Research Article
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Year 2022, Volume: 6 Issue: 1, 54 - 62, 30.01.2022
https://doi.org/10.31127/tuje.807019

Abstract

References

  • Adeli H & Hung S L (1995). Machine learning - neural networks, genetic algorithms and fuzzy systems. John Wiley & Sons Inc. ISBN: 9780471016335.
  • Allahverdi N & Saday F (2018). An artificial neural network study for predicting sex in bulls. 7th International Conference on Advanced Technologies (ICAT’18), 727-731, Antalya, Turkey.
  • Allahverdi N (2002). Uzman Sistemler. Atlas, Istanbul, Turkey (in Turkish). ISBN: 975-6574-11-9.
  • Allahverdi N (2020). Bulanık Mantık ve Tıptaki Uygulamaları. KTO Karatay Üniversitesi Yayınları, Konya, Turkey (in Turkish). ISBN:9786056934636.
  • Anderson G B (1997). Identification of embryonic sex by detection of H-Y antigens. Theriogenology, 27, 81-97.
  • Bobillo F & Straccia U (2008). Towards a Crisp Representation of Fuzzy Description Logics under Łukasiewicz Semantics. International Symposium on Methodologies for Intelligent Systems (ISMIS 2008), 309-318, Toronto, Canada.
  • Breiman L (2001). Random forests. Machine Learning, 45 (1), 5-32.
  • Erten O & Yılmaz O (2012). Techniques of sex-selected calf production in dairy cattle breeding. Van, Yüzüncü Yil Üniversitesi Journal of Veterinary Faculty, 23 (3), 155-157 (in Turkish).
  • Frank E, Hall M A & Witten I H (2016). The WEKA Workbench Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques. 4th ed. San Francisco, CA, USA: Morgan Kaufmann. ISBN:9780128042915.
  • Heide, E.M.M., Veerkamp, R.F., Pelt, M.L., Kamphuis, C., Athanasiadis, I. et al., (2019), Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle, Journal of Dairy Science, 102 (10), 9409-9421.
  • Huma ZE & Iqbal F (2019). Predicting the body weight of Balochi sheep using a machine learning approach. Turkish Journal of Veterinary and Animal Sciences, 43, 500-506.
  • Inanc M E, Çil B, Tekin K & Alemdar H (2018). The combination of CASA kinetic parameters and fluorescein staining as a fertility tool in cryopreserved bull semen. Turkish Journal of Veterinary and Animal Sciences, 42, 452-458.
  • Johnson L A, Cran D G & Polge C (1994). Recent advances in sex preselection of cattle: Flow cytometric sorting of X-Y-chromosome bearing sperm based on DNA to progeny. Theriogenology, 4, 51-56.
  • Martiskainen P, Jarvinen M, Skön J P, Tiirikainen J, Kolehmainen M, et al. (2009). Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines. Applied Animal Behaviour Science, 119 (1-2), 32-38.
  • Miekley B, Traulsen I & Krieter J (2013). Mastitis detection in dairy cows: the application of support vector machines. The Journal of Agricultural Science, 151 (6), 889-897.
  • Mikail N & Keskin I (2013). Application of the support vector machine to predict subclinical mastitis in dairy cattle. The Scientific World Journal, 1: 603897.
  • Nicolas G, Robinson TP, Wint W & Conchedda G (2016). Using Random Forest to Improve the Downscaling of Global Livestock Census Data. PLoS ONE, 11 (3), e0150424.
  • Niemann H & Meinecke B (1993). Embryo transfer und assoziierte biotechniken bei landwirtschaftlichen nutztieren. Ferdinand Enke Verlag, Stuttgart (In German). ISBN: 9783432254715.
  • Oztemel E (2016). Yapay Sinir Ağları. Papatya Yayınları. Istanbul, Turkey (in Turkish). ISBN: 9789756797396.
  • Parati K, Bongioni G, Aleandri R & Galli A (2006). Sex ratio determination in bovine semen: A new approach by quantitative real-time PCR. Theriogenology, 66, 2202–2209.
  • Seidel G EJ (2003). Economics of selecting for sex: the most important genetic trait. Theriogenology, 59, 585-598.
  • Sendag S, Aydin I & Celik HA (2005). Prenatal embryonic or fetal sex determination in cows. J Fac Vet Med., Univ. Erciyes, 2 (1), 39-44 (in Turkish).
  • Shevade S K, Keerthi SS, Bhattacharyya C & Murthy K R K (2000). Improvements to SMO algorithm for SVM regression. IEEE Transactions on Neural Networks, 11(5), 1188–1193.
  • Smola A J & Schölkopf B (1998). A tutorial on support vector regression. NeuroCOLT Technical Report TR 1998-030, Royal Holloway College, London, UK.
  • Ustun B, Melssen WJ, Buydens LMC (2006). Facilitating the application of support vector regression by using a universal Pearson VII function-based kernel. Chemometrics and Intelligent Laboratory Systems, 81, 29-20.
  • Vapnik V (1995). The Nature of Statistical Learning Theory. Springer-Verlag, New York. ISBN:9781475724400.
  • Vapnik V (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5), 988–999.
  • Vapnik VN, Vapnik V (1998). Statistical Learning Theory. New York, USA: Wiley. ISBN: 9780471030034.
  • Vásquez R P, Anguilar-Lasserre A A, Lopez-Segura M V, Rivero LC, Rodriguez-Duran AA & Rojaz-Luna AA (2019). Expert system based on a fuzzy logic model for the analysis of the sustainable livestock production dynamic system. Computers and Electronics in Agriculture, 161, 104-120.
  • Xu Y (2017). Research and implementation of improved random forest algorithm based on Spark. IEEE 2nd International Conference on Big Data Analysis, 499–503, Beijing, China.
  • Yao C, Spurlock DM, Armentano LE, Page Jr C D, VandeHaar MJ et al. (2013). Random Forests approach for identifying additive and epistatic single nucleotide polymorphisms associated with residual feed intake in dairy cattle. Journal of Dairy Science, 96 (10), 6716-6729.
  • Zadeh L (1965). Fuzzy sets. Information and Control, 8, 338-353.

