Browsing by Author "Canaza-Cayo, Ali William"

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    Article
    Machine Learning Algorithm Approaches for Predicting Body Weight in Tuis Huacaya Alpacas
    (Elsevier, 2025) Canaza-Cayo, Ali William; Rodriguez-Huanca, Francisco Halley; Huanca-Ilaquijo, Maria Celeste; Carvalheiro, Roberto; Romero-Torres, Maria Trinidad; Yucra-Yucra, Yovana Bertha; Churata-Huacani, Roxana
    The purpose of this study is to examine how machine learning (ML) techniques can predict the body weight of Huacaya alpacas (Vicugna pacos) based on body measurements. Eighteen body measurements (BM) and six different ML models were used. Body weight (BW) and BM: head length (HL), ear length (EL), head width (HW), interorbital distance (ID), head height (HH), neck length (NL), upper neck perimeter (UNP), lower neck perimeter (LNP), wither height (WH), back height (BH), rump height (RH), dorsal length (DL), distance between ischial tips (DBI), tail length (TL), Thoracic Perimeter (TP), Abdominal Perimeter (AP), anterior fore-shank perimeter (AFP) and hoof length (HoL), were collected from 153 10 month old Huacaya alpacas of both sexes, from the Quimsachata annex of the Instituto Nacional de Innovaci & oacute;n Agraria in Puno Department, Peru. The machine learning algorithms used to estimate body weight were Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), Multivariate Adaptive Regression Splines (MARS), Support Vector Machines for Regression (SVM), Random Forest (RF) and Classification and Regression Trees (CART). The performance of the models was evaluated by the mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R-2). Predictors with a high correlation (r >= 0.75) were removed from the dataset. The remaining predictors were then processed through variable selection procedures using the Boruta algorithm. The results from the Boruta algorithm confirmed that HW, UNP, BH, DBI, TL, TP, AP and HoL are important predictors of alpaca weight. The ML models were then trained on those selected predictors. RF had the highest R-2 values and lowest values of MAE, RMSE, and MAPE. In conclusion, the RF algorithm can be recommended for accurately estimating body weight in 10-month-old Huacaya alpacas of both sexes, based on the cohort evaluated in this study.
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    Article
    Use of Machine Learning Approaches for Body Weight Prediction in Peruvian Corriedale Sheep
    (Elsevier, 2024) Canaza-Cayo, Ali William; Churata Huacani, Roxana; Cakmakci, Cihan; Rodriguez-Huanca, Francisco Halley; Filzo, Julio Silvio de Sousa Bueno; Fernandes, Tales Jesus; De La Cruz, Yhan Carlos Rojas
    The goal of this study was to predict the body weight of Corriedale ewes using machine learning (ML) algorithms. Fourteen body measurements (BM) and six different machine learning models were used. Body weight (BW) and BM: wither height (WH), rump height (RH), thoracic perimeter (TP), abdominal perimeter (AP), foreshank length (FSL), fore-shank width (FSW), fore-shank perimeter (FSP), tail width (TW), tail perimeter (TPe), hip width (HW), loin width (LWi), shoulder width (SW), forelimb length (FL), and body length (BL), were collected from 100 Corriedale ewes between 1.5 and 2 years old from the Illpa Experimental Centre of the National University of Altiplano in Peru. The machine learning algorithms used to estimate body weight were Support Vector Machines for Regression (SVMR), Classification and Regression Trees (CART), Random Forest (RF), Model Average Neural Networks (MANN), Multivariate Adaptive Regression Splines (MARS) and eXtreme Gradient Boosting (XGBoost). The performance of the models was evaluated by the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Highly correlated predictors (r >= 075) were removed from the dataset. The remaining predictors were then subjected to variable selection procedures using the Boruta algorithm. Boruta results confirmed the importance of TP, LWi, BL, FSL, SW and HW as predictors of ewe weight. The ML models were then trained on those selected predictors. RF had the highest R2 values and lowest values of MAE, RMSE, and MAPE. In conclusion, the RF algorithm can be recommended for accurately estimating BW from body measurements of Corriedale sheep.