Application of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattle
dc.contributor.author | Mammadova, Nazira M. | |
dc.contributor.author | Keskin, Ismail | |
dc.date.accessioned | 2020-03-26T19:00:54Z | |
dc.date.available | 2020-03-26T19:00:54Z | |
dc.date.issued | 2015 | |
dc.department | Selçuk Üniversitesi | en_US |
dc.description.abstract | Mastitis 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.sponsorship | Scientific Research Project Office of Selcuk University, TurkeySelcuk University [10201056] | en_US |
dc.description.sponsorship | This 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.doi | 10.18805/ijar.5581 | en_US |
dc.identifier.endpage | 679 | en_US |
dc.identifier.issn | 0367-6722 | en_US |
dc.identifier.issue | 5 | en_US |
dc.identifier.scopusquality | Q2 | en_US |
dc.identifier.startpage | 671 | en_US |
dc.identifier.uri | https://dx.doi.org/10.18805/ijar.5581 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12395/31855 | |
dc.identifier.volume | 49 | en_US |
dc.identifier.wos | WOS:000365065900019 | en_US |
dc.identifier.wosquality | Q4 | en_US |
dc.indekslendigikaynak | Web of Science | en_US |
dc.indekslendigikaynak | Scopus | en_US |
dc.language.iso | en | en_US |
dc.publisher | AGRICULTURAL RESEARCH COMMUNICATION CENTRE | en_US |
dc.relation.ispartof | INDIAN JOURNAL OF ANIMAL RESEARCH | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.selcuk | 20240510_oaig | en_US |
dc.subject | Adaptive neuro-fuzzy inference system | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.subject | Artificial neural network | en_US |
dc.subject | Dairy cattle | en_US |
dc.subject | Neuro-Fuzzy Inference System | en_US |
dc.subject | Subclinical mastitis | en_US |
dc.title | Application of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattle | en_US |
dc.type | Article | en_US |