and Microcontrollers. IEEE Transactions on Neural Networks and Learning Systems, 27, pp. 1840-1850 (2016). [pdf]

[A39] I. Gómez, S.A. Cannas, O. Osenda, J.M. Jerez, and L. Franco. The Generalization Complexity Measure for Continuous Input Data. The Scientific World Journal, ID:815156 (2014). [pdf]

[A38] F. Ortega-Zamorano, J.M. Jerez, J.L. Subirats, I. Molina, and L. Franco. Smart sensor/actuator node reprogramming in changing environments using a neural network model. Engineering Applications of Artificial Intelligence, 30, pp. 179-188 (2014). [pdf]

[A37] R. Luque-Baena, D. Urda, G. Claros, L. Franco and J.M. Jerez. Robust gene signatures from microarray data using genetic algorithms enriched with biological pathway keywords. Journal of Biomedical Informatics, 49, pp. 32-44 (2014). [pdf]

[A36] F. Ortega-Zamorano, J.M. Jerez,  and  L. Franco. FPGA implementation of the C-Mantec Constructive Neural Network Algorithm. IEEE Transactions on Industrial Informatics, 10, pp. 1154-1161 (2014). [pdf]

[A35] R. Luque Baena, D. Urda, J.L. Subirats, L. Franco, and J.M. Jerez. Application of Genetic Algorithms and Constructive Neural Networks for the Analysis of Microarray Cancer Data. Theoretical Biology and Medical Modelling 11, S1(S7) (2014). [pdf]

[A34] N. Ribelles, L., Pérez-Villa, J.M. Jerez, B. Pajares, L. Vicioso, B. Jiménez, V. de Luque, L. Franco, E. Gallego, A. Márquez, M. Alvarez, A. Sánchez-Muñoz, L. Pérez-Rivas, and E. Alba. Pattern of recurrence of early breast cancer is different according to intrinsic subtype and proliferation index. Breast Cancer Research, 15, R98 (2013). [pdf] highly accessed 

[A33] D. Urda,  N. Ribelles, J.L. Subirats, L. Franco, and E. Alba. Addressing critical issues in the development of an Oncology Information System. International Journal of Medical Informatics, 82, pp. 398-407 (2013). [pdf]

[A32] L.G. Pérez-Rivas, J.M.  Jerez, J.M., C.E. Fernández-de Sousa, V. de Luque, C.  Quero, B.  Pajares, L. Franco, A. Sanchez-Muñoz, N. Ribelles and  E. Alba.  Serum protein levels following surgery in breast cancer patients: A protein microarray approach. International Journal of Oncology,41, pp. 2200-2206 (2012). [pdf]

[A31] D. Urda, J.L. Subirats, P.J. García-Laencina, L. Franco, J.L. Sancho-Gómez, and J.M. Jerez. WIMP: Web server tool for missing data imputation. Computer Methods and Programs in Biomedicine, 108, pp.1247-1254 (2012). [pdf]

[A30] D. Urda, E. Cañete, J.L. Subirats, L. Franco, L. Llopis, and J.M. Jerez. Energy-efficient Reprogramming in WSN using Constructive Neural Networks. International Journal of Innovative Computing, Information and Control, 8, pp. 7561-7578 (2012). [pdf]

[A29] J.L. Subirats,  L. Franco and J.M. Jerez. C-Mantec: a novel constructive neural network algorithm incorporating competition between neurons. Neural Networks, 26, pp 130-140 (2012). [pdf]

[A28] J.P. Neirotti and L. Franco. Computational capabilities of multilayer committee machines. Journal of Physics A: Mathematical & Theoretical, 43, 445103 (2010). [pdf] 

[A27] J.M. Jerez, I. Molina, P.J. García-Laencina, E. Alba, N. Ribelles, M. Martín and L. Franco. Missing Data Imputation Using Statistical and Machine Learning Methods in a Real Breast Cancer Problem. Artificial Intelligence in Medicine, 50, pp.105-115 (2010). [pdf]

[A26] J.L Subirats, J.M. Jerez, I. Gómez & L. Franco (2010). Multiclass pattern recognition extension for the new C-Mantec constructive neural network algorithm. Cognitive Computation, 2, pp. 285-290 (2010). [pdf]

[A25] I. Gómez,  L. Franco and J.M. Jerez. Neural Network Architecture Selection: Can function complexity help?. Neural Processing Letters, 30, pp. 71-87 (2009). [pdf] 

[A24] E.T. Rolls, F. Grabenhorst  and L. Franco. Prediction of Subjective Affective State from Brain Activations . Journal of Neurophysiology, 101, pp.1294-1308 (2009). [pdf]

[A23]  J.L. Subirats, J.M. Jerez and L. Franco. A New Decomposition Algorithm for Threshold Synthesis and Generalization of Boolean Functions. IEEE Transactions on Circuits and Systems I, 55, pp. 3188-3196 (2008). [pdf]

[A22]  L. Franco, J.L. Subirats and J.M. Jerez. MaxSet: An Algorithm for Finding a Good Approximation for the Largest Linearly Separable Set. Lecture Notes in Computer Science, 4668, pp. 648-656  (2007). [pdf]

[A21]  L. Franco, J.L. Subirats, I. Molina, E. Alba and J.M. Jerez. Early breast cancer prognosis prediction and rule extraction  using a new constructive neural network algorithm. Lecture Notes in Computer Science, 4507, pp. 1004-1011  (2007). [pdf]

