scholarly journals Artificial Neural Networks in the Prediction of Genetic Merit to Flowering Traits in Bean Cultivars

Agriculture ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 638
Author(s):  
Renato Domiciano Silva Rosado ◽  
Cosme Damião Cruz ◽  
Leiri Daiane Barili ◽  
José Eustáquio de Souza Carneiro ◽  
Pedro Crescêncio Souza Carneiro ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Breeding Value ◽  
Best Linear Unbiased Predictor ◽  
Genetic Merit ◽  
Complex Functions ◽  
Additive Gene ◽  
Best Linear Unbiased ◽  
Artificial Neural ◽  
The Individual

Flowering is an important agronomic trait that presents non-additive gene action. Genome-enabled prediction allow incorporating molecular information into the prediction of individual genetic merit. Artificial neural networks (ANN) recognize patterns of data and represent an alternative as a universal approximation of complex functions. In a Genomic Selection (GS) context, the ANN allows automatically to capture complicated factors such as epistasis and dominance. The objectives of this study were to predict the individual genetic merits of the traits associated with the flowering time in the common bean using the ANN approach, and to compare the predictive abilities obtained for ANN and Ridge Regression Best Linear Unbiased Predictor (RR-BLUP). We used a set of 80 bean cultivars and genotyping was performed with a set of 384 SNPs. The higher accuracy of the selective process of phenotypic values based on ANN output values resulted in a greater efficacy of the genomic estimated breeding value (GEBV). Through the root mean square error computational intelligence approaches via ANN, GEBV were shown to have greater efficacy than GS via RR-BLUP.

scholarly journals Selection in sugarcane families with artificial neural networks

2015 ◽  
Vol 15 (2) ◽  
pp. 72-78 ◽  
Author(s):  
Bruno Portela Brasileiro ◽  
Caillet Dornelles Marinho ◽  
Paulo Mafra de Almeida Costa ◽  
Cosme Damião Cruz ◽  
Luiz Alexandre Peternelli ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Selection Process ◽  
Selection Strategy ◽  
Ann Model ◽  
Best Linear Unbiased Predictor ◽  
Individual Level ◽  
Best Linear Unbiased ◽  
Artificial Neural ◽  
The Individual

The objective of this study was to evaluate Artificial Neural Networks (ANN) applied in an selection process within sugarcane families. The best ANN model produced no mistake, but was able to classify all genotypes correctly, i.e., the network made the same selective choice as the breeder during the simulation individual best linear unbiased predictor (BLUPIS), demonstrating the ability of the ANN to learn from the inputs and outputs provided in the training and validation phases. Since the ANN-based selection facilitates the identification of the best plants and the development of a new selection strategy in the best families, to ensure that the best genotypes of the population are evaluated in the following stages of the breeding program, we recommend to rank families by BLUP, followed by selection of the best families and finally, select the seedlings by ANN, from information at the individual level in the best families.

A method for reducing inbreeding with Best Linear Unbiased Prediction

1993 ◽  
Vol 1993 ◽  
pp. 33-33
Author(s):  
B Grundy ◽  
WG Hill
Keyword(s):  
Best Linear Unbiased Prediction ◽  
Breeding Value ◽  
Best Linear Unbiased Predictor ◽  
Linear Unbiased Prediction ◽  
Genetic Merit ◽  
Selection Decisions ◽  
Best Linear Unbiased ◽  
Estimated Breeding Value ◽  
Commercial Environment ◽  
Unbiased Prediction

An optimum way of selecting animals is through a prediction of their genetic merit (estimated breeding value, EBV), which can be achieved using a best linear unbiased predictor (BLUP) (Henderson, 1975). Selection decisions in a commercial environment, however, are rarely made solely on genetic merit but also on additional factors, an important example of which is to limit the accumulation of inbreeding. Comparison of rates of inbreeding under BLUP for a range of hentabilities highlights a trend of increasing inbreeding with decreasing heritability. It is therefore proposed that selection using a heritability which is artificially raised would yield lower rates of inbreeding than would otherwise be the case.

