The study of the variables involved in suicidal behavior is important from a social, medical, and economical point of view. Given the high number of potential variables of interest, a large population of subjects must be analysed in order to get conclusive results. In this paper, we describe a method based on self-organizing maps (SOMs) for finding the most relevant variables even when their re...

Abstract This paper presents the intelligent techniques approach for flood monitoring using Synthetic Aperture Radar (SAR) satellite images. We applied artificial neural networks and Self-Organizing Kohonen Maps (SOMs), to SAR image segmentation and classification. Our approach was used to process data from different SAR satellite instruments (ERS-2/SAR, ENVISAT/ASAR, RADARSAT-1/2) for differen...

Flow analysis has a wide range of applications both in engineering and science, and many techniques have been developed over the years that allow the data extraction and measurement of the flow dynamics. In particular, Particle Tracking Velocimetry (PTV) techniques have shown good results when combined with Artificial Neural Networks (ANN) techniques, especially using Self-Organizing Maps (SOM)...

With the rapid development of internet and information technologies, ranking methods only based on simple citation (link) networks are already insufficient for the growing requirement of information extraction and evaluation. We propose a method for evaluating the influence and importance of nodes that are contained in multiple dynamic networks. This method combines a modified PageRank algorith...

Self-Organizing map (SOM) is a widely used tool to find clustering and also to visualize high dimensional data. Several spherical SOMs have been proposed to create a more accurate representation of the data by removing the “border effect”. In this paper, we compare several spherical lattices for the purpose of implementation of a SOM. We then introduce a 2D rectangular grid data structure for r...

Utilization of remote sensing multiand hyperspectral imagery has shown a rapid increase in many areas of economic and scienti c signi cance over the past ten years. Hyperspectral sensors, in particular, are capable of capturing the detailed spectral signatures that uniquely characterize a great number of diverse surface materials. Interpretation of these very high-dimensional signatures, howeve...

|The capabilities of self-organizing maps (SOMs) in parametrizing data manifolds qualify them as candidates for blind separation algorithms. We study the virtues and problems of the SOM-based approach in a simple example. Also numerical simulations of more general cases have been performed. It shows that the performance is unquestionable in the case of a linear mixture only if the observed data...

We analyze the geographic location of 77,451 different Italian surnames (17,579,891 individuals) obtained from the lists of telephone subscribers of the year 1993. By using a specific neural network analysis (Self-Organizing Maps, SOMs), we automatically identify the geographic origin of 49,117 different surnames. To validate the methodology, we compare the results to a study, previously conduc...

This study presents an unsupervised feature selection approach for the discovery of significant patterns in seismic wavefields. We iteratively reduce the number of features generated from seismic time series by first considering significance of individual features. Significance testing is done by assessing the randomness of the time series with the Wald-Wolfowitz runs test and by comparing obse...

Availability of large full-text document collections in electronic form has created a need for intelligent information retrieval techniques. Especially the expanding World Wide Web presupposes methods for systematic exploration of miscellaneous document collections. In this paper we introduce a new method, the WEBSOM, for this task. Self-Organizing Maps (SOMs) are used to represent documents on...