Application of artificial intelligence methods for bovine gender prediction

Year 2022, Volume: 6 Issue: 1, 54 - 62, 30.01.2022
https://doi.org/10.31127/tuje.807019

Abstract

This study investigates determining the gender of calves using some artificial intelligence (AI) techniques. Gender identification is important in animal breeding, focusing on the desired outcome and planning. The data used to determine the gender of calves were the speed, magnitude, and density of the bull's semen. The analysis of the related studies showed that there was not a study on gender prediction of bovine with the application of AI methods. In this study, fuzzy logic (FL), artificial neural networks (ANN), support vector machines (SVM), and random forests (RF) were used. The efficiency of these approaches was verified by statistical analysis parameters such as accuracy, specificity, sensitivity (recall), precision, and F-score. The FL, ANN, SVM, and RF models had 84%, 96%, 97%, 99% accuracy, 93.75%, 96.88%, 100%, 100% sensitivity, 66.66%, 94.44%, 92.31%, 97.30% specificity, 83.33%, 96.88%, 95.31%, 98.44% precision results, respectively. Application of these AI techniques for prediction bovine gender proves that these methods may be used by semen breeders as supporting information tools. In particular, it was observed that the RF method yielded the highest accuracy results.  

References

  • Adeli H & Hung S L (1995). Machine learning - neural networks, genetic algorithms and fuzzy systems. John Wiley & Sons Inc. ISBN: 9780471016335.
  • Allahverdi N & Saday F (2018). An artificial neural network study for predicting sex in bulls. 7th International Conference on Advanced Technologies (ICAT’18), 727-731, Antalya, Turkey.
  • Allahverdi N (2002). Uzman Sistemler. Atlas, Istanbul, Turkey (in Turkish). ISBN: 975-6574-11-9.
  • Allahverdi N (2020). Bulanık Mantık ve Tıptaki Uygulamaları. KTO Karatay Üniversitesi Yayınları, Konya, Turkey (in Turkish). ISBN:9786056934636.
  • Anderson G B (1997). Identification of embryonic sex by detection of H-Y antigens. Theriogenology, 27, 81-97.
  • Bobillo F & Straccia U (2008). Towards a Crisp Representation of Fuzzy Description Logics under Łukasiewicz Semantics. International Symposium on Methodologies for Intelligent Systems (ISMIS 2008), 309-318, Toronto, Canada.
  • Breiman L (2001). Random forests. Machine Learning, 45 (1), 5-32.
  • Erten O & Yılmaz O (2012). Techniques of sex-selected calf production in dairy cattle breeding. Van, Yüzüncü Yil Üniversitesi Journal of Veterinary Faculty, 23 (3), 155-157 (in Turkish).
  • Frank E, Hall M A & Witten I H (2016). The WEKA Workbench Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques. 4th ed. San Francisco, CA, USA: Morgan Kaufmann. ISBN:9780128042915.
  • Heide, E.M.M., Veerkamp, R.F., Pelt, M.L., Kamphuis, C., Athanasiadis, I. et al., (2019), Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle, Journal of Dairy Science, 102 (10), 9409-9421.
  • Huma ZE & Iqbal F (2019). Predicting the body weight of Balochi sheep using a machine learning approach. Turkish Journal of Veterinary and Animal Sciences, 43, 500-506.
  • Inanc M E, Çil B, Tekin K & Alemdar H (2018). The combination of CASA kinetic parameters and fluorescein staining as a fertility tool in cryopreserved bull semen. Turkish Journal of Veterinary and Animal Sciences, 42, 452-458.