[A20]  L. Franco, E.T. Rolls, N.C. Aggelopoulos and J.M. Jerez. Neuronal selectivity, population sparseness, and  ergodicity in the inferior temporal visual cortex. Biological Cybernetics, 96, pp. 547-560  (2007). [pdf]

[A19] E.T. Rolls, L. Franco, N.C. Aggelopoulos and J.M. Jerez. Information in the first spike, the order of spikes, and the number of spikes provided by neurons in the inferior temporal visual cortex. Vision Research, 46, pp. 4193-4205 (2006). [pdf]

[A18] J.L. Subirats, I. Gómez, J.M. Jerez and L. Franco. Optimal Synthesis of Boolean Functions by Threshold Functions. Lecture Notes in Computer Science, 4131, pp. 983-992 (2006). [pdf]

[A17] I. Gómez, L. Franco, J.L. Subirats and J.M. Jerez. Neural Networks Architecture Selection: Size Depends on Function Complexity. Lecture Notes in Computer Science, 4131, pp. 122-129 (2006). [pdf]

[A16] L. Franco. Generalization ability of  Boolean functions implemented in feedforward neural networks. Neurocomputing, 70, pp. 351-361 (2006). [pdf]

[A15] L. Franco and M. Anthony. The influence of oppositely classified examples on the generalization complexity of Boolean functions. IEEE Transactions on Neural Networks,  17, pp. 578--590 (2006). [pdf]

[A14] V.G. Hadjianastassiou, L. Franco, J.M. Jerez, I.E. Evangelou,  D.R. Goldhill, P.P. Tekkis, L.J. Hands.  Optimal prediction of mortality  after abdominal aortic aneurism  repair with statistical models. Journal of Vascular Surgery,  43,  pp. 467--473 (2006). [pdf]

 [A13] J. Jerez, L. Franco, E. Alba,  A. Llombart-Cussac, A. Lluch, N. Ribelles, B. Munárriz and M. Martín. Improvement of Breast Cancer Relapse Prediction in High Risk Intervals Using Artificial Neural Networks. Breast Cancer Research and Treatment, 94,  pp. 265--272 (2005). [pdf]

[A12] E.T. Rolls,  L. Franco and S.M. Stringer. The perirhinal cortex and long term familiarity memory. Quarterly Journal of Experimental Psychology, Section B, 58, pp. 234--245 (2005). [pdf]

[A11] E.T. Rolls,  J-Z. Xiang and L. Franco. Object, space, and object-space representations in the human hippocampus. Journal of Neurophysiology, 94, pp. 833--844 (2005). [pdf]

[A10] L. Franco, J.M. Jerez, and J.M. Bravo-Montoya. Role of Function Complexity and Network Size in the Generalization Ability of Feedforward Networks.  Lecture Notes in Computer Science, 3512, pp. 1--8 (2005). [pdf]

[A9] N.C. Aggelopoulos,  L. Franco and E.T. Rolls. Object perception in natural scenes: encoding by inferior temporal cortex simultaneously recorded neurons. Journal of Neurophysiology,  93,  pp. 1342--1357 (2005). [pdf]

[A8] L. Franco, E.T. Rolls, A. Treves and N.C. Aggelopoulos. The use of decoding to analyze the contribution to the information of the correlations between the firing of simultaneously recorded neurons. Experimental Brain Research,  155 pp. 370--384, (2004). [pdf]

[A7] E.T. Rolls, N.C. Aggelopoulos, L. Franco, and A. Treves. Information encoding in the inferior temporal visual cortex: contributions of the firing rates and the correlations between the firing of neurons. Biological Cybernetics,  90, pp. 19--32  (2004). [pdf]

[A6] L. Franco and S.A. Cannas. Non-glassy ground state in a long-range antiferromagnetic frustrated model in the hypercubic cell. Physica A, 332, pp. 337--348 (2004). [pdf]

[A5] J.M. Jerez, L. Franco and  I. Molina. CBA generated receptive fields implemented in a Facial expression recognition task. Lecture Notes in Computer Science,  2686,  pp. 734--741 (2003). [pdf]

[A4] E.T. Rolls, L. Franco, N.C. Aggelopoulos, and S. Reece. An information theoretic approach to the contributions of the firing rates and correlations between the firing of neurons. Journal of Neurophysiology, 89, pp. 2810--2822  (2003). [pdf]

[A3] L. Franco and S.A. Cannas. Generalization Properties of Modular Networks: Implementing the Parity Function. IEEE Transactions on Neural Networks, 12, pp. 1306--1313 (2001). [pdf]

[A2] L. Franco and S.A. Cannas. Generalization and Selection of Examples in Feed-Forward Neural Networks. Neural Computation, 12, pp. 2405-- 2426 (2000). [pdf]

[A1] L. Franco and S.A. Cannas. Solving Arithmetic Problems Using Feed-Forward Neural Networks. Neurocomputing, 18, pp. 61--79 (1998). [pdf]

Proceedings, Lecture Notes, Book chapters & other publications  

[B42] F.J. Moreno-Barea,  Jerez, J.M., Franco, L.  Data Augmentation Meta-Classifier Scheme  for imbalanced data sets.  Proceedings IEEE SSCI’22, Symposium Series on Computational Intelligence, Singapur. In Press (2022).

