Comparison of artificial neural network and K-means for clustering dairy cattle

Atil, Hülya; Akıllı, Aslı

doi:10.1504/ijsami.2016.077266

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…Hence, these predictors had a brilliant impact on the DA findings. Somewhat of an infringement of these presumptions is prevalent and shows up to have little effect on results [ 18 ]. Another study performed by Görgülü [ 12 ] deduced that ANN could be used as a substitute to the multiple regression model to forecast milk production corrected at 305 days’ lactation period in Brown Swiss dairy cattle and cumulative milk yield in crossbred cattle [ 19 ].…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, many authors compared ANN method to classical statistical methods such as fuzzy logic [ 20 ], K -means for clustering milk-producing cattle [ 18 ], and multiple linear regressions for the anticipation of body weight in hair-bearing goats [ 21 ]. They found that ANN approach has a better performance in prediction, especially when the association between variables is complicated.…”

Section: Discussionmentioning

confidence: 99%

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Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models

2020

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Objective: The objective of this study was to assess the veracities of most admired strategy discriminant analysis (DA), in comparison to the artificial neural network (ANN) for the anticipation and classification of milk production level in Holstein Friesian cattle using their performances. Materials and Methods: A total of 3,460 performance records of imported and locally born Holstein Friesian cows were gathered during the period from 2000 to 2016 to compare two alternative techniques for predicting the level of production based on performance traits in dairy cattle with the use of statistical software (Statistical Package for the Social Sciences, version 20.0). Results: The findings of the comparison indicated that ANN was more impressive in the expectancy of milk production level than did an imitator statistical method based on DA. The accuracy of the ANN model was high for the winter season (79.5%), whereas it was 47.3% for DA. The current findings were assured via the areas under receiver operating characteristic curves (AUROC) for DA and ANN. AUROC curves were smaller in the condition of the DA model across different calving seasons compared with the ANN model. The inaccuracies of variations were significant at a 5% significance level utilizing paired sample t -test. Conclusion: ANN model can be used efficiently to predict the level of production across the different calving seasons compared to the DA model.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models

2020

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“…It basically represents a nonlinear statistical modelling tool. With the information learned through repeated experience it can provide classification, pattern recognition, optimisation and the realisation of forward-looking forecasts (Atil and Akilli, 2016). Table 1 demonstrates the homology between biological neuron and an artificial neuron.…”

Section: Homology Between Biological Neuron and Artificial Neuronmentioning

confidence: 99%

Artificial Neural Networks- An Introduction and Application in Animal Breeding and Production: A Review

Khanikar

Phookan

Gogoi

2022

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With the advent of newer methods and tools for accurate analysis, the outcomes of researches have become increasingly precise. This increase in reliability of research findings and forecasting is well demanded in various fields of science including those under animal breeding as well as its allied aspects such as production. One such recent breakthrough is an artificial neural network (ANN), which is a system of hardware and/or software programmed to function like the operation of neurons in the human brain. ANNs are a variety of deep learning technology, which also falls under the umbrella of artificial intelligence. The determination of milk yield, quality of meat animals, predicting breeding values, inferring demography and recombination, genome-enabled prediction are few of the sectors where ANN has proved to be a valuable asset. The correlation coefficient between the experimental values and those predicted with the help of ANN was found to be close to 1. These studies were a significant indication of the impactful ability of ANN. The early selection of superior bulls, culling of low performing ones, frequent prediction of breeding values and taking into account the non linearity encountered in biological research are few of the many benefits of using ANN in researches of animal genetics and production. Hence, considering its prodigious role, this current review has been written to highlight the role and impact of ANN in livestock studies.

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“…Azerbaijan scientist Lotfi A. Zadeh stated that for fuzzy (ambiguous) situations, which cannot be defined by probability distributions, mathematics is needed [1,10,11]. Zadeh wrote the first paper on fuzzy logic, titled "Fuzzy Sets," in 1965 [1].…”

Section: Fuzzy Logicmentioning

confidence: 99%

Fuzzy Control System for Predicting The Quality of Thermally Processed Raw Milk

Uzun

Allahverdi

2021

International Journal of Applied Mathematics Electronics and Computers

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Milk, which has a very important place in human nutrition, can be harmful in terms of human health when it is not produced, stored, processed, and necessary controls are performed under hygienic conditions. Even if raw milk contains a small number of bacteria, it is degraded in a very short time after the milking by the effects of microorganisms transmitted from the environment in various ways and constitutes the potential source of many pathogens that cause diseases in humans. Milk proteins contain essential amino acids that cannot be synthesized by the human body and must be taken from the outside. The microorganism load of milk is one of the most important indicators in determining milk quality and determining the hygienic properties of milk in the process from raw milk production to consumption. Fuzzy logic, which is frequently used in the solution of problems that occur in uncertain situations such as quality assessment in recent years, is one of the artificial intelligence methods. In this study, it was aimed to develop a fuzzy logic-based decision support system aimed at predicting the deterioration of heat-treated raw milk. As the subject of this project, it was developed to determine whether the milk produced in troops, farms and cooperatives is spoiled before the milk goes from production households to businesses.

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Comparison of artificial neural network and K-means for clustering dairy cattle

Cited by 9 publications

References 15 publications

Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models

Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models

Artificial Neural Networks- An Introduction and Application in Animal Breeding and Production: A Review

Fuzzy Control System for Predicting The Quality of Thermally Processed Raw Milk

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