  • Johnson L A, Cran D G & Polge C (1994). Recent advances in sex preselection of cattle: Flow cytometric sorting of X-Y-chromosome bearing sperm based on DNA to progeny. Theriogenology, 4, 51-56.
  • Martiskainen P, Jarvinen M, Skön J P, Tiirikainen J, Kolehmainen M, et al. (2009). Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines. Applied Animal Behaviour Science, 119 (1-2), 32-38.
  • Miekley B, Traulsen I & Krieter J (2013). Mastitis detection in dairy cows: the application of support vector machines. The Journal of Agricultural Science, 151 (6), 889-897.
  • Mikail N & Keskin I (2013). Application of the support vector machine to predict subclinical mastitis in dairy cattle. The Scientific World Journal, 1: 603897.
  • Nicolas G, Robinson TP, Wint W & Conchedda G (2016). Using Random Forest to Improve the Downscaling of Global Livestock Census Data. PLoS ONE, 11 (3), e0150424.
  • Niemann H & Meinecke B (1993). Embryo transfer und assoziierte biotechniken bei landwirtschaftlichen nutztieren. Ferdinand Enke Verlag, Stuttgart (In German). ISBN: 9783432254715.
  • Oztemel E (2016). Yapay Sinir Ağları. Papatya Yayınları. Istanbul, Turkey (in Turkish). ISBN: 9789756797396.
  • Parati K, Bongioni G, Aleandri R & Galli A (2006). Sex ratio determination in bovine semen: A new approach by quantitative real-time PCR. Theriogenology, 66, 2202–2209.
  • Seidel G EJ (2003). Economics of selecting for sex: the most important genetic trait. Theriogenology, 59, 585-598.
  • Sendag S, Aydin I & Celik HA (2005). Prenatal embryonic or fetal sex determination in cows. J Fac Vet Med., Univ. Erciyes, 2 (1), 39-44 (in Turkish).
  • Shevade S K, Keerthi SS, Bhattacharyya C & Murthy K R K (2000). Improvements to SMO algorithm for SVM regression. IEEE Transactions on Neural Networks, 11(5), 1188–1193.
  • Smola A J & Schölkopf B (1998). A tutorial on support vector regression. NeuroCOLT Technical Report TR 1998-030, Royal Holloway College, London, UK.
  • Ustun B, Melssen WJ, Buydens LMC (2006). Facilitating the application of support vector regression by using a universal Pearson VII function-based kernel. Chemometrics and Intelligent Laboratory Systems, 81, 29-20.
  • Vapnik V (1995). The Nature of Statistical Learning Theory. Springer-Verlag, New York. ISBN:9781475724400.
  • Vapnik V (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5), 988–999.
  • Vapnik VN, Vapnik V (1998). Statistical Learning Theory. New York, USA: Wiley. ISBN: 9780471030034.
  • Vásquez R P, Anguilar-Lasserre A A, Lopez-Segura M V, Rivero LC, Rodriguez-Duran AA & Rojaz-Luna AA (2019). Expert system based on a fuzzy logic model for the analysis of the sustainable livestock production dynamic system. Computers and Electronics in Agriculture, 161, 104-120.
  • Xu Y (2017). Research and implementation of improved random forest algorithm based on Spark. IEEE 2nd International Conference on Big Data Analysis, 499–503, Beijing, China.
  • Yao C, Spurlock DM, Armentano LE, Page Jr C D, VandeHaar MJ et al. (2013). Random Forests approach for identifying additive and epistatic single nucleotide polymorphisms associated with residual feed intake in dairy cattle. Journal of Dairy Science, 96 (10), 6716-6729.
  • Zadeh L (1965). Fuzzy sets. Information and Control, 8, 338-353.
There are 32 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Ali Öztürk 0000-0002-1797-2039

