An efficient method of indexing for image retrieval from pdf files

Una de las áreas que más interés está despertando actualmente entre los investigadores y usuarios de sistemas de Recuperación de Información es la recuperación de documentos que contengan imágenes relevantes a una necesidad de información. En este caso, el principal objetivo no es la recuperación de los documentos relevantes a la necesidad de información del usuario sino la obtención de las imágenes relevantes a dicha necesidad. En la actualidad, las colecciones de documentos se pueden encontrar en diversos formatos (html, xml, pdf, etc.). En este artículo presentamos un método eficaz para indexar una colección de documentos en formato pdf para mejorar la recuperación de imágenes contenidas en los documentos. Los experimentos realizados prueban que el método presentado obtiene mejores resultados que si se realizara una indexación del texto completo.One of the areas which is presently awakening more interest among researchers and users of Information Retrieval systems is the retrieval of documents containing images which are relevant to a need for information. In this case, the main objective is not the retrieval of the documents relevant to the user’s need for information, but the achievement of the images relevant to that need for information. At present, document collections can be found in a variety of formats (html, xml, pdf, etc). In this paper we present an efficient method to index a collection of documents in pdf format to improve the retrieval of images contained in documents. The experiments we carried out prove that the method presented here achieves better results than indexing the full text.Este trabajo ha sido parcialmente financiado por el Ministerio de Ciencia e Innovación, el Plan E del Gobierno Español y la Unión Europea con cargo al FEDER (TIN2009-14057-C03-03)

Mining interesting regions using an evolutionary algorithm

In this paper, we offer a new method to induce interesting knowledge from the relevant sets of data in databases for supervised learning. Thus, in this work, ELLIPSES is presented as a new method oriented to discover knowledge according to the expert's needs, by the detection of the most significant regions. The method essence is found in an evolutionary algorithm that finds these regions one after another. The expert decides which regions are significant and determines the stop criterion. The extracted knowledge is offered through two types of rules: Quantitative and Qualitative. The tool also offers a visualization of each rule by parallel coordinate systems. The ELLIPSES results are compared with C4.5 on UCI Repository datasets

OBLIC: Classification System Using Evolutionary Algorithm

We present a new classification system based on Evolutionary Algorithm (EA), OBLIC. This tool is an OBLIque Classification system whose function is to induce a set of classification rules no hierarchical from a database or training set. The core of the algorithm is a EA with real-coded and Pittsburgh approach. Each individual is composed by a no fixed classification rules set what split in regions the search space. The fitness of each classification is obtained by means of the exploration of these regions. The result of the tool is the best classification obtained in the evolutionary process

The habenula as a critical node in chronic stress-related anxiety

The habenula is activated in response to stressful and aversive events, resulting in exploratory inhibition. Although possible mechanisms for habenula activation have been proposed, the effects of chronic stress on the habenular structure have never been studied. Herein, we assessed changes in volume, cell density and dendritic structure of habenular cells after chronic stress exposure using stereological and 3D morphological analysis. This study shows for the first time that there is a hemispherical asymmetry in the medial habenula (MHb) of the adult rat, with the right MHb containing more neurons than its left counterpart. Additionally, it shows that chronic stress induces a bilateral atrophy of both the MHb and the lateral habenula (LHb). This atrophy was accompanied by a reduction of the number of neurons in the right MHb and the number of glial cells in the bilateral LHb, but not by changes in the dendritic arbors of multipolar neurons. Importantly, these structural changes were correlated with elevated levels of serum corticosterone and increased anxious-like behavior in stressed animals. To further assess the role of the habenula in stress-related anxiety, bilateral lesions of the LHb were performed; interestingly, in lesioned animals the chronic stress protocol did not trigger increases in circulating corticosterone or anxious-like behavior. This study highlights the role of the habenula in the stress responses and how its sub-regions are structurally impacted by chronic stress with physiological and behavioral consequences.Prof. Sharif Taha formerly of University of Utah for providing training and technical expertise on habenula research; Ana Lima and Mónica Dias for histological processing of samples; and Diana Afonso and Ana Veloso for help with the stress protocol. LRJ was supported by fellowships: UMINHO/BPD/27/2013 funded by CCDR-N and Programa Operacional Região Norte (ON.2) from QREN/FEDER; 2014/CON3/CAN23 from Fundação Luso-Americana; and UMINHO/BPD/63/2015 from Fundação Calouste Gulbenkian funded project (contract grant number P-139977). AN was supported by a fellowship from project ANR/NEU-OSD/0258/2012 funded by Fundação para a Ciência e Tecnologia (FCT) and Agence Nationale de la Recherche (ANR). Financial support for this work was provided by FEDER funds through the Operational Programme Competitiveness Factors - COMPETE and National Funds through FCT - Foundation for Science and Technology under the project POCI-01-0145-FEDER-007038; and by the project NORTE-01-0145-FEDER-000013, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF)info:eu-repo/semantics/publishedVersio

