Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

Abstract

The paper suggests the application of artificial neural networks (ANN) for the analysis of variables that significantly impact on the results of Hucul horses that participate at the National Breeding and Utility Championships for Hucul horses. The study exploits the results obtained during 2009–2015. The research material collected enabled the creation of a set of input data (for the artificial neural network), out of which independent learning and testing sets were isolated. The neural classification system in form of a multi-layered artificial neural network suggested in this paper was implemented in the programming environment Matlab, the 8.1.0.604 version. Each horse was described using features in three models. Experimental simulations were carried out separately for each model, conducting the learning and testing simulation process 10 times. In accepting the division of the evaluated group of horses into 10 classes for the analysis of the issue both the expert and network designated the classes, not without reservations due to imprecision of demarcations. The increase in class numbers would result in increased accuracy of selection (allocation to varied classes) of individuals. The average for 10 network responses which was 77% suggest an identical or a very similar horse class when compared with the expert’s value. Preliminary results of the application of artificial neural networks in predicting the utility value of Hucul horses, relying on a specific set of features seem promising.

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

“…This is also observed while selecting the family or families obtaining the best results ( Fig 6 ). At the same time, too little number of horses belonging to the breed are entered for participation in qualifying trails and in consequence for participation in the All-Polish performance championship [ 15 ].…”

“…Systems based on artificial intelligence have continued to evolve as research is undertaken in many fields of agriculture, including agronomy and animal husbandry. Horse studies have included, among others, estimating the need for surgery and the probability of survival in case of colic occurrence [ 13 ], biomechanical studies for gait phenotyping for breeding and genetic studies [ 14 ], predicting the performance value of Hucul horses [ 15 ], pain identification based on horse’s facial activity [ 16 ] and behavioral changes [ 17 ], as well as the occurrence of lameness based on movement trajectory [ 18 ]. The popularity of horse racing has contributed to a number of studies involving the prediction of race results [ 19 – 22 ], the identification of Arabian racehorses [ 23 ], and the optimization of a decision management model for competition organization [ 24 ].…”

“…A key issue is, in the case of small animal populations threatened with extinction, the assessment of their breeding and performance value, especially at the stage of selection and chioce of individuals for mating. Similarly, it should be emphasized that only a minor percentage of the Hucul horse population is usually subjected to such evaluation within the framework of the breed championships [ 15 ]. Thus, the development of a tool that allows the indication of characteristic traits and factors important for the evaluation of the breeding and performance value of Hucul horses to resolve this problem is deemed necessary.…”

Improvement of the Performance Balance between Thermal Comfort and Energy Use for a Building Space in the Mid-Spring Season

“…The ANN model can help many researchers to solve complex calculation problems utilizing several polynomials which make them hard to handle by conventional approaches. Especially, when analyzing the effects of convection, radiation, ventilation, and infiltration between envelopes, the ANN models for various types of buildings have successfully provided precise regression models to find hidden interactions between several architectural elements [10,11]. Regarding various operational strategies in combustion and circulation of fuel depending on the types of energy systems in buildings, the ANN algorithm has been preferred to generate signals for sensitive controls of valves or dampers.…”