He currently acts as Editor in Chief of the international journals “Information Fusion” (Elsevier) and “Progress in Artificial Intelligence” (Springer). He acts as editorial member of a dozen of journals.
He received the following honors and awards: ECCAI Fellow 2009, IFSA Fellow 2013, 2010 Spanish National Award on Computer Science ARITMEL to the “Spanish Engineer on Computer Science”, International Cajastur “Mamdani” Prize for Soft Computing (Fourth Edition, 2010), IEEE Transactions on Fuzzy System Outstanding 2008 and 2012 Paper Award (bestowed in 2011 and 2015 respectively), 2011 Lotfi A. Zadeh Prize Best paper Award of the International Fuzzy Systems Association, 2013 AEPIA Award to a scientific career in Artificial Intelligence, and 2014 XV Andalucía Research Prize Maimónides (by the regional government of Andalucía), 2017 Security Forum I+D+I Prize, and 2017 Andalucía Medal (by the regional government of Andalucía), 2018 “Granada: Science and Innovation City”.
He has been selected as a Highly Cited Researcher (in the fields of Computer Science and Engineering, respectively, 2014 to present, Clarivate Analytics).
His current research interests include among others, soft computing (including fuzzy modeling, evolutionary algorithms and deep learning), computing with words, information fusion and decision making, and data science (including data preprocessing, prediction and big data).
Quality Data to drive Deep Learning applications
Quality data requires a data preprocessing analysis to adapt the raw data to fulfill the input demands of each learning algorithm. Data preprocessing is an essential part of any data mining process.
In contrast to the classical classification models, the high abstraction capacity of CNNs allows them to work on the original high dimensional space, which reduces the need for manually preparing the input. However, a suitable preprocessing is still important to improve the quality of the result. One of the most used preprocessing techniques with CNNs is data augmentation for small image datasets, which increases the volume of the training dataset by applying several transformations to the original input. There are other guided preprocessing procedures for specific problems, like brightness and other images features.
In this talk we present the connection between deep learning and data guided preprocessing approaches throughout all families of methods used to improve the deep learning capabilities, together with some applications in different areas.