[PDF] Top 20 Assessment of genome-wide prediction by using Bayesian regularized neural networks
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Assessment of genome-wide prediction by using Bayesian regularized neural networks
... artificial neural networks (ANN). In animal breeding, a especial class of ANN called Bayesian Regularized Neural Network (BRNN) has been preferable since it does not demand a ... See full document
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PREDICTION OF PHENOTYPIC AND GENOTYPIC VALUES BY BLUPGWS AND NEURAL NETWORKS
... - Genome-wide selection (GWS) uses simultaneously the effect of the thousands markers covering the entire genome to predict genomic breeding values for individuals under ...benefits of ... See full document
9
Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee
... markers, genome wide selection (GWS) is a technique that has been widely used for selecting materials with higher performance based on information from molecular ...proposed by Meuwissen et ...the ... See full document
8
Prediction of disease and phenotype associations from genome-wide association studies.
... number of thick blue lines was seen for the gene level ...clustering of casual SNPs shared across ...ease of viewing as the number of relatively significant associations at this level was high ... See full document
10
Forecasting Rainfall Time Series with stochastic output approximated by neural networks Bayesian approach
... consist of 132 ...ensemble of 500 trials with a fractional Gaussian noise sequence of zero mean and variance of ...generated by the Hosking method [19] with the H parameter estimated ... See full document
6
Tests of moulding mixture by using various clay binder granularity
... day wide world produce of casts is estimated to 80 mil ...mixture of first binder generation where join is results of capillary pressure force and Van der Waals force ( forming mixture on the ... See full document
4
Probabilistic protein function prediction from heterogeneous genome-wide data.
... types of genome-wide ...graphs using a unified probabilistic ...improved prediction accuracy and coverage by integrating five types of genome-wide ...Also, ... See full document
7
Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks
... feedforward neural network. The structure of the model results from two components, the neural network (NN) deterministic component and a random component which is assumed to be normally ... See full document
14
Egg Hatchability Prediction by Multiple Linear Regression and Artificial Neural Networks
... used by the embryo are inside the ...measure of embryo livability, and it is directly related to the combined action of a large number of ... See full document
6
Feasibility of using neural networks to obtain simplified capacity curves for seismic assessment
... curves of the TS1, it is evident that ANN2 is unable to reproduce the entire domain of the problem, because F-test results for the ANN2 are much higher than zero, being even higher than ... See full document
14
Identification of the non-linear systems using internal recurrent neural networks
... utilization of neural networks took a distinct ampleness because of the following properties: distributed representation of information, capacity of generalization in case ... See full document
8
Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks
... planning of small and medium rural producers, especially in environments with adverse climatic conditions, such as the Brazilian semi-arid ...objective of this study was to evaluate the potential of ... See full document
5
Prediction of Skin Penetration using Artificial Neural Network
... predictability of the neural network model was compared to the experimental ...artificial neural network for prediction of Skin permeability ... See full document
6
A PROTOTYPE FOR CLASSIFICATION OF CLASSICAL MUSIC USING NEURAL NETWORKS
... consists of 40 input neurons (one for each extracted feature) a variable number of hidden neurons (described below) and 2, 3 or 5 output neurons, according to problem under ...total of 200 pieces for ... See full document
6
Bio-Measurements Estimation and Support in Knee Recovery through Machine Learning
... the neural networks will be trained using synthetic data, the main goal is two fold: (i) to achieve high accuracy in synthetic images, and (ii) to accurately transfer/generalize these capabilities ... See full document
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Assessment of Arias Intensity of historical earthquakes using modified Mercalli intensities and artificial neural networks
... most of the events are offshore and for those on- shore the surface geology does not often show any evident faulting, it is impossible to use a fault distance definition like the closest distance to the fault ... See full document
9
Sign Language Recognition using Neural Networks
... position of hand gestures as its positions are directly ...because of few requirements they are considered easy, natural and less costly compared to glove based approach ...proposed by ... See full document
6
ECG Biometrics using Deep Neural Networks
... dataset by a large margin, stating their intention to benchmark the proposed approach on CYBHi database, which was acquired off-the-person and contains two different recordings (see Section ...[56], using ... See full document
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PREDICTION OF GLIOMA USING GENETIC OPTIMIZED NEURAL NETWORK
... average of other pixels whose neighborhood has a similar geometrical ...averaging of these pixels results in noise cancellation and yields a pixel that is similar to its original ...extraction of MR ... See full document
13
Prediction of users’ future requests using neural network
... consist of two phases: offline pattern extraction and online recommendation (Li & Zaiane, ...patterns of users that stored in the web server ...session of active user is compared with these ... See full document
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