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Fuzzy Similarity Measures (FSMs) are widely used for comparison of fuzzy sets, as well as fuzzy rules. A multitude of different FSMs have been proposed so far. It is not straightforward to identify a single FSM that is the most suitable for a given task. In this paper, we investigate suitability of a few FSMs for the problem of reduction of number of rules for an image segmentation process. We use...
This research proposes an approach for text classification that uses a simple neural network called Dynamic Text Classifier Neural Network (DTCNN). The neural network uses as input vectors of words with variable dimension without information loss called Dynamic Token Vectors (DTV). The proposed neural network is designed for the classification of large and short text into categories. The learning...
The application fields of Hyperspectral Image (HI) analysis has been increasing in the last years because the availability of new devices and public data-sets. There are many published works demonstrating that it is possible to use hyperspectral imagery in order to detect targets and create material maps. Many of the proposed techniques require to have prior knowledge about the number of different...
New e-services come on-line each year at an exponential rate. Most of them have the need to analyze and interpret enormous quantities of data. However, many of them do not take into account the emotions and sentiments in the Web page for their analysis. Thus, in this work, we proposed a novel system to obtain data of interest from a Web search engine by analyzing the emotional and sentimental content...
This paper presents the design and implementation of a novel wireless sensor network system using the device Freescale MC1321X. The proposed network management system consists of two different modules: the first one performs network formation and maintenance under a policy of power consumption reduction, and the second one measures and collects sensory data over the built backbone. In the case of...
As Big Data becomes prevalent, the traditional models from Data Mining or Data Analysis, although very efficient, lack the speed necessary to process problems with data sets in the range of million samples. Therefore, the need for designing more efficient and faster algorithms for these new types of problems. Specifically, from the field of social network analysis, we have the influence maximization...
Wireless ad-hoc networks require a special management because of their hardware and energy limitations compared with wired networks. The problem of constructing a backbone structure over wireless ad-hoc networks has been widely researched. The basic problem is to minimize the wireless backbone size by taking into consideration the node's capabilities. Therefore, an efficient, self-organized, scalable,...
Many algorithms have been proposed for detecting anti-tank landmines and discriminating between mines and clutter objects using data generated by a ground penetrating radar (GPR) sensor. Our extensive testing of some of these algorithms has indicated that their performances are strongly dependent upon a variety of factors that are correlated with geographical and environmental conditions. It is typically...
In this work, virtual backbone generation in ad-hoc networks under constraints of limited energy resources is addressed through a novel global optimization method. It is based on the maximal independent set approach which is stated as a multi-objective optimization problem to represent the different functional constraints of the backbone generation. A discrete version of a Particle Swarm Optimization...
In this paper, we present a novel algorithm for learning fuzzy measures for Choquet integration. There are two novel aspects of the algorithm: it seeks to explicitly reduce the number of nonzero parameters in the measure to eliminate noninformative or useless information sources and it uses a Bayesian model for parameter estimation which has not been previously applied to the fuzzy measure learning...
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