297 related articles for article (PubMed ID: 31605926)
1. Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep Learning and Gradient-Boosted Trees outperform other models.
Ebrahimi M; Mohammadi-Dehcheshmeh M; Ebrahimie E; Petrovski KR
Comput Biol Med; 2019 Nov; 114():103456. PubMed ID: 31605926
[TBL] [Abstract][Full Text] [Related]
2. A large-scale study of indicators of sub-clinical mastitis in dairy cattle by attribute weighting analysis of milk composition features: highlighting the predictive power of lactose and electrical conductivity.
Ebrahimie E; Ebrahimi F; Ebrahimi M; Tomlinson S; Petrovski KR
J Dairy Res; 2018 May; 85(2):193-200. PubMed ID: 29785910
[TBL] [Abstract][Full Text] [Related]
3. Development of a new clinical mastitis detection method for automatic milking systems.
Khatun M; Thomson PC; Kerrisk KL; Lyons NA; Clark CEF; Molfino J; García SC
J Dairy Sci; 2018 Oct; 101(10):9385-9395. PubMed ID: 30055925
[TBL] [Abstract][Full Text] [Related]
4. Performance comparison of machine learning models used for predicting subclinical mastitis in dairy cows: Bagging, boosting, stacking, and super-learner ensembles versus single machine learning models.
Satoła A; Satoła K
J Dairy Sci; 2024 Jun; 107(6):3959-3972. PubMed ID: 38310958
[TBL] [Abstract][Full Text] [Related]
5. Risk prediction model of clinical mastitis in lactating dairy cows based on machine learning algorithms.
Luo W; Dong Q; Feng Y
Prev Vet Med; 2023 Dec; 221():106059. PubMed ID: 37951013
[TBL] [Abstract][Full Text] [Related]
6. Suitability of somatic cell count, electrical conductivity, and lactate dehydrogenase activity in foremilk before versus after alveolar milk ejection for mastitis detection.
Khatun M; Bruckmaier RM; Thomson PC; House J; García SC
J Dairy Sci; 2019 Oct; 102(10):9200-9212. PubMed ID: 31351709
[TBL] [Abstract][Full Text] [Related]
7. Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction.
Kamphuis C; Mollenhorst H; Heesterbeek JA; Hogeveen H
J Dairy Sci; 2010 Aug; 93(8):3616-27. PubMed ID: 20655431
[TBL] [Abstract][Full Text] [Related]
8. Measurement of oxygen concentration for detection of subclinical mastitis.
Wittek T; Mader C; Ribitsch V; Burgstaller J
Schweiz Arch Tierheilkd; 2019 Oct; 161(10):659-665. PubMed ID: 31586928
[TBL] [Abstract][Full Text] [Related]
9. Bayesian integration of sensor information and a multivariate dynamic linear model for prediction of dairy cow mastitis.
Jensen DB; Hogeveen H; De Vries A
J Dairy Sci; 2016 Sep; 99(9):7344-7361. PubMed ID: 27320667
[TBL] [Abstract][Full Text] [Related]
10. Short communication: Protease activity measurement in milk as a diagnostic test for clinical mastitis in dairy cows.
Koop G; van Werven T; Roffel S; Hogeveen H; Nazmi K; Bikker FJ
J Dairy Sci; 2015 Jul; 98(7):4613-8. PubMed ID: 25981067
[TBL] [Abstract][Full Text] [Related]
11. Multivariable time series classification for clinical mastitis detection and prediction in automated milking systems.
Fan X; Watters RD; Nydam DV; Virkler PD; Wieland M; Reed KF
J Dairy Sci; 2023 May; 106(5):3448-3464. PubMed ID: 36935240
[TBL] [Abstract][Full Text] [Related]
12. Farm-level risk factors for bovine mastitis in Dutch automatic milking dairy herds.
Deng Z; Koop G; Lam TJGM; van der Lans IA; Vernooij JCM; Hogeveen H
J Dairy Sci; 2019 May; 102(5):4522-4535. PubMed ID: 30852004
[TBL] [Abstract][Full Text] [Related]
13. Application of the support vector machine to predict subclinical mastitis in dairy cattle.
Mammadova N; Keskin I
ScientificWorldJournal; 2013; 2013():603897. PubMed ID: 24574862
[TBL] [Abstract][Full Text] [Related]
14. Using Sensor Data to Detect Lameness and Mastitis Treatment Events in Dairy Cows: A Comparison of Classification Models.
Post C; Rietz C; Büscher W; Müller U
Sensors (Basel); 2020 Jul; 20(14):. PubMed ID: 32664417
[TBL] [Abstract][Full Text] [Related]
15. Diagnostic Screening of Bovine Mastitis Using MALDI-TOF MS Direct-Spotting of Milk and Machine Learning.
Thompson J; Everhart Nunn SL; Sarkar S; Clayton B
Vet Sci; 2023 Jan; 10(2):. PubMed ID: 36851405
[TBL] [Abstract][Full Text] [Related]
16. Indicators of inflammation in the diagnosis of mastitis.
Pyörälä S
Vet Res; 2003; 34(5):565-78. PubMed ID: 14556695
[TBL] [Abstract][Full Text] [Related]
17. Detection of subclinical mastitis from on-line milking parlor data.
Nielen M; Schukken YH; Brand A; Deluyker HA; Maatje K
J Dairy Sci; 1995 May; 78(5):1039-49. PubMed ID: 7622715
[TBL] [Abstract][Full Text] [Related]
18. The Prediction of Clinical Mastitis in Dairy Cows Based on Milk Yield, Rumination Time, and Milk Electrical Conductivity Using Machine Learning Algorithms.
Tian H; Zhou X; Wang H; Xu C; Zhao Z; Xu W; Deng Z
Animals (Basel); 2024 Jan; 14(3):. PubMed ID: 38338070
[TBL] [Abstract][Full Text] [Related]
19. Quarter-milking-, quarter-, udder- and lactation-level risk factors and indicators for clinical mastitis during lactation in pasture-fed dairy cows managed in an automatic milking system.
Hammer JF; Morton JM; Kerrisk KL
Aust Vet J; 2012 May; 90(5):167-74. PubMed ID: 22510075
[TBL] [Abstract][Full Text] [Related]
20. The determination of mastitis severity at 4-level using Milk physical properties: A deep learning approach via MLP and evaluation at different SCC thresholds.
Yesil MI; Goncu S
Res Vet Sci; 2024 Jul; 174():105310. PubMed ID: 38795430
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
