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

  • CS and OR in Big Data and Cloud Com
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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.

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References

  • Bennett, C. C., & Hauser, K. (2013). Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach. Artificial Intelligence in Medicine, 57, 9–19.

    Article  Google Scholar 

  • Berkan, R. C., & Trubatch, S. (1997). Fuzzy system design principles. New York: Wiley-IEEE Press.

    Google Scholar 

  • Berner, E. S. (2007). Clinical decision support systems: Theory and practice. New York: Springer.

    Book  Google Scholar 

  • Bojadziev, G., & Bojadziev, M. (2007). Fuzzy logic. In Fuzzy logic for business, finance and management (pp. 37–59).

  • Brown-Brandl, T. M., & Eigenberg, R. A. (2011). Development of a livestock feeding behavior monitoring system. Transactions of the ASABE, 54(5), 1913–1920.

    Article  Google Scholar 

  • Chai, Y., Jia, L., & Zhang, Z. (2009). Mamdani model based adaptive neural fuzzy inference system and its application. International Journal of Computational Intelligence, 5(1), 22–29.

    Google Scholar 

  • Cveticanin, D. (2003). New approach to the dynamic weighing of livestock. Biosystems Engineering, 86(2), 247–252.

    Article  Google Scholar 

  • Charitos, T., van der Gaag, L. C., Visscher, S., Schurink, C. A. M., & LUCAS, P. J. F. (2009). A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients. Expert Systems with Applications, 36, 1249–1258.

    Article  Google Scholar 

  • Devore, J. (2015). Probability and statistics for engineering and the sciences. Boston: Cengage Learning.

    Google Scholar 

  • Engel, B. A., Srinivasan, R., Rewerts, C., Goodchild, M. F., Parks, B. O., & Steyaert, L. T. (1993). A spatial decision support system for modeling and managing agricultural non-point-source pollution. Oxford: Oxford University Press Inc.

    Google Scholar 

  • Gates, R. S., Chao, K., & Sigrimis, N. (2001). Identifying design parameters for fuzzy control of staged ventilation control systems. Computers and Electronics in Agriculture, 31(1), 61–74.

    Article  Google Scholar 

  • Gelfand, A. E., & Dey, D. K. (1994). Bayesian model choice: Asymptotics and exact calculations. Journal of the Royal Statistical Society. Series B (Methodological), 56(2), 501–514.

  • Grothmann, A., Nydegger, F., Mortiz, C. & Bisaglia, C. (2012). Automatic feeding systems for dairy cattle—potential for optimization in dairy farming. In Proceedings of International Conference Of Agricultural Engineering CIGR-AgEng2012. Valencia, Spain, 8–12 July (2012).

  • Herd, R. M. (1992). A computerised individual feeding system for beef cattle. Computers and Electronics in Agriculture, 7(3), 261–267.

    Article  Google Scholar 

  • Hudson, D. L. (2006). Medical expert systems. In Encyclopedia of Biomedical Engineering. John Wiley and Sons, doi:10.1002/9780471740360.ebs0751.

  • Jensen, F. V. (2000). An Introduction to Bayesian Networks. London: UCL Press Limited.

    Google Scholar 

  • Korb, K. B., & Nicholson, A. E. (2010). Bayesian artificial intelligence. Boca Raton: CRC press.

    Google Scholar 

  • Kramer, E., Cavero, D., Stamer, E., & Krieter, J. (2009). Mastitis and lameness detection in dairy cows by application of fuzzy logic. Livestock Science, 125(1), 92–96.

    Article  Google Scholar 

  • Liu, P. Z., Bi, S. S., Liang, Y., Xue, X. Y., & Xu, C. Z. (2009). Design and implementation of intelligent terminal for livestock breeding environment. Computer Engineering, 35(19), 20–22.

    Google Scholar 

  • Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (2013). Machine learning: An artificial intelligence approach. Berlin: Springer.

  • Ogiela, L. (2013). Cognitive informatics in image semantics description, identification and automatic pattern understanding. Neurocomputing, 122, 58–69.

    Article  Google Scholar 

  • Ogiela, L., & Ogiela, M. R. (2014). Cognitive systems and bio-inspired computing in homeland security. Journal of Network and Computer Applications, 38, 34–42.

    Article  Google Scholar 

  • Onisko, A., Luca, P., & Druzdzel, M. J. (2001). Comparison of rule-based and Bayesian network approaches in medical diagnostic systems. In Artificial Intelligence in Medicine (8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001 Cascais, Portugal, July 1–4, 2001, Proceedings), Lecture Notes in Computer Science, vol. 2101 (pp. 283–292).

  • Pan, H. P., & McMichael, D. W. (1998). Fuzzy causal probabilistic networks—a new ideal and practical inference engine. In Proceedings first international conference on multisource–Multisensor data fusion(FUSION’98) (pp. 101–108). July, Las Vegas, USA. ISBN 1-892512-00-9.

  • Pan, L., & Yang, S. X. (2007). Analysing livestock farm odour using an adaptive neuro-fuzzy approach. Biosystems Engineering, 97(3), 387–393.

    Article  Google Scholar 

  • Santana, Á.L., Conde, G.B., Rego, L.P., Rocha, C.A., Cardoso, D.L., Costa, J.C., Bezerra, U.H., & Francês, C.R. (2012). PREDICT-decision support system for load forecasting and inference: A new undertaking for Brazilian power suppliers. International Journal of Electrical Power & Energy Systems, 38(1), 33–45.

  • Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., et al. (2001). Clinical decision support systems for the practice of evidence-based medicine. Journal of the American Medical Informatics Association, 8(6), 527–534.

    Article  Google Scholar 

  • Traulsen, I., & Krieter, J. (2012). Assessing airborne transmission of foot and mouth disease using fuzzy logic. Expert Systems with Applications, 39(5), 5071–5077.

    Article  Google Scholar 

  • Yam, R. C. M., Tse, P. W., Li, L., & Tu, P. (2001). Intelligent predictive decision support system for condition-based maintenance. The International Journal of Advanced Manufacturing Technology, 17(5), 383–391.

    Article  Google Scholar 

  • Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1), 3–28.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government. (MEST) (No. 2014R1A1A2059853). This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-H8601-16-1007) supervised by the IITP (Institute for Information & communications Technology Promotion).

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Correspondence to Yongyun Cho.

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Sivamani, S., Choi, J. & Cho, Y. A service model for nutrition supplement prediction based on Fuzzy Bayes model using bigdata in livestock. Ann Oper Res 265, 257–268 (2018). https://doi.org/10.1007/s10479-017-2490-7

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  • DOI: https://doi.org/10.1007/s10479-017-2490-7

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