scholarly journals CONNECTIONIST TECHNIQUES FOR THE IDENTIFICATION AND SUPPRESSION OF INTERFERING UNDERLYING FACTORS

2003 ◽  
Vol 17 (08) ◽  
pp. 1447-1466 ◽  
Author(s):  
EMILIO CORCHADO ◽  
COLIN FYFE
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Difficult Problem ◽  
Network System ◽  
Visual Inputs ◽  
Neural Network System ◽  
Underlying Causes ◽  
Artificial Neural ◽  
Vertical Bars ◽  
The Individual

We consider the difficult problem of identification of independent causes from a mixture of them when these causes interfere with one another in a particular manner: those considered are visual inputs to a neural network system which are created by independent underlying causes which may occlude each other. The prototypical problem in this area is a mixture of horizontal and vertical bars in which each horizontal bar interferes with the representation of each vertical bar and vice versa. Previous researchers have developed artificial neural networks which can identify the individual causes; we seek to go further in that we create artificial neural networks which identify all the horizontal bars from only such a mixture. This task is a necessary precursor to the development of the concept of "horizontal" or "vertical".

scholarly journals PREDICTION OF PHENOTYPIC AND GENOTYPIC VALUES BY BLUP/GWS AND NEURAL NETWORKS

2018 ◽  
Vol 31 (3) ◽  
pp. 532-540 ◽  
Author(s):  
ALISSON ESDRAS COUTINHO ◽  
DIOGO GONÇALVES NEDER ◽  
MAIRYKON COÊLHO DA SILVA ◽  
ELIANE CRISTINA ARCELINO ◽  
SILVAN GOMES DE BRITO ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Breeding Cycle ◽  
Prediction Methods ◽  
Best Linear Unbiased Predictor ◽  
Entire Genome ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Genome Wide ◽  
Best Linear Unbiased

ABSTRACT Genome-wide selection (GWS) uses simultaneously the effect of the thousands markers covering the entire genome to predict genomic breeding values for individuals under selection. The possible benefits of GWS are the reduction of the breeding cycle, increase in gains per unit of time, and decrease of costs. However, the success of the GWS is dependent on the choice of the method to predict the effects of markers. Thus, the objective of this work was to predict genomic breeding values (GEBV) through artificial neural networks (ANN), based on the estimation of the effect of the markers, compared to the Ridge Regression-Best Linear Unbiased Predictor/Genome Wide Selection (RR-BLUP/GWS). Simulations were performed by software R to provide correlations concerning ANN and RR-BLUP/GWS. The prediction methods were evaluated using correlations between phenotypic and genotypic values and predicted GEBV. The results showed the superiority of the ANN in predicting GEBV in simulations with higher and lower marker densities, with higher levels of linkage disequilibrium and heritability.

scholarly journals Using Artificial Neural Networks for Equalizing Time Series Considering Seasonal Fluctuations

2019 ◽  
Vol 71 ◽  
pp. 01003
Author(s):  
J. Vrbka ◽  
J. Horák ◽  
V. Machová
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Seasonal Fluctuations ◽  
Republic Of China ◽  
The People ◽  
Data Points ◽  
Artificial Neural ◽  
The Individual

The objective of this contribution is to prepare a methodology of using artificial neural networks for equalizing time series when considering seasonal fluctuations on the example of the Czech Republic import from the People´s Republic of China. If we focus on the relation of neural networks and time series, it is possible to state that both the purpose of time series themselves and the nature of all the data are what matters. The purpose of neural networks is to record the process of time series and to forecast individual data points in the best possible way. From the discussion part it follows that adding other variables significantly improves the quality of the equalized time series. Not only the performance of the networks is very high, but the individual MLP networks are also able to capture the seasonal fluctuations in the development of the monitored variable, which is the CR import from the PRC.

