The use of artificial neural network for modeling in vitro rumen methane production using the CNCPS carbohydrate fractions as dietary variables

Dong, Ruilan; Zhao, Guanglei

doi:10.1016/j.livsci.2013.12.033

Cited by 14 publications

(9 citation statements)

References 17 publications

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“…However, their integration with MM, more specifically mechanistic modeling, is embryonic. In cattle production, few studies in animal welfare (Dutta et al, 2015), genome-wide predictions (González-Recio et al, 2014) and breed classification (Santoni et al, 2015), genomics’ expected progeny difference (Okut et al, 2013), anatomical biometrics for animal identification/recognition (Kumar et al, 2018), animal growth (Alonso et al, 2013; Alonso et al, 2015), and rumen functioning (Craninx et al, 2008; Dong and Zhao, 2014) have used AI technologies alone or in combination with other statistical methods. Craninx et al (2008), for instance, compared the adequacy of ML to multilinear regression techniques for predicting ruminal volatile fatty acids production, measured by milk fatty acid composition, using data from 10 studies ( n = 138 observations) of rumen cannulated dairy cows.…”

Section: Hybrid Knowledge- and Data-driven Mathematical Modelingmentioning

confidence: 99%

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2

Tedeschi

2019

Journal of Animal Science

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This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models ( MM ) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate real-life situations into mathematical formulations to describe existing patterns or forecast future behaviors in real-life situations. The appropriateness of the virtual representation of real-life situations through MM depends on the modeler’s ability to synthesize essential concepts and associate their interrelationships with measured data. The development of MM paralleled the evolution of digital computing. The scientific community has only slightly accepted and used MM, in part because scientists are trained in experimental research and not systems thinking. The scientific advancements in ruminant production have been tangible but incipient because we are still learning how to connect experimental research data and concepts through MM, a process that is still obscure to many scientists. Our inability to ask the right questions and to define the boundaries of our problem when developing models might have limited the breadth and depth of MM in agriculture. Artificial intelligence ( AI ) has been developed in tandem with the need to analyze big data using high-performance computing. However, the emergence of AI, a computational technology that is data-intensive and requires less systems thinking of how things are interrelated, may further reduce the interest in mechanistic, conceptual MM. Artificial intelligence might provide, however, a paradigm shift in MM, including nutrition modeling, by creating novel opportunities to understand the underlying mechanisms when integrating large amounts of quantifiable data. Associating AI with mechanistic models may eventually lead to the development of hybrid mechanistic machine-learning modeling. Modelers must learn how to integrate powerful data-driven tools and knowledge-driven approaches into functional models that are sustainable and resilient. The successful future of MM might rely on the development of redesigned models that can integrate existing technological advancements in data analytics to take advantage of accumulated scientific knowledge. However, the next evolution may require the creation of novel technologies for data gathering and analyses and the rethinking of innovative MM concepts rather than spending resources in collecting futile data or amending old technologies.

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Section: Hybrid Knowledge- and Data-driven Mathematical Modelingmentioning

confidence: 99%

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2

Tedeschi

2019

Journal of Animal Science

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“…Both ANN‐BP and ANN‐KF models (on validation data) predicted the log reduction of E. coli K12 on goat meat surfaces well. However, prediction performance was better in mid‐range treatment times (15, 30 and 45 s) compared with treatment times 5 or 60 s. It is not uncommon for ANN models to display better prediction for certain treatments and not others (Dong & Zhao, 2014). Since the number of neurons in the hidden layer and the number of variables in the output layer could affect the performance of neural nets, a model with complex networks (i.e.…”

Section: Resultsmentioning

confidence: 99%

Inactivation ofEscherichia coliK12 by pulsed UV light on goat meat and beef: microbial responses and modelling

Bryant

Degala

Mahapatra

et al. 2020

Int J of Food Sci Tech

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“…Few studies have considered predicting the relationship between the rumen fill scores, feed and animal characteristics to improve the predictive capacity of ANN models. Artificial neural networks have been successfully used in the simulation of milk production in goats (Fernandez et al 2006), in vitro methane and carbon dioxide production in the rumen (Dong and Zhao 2014) and for modelling the solid and liquid passage rate in ruminants (Moyo et al 2017;Moyo and Nsahlai 2018). Craninx et al (2008) used the machine learning of artificial neural networks with different architectures and training algorithms to determine the prediction accuracy of the chosen model for the branched chain milk fatty acid content in the rumen.…”

Section: Artificial Neural Network In Animal Sciencementioning

confidence: 99%

Progressive trends on the application of artificial neural networks in animal sciences - A review

Bauer¹

2022

Vet. Med. - Czech

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In recent years, artificial neural networks have become the subject of intensive research in a number of scientific areas. The high performance and operational speed of neural models open up a wide spectrum of applications in various areas of life sciences. Objectives pursued by many scientists, who use neural modelling in their research, focus – among others – on intensifying real-time calculations. This study shows the possibility of using Multilayer-Perceptron (MLP) and Radial Basis Function (RBF) models of artificial neural networks for the future development of new methods for animal science. The process should be explained explicitly to make the MLP and RBF models more readily accepted by more researchers. This study describes and recommends certain models as well as uses forecasting methods, which are represented by the chosen neural network topologies, in particular MLP and RBF models for more successful operations in the field of animals sciences.

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The use of artificial neural network for modeling in vitro rumen methane production using the CNCPS carbohydrate fractions as dietary variables

Cited by 14 publications

References 17 publications

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2

Inactivation ofEscherichia coliK12 by pulsed UV light on goat meat and beef: microbial responses and modelling

Progressive trends on the application of artificial neural networks in animal sciences - A review

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