Fuzzy min-max neural networks for categorical data: application to missing data imputation

The fuzzy min–max neural network classifier is a supervised learning method. This classifier takes the hybrid neural networks and fuzzy systems approach. All input variables in the network are required to correspond to continuously valued variables, and this can be a significant constraint in many real-world situations where there are not only quantitative but also categorical data. The usual way of dealing with this type of variables is to replace the categorical by numerical values and treat them as if they were continuously valued. But this method, implicitly defines a possibly unsuitable metric for the categories. A number of different procedures have been proposed to tackle the problem. In this article, we present a new method. The procedure extends the fuzzy min–max neural network input to categorical variables by introducing new fuzzy sets, a new operation, and a new architecture. This provides for greater flexibility and wider application. The proposed method is then applied to missing data imputation in voting intention polls. The micro data—the set of the respondents’ individual answers to the questions—of this type of poll are especially suited for evaluating the method since they include a large number of numerical and categorical attributes.

Publishing Orthology and Diseases Information in the Linked Open Data Cloud

The Linked Data initiative offers a straight method to publish structured data in the World Wide Web and link it to other data, resulting in a world wide network of semantically codified data known as the Linked Open Data cloud. The size of the Linked Open Data cloud, i.e. the amount of data published using Linked Data principles, is growing exponentially, including life sciences data. However, key information for biological research is still missing in the Linked Open Data cloud. For example, the relation between orthologs genes and genetic diseases is absent, even though such information can be used for hypothesis generation regarding human diseases. The OGOLOD system, an extension of the OGO Knowledge Base, publishes orthologs/diseases information using Linked Data. This gives the scientists the ability to query the structured information in connection with other Linked Data and to discover new information related to orthologs and human diseases in the cloud.

Data-Intensive architecture for scientific knowledge discovery

This paper presents a data-intensive architecture that demonstrates the ability to support applications from a wide range of application domains, and support the different types of users involved in defining, designing and executing data-intensive processing tasks. The prototype architecture is introduced, and the pivotal role of DISPEL as a canonical language is explained. The architecture promotes the exploration and exploitation of distributed and heterogeneous data and spans the complete knowledge discovery process, from data preparation, to analysis, to evaluation and reiteration. The architecture evaluation included large-scale applications from astronomy, cosmology, hydrology, functional genetics, imaging processing and seismology.

Regularized logistic regression and multi-objective variable selection for classifying MEG data

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.

Data Mining with Enhanced Neural Networks-CMMSE

Abstract This paper presents a new method to extract knowledge from existing data sets, that is, to extract symbolic rules using the weights of an Artificial Neural Network. The method has been applied to a neural network with special architecture named Enhanced Neural Network (ENN). This architecture improves the results that have been obtained with multilayer perceptron (MLP). The relationship among the knowledge stored in the weights, the performance of the network and the new implemented algorithm to acquire rules from the weights is explained. The method itself gives a model to follow in the knowledge acquisition with ENN.

Grapheme-color synesthetes show peculiarities in their emotional brain: cortical and subcortical evidence from VBM analysis of 3D-T1 and DTI data

Grapheme-color synesthesia is a neurological phenomenon in which viewing achromatic letters/numbers leads to automatic and involuntary color experiences. In this study, voxel-based morphometry analyses were performed on T1 images and fractional anisotropy measures to examine the whole brain in associator grapheme-color synesthetes. These analyses provide new evidence of variations in emotional areas (both at the cortical and subcortical levels), findings that help understand the emotional component as a relevant aspect of the synesthetic experience. Additionally, this study replicates previous findings in the left intraparietal sulcus and, for the first time, reports the existence of anatomical differences in subcortical gray nuclei of developmental grapheme-color synesthetes, providing a link between acquired and developmental synesthesia. This empirical evidence, which goes beyond modality-specific areas, could lead to a better understanding of grapheme-color synesthesia as well as of other modalities of the phenomenon.