A Multidisciplinary Design and Optimization Methodology in Electronics Packaging: Application to BGA Design

2003 ◽  
Author(s):  
Hamid Hadim ◽  
Tohru Suwa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Objective Function ◽  
Thermal Strain ◽  
Design Parameters ◽  
Design And Optimization ◽  
Trade Offs ◽  
Artificial Neural ◽  
Optimization Methodology ◽  
The Individual

In this manuscript a systematic multidisciplinary electronic packaging design and optimization methodology based on the artificial neural networks technique is presented. This method is applied to a Ball Grid Array (BGA) package design as an example. Multidisciplinary criteria including thermal, structural (thermal strain), electromagnetic leakage, and cost are optimized simultaneously. A simplified routability criterion is also considered as a constraint. The artificial neural networks technique is used for thermal and structural performance predictions. Large calculation time reduction is achieved using the artificial neural networks, which also provide enough information to specify the individual weights for each design discipline within the objective function used for optimization. This methodology is able to provide the designers a clear view of the design trade-offs, which are represented in the objective function using various design parameters. This methodology can be applied to any electronic product design at any packaging level.

scholarly journals Epilepsy Detection Using Artificial Neural Networks

2020 ◽  
Vol 8 (4) ◽  
pp. 323-328
Author(s):  
Matheus Adler Soares Pinto ◽  
Bruno Rocha Gomes ◽  
João Pedro Moreno Vale ◽  
André Luis Rolim de Castro Silva ◽  
Joadson Teixeira Castro das Chagas ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sudden Death ◽  
Epileptic Seizures ◽  
Accurate Diagnosis ◽  
Brain Cells ◽  
Artificial Neural ◽  
The Individual ◽  
Electroencephalogram Eeg

Epilepsy is a neurological disorder, where there is a cluster of brain cells that behave in a hyperexcitable manner, the individual can promote injuries, trauma or, in more severe cases, sudden death. Electroencephalogram (EEG) is the most used way to detect epileptic seizures. Therefore, more simplified methods of analysis of the EEG can help in the diagnosis and treatment of these individuals more quickly. In this study, we extracted pertinent EEG characteristics to assess the epileptic seizure period. We use Perceptron Multilayer artificial neural networks to classify the period of the crisis, obtaining a more efficient diagnosis. The multilayer neural network obtained an accuracy of 98%. Thus, the strategy of extracting characteristics and the architecture of the assigned network were sufficient for a rapid and accurate diagnosis of epilepsy.

scholarly journals Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1832
Author(s):  
Wojciech Sitek ◽  
Jacek Trzaska
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Vital Role ◽  
Materials Engineering ◽  
The Neural Network ◽  
Critical Issues ◽  
Artificial Neural ◽  
The Individual

Artificial neural networks are an effective and frequently used modelling method in regression and classification tasks in the area of steels and metal alloys. New publications show examples of the use of artificial neural networks in this area, which appear regularly. The paper presents an overview of these publications. Attention was paid to critical issues related to the design of artificial neural networks. There have been presented our suggestions regarding the individual stages of creating and evaluating neural models. Among other things, attention was paid to the vital role of the dataset, which is used to train and test the neural network and its relationship to the artificial neural network topology. Examples of approaches to designing neural networks by other researchers in this area are presented.

scholarly journals Artificial neural networks and regression analysis for volume estimation in native species

2021 ◽  
Vol 25 (10) ◽  
pp. 664-669
Author(s):  
Lucas M. Amorim ◽  
Elton da S. Leite ◽  
Deoclides R. de Souza ◽  
Liniker F. da Silva ◽  
Carlos R. de Mello ◽  
...  
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Native Species ◽  
Middle Layer ◽  
Volume Measurement ◽  
Volume Estimation ◽  
Individual Volume ◽  
Artificial Neural ◽  
The Individual

ABSTRACT Modeling is an important tool to estimate forest production in planted areas. Although this issue has been studied worldwide, knowledge regarding volume measurement in specific locations such as Northeast Brazil is still scarce. The present study aimed to evaluated the effectiveness of artificial neural networks (ANNs) and regression analysis in estimating the timber volume of homogeneous stands of Anadantera macrocarpa, Genipa americana, and Mimosa casalpinifolia, in order to better predict the growth and production of these species. Both methods were suitable for estimating the individual volume in 7-year-old stands with different spacing. The Spurr regression model showed better statistical results and dispersion of unbiased errors for Anadantera macrocarpa and Genipa americana, whereas the Shumacher-Hall model provided more accurate volume estimates for Mimosa caesalpinifolia. The ANNs calibrated with two neurons in the middle layer exhibited the best fit for all three species. As such, artificial neural networks can be recommended to estimate the individual volumes of the species analyzed in the study area.

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