[B41] F.J. Moreno-Barea,  Jerez, J.M., Franco, L. GAN-Based Data Augmentation for Prediction Improvement Using Gene Expression Data in Cancer. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13352. Springer, Cham.  (2022). https://doi.org/10.1007/978-3-031-08757-8_3

[B40] F.J. Moreno-Barea,  Franco, L., Elizondo, D., Grootveld, M.  Data Augmentation Techniques to Improve Metabolomic Analysis in Niemann-Pick Type C Disease. (2022). In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13352. Springer,  (2022). https://doi.org/10.1007/978-3-031-08757-8_8

[B39] G. E. Juárez, F. D. Menéndez, C. H. Lafuente, J. Pérez, L. Franco and C. R. Rivero, Analysis and simulation of social behavior during the COVID-19 pandemic in Argentina, using intelligent agents. 2022 IEEE World Engineering Education Conference (EDUNINE), (2022), pp. 1-6. IEEE xplore.

[B38] C, Rodriguez-Rivero, J. Puchet, D. Patiño, P. Otaño,  L. Franco, & G. Juárez. Short-Term Rainfall Forecasting with E-LSTM Recurrent Neural Networks Using Small Datasets. Lecture Notes in Computer Science, 12465, pp. 258-270. (2020). Proceedings ICIC'2020, Bari, Italia.

[B37] G. García-López, José M. Jerez Aragonés,  L. Franco, & F. Veredas. A transfer-learning approach to feature extraction from cancer transcriptomes with deep autoencoders. Lecture Notes in Computer Science, 11506, pp. 912-924. (2019). Proceedings IWANN'2019, Gran Canaria, España.

[B36] D. Urda-Muñoz,  L. Franco, I. Turias, F. Veredas. Addition of pathway-based information to improve prediction in transcriptomics. Lecture Notes in Bioinformatics, 11466, pp. 200-208 (2019). Proceedings of the 2019 IWBBIO, Granada, España. 

[B35] F.J. Moreno-Barea, F. Strazzera, J.M. Jerez, D. Urda, & L. Franco. Forward Noise Adjustment Scheme for Data Augmentation. Proceedings of the 2018 Symposium Series on Computational Intelligence, Bengaluru, India. (2018). [pdf]

[B34] A.L. Fraga, M.G. Gramajo, F. Trejo, S. García, G. Juárez,  & L. Franco. SIMNET: Simulation-based exercises for Computer Network Curriculum through Gamification and Augmented Reality. Lecture Notes in Networks and Systems (Smart Industry & Smart Education), 47, pp. 627-635. (2019). ISBN: 978-3-319-95677-0. [pdf]

[B33]  D. Urda, L. Franco and J.M. Jerez. Classification of high dimensional data using LASSO ensembles. Proceedings IEEE SSCI'17, Symposium Series on Computational Intelligence, Honolulu, Hawaii, U.S.A. (2017). [pdf]

[B32]  C. Rodriguez Rivero, Y. Tupac, J. Pucheta, G. Juarez, L. Franco and P. Otaño. Time-series Prediction with BEMCA Approach: application to short rainfall series. Proceedings IEEE LA-CCI'17, Latin American Conference on Computational Intelligence, Cuzco, Perú. (2017). [pdf]

[B31] D. Urda, J. Montes-Torres, F. Moreno, L.  Franco & J.M. Jerez.  Deep learning to analyze RNA-Seq gene expression data. Lecture Notes in Computer Science, 10306, pp. 50-59. (2017).

[B30] J.L. Subirats, H. Mesa, F. Ortega, G.E. Juárez, J.M. Jerez, I. Turias,  & L. Franco. Solving Scheduling Problems with Genetic Algorithms using a Priority Encoding Scheme. Lecture Notes in Computer Science, 10305, pp. 52-61. (2017).

[B29] I.J. Turias, J.M. Jerez, L. Franco, H. Mesa, J.J. Ruiz Aguilar, J.A. Moscoso & M.J. Jiménez.  Prediction of carbon monoxide (co) atmospheric pollution concentrations using meteorological variables.  Proceeedings Air Pollution Conference 2017. WIT Press Conference Papers. 25-27 April (2017).

[B28]  D. Urda, R. Luque, N. Sánchez, L. Franco and J.M. Jerez. Machine learning models to search relevant genetic signatures in clinical context. Proceedings IJCNN, International Joint Conference on Neural Networks, Anchorage, Alaska,  pp.1649-1656. (2017).

[B27]  D. Urda, F. Aragón, L. Franco, F. Veredas and J.M. Jerez. L1-regularization model enriched with biological knowledge. Proceedings IWBBIO, Granada, (2017).

[B26]  J. Rodriguez-Alabarce, F. Ortega-Zamorano,  J.M. Jerez, K. Goreishi,  and L. Franco.  Thermal Comfort Estimation using a Neurocomputational Model. Proceedings IEEE Latin American Conference on Computational Intelligence,  (2016). best
        paper award thermal comfort[Best Paper Award]

[B25]  C. Rodríguez-Rivero,  J. Pucheta, A. Orjuela-Cañon and L. Franco. Noisy Chaotic time series forecast approximated by combining Renyi entropy with Energy associated to series method: application to rainfall series. Proc. IEEE Latin American Conference on Computational Intelligence, (2016).

[B24]  F. Ortega-Zamorano,  J.M. Jerez, I. Gómez,  and L. Franco. Deep Neural Network Architecture Implementation on FPGAs Using a Layer Multiplexing Scheme. Springer Series on Advances in Intelligent Systems and Computing, 474, pp. 79-86, (2016). ISBN: 978-3-319-40162-1

[B23] F. Ortega-Zamorano,  J.M. Jerez, G. Juárez, and L. Franco. FPGA implementation comparison between C-Mantec and Back-Propagation Neural Network Algorithms. Lecture Notes in Computer Science, 9095, pp. 197-208. (2015).

