首页|New Research on Machine Learning from State University of Campinas (UNICAMP) Summarized (Predicting Risk of Ammonia Exposure in Broiler Housing: Correlation with Incidence of Health Issues)
New Research on Machine Learning from State University of Campinas (UNICAMP) Summarized (Predicting Risk of Ammonia Exposure in Broiler Housing: Correlation with Incidence of Health Issues)
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Investigators publish new report on artificial intelligence. According to news originating from Sao Paulo, Brazil, by NewsRx correspondents, research stated, “The study aimed to forecast ammonia exposure risk in broiler chicken production, correlating it with health injuries using machine learning. Two chicken breeds, fast-growing (Ross®) and slow-growing (Hubbard®), were compared at different densities.” Our news editors obtained a quote from the research from State University of Campinas (UNICAMP): “Slow-growing birds had a constant density of 32 kg m-2, while fast-growing birds had low (16 kg m- 2) and high (32 kg m-2) densities. Initial feeding was uniform, but nutritional demands led to varied diets later. Environmental data underwent selection, pre-processing, transformation, mining, analysis, and interpretation. Classification algorithms (decision tree, SMO, Naive Bayes, and Multilayer Perceptron) were employed for predicting ammonia risk (10-14 pmm, Moderate risk). Cross-validation was used for model parameterization. The Spearman correlation coefficient assessed the link between predicted ammonia risk and health injuries, such as pododermatitis, vision/affected, and mucosal injuries. These injuries encompassed trachea, bronchi, lungs, eyes, paws, and other issues. The Multilayer Perceptron model emerged as the best predictor, exceeding 98% accuracy in forecasting injuries caused by ammonia. The correlation coefficient demonstrated a strong association between elevated ammonia risks and chicken injuries. Birds exposed to higher ammonia concentrations exhibited a more robust correlation.”
State University of Campinas (UNICAMP)Sao PauloBrazilSouth AmericaAmmoniaCyborgsEmerging TechnologiesMachine LearningNitrogen CompoundsPerceptronRisk and Prevention
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