{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T09:34:56Z","timestamp":1769765696021,"version":"3.49.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"3-4","license":[{"start":{"date-parts":[[2006,3,28]],"date-time":"2006-03-28T00:00:00Z","timestamp":1143504000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2006,6]]},"DOI":"10.1007\/s00521-006-0037-y","type":"journal-article","created":{"date-parts":[[2006,3,27]],"date-time":"2006-03-27T12:32:58Z","timestamp":1143462778000},"page":"359-365","source":"Crossref","is-referenced-by-count":23,"title":["Empirical comparisons of feed-forward connectionist and conventional regression models for prediction of first lactation 305-day milk yield in Karan Fries dairy cows"],"prefix":"10.1007","volume":"15","author":[{"given":"Adesh K.","family":"Sharma","sequence":"first","affiliation":[]},{"given":"R. K.","family":"Sharma","sequence":"additional","affiliation":[]},{"given":"H. S.","family":"Kasana","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2006,3,28]]},"reference":[{"key":"37_CR1","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/S0168-1699(98)00018-0","volume":"20","author":"F Salehi","year":"1998","unstructured":"Salehi F, Lacroix R, Wade KM (1998) Improving dairy yield predictions through combined record classifiers and specialized artificial neural networks. Comput Electron Agric 20:199\u2013213","journal-title":"Comput Electron Agric"},{"key":"37_CR2","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1111\/j.1365-2621.1995.tb06253.x","volume":"60","author":"B Vallejo-Cordoba","year":"1995","unstructured":"Vallejo-Cordoba B, Artega GE, Nakai S (1995) Predicting milk shelf-life based on artificial neural networks and headspace gas chromatographic data. J Food Sci 60:885\u2013888","journal-title":"J Food Sci"},{"key":"37_CR3","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/S0168-1699(02)00051-0","volume":"35","author":"P Kominakis","year":"2002","unstructured":"Kominakis P, Abas Z, Maltaris I, Rogdakis E (2002) A preliminary study of the application of artificial neural networks to prediction of milk yield in dairy sheep. Comput Electron Agric 35:35\u201348","journal-title":"Comput Electron Agric"},{"key":"37_CR4","unstructured":"Sharma RK, Sharma AK (2004) Neuro-computing paradigms with application to dairy production. In: Sharma Ashwini et\u00a0al (eds) Lecture compendium, National Training Programme on Information Resources on Genetics and Documentation Techniques for Livestock Improvement, Centre for Advanced Studies - AG & B, Dairy Cattle Breeding Division, NDRI, Karnal, India, pp 173\u2013178"},{"key":"37_CR5","doi-asserted-by":"crossref","first-page":"253","DOI":"10.13031\/2013.17144","volume":"41","author":"F Salehi","year":"1998","unstructured":"Salehi F, Lacroix R, Wade KM (1998) Effects of learning parameters and data presentation on the performance of back-propagation networks for milk yield prediction. Trans Am Soc Agric Eng 41:253\u2013259","journal-title":"Trans Am Soc Agric Eng"},{"key":"37_CR6","doi-asserted-by":"crossref","first-page":"307","DOI":"10.4141\/A02-002","volume":"83","author":"W Grzesiak","year":"2003","unstructured":"Grzesiak W, Lacroix R, Wojcik J, Blaszczyk P (2003) A comparison of neural network and multiple regression predictions for 305-day lactation yield using partial lactation records. Can J Anim Sci 83:307\u2013310","journal-title":"Can J Anim Sci"},{"key":"37_CR7","doi-asserted-by":"crossref","first-page":"839","DOI":"10.13031\/2013.21294","volume":"40","author":"R Lacroix","year":"1997","unstructured":"Lacroix R, Salehi F, Yang XZ, Wade KM (1997) Effects of data preprocessing on the performance of artificial neural network for