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|Title: ||The use of artificial neural networks to diagnose mastitis in dairy cattle|
|Author: ||López-Benavides, Mario G.|
Hickford, Jon G. H.
|Date: ||Jul-2003 |
|Citation: ||López-Benavides, M. G., Samarasinghe, S., & Hickford, J. G. H. (2003). The use of artificial neural networks to diagnose mastitis in dairy cattle. In Proceedings of the International Joint Conference on Neural Networks 2003 Doubletree Hotel-Jantzen Beach, Portland, Oregon, July 20-24, 2003 (pp. 582-585). Piscataway, NJ: IEEE.|
|Item Type: ||Conference Contribution - Paper in Published Proceedings|
|Abstract: ||The use of milk sample categorization for diagnosing mastitis using Kohonen's self-organizing feature map (SOFM) is reported. Milk trait data of 14 weeks of milking from commercial dairy cows in New Zealand was used to train and test a SOFM network. The SOFM network was useful in discriminating data patterns into four separate mastitis categories. Several other artificial neural networks were tested to predict the missing data from the recorded milk traits. A multi-layer perceptron (MLP) network proved to be most accurate (R² = 0.84, r = 0.92) when compared to other MLP (R² = 0.83, r = 0.92), Elman (R² = 0.80, r = 0.92), Jordan (R² = 0.81, r = 0.92) or linear regression (R² = 0.72, r = 0.85) methods. It is concluded that the SOFM can be used as a decision tool for the dairy farmer to reduce the incidence of mastitis in the dairy herd.|
|Persistent URL (URI): ||http://hdl.handle.net/10182/1994|
|Related: ||Originally published online at IEEE Xplore.|
|Related URI: ||http://dx.doi.org/10.1109/IJCNN.2003.1223420|
|Rights: ||© 2003 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Appears in Collections:||Department of Agricultural Sciences|
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