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The complex Interconnections between markers and polygenic genotype value suggested that the regression was not enough for describing the relation between genes and traits. Artificial neural networks (ANNs) could perform well for optimization in complex non-linear systems. Recently, artificial neural networks had been successfully used to predict the polygenic genotype value, and the different learning...
In the past, a prediction equation based on the single nucleotide polymorphisms (SNP) is derived to calculate genomic breeding values (GEBV). However, the genome is very complex; a function could not reflect the relation between markers and phenotypes. Unlike the methods of regression, artificial neural networks (ANNs) could perform well for optimization in complex non-linear systems, however, artificial...
Although linear multivariate approaches used to analyze large genetic data sets did not allow a large part of the total variance to be explained, strong distortions with nonlinear data sets, horseshoe effects had always been found. Artificial neural networks could gather their knowledge by detecting the patterns and relationships in data and learn through experience, and could perform well for optimization...
As a new detecting landmine method, Ground Penetrating Radar (GPR) is introduced into the field of detecting buried landmine. In order to improve the detection accuracy, A approach based on the Support Vector Machine (SVMs) is presented in the paper. The Support Vector Machines (SVMs) has solved the inevitable partial minimum problem and overcome the disadvantage which the traditional neural network...
In order to improve the performance of traditional intrusion detection system (IDS) based on neural network, we design and implement an integrated model of IDS based on rough set and wavelet neural network (RWNN-IDS). This paper focuses on applying RWNN for attacks recognition. We first present a conditional information entropy based algorithm to select the smallest features set, which can ensure...
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