dairy yield prediction and cow culling classification. Trans Am Soc Agric Eng 40:839\u2013846","journal-title":"Trans Am Soc Agric Eng"},{"key":"37_CR8","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.13031\/2013.27984","volume":"38","author":"R Lacroix","year":"1995","unstructured":"Lacroix R, Wade KM, Kok R, Hayes JF (1995) Prediction of cow performance with a connectionist model. Trans Am Soc Agric Eng 38:1573\u20131579","journal-title":"Trans Am Soc Agric Eng"},{"key":"37_CR9","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/S0168-1699(01)00151-X","volume":"32","author":"L Sanzogni","year":"2001","unstructured":"Sanzogni L, Kerr D (2001) Milk production estimates using feed-forward artificial neural networks. Comput Electron Agric 32:21\u201330","journal-title":"Comput Electron Agric"},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Torres Mercedes, Hervas Cesar, Amador Francisco (2005) Approximating the sheep milk production curve through the use of artificial neural networks and genetic algorithms. Comput Oper Res 32(10):2653\u20132670. DOI: 10.1016\/j.cor.2004.06.025","DOI":"10.1016\/j.cor.2004.06.025"},{"key":"37_CR11","doi-asserted-by":"crossref","unstructured":"Kerr DV, Chaseling J, Chopping GD, Davison TM, Busby G (1998) A study of the effect of inputs on level of production of dairy farms in Queensland\u2014a comparative analysis of survey data. Aust J Exp Agric 38(5):419\u2013425. DOI: 10.1071\/EA97153","DOI":"10.1071\/EA97153"},{"key":"37_CR12","doi-asserted-by":"crossref","first-page":"711","DOI":"10.3168\/jds.S0022-0302(00)74933-2","volume":"83","author":"CW Heald","year":"2000","unstructured":"Heald CW, Kim T, Sischo WM, Cooper JB, Wolfgang DR (2000) A computerized mastitis decision aid using farm-based records: an artificial neural network approach. J Dairy Sci 83:711\u2013720","journal-title":"J Dairy Sci"},{"key":"37_CR13","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.3168\/jds.S0022-0302(95)76720-0","volume":"78","author":"M Nielen","year":"1995","unstructured":"Nielen M, Schukken YH, Brand A, Deluyker HA, Maatje K (1995) Detection of sub-clinical mastitis from on-line milking parlor data. J Dairy Sci 78:1039\u20131049","journal-title":"J Dairy Sci"},{"key":"37_CR14","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.3168\/jds.S0022-0302(95)76721-2","volume":"78","author":"M Nielen","year":"1995","unstructured":"Nielen M, Schukken YH, Brand A, Haring S, Ferwerda-Van Zonneveld RT (1995) Comparison of analysis techniques for on-line detection of clinical mastitis. J Dairy Sci 78:1050\u20131061","journal-title":"J Dairy Sci"},{"key":"37_CR15","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.13031\/2013.13254","volume":"42","author":"XZ Yang","year":"1999","unstructured":"Yang XZ, Lacroix R, Wade KM (1999) Neural detection of mastitis from dairy herd improvement records. Trans Am Soc Agric Eng 42:1063\u20131071","journal-title":"Trans Am Soc Agric Eng"},{"key":"37_CR16","doi-asserted-by":"crossref","unstructured":"L\u00fcpez-Benavides MG, Samarasinghe S, Hickford JGH (2003) The use of artificial neural networks to diagnose mastitis in dairy cattle. In: Proceedings of the IEEE international joint conference on neural networks, July 2003, 1(20\u201324):582\u201355","DOI":"10.1109\/IJCNN.2003.1223420"},{"key":"37_CR17","doi-asserted-by":"crossref","first-page":"2393","DOI":"10.3168\/jds.S0022-0302(00)75129-0","volume":"83","author":"J Paquet","year":"2000","unstructured":"Paquet J, Lacroix C, Thibault J (2000) Modeling of pH and acidity for individual cheese production. J Dairy Sci 83:2393\u20132409","journal-title":"J Dairy Sci"},{"key":"37_CR18","doi-asserted-by":"crossref","unstructured":"Karla VR, Bakker