Application of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattle

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dc.contributor.authorMammadova, Nazira M.
dc.contributor.authorKeskin, Ismail
dc.date.accessioned2020-03-26T19:00:54Z
dc.date.available2020-03-26T19:00:54Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractMastitis is an important problem, while I guess AT 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 AT 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.en_US
dc.description.sponsorshipScientific Research Project Office of Selcuk University, TurkeySelcuk University [10201056]en_US
dc.description.sponsorshipThis research was supported as a doctoral thesis by a grant from The Scientific Research Project Office of Selcuk University, Turkey (Project No: 10201056). The authors wish to thank the staff of KARYEM A<SUP>a</SUP>, Konya, TURKEY.en_US
dc.identifier.doi10.18805/ijar.5581en_US
dc.identifier.endpage679en_US
dc.identifier.issn0367-6722en_US
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage671en_US
dc.identifier.urihttps://dx.doi.org/10.18805/ijar.5581
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31855
dc.identifier.volume49en_US
dc.identifier.wosWOS:000365065900019en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAGRICULTURAL RESEARCH COMMUNICATION CENTREen_US
dc.relation.ispartofINDIAN JOURNAL OF ANIMAL RESEARCHen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectAdaptive neuro-fuzzy inference systemen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectArtificial neural networken_US
dc.subjectDairy cattleen_US
dc.subjectNeuro-Fuzzy Inference Systemen_US
dc.subjectSubclinical mastitisen_US
dc.titleApplication of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattleen_US
dc.typeArticleen_US

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