[B22]  F. Ortega-Zamorano,  J.M. Jerez, G. Juárez, J.O. Pérez and L. Franco. High Precision FPGA Implementation of Neural Network Activation functions. Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI'2014), pp. 55-60, (2014). ISBN: 978-1-4799-4486-6

[B21]  D. Urda, S. Chambers, I. Jarman, P. Lisboa, L. Franco and J.M. Jerez. Use of q-values to improve a genetic algorithm to identify robust gene signatures. Lecture Notes in Computer Science, 8623, pp. 199-206, (2015).

[B20]  F. Ortega-Zamorano, J.L. Subirats, J.M. Jerez, I. Molina and L. Franco. Implementation of the C-Mantec Neural Network Constructive Algorithm in an Arduino Uno Microcontroller. Lecture Notes in Computer Science, 7902, pp. 80-87, (2013). ISBN: 978-3-642-38678-7

[B19]  J.L. Subirats, R.M. Luque, D. Urda, F. Ortega, J.M. Jerez and L. Franco. Committee C-Mantec: A Probabilistic Constructive Neural Network. Lecture Notes in Computer Science, 7902, pp. 339-346, (2013). ISBN: 978-3-642-38678-7

[B18]  D. Urda, R.M. Luque, M.J.  Jiménez, I. Turias, L. Franco and J. M. Jerez. A constructive neural network to predict pitting corrosion status of stainless steel. Lecture Notes in Computer Science, 7902, pp. 88-95, (2013). ISBN: 978-3-642-38678-7

[B17]  R.M. Luque-Baena, D. Urda, J.L. Subirats, L. Franco, and J.M. Jerez. Analysis of Cancer Microarray Data using Constructive Neural Networks and Genetic Algorithms. Proceedings of the IWBBIO, International Work-Conference on Bioinformatics and Biomedical Engineering, pp. 55-64 (2013). ISBN: 978-84-15814-13-9

[B16]  J.J. Carneros, J.M. Jerez, I. Gómez & L. Franco. Data Discretization Using the Extreme Learning Machine Neural Network. Lecture  Notes in   Computer Science, 7666, pp. 281-288. Proceedings of the ICONIP 2012 Conference, Qatar, November 2012. [pdf]

[B15]  Y. Couce,  L. Franco, D. Urda, J.L. Subirats  and J.M. Jerez. Hybrid (Generalization-Correlation) method for feature selection in high dimensional DNA microarray prediction problems. Lecture Notes in Artificial Intelligence, 6692, pp. 202-209. Proceedings of the IWANN 2011 conference, Torremolinos, Spain, June 8-10, (2011). ISBN: 978-3-642-21498-1 [pdf]

[B14]  D. Urda, J.L. Subirats, L. Franco,  and J.M. Jerez. Constructive neural networks to predict breast cancer outcome by using gene expression profiles. Lecture Notes in Artificial Intelligence, 6096, pp. 317-326. Proceedings of the IEA-AIE, Córdoba, Spain, June 1-3, (2010). ISBN: 978-3-642-13021-2. [pdf]

[B13]  I. Gómez, L. Franco, J.M. Jerez & J.L. Subirats. Extension of the generalization complexity measure to real valued input data sets. Lecture Notes in Computer Science, 6063, pp. 86-94. Proceedings of the ISNN'2010, Shanghai, China, June 6-9, (2010). ISBN: 978-3-642-13277-3. [pdf]

[B12] N. Ribelles, J.M. Jerez, D. Urda, J.L. Subirats, A. Marquez,  C. Quero, E. Torres, L. Franco & E. Alba. Galén: Sistema de Información para la gestión y coordinación de procesos en un servicio de Oncología. RevistaeSalud, 6 (21) (2010).  [pdf]

[B11]  J.L. Subirats, L. Franco, I. Molina and J.M. Jerez. Active learning using a constructive neural network algorithm. Lecture Notes in Computer Science, 5164, pp. 803-811. Proceedings of the International Conference in Artificial Neural Networks (ICANN), Praga, República Checa, 3-6 September. (2008). [pdf]

[B10] J.L. Subirats, L. Franco, I. Gómez and J.M. Jerez. Computational capabilities of feedforward neural networks: the role of the output function. Proceedings of the XII CAEPIA'07, Salamanca, Spain, vol. II, pp. 231-238. ISBN: 978-84-611-8848-2. November 12-16 (2007).  [pdf]

[B9] L. Franco, J.L. Subirats, M. Anthony and J.M. Jerez. A New Constructive Approach for the Set of Linearly Separable Functions. Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN'06 - WCCI'06), Vancouver, Canada, pp. 9541-9546. ISBN: 0-7803-9490-9. July 16-21 (2006).  [pdf]

[B8] J.M. Jerez, I. Molina, L. Franco, and  J.L. Subirats. Missing Data Imputation in Breast Cancer Prognosis. Proceedings of the IASTED International Multiconference in Biomedical Engineering, Innsbruck, Austria, pp. 323-328. ISBN: 0-88986-576-0. February 15-17 (2006). [pdf]

[B7] L. Franco,  J.M. Jerez  and E. Alba. Artificial neural networks and prognosis in medicine. Survival analysis in breast cancer patients.  Proceedings of the European Symposium on Artificial Neural Networks (ESANN'05), Bruges, Belgium, pp. 91-102. ISBN 2-930307-05-6. April 27-29 (2005). [pdf]

[B6] L. Franco and M. Anthony. On a Generalization complexity measure for Boolean functions. Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN'04), Budapest, Hungary, pp. 973-978. ISBN 0-7803-8359. July 25-28 (2004). [pdf]

[B5] L. Franco and J.M. Jerez. Modularity: a Natural Way of Improving Generalization. Proceedings of the 3rd WSES International Conference on Neural Networks, Interlaken, Switzerland.  ISBN: 960-8052-48-3. February 11-15. (2002). [abstract]

[B4] L. Franco and A. Treves. A Neural Network Face Expression Recognition System using Unsupervised Local Processing.  Proceedings of the Second International Symposium on Image and Signal Processing and Analysis (ISPA 01), Croatia, pp. 628-632. ISBN: 953-96769-4-0.  June 19-21. (2001). [pdf]

[B3] L. Franco, A. Montagnini  and A. Treves. A modular Neural Network for Facial Expression Recognition. Proceedings of the International Conference of Cognitive Modeling (ICCM). Halifax, VA. July 26–28 (2001).

