2022
DOI: 10.1007/s11869-022-01225-9
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Applicability of statistical and machine learning–based regression algorithms in modeling of carbon dioxide emission in experimental pig barns

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Cited by 10 publications
(6 citation statements)
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“…From Fig 3, there was a positive correlation between growthrelated factors and body weights, specifically the height of pig (HP), age (AG), length (LP) and girth length (GL). Other researchers have reported similar findings, in where FI (Pierozan et al, 2016), DW (Arulmozhi et al, 2020), HP (Yang et al, 2019), AG (Birteeb et al, 2015), LP (Banik et al, 2021), GL (Banik et al, 2021) and RCO 2 (Basak et al, 2022) are highly associated with pig's body weight. In addition, a negative correlation existed between PBW and the pig's body temperature (PBT).…”
Section: Input Variables Selectionsupporting
confidence: 60%
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“…From Fig 3, there was a positive correlation between growthrelated factors and body weights, specifically the height of pig (HP), age (AG), length (LP) and girth length (GL). Other researchers have reported similar findings, in where FI (Pierozan et al, 2016), DW (Arulmozhi et al, 2020), HP (Yang et al, 2019), AG (Birteeb et al, 2015), LP (Banik et al, 2021), GL (Banik et al, 2021) and RCO 2 (Basak et al, 2022) are highly associated with pig's body weight. In addition, a negative correlation existed between PBW and the pig's body temperature (PBT).…”
Section: Input Variables Selectionsupporting
confidence: 60%
“…non-linear models such as ANNs, Bayesian classification and genetic expression are acceptable when the variables under consideration have complicated and non-linear correlation (Basak et al, 2020;Basak et al, 2022). It has been noted that non-linear methods accurately interpret variable relationship and are better able to predict output variables than linear-based approaches (Basak et al, 2022).…”
Section: Applications Of Artificial Neural Network and Multiple Linea...mentioning
confidence: 99%
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“…Gautam et al [92] developed an offline prediction model to estimate future thermal conditions from building data collected in operation. Basak et al [93,94] used statistical and machine-learning methods to model CH 4 and CO 2 emissions from pig manure. Rodriguez et al [95] developed a CO 2 emission prediction model using neural networks.…”
Section: Applications Of Machine Learningmentioning
confidence: 99%