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Njubi DM, Wakhungu J, Badamana MS. "Milk yield prediction in Kenyan Holstein- Friesian cattle using computer neural networks system.". 2009. Abstract

An essential aspect of understanding any natural system is the ability to acquire knowledge through experience and to adapt to new situations. This study, investigates the use of back-propagation Artificial Neural Networks (ANN) (an artificial intelligence technique) approach to model and predict the performance of daughter first lactation milk yield in recorded dairy cattle herds in Kenya. Such prediction is a prerequisite to selection which ultimately leads to optimal breeding strategies and increased annual genetic progress. Data consisting of 6095 lactation records made by the Kenyan Holstein-Friesian cows from 76 officially milk recorded herds of 445 sires, 1956 dams and 2267 daughters and collected over the period 1988 -2005 were used to predict the first lactation performance of the female offspring based on recorded genetic traits of their parents using computer Neural Networks (NN). Weka and MATLAB softwares were used for NN analyses while SAS (2003) and Derivative free Restricted maximum likelihood (DFREML) (Meyer 1989) computer packages were used for statistical analyses. Different ANN were modeled and the best performing number of hidden layers and neurons and training algorithms retained. The performance of the ANN model in simulating daughter performance was compared with the industry default technique linear regression (LR) model. The best NN model had one hidden layer with 8 hidden nodes and tangent sigmoid transfer function for hidden layer. The correlation coefficients between the observed and the estimated daughter milk yield for the two estimation methods was generally high (>0.80). Including sire information resulted to more accurate predictions by a neural network as shown by reduced root mean square error. Generally more accurate prediction was obtained by a neural network approach than by linear regression. This suggests a non-linear relationship exists among the feature variables in the data and that these are learned by the hidden layer of the NN. Thus, prediction tests show that the ANN models used in this study

Njubi DM, Wakhungu J, Badamana MS. "Mating decision support system using computer neural network model in Kenyan Holstein-Friesian dairy cattle.". 2009. Abstract

Knowledge discovery in databases (KDD) should provide not only accurate predictions but also comprehensible rules. In this paper, we demonstrate that the machine learning approach of rule extraction from a computer trained neural network system can successfully be applied to milk production analyses in dairy cattle. Such extracted knowledge should be useful in interpretation and understanding how the neural network (NN) model makes its decision. Data consisting of 6095 lactation records made by cows from 76 officially milk recorded Holstein Friesian herds in the period 1988-2005 were used to extract rules using neural network. Two different methods of attribute categorization; auto-class and the domain expert were used. For automated knowledge acquisition, rule induction used Weka software while SAS was used in domain expert. The neural nets were first trained to identify outputs for different inputs. The trained networks were then used for rule extraction. The study showed that the decision trees generated from the trained network had higher accuracy than decision trees created directly from the data. The study also indicated a need for a process to determine important inputs before using a neural net and showed that reduced input sets may produce more accurate neural nets and more compact decision trees. The “black-box” nature of neural networks was explained by extracting rules with both the domain expert and autoclass for both the continuous and the discrete valued inputs with rule sets performing better on the ‘low’ and ‘high’ levels. It follows from these analyses that performance at the two extremes was more important than average performance. It implied that the end user was particularly concerned with identifying mating with good potential and avoid mating with poor potential animals. The decision tree showed that when the herd performance was low then the foremost limiting factor was the dam performance whereas for medium and high herd performance sire level performance was the limiting factor. Through sensitivity analysis the most important and sensible factors with respect to productivity were sire breeding value and herd performance. It was, therefore, concluded that neural network rule extraction and decision tables were powerful management tools that allow the building of advanced and user-friendly decision-support systems for mating strategy designs and their evaluation.

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