[B2] L. Franco and S.A. Cannas. Improving Network Generalization through Selection of Examples. Proceedings of IV Argentinian Meeting on Computer Science (CACIC 98). Neuquén. Argentina, pp. 373-382. October 26-31 (1998). [pdf]

[B1] L. Franco and S. A. Cannas. Energy landscape and learning on a neural network for the sum problem. Proceedings of the Second International Workshop on (semi) numerical techniques in polynomial equation solving TERA 97, Trabajos de Matemática serie B, FaMAF, Universidad Nacional de Cordoba, Argentina, pp. 28-33. September 4-6 (1997). [pdf]

Teaching related publications & projects   

[C2]   J.M. Jerez, D. Bueno,  I.A. Molina, D. Urda, and L. Franco. Improving Motivation in Learning Programming Skills for Engineering Students.  International Journal of Engineering Education, 28, pp. 202-208. (2012). [pdf]

[C1]   M. C. Alonso, J. Béjar, A. Cabeza, A.G. De la Torre, L. Franco, E. García-Rosado, Y. Vida. Elaboración de unidades didácticas con herramientas docentes Interdisciplinares para la adaptación de asignaturas al EEES. IV Jornadas de Innovación Educativa y Enseñanza Virtual en la Universidad de Málaga. (2010). [pdf]

Grants supporting our research activities:

[G7] UMA20-FEDERJA-045, Junta de Andalucía, Proyectos FEDER, AIomics: Aplicaciones de Técnicas de Inteligencia Artificial a Conjuntos de Datos
Ómicos Para la Predicción en Cáncer.  57.182€. IPs: Leonardo Franco y Miguel Atencia.

[G6] TIN2017-88728-C2-1-R Avances en el diseño y adaptación de algoritmos de aprendizaje profundo para suaplicación a problemas en las areas de biomedicina y contaminación atmosférica. 94.501 €. IPs: José M. Jerez Aragonés y Leonardo Franco.

[G5]  TIN2014-58516-c2-1-R, MICIIN:  Diseño de estrategias adaptativas en sensores inteligentes y aplicación a la predicción de contaminantes atmosféricos en entornos locales. 83.611€.  IP: Leonardo Franco - José M. Jerez Aragonés.

[G4]  P10-TIC-5770, Junta de Andalucía: Modelo Neurocomputacional de confort térmico en espacios públicos urbanos. 136.080 € IP: Leonardo Franco.

[G3]  TIN2008-04985, MICIIN : Nuevas estrategias en el diseño de sistemas neurocomputacionales. Aplicación al procesamiento de información en oncología. 89.540€.  IP: Leonardo Franco.

[G2]  A/6030/06 y A/12805/07, AECI: Inferencia Bayesiana mediante redes neuronales y su aplicación a biomedicina. 42.500 €. IP: Leonardo Franco.

[G1]  Proyecto Ramón y Cajal, MICIIN, Financiación adicional: Redes Neuronales: Estudio de la complejidad de generalización y aplicaciones a la predicción de recidiva de cáncer de mama. 12.000€. IP: Leonardo Franco.

[GE4] TIN2010-16556, MICIIN, Sistemas inteligentes bioinspirados aplicados a medicina personalizada, 92.000 €. IP: José M. Jerez Aragonés. (10/01/2011 ---31/12/2013)

[GE3] P08-TIC-04026, Junta de Andalucía, Proyectos de Investigación de Excelencia: Diseño de métodos constructivos en sistemas neurocomputacionales y aplicación a minería de datos en Oncología,  225.635,68 €. IP: José M. Jerez Aragonés. (12/01/2008 ---11/01/2012)

[GE2]  TIN2005-02984 , MICIIN: :  Diseño de un sistema neurocomputacional para predicción de recidiva en cáncer de mama operable . Estudio de la relación complejidad-generalización en redes neuronales artificiales. 87.000€.  IP: José M. Jerez Aragonés

[GE1]  P06-TIC-01615, Junta de Andalucía: Sistemas de teledetección Inteligentes. 190.536 €. IP: José Muñoz Perez


[T2] L. Franco. Learning and Generalization Properties of Feed-Forward Neural Networks. Ph.D. Thesis. Biblioteca FaMAF. Universidad Nacional de Córdoba. Argentina. (2000). [pdf]  (In Spanish)

[T1] L. Franco. Study of Learning Properties of Feed-Forward Neural Networks using Generalized Simulated Annealing algorithms. Thesis submitted within the requirements to obtain the degree of Licenciado in Physics. FaMAF Library. Argentina. (1995). [abstract]

Some Recent & Future Presentations: 

"Bases teóricas para la aplicación de la Inteligencia Artificial en Investigación clínica. Nuevos conceptos." 1er Simposio de Inteligencia Artificial en Oncología Médica. Centro Pompidou, Málaga. 21 de Abril de 2022.

