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Meza-Lovon G.L.,National University of San Agustín | Meza-Lovon G.L.,San Pablo Catholic University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

Over the last years, the interest in preserving digitally ancient documents has increased resulting in databases with a huge amount of image data. Most of these documents are not transcribed and thus querying operations are limited to basic searching. We propose a novel approach for transcribing historical documents and present results of our initial experiments. Our method divides a text-line image into frames and constructs a graph using the framed image. Then Dijkstra algorithm is applied to find the line transcription. Experiments show a character accuracy of 79.3%. © Springer-Verlag Berlin Heidelberg 2012.


Soto-Anari M.,San Pablo Catholic University | Flores-Valdivia G.,Asociacion Peruana de Enfermedad de Alzheimer y Otras Demencias | Fernandez-Guinea S.,Complutense University of Madrid
Revista de Neurologia | Year: 2013

Introduction. Cognitive reserve modulates between neurodegenerative processes and the clinical manifestations of cognitive impairment and dementia. This construct is associated with the capacity to optimise the execution of tasks by recruiting neuronal networks and with the use of alternative cognitive strategies that would be mediated by formal educational processes. Aim. To analyse the level of reading skills as a measure of cognitive reserve and as a reliable predictor of performance in tests for evaluating different cognitive domains. Subjects and methods. The sample consisted of 87 healthy subjects who were asked to complete the Word Naming test as an indicator of the level of reading skills; this allowed us to divide the sample into subjects with a low and a high level of reading ability. A broad neuropsychological battery was then applied. Results. The subjects with a low level of reading skills displayed lower general cognitive performance, reduced processing speed and cognitive deficits. Furthermore, the level of reading skills is a better predictor of performance in executive functions and general cognitive performance than the variables age, years of schooling and education. Conclusions. The level of reading skills has shown itself to be a good measure of cognitive reserve and a reliable predictor of executive and cognitive functioning in ageing. © 2013 Revista de Neurología.


Mayhua-Lopez E.,Charles III University of Madrid | Mayhua-Lopez E.,San Pablo Catholic University | Gomez-Verdejo V.,Charles III University of Madrid | Figueiras-Vidal A.R.,Charles III University of Madrid
IEEE Transactions on Neural Networks and Learning Systems | Year: 2012

In this brief, we propose to increase the capabilities of standard real AdaBoost (RAB) architectures by replacing their linear combinations with a fusion controlled by a gate with fixed kernels. Experimental results in a series of well-known benchmark problems support the effectiveness of this approach in improving classification performance. Although the need for cross-validation processes obviously leads to higher training requirements and more computational effort, the operation load is never much higher; in many cases it is even lower than that of competitive RAB schemes. © 2012 IEEE.


Santisteban J.,San Pablo Catholic University | Tejada-Carcamo J.,San Pablo Catholic University
Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 | Year: 2015

How can we retrieve meaningful information from a large and sparse graph?. Traditional approaches focus on generic clustering techniques and discovering dense cumulus in a network graph, however, they tend to omit interesting patterns such as the paradigmatic relations. In this paper, we propose a novel graph clustering technique modelling the relations of a node using the paradigmatic analysis. We exploit node's relations to extract its existing sets of signifiers. The newly found clusters represent a different view of a graph, which provides interesting insights into the structure of a sparse network graph. Our proposed algorithm PaC (Paradigmatic Clustering) for clustering graphs uses paradigmatic analysis supported by a asymmetric similarity, in contrast to traditional graph clustering methods, our algorithm yields worthy results in tasks of word-sense disambiguation. In addition we propose a novel paradigmatic similarity measure. Extensive experiments and empirical analysis are used to evaluate our algorithm on synthetic and real data. © 2015 IEEE.


Mayhua-Lopez E.,San Pablo Catholic University | Gomez-Verdejo V.,Charles III University of Madrid | Figueiras-Vidal A.R.,Charles III University of Madrid
Information Fusion | Year: 2015

Boosting algorithms pay attention to the particular structure of the training data when learning, by means of iteratively emphasizing the importance of the training samples according to their difficulty for being correctly classified. If common kernel Support Vector Machines (SVMs) are used as basic learners to construct a Real AdaBoost ensemble, the resulting ensemble can be easily compacted into a monolithic architecture by simply combining the weights that correspond to the same kernels when they appear in different learners, avoiding to increase the operation computational effort for the above potential advantage. This way, the performance advantage that boosting provides can be obtained for monolithic SVMs, i.e., without paying in classification computational effort because many learners are needed. However, SVMs are both stable and strong, and their use for boosting requires to unstabilize and to weaken them. Yet previous attempts in this direction show a moderate success. In this paper, we propose a combination of a new and appropriately designed subsampling process and an SVM algorithm which permits sparsity control to solve the difficulties in boosting SVMs for obtaining improved performance designs. Experimental results support the effectiveness of the approach, not only in performance, but also in compactness of the resulting classifiers, as well as that combining both design ideas is needed to arrive to these advantageous designs. © 2014 Elsevier B.V.All rights reserved.


