Artificial Neural Networks for Predicting Animal Thermal Comfort

Borges, Pedro H. M.; Mendoza, Zaíra M. S. H.; Morais, Pedro Hurtado de Mendoza; Santos, Ronei Lopes dos

doi:10.1590/1809-4430-eng.agric.v38n6p844-856/2018

Cited by 8 publications

(8 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…ANOVA is a method used to test the quality of three or more population means based on analysis of sample variances. Because the sample data were assigned into groups according to a characteristic (a factor) (16) , ANOVA was used to compare numerical variables among clusters 1, 2, 3, and 4. The Chi-squared test was used to compare categorical variables.…”

Section: Data Analysis and Statisticsmentioning

confidence: 99%

Clustering the engagement of Brazilian nurses in political advocacy

et al. 2022

View full text Add to dashboard Cite

Objectives: to analyze the level of nurses’ engagement in political advocacy by performing cluster analysis. Methods: observational study, with a quantitative approach. A total of 184 nurses working in primary, secondary, and tertiary care in a city in the south of Brazil completed the Policy Advocacy Engagement Scale. Data analysis consisted of descriptive statistics, cluster analysis, analysis of variance, and chi2. The Institutional Review Board approved the study. Results: four differentiated clusters were found according to professional experience, level of healthcare complexity, and unit. The cluster analysis revealed that patient advocacy for community-based obtained the highest mean, indicating that political advocacy is effective in organizational environments and that professional qualification favors greater engagement in political advocacy. Conclusions: the results reveal that nurses play an active role in political advocacy, seeking to promote positive changes in health, especially those working in tertiary care, the nurse group that obtained the highest means.

show abstract

Section: Data Analysis and Statisticsmentioning

confidence: 99%

Clustering the engagement of Brazilian nurses in political advocacy

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The different typologies of plant and animal production buildings 15,16 , the construction materials and the physical structures of these buildings, air quality inside the buildings, the thermal environment, the presence of contaminants in the air, animal behavior in terms of physiology and of the behavioral expression itself 9,[17][18][19][20] , among other topics are studied within the field of Rural Buildings and Ambience (RB&A).…”

Section: Introductionmentioning

confidence: 99%

Representativeness of the field of rural buildings and ambience in Brazilian journals / Representatividade do campo dos edifícios rurais e do ambiente nas revistas brasileiras

Andrade¹,

Sampaio²,

Terezo³

et al. 2021

BJDV

View full text Add to dashboard Cite

show abstract

“…Esos artificios computacionales son flexibles y robustos ante las amplias fluctuaciones e inestabilidades del microclima, bien como tienen elevada capacidad para el reconocimiento y repetición de padrones. Esas propiedades fueron comprobadas en los estudios realizados por Bilgili & Sahin (2010), Wu et al (2010), Yasar et al (2012), Depiné et al (2013) y Borges et al (2018Borges et al ( , 2019Borges et al ( , 2021a.…”

Section: Introductionunclassified

Previsión del ambiente térmico para el ganado lechero mediante redes neuronales artificiales

Borges¹,

Mendoza²,

Morais³

2021

SFJEAS

View full text Add to dashboard Cite

El presente estudio tuvo como objetivo la previsión del ambiente térmico para el ganado lechero mediante redes neuronales artificiales, de acordó con la temperatura y humedad diaria. En la investigación se utilizaron los valores diarios de esas variables, disponibles en el Instituto Nacional de Meteorología de Brasil. Los datos correspondieron a las series históricas registradas en estaciones convencionales con tiempo de operación superior a 30 años hasta 2020. A continuación, se seleccionaron los municipios Canarana, Matupá, Nova Xavantina y Santo Antônio de Leverger, localizados en Mato Grosso, Brasil. Con base en los dados climatológicos, se estimó el Índice de Temperatura y Humedad diario en el calendario Juliano. Posteriormente, se probaron 35 arquitecturas de redes neuronales artificiales con tipología perceptrón de múltiples camadas, siendo la variable de entrada el día Juliano y la de salida el Índice de Temperatura y Humedad. La idoneidad de las redes fue verificada por el coeficiente de determinación, el error absoluto medio, el error cuadrático medio, el porciento medio del error absoluto y la normalidad de los residuos. No hubo diferencias entre los valores estimados por las redes y los obtenidos a partir de las series históricas. La rede de mejor desempeño y eficiencia para cada municipio, también fue comprobada por el análisis gráfico de los residuos. Se concluyó que las redes neuronales con tipología perceptron de dos camadas ocultas fueron apropiadas en el pronóstico del ambiente térmico natural para el ganado lechero. The present study aimed to forecast the thermal environment for dairy cattle through artificial neural networks, according to the daily temperature and humidity. The research used the daily values of these variables, available in the National Institute of Meteorology of Brazil. The data corresponded to the historical series registered in conventional stations with an operating time of more than 30 years until 2020. Next, the municipalities Canarana, Matupá, Nova Xavantina and Santo Antônio de Leverger, located in Mato Grosso, Brazil, were selected. Based on the climatological data, the Temperature and Humidity Index was determined for each day of the year in the Julian calendar. Subsequently, 35 artificial neural network architectures with multiple layer perceptron typology were tested, the input variable being the Julian day and the output variable being the Temperature and Humidity Index. The suitability of the networks was verified by the coefficient of determination, the mean absolute error, the mean square error, the mean percentage of the absolute error and the normality of the residuals. There were no differences between the values estimated by the networks and those obtained from the historical series. The network with the best performance and efficiency for each municipality was also verified by the graphic analysis of the residuals. It was concluded that the neural networks with perceptron typology of two hidden layers were appropriate in the forecast of the natural thermal environment for dairy cattle.

show abstract

Artificial Neural Networks for Predicting Animal Thermal Comfort

Cited by 8 publications

References 32 publications

Clustering the engagement of Brazilian nurses in political advocacy

Clustering the engagement of Brazilian nurses in political advocacy

Representativeness of the field of rural buildings and ambience in Brazilian journals / Representatividade do campo dos edifícios rurais e do ambiente nas revistas brasileiras

Previsión del ambiente térmico para el ganado lechero mediante redes neuronales artificiales

Contact Info

Product

Resources

About