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Home / Equine Research Bank / PLoS One / Assessing the utility value of Hucul horses using classifica...
PloS one2022; 17(7); e0271340; doi: 10.1371/journal.pone.0271340

Assessing the utility value of Hucul horses using classification models, based on artificial neural networks.

Abstract: The aim of this study was to evaluate factors influencing the performance of Hucul horses and to develop a prediction model, based on artificial neural (AI) networks for predict horses' classification, relying on their performance value assessment during the annual Hucul championships. The Feedforward multilayer artificial neural networks, learned using supervised methods and implemented in Matlab programming environment were applied. Artificial neural networks with one and two hidden layers with different numbers of neurons equipped with a tangensoidal transition function, learned using the Levenberg-Marqiuardt method, were applied for the analysis. Although results showed that 7-year-old horses had the highest number of wins, the 11-year-old horses were observed to have had the best results when accessed relative to the total number of horses for a given year. Although horses from the Hroby line had the most starts in 2009-2019, those of the Goral line had the most wins. While predicting the horses' efficiency for the first 6 positions during the utility championship, the neural network consisting of 12 neurons in hidden layer performed the best, obtaining 69,65% efficiency. The highest horse efficiency classification was obtained for the four-layered network with 12 and 8 neurons in the hidden layers. An 81.3% efficiency was obtained while evaluating the correctness of the prediction for horses occupying positions 1 to 3. The use of AI seems to be indispensable in assessing the performance value of Hucul horses. It is necessary to determine the relation between horses' traits and their utility value by means of trait selection methods, accompanied with expert advice. It is also advisable to conduct research using deep neural networks.
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Publication Date: 2022-07-26 PubMed ID: 35881630PubMed Central: PMC9321442DOI: 10.1371/journal.pone.0271340Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The study explores the use of artificial neural networks in predicting the performance and classification of Hucul horses based on data gathered during annual competitions. By applying different artificial intelligence algorithms, the researchers found that certain features and patterns can suggest the potential success of a horse.

Research Method

  • In order to understand factors that influence the performance of Hucul horses, the researchers gathered data from annual horse championships.
  • This data was then used to train artificial neural networks which were implemented in MATLAB, a numerical computing environment often used for machine learning applications. This method is called supervised learning, a type of machine learning where the model learns from labeled training data.
  • The artificial neural networks were made up of one or two hidden layers containing a varying number of neurons. Each of these neurons was equipped with a tangensoidal transition function – an activation function that is used to calculate the output of artificial neural networks.
  • The researchers applied a specific type of optimization algorithm known as the Levenberg-Marquardt method to adjust the weights and biases in the network during the learning process.

Research Findings

  • The study found several interesting patterns. For instance, while 7-year-old horses had the most total wins, when considered proportionally to the number of horses in each age group, 11-year-old horses performed better.
  • The horses from the Hroby line had the most starts in the 2009-2019 period studied, but the Goral line had the most wins.
  • A neural network consisting of 12 neurons in a single hidden layer proved to be the most successful at predicting horses’ performance in the first six positions during championships, achieving 69.65% efficiency.
  • However, when predicting positions 1 to 3, the highest horse efficiency classification was achieved by a four-layer neural network with 12 and 8 neurons in the hidden layers, reaching an efficiency of 81.3%.

Recommendations and Implications

  • The study suggests that artificial intelligence is extremely useful in predicting the performance of Hucul horses and other live creatures.
  • It underlines the need for further research to identify the correlation between horses’ traits and their performance using trait selection methods. This should be complemented by expert advice.
  • The study also suggests that future research be conducted using deep neural networks for better accuracy and precision in performance prediction.

Cite This Article

APA
Topczewska J, Bartman J, Kwater T. (2022). Assessing the utility value of Hucul horses using classification models, based on artificial neural networks. PLoS One, 17(7), e0271340. https://doi.org/10.1371/journal.pone.0271340

Publication

PloS one
ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 17
Issue: 7
Pages: e0271340
PII: e0271340

Researcher Affiliations

Topczewska, Jadwiga
  • College of Natural Sciences, University of Rzeszów, Rzeszow, Poland.
Bartman, Jacek
  • College of Natural Sciences, University of Rzeszów, Rzeszow, Poland.
Kwater, Tadeusz
  • Institute of Technical Engineering, State University of Technology and Economics in Jarosław, Jarosław, Poland.

MeSH Terms

  • Animals
  • Horses
  • Neural Networks, Computer
  • Phenotype

Conflict of Interest Statement

The authors have declared that no competing interests exit.

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