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

Author

Listed:
  • Saraswathi Sivamani

    (Sunchon National University)

  • Jongsun Choi

    (Soongsil University)

  • Yongyun Cho

    (Sunchon National University)

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.

Suggested Citation

  • Saraswathi Sivamani & Jongsun Choi & Yongyun Cho, 2018. "A service model for nutrition supplement prediction based on Fuzzy Bayes model using bigdata in livestock," Annals of Operations Research, Springer, vol. 265(2), pages 257-268, June.
    • Handle: RePEc:spr:annopr:v:265:y:2018:i:2:d:10.1007_s10479-017-2490-7
    DOI: 10.1007/s10479-017-2490-7
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    Cited by:

    1. Guangyong Yang & Guojun Ji & Kim Hua Tan, 2022. "Impact of artificial intelligence adoption on online returns policies," Annals of Operations Research, Springer, vol. 308(1), pages 703-726, January.

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