A service model for nutrition supplement prediction based on Fuzzy Bayes model using bigdata in livestock

Saraswathi Sivamani (), Jongsun Choi () and Yongyun Cho ()
Additional contact information
Saraswathi Sivamani: Sunchon National University
Jongsun Choi: Soongsil University
Yongyun Cho: Sunchon National University

Annals of Operations Research, 2018, vol. 265, issue 2, No 6, 257-268

Abstract: Abstract The paper proposes a novel method in the decision support system for the nutritional management of livestock using the Bayesian model based on fuzzy rules. The objective is to analysis the decision based on fuzzy rules over the nutrition management that helps to improve the health of the livestock. Bayesian logic mainly focuses on the probabilities of the food intake with respect to the Food Intake Amount, Cow Stage and weight of the livestock. The conditional probability of the Bayesian reasoning is introduced along with the fuzzy rule, to determine the health status of the livestock. The fuzzy logic technique helps to decide on the decision system, when there are more than one dependencies. In this paper, the total digestible nutrient of the cow is determined over the period of time to get the rate of probability, and the fuzzy rule is applied to determine the health status of the cow, to predict the nutritional intake in the livestock.

Keywords: Fuzzy logic; Bayesian model; Big data; Livestock nutrition management; Conditional probability (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-017-2490-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:265:y:2018:i:2:d:10.1007_s10479-017-2490-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-017-2490-7

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().