Novruz Allahverdı 0000-0001-9807-884X

Fatih Saday 0000-0001-7496-2796

Publication Date January 30, 2022
Published in Issue Year 2022 Volume: 6 Issue: 1

Cite

APA Öztürk, A., Allahverdı, N., & Saday, F. (2022). Application of artificial intelligence methods for bovine gender prediction. Turkish Journal of Engineering, 6(1), 54-62. https://doi.org/10.31127/tuje.807019
AMA Öztürk A, Allahverdı N, Saday F. Application of artificial intelligence methods for bovine gender prediction. TUJE. January 2022;6(1):54-62. doi:10.31127/tuje.807019
Chicago Öztürk, Ali, Novruz Allahverdı, and Fatih Saday. “Application of Artificial Intelligence Methods for Bovine Gender Prediction”. Turkish Journal of Engineering 6, no. 1 (January 2022): 54-62. https://doi.org/10.31127/tuje.807019.
EndNote Öztürk A, Allahverdı N, Saday F (January 1, 2022) Application of artificial intelligence methods for bovine gender prediction. Turkish Journal of Engineering 6 1 54–62.
IEEE A. Öztürk, N. Allahverdı, and F. Saday, “Application of artificial intelligence methods for bovine gender prediction”, TUJE, vol. 6, no. 1, pp. 54–62, 2022, doi: 10.31127/tuje.807019.
ISNAD Öztürk, Ali et al. “Application of Artificial Intelligence Methods for Bovine Gender Prediction”. Turkish Journal of Engineering 6/1 (January 2022), 54-62. https://doi.org/10.31127/tuje.807019.
JAMA Öztürk A, Allahverdı N, Saday F. Application of artificial intelligence methods for bovine gender prediction. TUJE. 2022;6:54–62.
MLA Öztürk, Ali et al. “Application of Artificial Intelligence Methods for Bovine Gender Prediction”. Turkish Journal of Engineering, vol. 6, no. 1, 2022, pp. 54-62, doi:10.31127/tuje.807019.
Vancouver Öztürk A, Allahverdı N, Saday F. Application of artificial intelligence methods for bovine gender prediction. TUJE. 2022;6(1):54-62.
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