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.
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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holder.
Books:
[B3]
Computational Science –
ICCS 2024. Franco, L., de Mulatier, C.,
Paszynski, M., Krzhizhanovskaya,
V., Dongarra, J., Sloot, P.M.A.
24th International
Conference, Malaga, Spain, July 2–4, 2024, Proceedings,
Parts I-VII. [Book]
[B2] Constructive
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] 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]S.
Bottini, F. Emmert-Streib & L. Franco (2024).
Editorial: AI and Multi-Omics for
Rare diseases: Challenges, Advances and Perspectives.
FRONTIERS IN MOLECULAR BIOSCIENCES,
11:1392943. [pdf]
[A54] 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]
[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]
[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]
[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
[B44] F.J. Moreno-Barea,
Jerez, J.M., Franco, L. (2024). Data augmentation to improve
molecular subtype prognosis prediction in breast cancer.
International Conference on Computational Science. Lecture Notes
in Computer Science,
[B43] F.Alhoseinifakhraei, S., Franco, L. (2024).Semantic Web
usage in the tourism industry in Andalusia, Spain. International
Conference on Tourism, Management and Hospitality.
[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. pp. 1392-1399, doi:
10.1109/SSCI51031.2022.10022209.
(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]
[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
Thesis:
[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.
"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, DeMontfortUniversity, 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 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 23: Marketing Digital en
el Metaverso. Aplicaciones de la Inteligencia Artificial.
María Rosa Villatoro Lozano. Junio de 2024.
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. 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
Miscellaneous
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.
DLBM Invited
speaker at INTERNATIONAL SYMPOSIUM ON DEEP LEARNING AND
DATA MINING IN BIOLOGY AND MEDICINE, 6-7 February 2019
Lyon (France).
IWANN'2019. Organizing
Special Session on "Deep Learning models in Heathcare and
Biomedicine". FGUMA open course on Deep
Learning, 2018-2019-2020.
IBIMA selected our publication
Ortega et al. [A44] as candidate for the
IBIMA-DIVULGA award, 2018.
Computational Science –
ICCS 2024. 
Escenario de
Emisiones de Gases de Efecto Invernadero en la Ciudad de
Málaga. 
[Best
Paper Award]
