Scopus
Application of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattle
Indian Journal of Animal Research · Ekim 2015
Makale Bilgileri
DergiIndian Journal of Animal Research
Yayın TarihiEkim 2015
Cilt / Sayfa49 · 671-679
Scopus ID2-s2.0-84944555125
Özet
Mastitis is an important problem, while I guess AI is a possible solution to detect subclinical mastitis in Holstein cows milked with automatic milking systems. Mastitis alerts were generated via ANN and ANFIS model with the input data of lactation rank (current lactation number), milk yield, electrical conductivity, average milking duration and season. The output variable was somatic cell counts obtained from milk samples collected monthly throughout the 15 months of the sampling period. Cattle were judged healthy or infected based on somatic cell counts. This study undertook a detailed scrutiny of ANN, and ANFIS AI methodology; constructed and examined models for each; and chose optimal methods based on that examination. The two mastitis detection models were evaluated as to sensitivity, specificity and error rate. The ANN model yielded 80% sensitivity, 91% specificity, and 64% error and the ANFIS, 55%, 91% and 35%. These results suggest the ANN model is better predictor of subclinical mastitis than ANN based on Z-test (the hypothesis control for the difference between rates). AI models such as these are useful tools in the development of mastitis detection models. Prediction error rates can be decreased through the use of more informative parameters.
Yazarlar (2)
1
N. Mikail
2
İsmail Keskin
Anahtar Kelimeler
Adaptive neuro-fuzzy inference system
Artificial Intelligence
Artificial neural network
Dairy cattle
Neuro-fuzzy Inference System
Subclinical mastitis
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Siirt Üniversitesi
Siirt Turkey
Metrikler
20
Atıf
2
Yazar
6
Anahtar Kelime