Braga S.L.,Pontifical Catholic University of Rio de Janeiro | Milon J.J.,San Pablo Catholic University
International Journal of Heat and Mass Transfer | Year: 2012

An experimental device was developed to study dendritic ice growth in supercooled water inside cylindrical capsules. The capsule materials investigated were acrylic, PVC (polyvinyl chloride), bronze and aluminum. The internal diameter of all the capsules was 45 mm. The results indicate that dendritic ice appears only in supercooled water at the start of nucleation. Blockage by dendritic ice growth was classified according to the capsule material and the coolant temperature. Total blockage (100%) and partial blockage (25%, 50% and 75%) was observed. Dendritic ice growth was shown and analyzed in photographic sequences. © 2012 Elsevier Ltd. All rights reserved.


Ludena-Choez J.,Charles III University of Madrid | Ludena-Choez J.,San Pablo Catholic University | Gallardo-Antolin A.,Charles III University of Madrid
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

In this paper, we propose a new front-end for Acoustic Event Classification tasks (AEC). First, we study the spectral contents of different acoustic events by applying Non-Negative Matrix Factorization (NMF) on their spectral magnitude and compare them with the structure of speech spectra. Second, from the findings of this study, we propose a new parameterization for AEC, which is an extension of the conventional Mel Frequency Cepstrum Coefficients (MFCC) and is based on the high pass filtering of acoustic event spectra. Also, the influence of different frequency scales on the classification rate of the whole system is studied. The evaluation of the proposed features for AEC shows that relative error reductions about 12% at segment level and about 11% at target event level with respect to the conventional MFCC are achieved. © 2013 Springer-Verlag Berlin Heidelberg.


Echegaray-Calderon O.A.,San Pablo Catholic University | Barrios-Aranibar D.,San Pablo Catholic University
2015 Latin-America Congress on Computational Intelligence, LA-CCI 2015 | Year: 2016

In this research, we propose to use a Genetic Algorithm with an Artificial Neural Network as fitness function in order to solve one of the most important problems in predicting academic success in higher education environments. Which is to find what are the factors that affect the students' academic performance. Also, using the same Artificial Neural Network as a predictor. To solve the problem, each individual of the genetic algorithm represents a group of factors, which will be evaluated with the fitness function seeking to obtain the optimal individual (group of factors) to predict academic performance. Then, with the same Artificial Neural Network we will classify students' academic grades in order to predict their semester final grades. With this technique, it was possible to reduce the initial amount of 39 factors (founded in the literature) to only 8. The prediction accuracy is 84.86%. © 2015 IEEE.


Chire Saire J.E.,San Pablo Catholic University | Tupac V.Y.J.,San Pablo Catholic University
2015 Latin-America Congress on Computational Intelligence, LA-CCI 2015 | Year: 2016

This work proposes, implements and evaluates the FP-QIEA-R model as a new quantum inspired evolutionary algorithm based on the concept of quantum superposition that allows the optimization process to be carried on with a smaller number of evaluations. This model is based on a QIEA-R, but instead of just using quantum individuals based on uniform probability density functions, where the update consists on change the width and mean of each pdf; this proposal uses a combined mechanism inspired in particle filter and multilinear regression, re-sampling and relative frequency with the QIEA-R to estimate the probability density functions in a better way. To evaluate this proposal, some experiments under benchmark functions are presented. The obtained statistics from the outcomes show the improved performance of this proposal optimizing numerical problems. © 2015 IEEE.


Camargo J.A.,San Pablo Catholic University | Barrios-Aranibar D.,San Pablo Catholic University
Advances in Intelligent Systems and Computing | Year: 2016

Nowadays Decentralized Partial Observable Markov Decision Process framework represents the actual state of art in Multi-Agent System. Dec-POMDP incorporates the concepts of independent view and message exchange to the original POMDPmodel, opening newpossibilities about the independent views for each agent in the system. Nevertheless there are some limitations about the communication. About communication on MAS, Dec-POMDP is still focused in the message structure and content instead of the communication relationship between agents, which is our focus. On the other hand, the convergence on MAS is about the group of agents convergence as a whole, to achieve it the collaboration between the agents is necessary. The collaboration and/or communication cost in MAS is high, in computational cost terms, to improve this is important to limit the communication between agents to the only necessary cases. The present approach is focused in the impact of the communication limitation on MAS, and how it may improve the use of system resources, by reducing computational, without harming the global convergence. In this sense δ-radius is a unified algorithm, based on Influence Value Reinforcement Learning and Independent Learning models, that allows restriction of the communication by the variation of δ. © Springer International Publishing Switzerland 2016.

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