Publicado en Patter Recognition ( PDF ) En este trabajo, abordamos los problemas de la actividad humana y el reconocimiento de los gestos de las manos utilizando secuencias de datos en 3D obtenidas a partir de esqueletos de cuerpo entero y de manos, respectivamente. Para ello, proponemos un enfoque basado en el aprendizaje profundo para que el 3D temporal plantee problemas de reconocimiento basado en la combinación de una Red Neural Convolucional (CNN) y una red recurrente de Larga Memoria a Corto Plazo (LSTM). También presentamos una estrategia de formación en dos etapas que, en primer lugar, se centra en la formación de CNN y, en segundo lugar, ajusta el método completo (CNN+LSTM). Las pruebas experimentales demostraron que nuestro método de entrenamiento obtiene mejores resultados que una estrategia de entrenamiento de una sola etapa. Además, proponemos un método de aumento de datos que también ha sido validado experimentalmente. Por último, realizamos un amplio estudio experiment...

Publicado en Lecture Notes in Computer Science Abstract Nowadays, Deep Learning is one of the most popular techniques which is used in several fields like handwriting text recognition. This paper presents our propose for a handwritten digit sequences recognition system. Our system, based in two stage model, is composed by Convolutional Neural Networks and Recurrent Neural Networks. Moreover, it is trained using on-demand scheme to recognize numbers from digits of the MNIST dataset. We will see that, with these training samples is not necessary segment or normalize the input images. Average recognition results were on 88,6% of accuracy in numbers of variable-length, between 1 and 10 digits. This accuracy is independent on the number length. Moreover, in most of the wrongly predicted numbers there was only one digit error.

Publicado en Neurocomputing Abstract This paper proposes the use of a new neural network architecture that combines a deep convolutional neural network with an encoder–decoder, called sequence to sequence, to solve the problem of recognizing isolated handwritten words. The proposed architecture aims to identify the characters and contextualize them with their neighbors to recognize any given word. Our model proposes a novel way to extract relevant visual features from a word image. It combines the use of a horizontal sliding window, to extract image patches, and the application of the LeNet-5 convolutional architecture to identify the characters. Extracted features are modeled using a sequence-to-sequence architecture to encode the visual characteristics and then to decode the sequence of characters in the handwritten text image. We test the proposed model on two handwritten databases (IAM and RIMES) under several experiments to determine the optimal parameterization of the model. C...

Patrocinado por Ibero-American Programme of Science and Technology for the Development (CYTED), Years: 2018-2021.

Publicado en Complexity Abstract Demographic handwriting-based classification problems, such as gender and handedness categorizations, present interesting applications in disciplines like Forensic Biometrics. This work describes an experimental study on the suitability of deep neural networks to three automatic demographic problems: gender, handedness, and combined gender-and-handedness classifications, respectively. Our research was carried out on two public handwriting databases: the IAM dataset containing English texts and the KHATT one with Arabic texts. The considered problems present a high intrinsic difficulty when extracting specific relevant features for discriminating the involved subclasses. Our solution is based on convolutional neural networks since these models had proven better capabilities to extract good features when compared to hand-crafted ones. Our work also describes the first approach to the combined gender-and-handedness prediction, which has not been addres...

Publicado en Expert Systems with Applications Abstract Automatic linguistic description of the available data about complex phenomena is a challenging task that is receiving the attention of data scientists in recent years. As an evolution of previous research results, there is a need of creating new linguistic computational models that allow us dealing with more complex phenomena and more complex descriptions of a growing amount of heterogeneous and real-time data. This paper contributes to this field by presenting three new ways of describing added-value information automatically extracted from data. Also, we extend previous computational models by including a description of the reliability of the available input data. Namely, we face this challenge by using a new implementation of the concept of Z-number proposed by Zadeh. We demonstrate the possibilities of the proposed extension with a practical application. The application generates automatic linguistic repor...

Publicado en Plos One Abstract One reason for the aggressiveness of the pancreatic cancer is that it is diagnosed late, which often limits both the therapeutic options that are available and patient survival. The long-term survival of pancreatic cancer patients is not possible if the tumor is not resected, even among patients who receive chemotherapy in the earliest stages. The main objective of this study was to create a prediction model for in-hospital mortality after a pancreatectomy in pancreatic cancer patients. We performed a retrospective study of all pancreatic resections in pancreatic cancer patients in Spanish public hospitals (2013). Data were obtained from records in the Minimum Basic Data Set. To develop the prediction model, we used a boosting method. The in-hospital mortality of pancreatic resections in pancreatic cancer patients was 8.48% in Spain. Our model showed high predictive accuracy, with an AUC of 0.91 and a Brier score of 0.09, which...