Applying data mining to software development projects : a case study

One of the main challenges that the project managers have during the building process of a software development project (SDP) is to optimise the values of the parameters that measure the viability of the final process. The accomplishment of this task, something that was not easy at the beginning, was helped with the appearance of dynamic models and simulation environments. The application of data mining techniques to the managing of Software Development Projects (SDP) is not an uncommon phenomenon, as in any other productive process that generates information in the way of input data and output variables. In this paper, we present and analyze the results obtained from a tool, developed by the authors, based on a Knowledge Discovery in Databases (KDD) technique. One of the most important contributions of these techniques to the software engineering field is the possibility of improving the management process of an SDP. The purpose is to provide accurate decision rules in order to help the project manager to take decisions during the development

Age-Stratified Analysis of COVID-19 Outcome Using Machine Learning Predictive Models

Since the emergence of COVID-19, most health systems around the world have experienced a series of spikes in the number of infected patients, leading to collapse of the health systems in many countries. The use of clinical laboratory tests can serve as a discriminatory method for disease severity, defining the profile of patients with a higher risk of mortality. In this paper, we study the results of applying predictive models to data regarding COVID-19 outcome, using three datasets after age stratification of patients. The extreme gradient boosting (XGBoost) algorithm was employed as the predictive method, yielding excellent results. The area under the receiving operator characteristic curve (AUROC) value was 0.97 for the subgroup of patients up to 65 years of age. In addition, SHAP (Shapley additive explanations) was used to analyze the feature importance in the resulting model

Acceso a la información bilingüe utilizando ontologías específicas del dominio biomédico

Unos de los enfoques más prometedores en la Recuperación de Información Croslingüe es la utilización de recursos léxico-semánticos para realizar una indexación conceptual de los documentos y consultas. Hemos seguido esta aproximación para proponer un sistema de acceso a la información para profesionales sanitarios, que facilita la preparación de casos clínicos, y la realización de estudios e investigaciones. En nuestra propuesta se conecta la documentación de los pacientes (la historia clínica), en castellano, con la información científica relacionada (artículos científicos), en inglés y castellano, usando para ellos recursos de gran cobertura y calidad como la ontología SNOMED. Se describe asimismo como se gestiona la confidencialidad de la información.One of the most promising approaches to Cross-Language Information Retrieval is the utilization of lexical-semantic resources for concept-indexing documents and queries. We have followed this approach in a proposal of an Information Access system designed for medicine professionals, aiming at easing the preparation of clinical cases, and the development of studies and research. In our proposal, the clinical record information, in Spanish, is connected to related scientific information (research papers), in English and Spanish, by using high quality and coverage resources like the SNOMED ontology. We also describe how we have addressed information privacy

Oblique classification rule induction method using an evolutionary algorithm

En este articulo presentamos un nuevo metodología denominado OBLIC, para inducción de reglas de clasificación oblicuas no jerárquicas a partir de un conjunto de datos etiquetados. La base del método es un algoritmo evolutivo con codificación real para los individuos y basado en la estrategia de Pittsburgh. Así, cada individuo esta compuesto por un conjunto de reglas de clasificación que dividen el espacio de búsqueda en regiones para cada una de las clases del conjunto de datos. La función de bondad determina la exactitud de cada individuo mediante la exploración de estas regiones. El modelo de clasificación es deducido a partir del mejor individuo obtenido durante el proceso evolutivo. Para analizar los resultados se ofrece una comparativa entre OBLIC, C4.5 y 0C1 sobre un conjunto de bases de datos del UCI Repositorio.In this article we present a new methodology called OBLIC, for the induction of non-hierarchical oblique classification rules from a set of labeled data. The basis of the method is an evolutionary algorithm with real coding for individuals and based on the Pittsburgh strategy. Thus, each individual is composed of a set of classification rules that divide the search space into regions for each of the classes in the data set. The goodness function determines the correctness of each individual by exploring these regions. The classification model is deduced from the best individual obtained during the evolutionary process. To analyze the results, a comparison between OBLIC, C4.5 and 0C1 is offered on a set of databases from the UCI Repository

Método de inducción de reglas de clasificación oblicuas mediante un algoritmo evolutivo

En este artículo presentamos un nuevo método, denominado OBLIC, para inducción de reglas de clasificación oblicuas no jerárquicas a partir de un conjunto de datos etiquetados.Palabras clave: Minería de Datos, Aprendizaje Supervisado, Clasificación, Algoritmos Evolutivos

Visualization techniques of management rules for software development projects

The application of data mining techniques to the managing of software development projects (SDP) is not an uncommon phenomenon, as in any other productive process that generates information in the way of input data and output variables. In this paper, a set of tools developed by the authors, that generate, in a visual way, managing rules suitable to cover minimum goals in a SDP are presented. Although the techniques used are able to generate quantitative rules, giving numeric values suitable for these goals, the visual representation of these rules helps their easy and quick understanding by a manager of a SDP. The application to a database generated from the simulation of a project allows to establish a profitable comparison and to demonstrate the validity of the techniques