Assessment of diastolic chamber properties of the right ventricle by global fitting of pressure-volume data and conformational analysis of 3D + T echocardiographic sequences

Assessment of diastolic chamber properties of the right ventricle by global fitting of pressure-volume data and conformational analysis of 3D + T echocardiographic sequences

High-speed trains and tourists: what is the link? Evidence from the French and Spanish capitals

The objective of this paper is to analyse the factors influencing tourists? choice of a destination and the role of High Speed Rail (HSR) systems in this choice. The methodology proposed consists in analysing two capitals in Europe, i.e. Paris and Madrid where HSR services are important, to investigate the factors influencing holidaymakers in choosing these cities, and the role of HSR in this choice. The main outcome of this paper is to show that several factors influence the choice of a tourist, like the presence of architectural sites, the quality of promotion of the destination itself, the presence of events, and also HSR services. However we found that the HSR system has affected the choice of Paris and Madrid in a different way. Concerning the French case study, HSR is considered a real transport mode alternative among tourists, therefore HSR is chosen to reach Paris as well as for revisiting it. On the other hand, Madrid is chosen by tourists irrespective on the presence of HSR, while HSR is chosen for reaching cities close to Madrid. Data collected from the two surveys have been used for a further quantitative analysis. Models have been specified and calibrated to identify the factors influencing holidaymakers to revisit Paris and Madrid and the role of HSR in this choice has been highlighted.

The Imaging and Slitless Spectroscopy Instrument for Surveys (ISSIS): expected radiometric performance, operation modes and data handling

ISSIS is the instrument for imaging and slitless spectroscopy on-board WSO-UV. In this article, a detailed comparison between ISSIS expected radiometric performance and other ultraviolet instruments is shown. In addition, we present preliminary information on the performance verification tests and on the foreseen procedures for in-flight operation and data handling.

Explaining light vehicle demand evolution in interurban toll roads: a dynamic panel data analysis in Spain

Tolls have increasingly become a common mechanism to fund road projects in recent decades. Therefore, improving knowledge of demand behavior constitutes a key aspect for stakeholders dealing with the management of toll roads. However, the literature concerning demand elasticity estimates for interurban toll roads is still limited due to their relatively scarce number in the international context. Furthermore, existing research has left some aspects to be investigated, among others, the choice of GDP as the most common socioeconomic variable to explain traffic growth over time. This paper intends to determine the variables that better explain the evolution of light vehicle demand in toll roads throughout the years. To that end, we establish a dynamic panel data methodology aimed at identifying the key socioeconomic variables explaining changes in light vehicle demand over time. The results show that, despite some usefulness, GDP does not constitute the most appropriate explanatory variable, while other parameters such as employment or GDP per capita lead to more stable and consistent results. The methodology is applied to Spanish toll roads for the 1990?2011 period, which constitutes a very interesting case on variations in toll road use, as road demand has experienced a significant decrease since the beginning of the economic crisis in 2008.

Language resources and linked data: a practical perspective

Recently, experts and practitioners in language resources have started recognizing the benefits of the linked data (LD) paradigm for the representation and exploitation of linguistic data on the Web. The adoption of the LD principles is leading to an emerging ecosystem of multilingual open resources that conform to the Linguistic Linked Open Data Cloud, in which datasets of linguistic data are interconnected and represented following common vocabularies, which facilitates linguistic information discovery, integration and access. In order to contribute to this initiative, this paper summarizes several key aspects of the representation of linguistic information as linked data from a practical perspective. The main goal of this document is to provide the basic ideas and tools for migrating language resources (lexicons, corpora, etc.) as LD on the Web and to develop some useful NLP tasks with them (e.g., word sense disambiguation). Such material was the basis of a tutorial imparted at the EKAW’14 conference, which is also reported in the paper.

MedVir: an interactive representation system of multidimensional medical data applied to Traumatic Brain Injury's rehabilitation prediction

Clinicians could model the brain injury of a patient through his brain activity. However, how this model is defined and how it changes when the patient is recovering are questions yet unanswered. In this paper, the use of MedVir framework is proposed with the aim of answering these questions. Based on complex data mining techniques, this provides not only the differentiation between TBI patients and control subjects (with a 72% of accuracy using 0.632 Bootstrap validation), but also the ability to detect whether a patient may recover or not, and all of that in a quick and easy way through a visualization technique which allows interaction.