“Avances y aplicaciones de la Inteligencia Computacional en Medicina de Precisión”, Ciclo de conferencias sobre inteligencia computacional 2022, Universidad de Quevedo, Ecuador, 27 de Enero de 2022.

"Artificial Intelligence Applications for Precision Medicine", 15 de Octubre de 2021, Jornadas de Formación dentro del proyecto Europeo  PARENT, Cádiz.

"Internet of things y su papel en la transformación de la asistencia sanitaria", 19 de Octubre de 2020, Congreso de la Sociedad Española de Oncología Médica (SEOM 2020).

"Aplicaciones de Deep Learning a problemas con pocos datos: data augmentation y transfer learning". 1 de Julio de 2020. Famaf-UNC

"Internet of things y su papel en la transformación de la asistencia sanitaria y la medicina del futuro", Cursos de Verano sobre "Ciudades Inteligentes: medio ambiente, transporte y salud" de la Universidad de Cádiz, 24 de Julio de 2019.

"The Artificial Intelligence Revolution: Recent advances and applications in healthcare", Université  Cote d'Azur, Niza, 27th June, 2019.

"Applying Deep Learning to Biomedical problems", International Symposium on Deep Learning and data mining in biology and medicine, 6-7  February 2019, UMPA, Lyon, France.

"From BackPropagation to Deep Learning", Research Seminar, Málaga, Spain, March 2016.

"Neural Network Prediction Capabilities and Applications" given at UMI-IFAECI (CNRS-CONICET-University of Buenos Aires), September 2016.

"Computational Intelligence applications in Smart Sensor Systems", I Seminario sobre Inteligencia Computacional, Yachay Tech, Ecuador, Febrero de 2016.

"Impacto de los drones en la sociedad actual", Café científico SIDETEC, Tucumán, 7 de Abril de 2015.

"Data Mining & Applications to Biological Data", Faculty of Life and Health Sciences, De Montfort University, Leicester, United Kingdom, May, 2014.

"Data Discretization Using the Extreme Learning Machine Neural Network", ICONIP'2012, Qatar, Noviembre de 2012.

"Inteligencia Artificial: Últimos avances en el área de las redes neuronales", Café Científico organizado por la SIDETEC, Tucumán.  4 de Agosto de 2012.

"Redes Neuronales". Ponencia Magistral invitada en el congreso IEEE BETCON'2012, La Paz, Bolivia. 16 de Julio de 2012.

"Nuevas estrategias en el aprendizaje de Redes Neuronales mediante el uso de algoritmos Constructivos", FACET UNT, Tucumán, Argentina, 5th August 2011.

"Extension of the Generalization Complexity Measure to Real Valued Input Data Sets", ISNN 2010, Shanghai, China, 7th June 2010.

"Information encoding in the inferior temporal code", Max Planck Institute for Cybernetics, Tubingën, January 23rd 2009.

"Active learning using a constructive neural network algorithm", ICANN' 2008, Prague, Czech Republic, September 6th, 2008.

"Codificación de la información en la corteza visual: Experimentos y modelos relacionados con el reconocimiento de objetos " 

X Neurotaller Argentino de Neurociencia, Huerta Grande,  Córdoba, Argentina, 11-13 Abril de 2008.     

"Early breast cancer prognosis prediction and rule extraction using a new constructive neural network algorithm". IWANN 2007, San Sebastián, España, June  2007.

"Sensory processing", Curso de Neurociencia computacional en la 2da Escuela Argentina de Biología y Matemática, BIOMAT 2007,  La Falda, Córdoba, Argentina, 29 Junio -7 Julio de 2007.

"Modelos de Predicción" (Predictive models). Curso de Doctorado en Oncología, Facultad de Medicina, Málaga,  April 20

"Codificación Neuronal y el reconocimiento de objetos en la corteza infero-temporal",  Jornadas de Ciencia y Sociedad a los 50 años de FaMAF, Córdoba, Argentina, Diciembre de 2006.

"A new constructive approach for the set of linearly separable functions", World Conference on Computational Intelligence (WCCI) , IJCNN'07, Vancouver, Canada,  July 2006.

"Synthesis of Boolean functions by threshold circuits", Instituto Nacional de Microelectrónica de Sevilla, Sevilla,  July 2006.

"Generalization Properties and Computational Capabilities of Feed-forward Neural Networks", Neural Computing Research Group, Aston University, May 2006.

"The Importance of function complexity on the neural network architecture selection process ", Centre for Computational Intelligence, DeMontfort University, May 2006.

"Modelos de Predicción" (Predictive models). Curso de Doctorado en Oncología, Facultad de Medicina, Málaga,  May 2006

"Information transmission in the inferior temporal visual cortex". Cosyne'06. Salt Lake City, March 2006.

"Role of function complexity and network size in the generalization ability of feedforward networks". IWANN'05, Vilanova y la Geltrú. June 2005

"Information theory and population coding",  MSc in Neuroscience Lecture. University of Oxford, February 2004.

"The  generalization complexity of Boolean functions",  Universidad de Málaga, November 2003.

PhD Thesis, Master (MSc) and Student Projects (PFCs & TFGs) supervised:

PhD 5: Desarrollo de técnicas de aumento de datos para la aplicación de aprendizaje profundo en problemas de bioinformática. Autor:  Francisco Javier Moreno Barea.  18 de Enero de 2023.  Codirigida con J.M. Jerez Aragonés.

