In this work SymbPar, a parallel co-evolutionary algorithm for automatically design the Radial Basis Function Networks, is proposed. It tries to solve the problem of huge execution time of Symbiotic-CHC-RBF, in which ...
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In this paper a study of two approaches of a meta-algorithm, Meta-CHC-RBF, is presented. The main goal of this algorithm is to automatically design Radial Basis Function Networks (RBFNs) finding a suitable configurati...
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This year, the 3rd ACM International Workshop on Context-Awareness for Self-Managing Systems (Devices, Applications and Networks) (CASEMANS 2009) is organized in Nara, Japan. This article summarizes the objectives of ...
ISBN:
(纸本)9781605584393
This year, the 3rd ACM International Workshop on Context-Awareness for Self-Managing Systems (Devices, Applications and Networks) (CASEMANS 2009) is organized in Nara, Japan. This article summarizes the objectives of the CASE-MANS 2009 workshop and gives an overview of the papers that are selected for presentation and publication. Copyright 2009 ACM.
In current Proteomics research, prediction of protein-protein interactions (PPIs) is one of the main goals, since PPIs explain most of the cellular biological processes. In the present work, we propose a method for pr...
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In current Proteomics research, prediction of protein-protein interactions (PPIs) is one of the main goals, since PPIs explain most of the cellular biological processes. In the present work, we propose a method for prediction of protein-protein interactions in yeast. Our proposal is based on the well-known classification paradigm called support vector machines and a well-known feature selection method (Relief) using genomics/proteomics information. In order to obtain higher values of specificity and sensitivity in predicting PPIs, we use a high reliable set of positive and negative examples from which to extract a set of proteomic/genomic features. We also introduce a similarity measure for pairs of proteins to calculate additional features from well-known databases, that allow us to improve the prediction capability of our approach. After applying a feature selection method, we construct SVM classifiers that obtain a low error rate in the prediction for each pair of proteins. Finally, we analyse and compare the prediction quality of the method proposed with other high-confidence datasets from other works.
Current consumer-grade computers and game devices incorporate very powerful processors that can be used to accelerate many classes of scientific codes. However, programming multi-core chips, hybrid multi-processors or...
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The development of intelligent environments is considered an important step toward the realization of the ambient intelligence vision. Intelligent environments are technologically augmented everyday spaces that intuit...
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In this paper, we propose a new synthetic aperture radar (SAR) image segmentation scheme. Firstly, the SAR image is over-segmented using the mean shift (MS) algorithm while the original image discontinuity characteris...
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Software Defined Radio (SDR) is favored by the wireless industry as the platform of choice for implementing physical layers of wireless protocols due to its significant benefits of reduced development costs and accele...
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The present work describes a reliable improved gradient optical flow estimation system using FPGAs. This structure is based on space-temporal processing and the use of steerable filters. This model can be enhanced usi...
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The present work describes a reliable improved gradient optical flow estimation system using FPGAs. This structure is based on space-temporal processing and the use of steerable filters. This model can be enhanced using psychophysical and bioinspired properties according to biological vision in order to mimic the singularity and the performance of mammalians. Experimental results and the resources used to analyze the associate customizability of the system are discussed.
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