HHC (1995) Neural-network-based model predictive control: a case study. In: Proceedings of the 2nd New Zealand two-stream international conference on artificial neural networks and expert systems, (ANNES \u201995), Dunedin, New Zealand, November 20\u201323, pp 355","DOI":"10.1109\/ANNES.1995.499507"},{"key":"37_CR19","doi-asserted-by":"crossref","first-page":"2779","DOI":"10.3168\/jds.S0022-0302(04)73405-0","volume":"87","author":"Y Etzion","year":"2004","unstructured":"Etzion Y, Linker R, Cogan U, Shmulevich I (2004) Determination of protein concentration in raw milk by mid-infrared Fourier transform infrared\/attenuated total reflectance spectroscopy. J Dairy Sci 87:2779\u20132788","journal-title":"J Dairy Sci"},{"key":"37_CR20","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/BF01413822","volume":"4","author":"GD Finn","year":"1996","unstructured":"Finn GD, Lister R, Szabo T, Simonetta Don, Mulder H, Young R (1996) Neural networks applied to a large biological database to analyse dairy breeding patterns. Neural Comput Appl 4:237\u2013253","journal-title":"Neural Comput Appl"},{"key":"37_CR21","unstructured":"Gardener JW, Hines EL, Molinier F, Bartlett PN, Mottram TT (1999) Prediction of health of dairy cattle from breath samples using neural network with parametric model of dynamic response of array of semi-conducting gas sensors. In: IEEE proceedings on science, measurement and technology, March 1999, 146(2):102\u2013106. DOI: 10.1049\/ip-smt: 19990100"},{"key":"37_CR22","unstructured":"Padhi Radhakant, Balakrishnan SN (2004) Development and analysis of a feedback treatment strategy for parturient paresis of cows. In: IEEE transactions on control systems technology 12(1):52\u201364. DOI: 10. 1109\/TCST.2003.821962"},{"key":"37_CR23","unstructured":"Gurnani M, Sethi RK, Nagarcenkar R (1986) Development of Karan Fries cattle at NDRI, Karnal. Dairy information bulletin, vol 3. National Dairy Research Institute Press, Karnal pp 1\u20132"},{"key":"37_CR24","unstructured":"Demuth Howard, Beale Mark (2004) Neural network toolbox for use with MATLAB, User\u2019s Guide. Available at: The MathWorks Inc., Natick, website: http:\/\/www.mathworks.com\/ . Accessed 8 Feb 2004"},{"key":"37_CR25","unstructured":"Guedes Guilherme Luiz Lepsch (2004) FORCE (FORTRAN compiler and editor) version 2.0. Freeware available on the Internet at website: http:\/\/www.forceproject.hpq.com.br\/ . Accessed 10 March 2004"},{"key":"37_CR26","volume-title":"Neural network design","author":"MT Hagan","year":"2004","unstructured":"Hagan MT, Demuth HB, Beale M (2004) Neural network design. PWS Publishing Co., Boston"},{"key":"37_CR27","unstructured":"Sharma Adesh K, Sharma RK, Kasana HS (2005) Performance evaluation of some back-propagation training algorithms for prediction of milk yield in dairy cattle. In: Kasana HS et\u00a0al (eds) Proceedings of national conference on frontiers in applied and computational mathematics organized by TIET, Patiala (India), pp 230\u2013238, Allied Publishers Pvt. Ltd., New Delhi"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-006-0037-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-006-0037-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-006-0037-y","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,29]],"date-time":"2019-05-29T02:06:54Z","timestamp":1559095614000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-006-0037-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006,3,28]]},"references-count":27,"journal-issue":{"issue":"3-4","published-print":{"date-parts":[[2006,6]]}},"alternative-id":["37"],"URL":"https:\/\/doi.org\/10.1007\/s00521-006-0037-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2006,3,28]]}}}