PhD 4: Algoritmos de aprendizaje neurocomputacionales para su implementación hardware. Autor:  Francisco Ortega Zamorano.  19 de Junio de  2015.  Codirigida con J.M. Jerez Aragonés.

PhD 3: Diseño de sistemas neurocomputacionales en el ámbito de la biomedicina. Autor:  Daniel Urda Muñoz.  18 de Febrero de  2015.  Codirigida con J.M. Jerez Aragonés.   
PhD 2: Diseño de Algoritmos Constructivos de Redes Neuronales para la Síntesis de Arquitecturas con Gran Capacidad de Generalización. Autor:  José Luis Subirats Contreras.  19 de Junio de  2013.  Codirigida con J.M. Jerez Aragonés.      

PhD 1: Complejidad de Generalización en Redes Neuronales: Aplicación al problema de selección de arquitecturas para datos de entrada continua. Autor:  Iván Gómez Gallego, Octubre de 2012.  Codirigida con J.M. Jerez Aragonés.   

MSc 22: Publicidad con influencers en TikTok:  Análisis de casos de éxito y propuesta de aplicación. Ana López García. Julio de 2023.

MSc 21: Estrategias de gamificación aplicadas al Marketing Digital. Jesús Luna Mejías. Octubre de 2022.

MSc 20: Sistemas de recomendación: estado actual y percepción de los usuarios. Tania Sánchez Pedroza. Octubre de 2022.

MSc 19: Plan de Marketing Digital para la empresa Tecnoturbines. María Antonia Mejías. Octubre de 2021.

MSc 18: Estrategias de marketing digital para empresas de hostelería Malagueñas. Pedro Belver Ceres. Octubre de 2020.

MSc 17: Asesoramiento Digital. Análsis e implementación. Alba del Toro Gómez-Hidalgo. Octubre de 2020.

MSc 16: Uso y Aplicaciones de la Realidad Aumentada en Hostelería. Ana María Herrera Cebreros. Marzo de 2020. 

MSc 15: Aplicación de chatbots en marketing digital. Lorena Bautista Romero. Octubre de 2019.

MSc 14: Bridging the gap between Deep Learning and Neuroscience. Fiammetta Strassera. Octubre de 2018.

MSc 13: Deep Learning en Electroencefalografía. Victoria Eugenia Fernández Sánchez. Octubre de 2018.

MSc 12: Buscadores y metabuscadores en el sector hotelero. Jesús Cádiz Palacios. Octubre de 2018.

MSc 11: Aplicación de técnicas de Big Data y Business Analytics en pequeñas y medianas empresas. Rosario Jiménez Herrera. Octubre de 2018.

MSc10: Análisis de estrategias basadas en Neuromarketing para determinar la importancia del COLOR en los anuncios de publicidad SEM. Sergio Solís Serrano. Marzo de 2017.

MSc 9: Transformación digital de una empresa de seguros: caso TM Seguros. Rocío Espinar Carnero. Marzo de 2017.

MSc 8: Estrategias creativas en social media y creación de contenidos para una empresa de moda. Caso Gisela Intimates. Estefanía Márquez Tejada. Marzo de 2016.

          MSc 7: Análisis de opinión utilizando la red social Twitter. Marta Pacheco Compaña. Marzo de 2016.

         MSc 6: Modelos de confort térmico mediante redes neuronales artificiales. José A. Rodriguez Alabarce. Enero de  2016. Codirigida con Jesús Martínez Cruz.
        MSc 5:  Implementación de un algoritmo de red neuronal constructivo en un microcontrolador Arduino. Francisco Ortega Zamorano, Julio de 2013.
         MSc 4:  Aplicación de algoritmos de clustering y clasificación a datos de pacientes operados de cáncer de mama. Sindy Rengifo, 2011.

          MSc 3:  Selección de variables relevantes en datos de microarrays de ADN, Yasel Couce Sardiñas, 2011.

          MSc 2:  XONTEC: Un nuevo método de redes neuronales constructivas, José Luis Subirats Contreras, 2010.

          MSc 1: Análisis del ``Memory-Prediction FrameWork'' para aplicaciones inteligentes, Diego Rosado, Junio de 2008.

         TFG 19: Traducciones en tiempo real mediante procesamiento del lenguaje natural en un entorno de realidad aumentada, Álvaro Lloret, Julio de 2022.          

         TFG 18: Aumento de datos mediante redes GAN para mejora en la predicción utilizando conjuntos de patrones de naturaleza ómica, Sandra Castro Labrador, Julio de 2021.

          TFG 17:  Aplicación de Deep Learning a la predicción de turismo en la Costa del Sol, Hind Labzioui, Julio de 2020.

          TFG 16: Aproximación a la predicción de proteínas mediante técnicas de aprendizaje computacional. Guillermo Rejón. Septiembre de 2018.

TFG 15: Mahjong - Implementación y desarrollo de agente inteligente: Desarrollo de cliente y agente inteligente, Álex Javier Porras Palma, Septiembre de 2018.

TFG 14: Mahjong - Implementación y desarrollo de agente inteligente: Implementación del juego, José Benítez Doblado, Septiembre de 2018.

TFG 13: Análisis de citoquinas en pacientes adictos a la cocaína, Rosa María Maza Quiroga, Julio de  2017.

PFC 12: Implementación del algoritmo DASG en lenguaje R, José Javier Guerrero Montero, Enero de 2016.

PFC 11: Personal Trainer: Aplicación Web para la gestión deportiva,  Miguel Chamizo Rosales, Enero de 2016.

PFC 10: Photo Business Agent (Aurora), David Gómez Ruiz, Enero  de 2016.

