ArticleTropical animal health and production2023
Using the artificial bee colony technique to optimize machine learning algorithms in estimating the mature weight of camels.
Article in Tropical animal health and production, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- ExploringCurrent issues in molecular biology · 2026Article
- Prediction of Mature Body Weight of Indigenous Camel (Animals : an open access journal from MDPI · 2025Article
- Machine Learning Analysis of the Impact of Silver Nitrate and Silver Nanoparticles on Wheat (Plants (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
Funding
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Abstract
This paper aims to predict male and female camels' mature weight (MW) through various morphological traits using hybrid machine learning (ML) algorithms. For this aim, biometrical measurements such as birth weight (BW), length of face (FL), length of the neck (NL), a girth of the heart (HG), body length (BL), withers height (WH), and hind leg length (HLL) were used to estimate the mature weight for eight camel breeds of Pakistan. In this study, multivariate adaptive regression splines (MARS), random forest (RF), and support vector machine (SVM) were applied to develop prediction models. Furthermore, the artificial bee colony (ABC) algorithm is employed to optimize ML models' internal parameters and improve prediction accuracy. The predictive performance of ML and hybrid models was evaluated on a testing dataset using goodness-of-fit measures such as mean absolute deviation (MAD), mean absolute percentage error (MAPE), coefficient of determination (R
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