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New Types of Computational Perceptions: Linguistic Descriptions in Deforestation Analysis

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

Prediction of in-hospital mortality after pancreatic resection in pancreatic cancer patients: A boosting approach via a population-based study using health administrative data

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

A cognitive architecture framework for critical situation awareness systems

Publicado en Lecture Notes in Computer Science Abstract Goal-oriented human-machine situation-awareness systems focus on the challenges related to perception of the elements of an environment and their state, within a time-space window, the comprehension of their meaning and the estimation of their state in the future. Present computer-supported situation awareness systems provide real-time information fusion from different sources, basic data analysis and recognition, and presentation of the corresponding data using some augmented reality principles. However, a still open research challenge is to develop advanced supervisory systems, platforms and frameworks that support higher-level cognitive activities, integrate domain specific associated knowledge, learning capabilities and decision support. To address these challenges, a novel cognitive architecture framework is presented in this paper, which emphasizes the role of the Associated Reality as a new cognitive lay

Convolutional Neural Networks and Long Short-Term Memory for skeleton-based human activity and hand gesture recognition

Publicado en Pattern Recognition Abstract In this work, we address human activity and hand gesture recognition problems using 3D data sequences obtained from full-body and hand skeletons, respectively. To this aim, we propose a deep learning-based approach for temporal 3D pose recognition problems based on a combination of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) recurrent network. We also present a two-stage training strategy which firstly focuses on CNN training and, secondly, adjusts the full method (CNN+LSTM). Experimental testing demonstrated that our training method obtains better results than a single-stage training strategy. Additionally, we propose a data augmentation method that has also been validated experimentally. Finally, we perform an extensive experimental study on publicly available data benchmarks. The results obtained show how the proposed approach reaches state-of-the-art performance when compared to the methods i

Using a synthetic character database for training deep learning models applied to offline handwritten recognition

Publicado en Advances in Intelligent Systems and Computing  We present our current work on building a deep learning architecture for the offline handwritten character recognition problem. The proposed system is based on training a deep Convolutional Neural Network (CNN) to recognize handwritten characters, using a new synthetic character database derived from UNIPEN dataset. The presented approach is inspired in some successfully-used neural architectures for image classification, specially the VGG-CNN. Our system reads each word with the help of a sliding window in a similar way to how humans do. An innovative feature of our proposal is using a synthetic character database specifically built, in a optimized way, to identify the characters as component elements of the words. Experiments with this new training synthetic dataset produced recognition rates of 98.4% for uppercase and 96.3% for lowercase, respectively.