PFC 9: Análisis de datos de microarray de adn utilizando el algoritmo constructivo c-mantec para prognosis de cáncer de mama, Mª Isabel Vinuesa Muñoz, Julio de 2015.

PFC8: Desarrollo, implementación y aplicación de un algoritmo constructivo de redes neuronales utilizando funciones de base radial, Nicolás J. Gil Ruiz, Octubre de 2013.

PFC7: Aplicación de Redes neuronales probabilísticas en identificación de opiniones cualificadas de auditoría, Omar Hassani Zerrouk, Octubre de  2013.

PFC6: Desarrollo e implementación de un nuevo algoritmo para la discretización de datos usando redes neuronales y su aplicación a problemas de clasificación, Juan Jesús Carneros Godoy, Diciembre de 2011. El artículo [B16] fue obtenido a partir de este PFC.

PFC5: Implementación de un algoritmo para el tratamiento de datos de tipo real, ordinal y categórico y su transformación en datos binarios. Aplicación al uso del algoritmo constructivo DASG en problemas de clasificación. Esther Ramos Retamero, Julio de  2009.

PFC4: Método para evitar el sobre-entrenamiento en el algoritmo DASG, su implementación en el entorno WEKA y su aplicación a problemas de predicción de cáncer de mama, Raúl Pérez Arrebola, Junio de 2009.

PFC3: Sistema integral para la coordinación de procesos en SOM. Daniel Urda Muñoz, 2008.

          PFC2: Estudio de la capacidad de generalización en redes neuronales,  José Luis Subirats Contreras, Marzo de 2006.

PFC1: Predicción de recidiva de cáncer de mama usando redes neuronales artificiales: aplicaciones a la base de datos del GEICAM. Eduardo Jerez, Junio de 2005.

External Collaborators:

Dr. Martin Anthony, Centre for Discrete Mathematics, London School of Economics (LSE), London, United Kingdom.
Dr. Sergio A. Cannas. FaMAF, Córdoba, Argentina.

   Dr. David Elizondo, De Montfort University, Leicester, United Kingdom.

   Dr. Vassilis Hadjianasstassiou,  St. Thomas Hospital, London, United Kingdom. 

   Dr. Paulo Lisboa, John Moores University, Liverpool, United Kingdom.

   Dr. Juan P. Neirotti, Aston University, United Kingdom.

   Dr. Omar Osenda, FaMAF, Córdoba, Argentina.

   Dr. Cristián Rodriguez Rivero, University of California Davis, U.S.A.
   Dr. Edmund T. Rolls, University of Oxford, Oxford, United Kingdom.

  Dr. Ruxandra Stoean, Craiova University, Romania.  

   Dr. Alessandro Treves, SISSA, Trieste, Italy.

   Dr. Ignacio Turias, Universidad de Cádiz, Algeciras, Spain.

   Dr. Andreas Wendemuth, Magdeburg, Germany.

Local Collaborators (Members and collaborators of the ICB research group) :

     Dr. Emilio Alba

Dr. Miguel Atencia

    Dr. Gonzalo Claros   
    Dr. Iván Gómez

    Dr. José M. Jerez Aragonés

    Dr. Gonzalo Joya Caparrós

    Dr. Rafael Luque-Baena
    Dr. Ignacio Molina    
    Dr. Francisco Ortega

    Dr. Nuria Ribelles
    Dr. José L. Subirats

    Dr. Daniel Urda Muñoz

    Dr. Francisco Veredas Navarro   

    MSc. Gustavo E. Juárez   
    MSc. Héctor Mesa

    MSc. Julio Montes Torres

    MSc. Francisco Javier Moreno

    MSc. Fiammetta Strazzera

    MSc. Roberth Figueroa



SCEIE-UMSA: Panelista invitado: Condiciones y experiencias del sistema universitario europeo y de la formación profesionalizante en tiempos de pandemia. 16 de Junio de 2020. Educación, Tecnología y Ciencia. Organizado por la Universidad de San Marcos, La Paz, Bolivia.


IWANN'2019. Organizing Special Session on "Deep Learning models in Heathcare and Biomedicine".
FGUMA open course on Deep Learning, 2018-2019-2020.    fguma DL deep klearning aprendizaje profundo
                  curso estudiantes

IBIMA selected our publication Ortega et al. [A44] as candidate for the IBIMA-DIVULGA award, 2018.

Yachay Tech reports about our latest work, 2016.

Agencia SINC reports about our research on "Smart sensors for real time prediction", March 2015.

IEEE CIS DAY organizer at BETCON 2013. Santa Cruz de la Sierra, Bolivia, 10-12 de Abril de 2013.

An interview (In spanish) about Neuroscience for the Radio program "Hablemos de Ciencia con Avid".

IEEE FOCI 2013 will take place in Singapore as part of the IEEE SSCI 2013  (I am part of the organizing committee).

Invited speaker to Betcon 2012, La Paz, Bolivia

IWANN'2011 will take place in Torremolinos, Málaga  (I am part of the local committee).

IEEE FOCI 2011 will be in Paris (I am part of the Programme committee).

Special Session on Constructive Neural Networks at ICANN'08

X Taller Argentino de Neurociencias.     Nota del Diario La Voz del Interior (Córdoba, Argentina)

2nd Argentine school on Mathematics and Biology, BIOMAT-07    

An interview (in spanish) for Málaga Hoy about  our  research programme on Breast Cancer (2005).

An Interview (in spanish) for La Gaceta about my research in Oxford (2002).

APARU   Association of Professionals from Argentina working in the UK.


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