A preliminary study of the application of artificial neural networks to prediction of milk yield in dairy sheep

2002 ◽  
Vol 35 (1) ◽  
pp. 35-48 ◽  
Author(s):  
A.P. Kominakis ◽  
Z. Abas ◽  
I. Maltaris ◽  
E. Rogdakis
2021 ◽  
Vol 12 (1) ◽  
pp. 117
Author(s):  
J.C. Angeles-Hernandez ◽  
A.J. Chay-Canul ◽  
F.A. Castro-Espinoza ◽  
M Benaouda ◽  
B. Castro-Hernández ◽  
...  

2000 ◽  
Vol 80 (3) ◽  
pp. 415-426 ◽  
Author(s):  
X. Z. Yang ◽  
R. Lacroix ◽  
K. M. Wade

A data set comprising milk-recording and conformation data was used to investigate the usefulness of artificial neural networks in detecting influential variables in the prediction of incidences of clinical mastitis. Specifically, these data contained test-day records from dairy herd analysis, phenotypic cow scores for conformation and genetic conformation proofs for cows and their sires. The data were analysed using the milk-recording data only, the conformation data only, and a combination of the two. Results from sensitivity analyses, performed with trained neural nets, indicated that stage of lactation, milk yield on test day, cumulative milk yield and somatic cell count were the major production factors influencing the ability to detect the occurrence of clinical mastitis. Among the conformation traits, such variables as phenotypic scores for rear-teat placement, dairy character and size, cow proof for dairy character, sire reliability for final score and sire proofs for pin-setting (desirability) and loin strength were found to have some influence on the network's predictive ability, although they were all very minor in relation to the production variables mentioned. As a group, cow genetic proofs seemed more important than either sire genetic proofs or cow phenotypic scores. Given the neural network's general abilities to determine the major factors related to the presence or absence of mastitis on a given test day, it may be appropriate to investigate the possibility of using this technology for actual prediction purposes. Key words: Artificial neural networks, clinical mastitis, milk-recording data, conformation traits, sensitivity analysis, milk production


2019 ◽  
Vol 37 (1) ◽  
pp. 129-144 ◽  
Author(s):  
Shane Steinert-Threlkeld

Abstract A semantic universal, which we here dub the Veridical Uniformity Universal, has recently been argued to hold of responsive verbs (those that take both declarative and interrogative complements). This paper offers a preliminary explanation of this universal: verbs satisfying it are easier to learn than those that do not. This claim is supported by a computational experiment using artificial neural networks, mirroring a recent proposal for explaining semantic universals of quantifiers. This preliminary study opens up many avenues for future work on explaining semantic universals more generally, which are discussed in the conclusion.


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