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The Importance of Feedforward Neural Network in Developing Small Ruminant Breed Lineage Prediction System

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AI and Business, and Innovation Research: Understanding the Potential and Risks of AI for Modern Enterprises

Abstract

Sheep are crucial to Malaysian Muslims, which accounts to 60% of the population. Yet there is not enough supply due to a high death rate brought on by diseases like Tetanus and Foot and Mouth Disease (FMD), among others. Objective of the study is to create a uniform data collection system that can be adopted across farms. The system will be developed using a standard web-development framework and will be utilizing the Feedforward Artificial Neural Network (FANN) to process the data. The outcome of the system will be based on accuracy, time consumption and reliability. The results of this study will be displayed in diagrams and user interface of the system. Although certain farms recorded their data digitally in separate database tables and Excel sheets, bulk of the data was recorded in one master sheet and one master table from a certain point in time. This means that there is movement in staffing where previous worker resigns and new worker is hired without any proper handover between the two which creates inconsistent data being recorded. History could not be established and data from old records could not be synced with newly recorded data as the values differ from each other. This paper focuses of sheep farms around Peninsular Malaysia. Future areas of research may include adopting our system on other types of farm animals. Analyzed data from our study will be integrated with our Feedforward Neural Network algorithm.

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References

  1. Peng, W., Berry, E.M.: The Concept of Food Security (2018). https://doi.org/10.1016/B978-0-08-100596-5.22314-7

  2. Jouneau, L., et al.: The antibody response induced FMDV vaccines in sheep correlates with early transcriptomic responses in blood. NPJ Vacc. 5(1), 151 (2020). https://doi.org/10.1038/s41541-019-0151-3

    Article  Google Scholar 

  3. Lotfollahzadeh, S., Heydari, M., Mohebbi, M.R., Hashemian, M.: Tetanus outbreak in a sheep flock due to ear tagging. Vet. Med. Sci. 5(2), 146–150 (2019). https://doi.org/10.1002/vms3.139

    Article  Google Scholar 

  4. In Depth: Sheep and Goat Meat to Malaysia|Meat and Livestock Australia. https://www.mla.com.au/prices-markets/market-news/2018/in-depth-sheep-and-goat-meat-to-malaysia/. Accessed 17 Nov 2021

  5. Milerski, M.: The effect of inbreeding on the growth ability of meat sheep breeds in the. Czech Republic 2021(04), 122–128 (2021)

    Google Scholar 

  6. Doekes, H.P., Veerkamp, R.F., Bijma, P., De Jong, G., Hiemstra, S.J., Windig, J.J.: Inbreeding depression due to recent and ancient inbreeding in Dutch Holstein-Friesian dairy cattle. Genet. Sel. Evol. 51(1), 1–16 (2019). https://doi.org/10.1186/s12711-019-0497-z

    Article  Google Scholar 

  7. Marcos, A., Perez, A.M.: Quantitative risk assessment of foot-and-mouth disease (FMD) virus introduction into the FMD-free zone without vaccination of Argentina through legal and illegal trade of bone-in beef and unvaccinated susceptible species. Front. Vet. Sci. 6, 1–12 (2019). https://doi.org/10.3389/fvets.2019.00078

    Article  Google Scholar 

  8. Takatsuka, K., Sekiguchi, S., Yamaba, H., Aburada, K., Mukunoki, M., Okazaki, N.: FMD-VS: a virtual sensor to index FMD virus scattering. PLoS ONE 15(9), 1–25 (2020). https://doi.org/10.1371/journal.pone.0237961

    Article  Google Scholar 

  9. Ehuwa, O., Jaiswal, A.K., Jaiswal, S.: Salmonella, food safety and food handling practices. Foods 10(5), 1–16 (2021). https://doi.org/10.3390/foods10050907

    Article  Google Scholar 

  10. Foodborne Pathogens|FDA. https://www.fda.gov/food/outbreaks-foodborne-illness/foodborne-pathogens. Accessed 08 March 2022

  11. Ekici, E., Gozde, D.: Escherichia coli and food safety. In: The Universe of Escherichia coli. IntechOpen, Istanbul (2019)

    Google Scholar 

  12. Escherichia coli O157:H7 Infection (E. coli O157) and Hemolytic Uremic Syndrome (HUS)—Minnesota Department of Health. https://www.health.state.mn.us/diseases/ecoli/index.html. Accessed 08 March 2022

  13. Hussin, R: Malaysia is entering a serious food security conundrum|The Star. In: The Star (2022). https://www.thestar.com.my/opinion/letters/2022/05/20/malaysia-is-entering-a-serious-food-security-conundrum. Accessed 07 Jul 2022

  14. Amin, N.A.M.: Populasi lembu, kambing menurun di Johor. Sinar Harian (2022). https://www.sinarharian.com.my/article/112944/EDISI/Johor/Populasi-lembu-kambing-menurun-di-Johor. Accessed 07 Jul 2022

  15. Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S., Dehmer, M.: An introductory review of deep learning for prediction models with big data. Front. Artif. Intell. 3, 1–23 (2020). https://doi.org/10.3389/frai.2020.00004

    Article  Google Scholar 

  16. Sheep Rearing; Department of Agriculture Sarawak. https://doa.sarawak.gov.my/page-0-0-141-Sheep-Rearing.html. Accessed 17 Nov 2021

  17. Ehrhardt, R.: Tips for improving out-of-season reproduction—sheep and goats. Michigan State University (2020). https://www.canr.msu.edu/news/tips-for-improving-out-of-season-reproduction. Accessed 08 March 2022

  18. Sheep 201: A Beginner’s Guide to Raising Sheep. http://www.sheep101.info/201/breedingsystems.html. Accessed 17 Nov 2021

  19. Zhumadillayev, N., Yuldashbaev, Y., Karynbaev, A., Khudaiberdiev, A., Efendiev, B.: Exterior features and productivity of the Kazakh fine-wool breed of sheep and its crossbreeds with meat breeds. E3S Web Conf. 262, 2620 (2021). https://doi.org/10.1051/e3sconf/202126202019

    Article  Google Scholar 

  20. Rather, M.: Sheep Breeding Practice in India (2020)

    Google Scholar 

  21. Smith, K., Fennessy, P.: Using Estimated Breeding Values in Plant Breeding (2021)

    Google Scholar 

  22. Fmd, T., States, U.: Foot-and-Mouth Disease (2021)

    Google Scholar 

  23. Blacksell, S.D., Siengsanan-Lamont, J., Kamolsiripichaiporn, S., Gleeson, L.J., Windsor, P.A.: A history of FMD research and control programmes in Southeast Asia: lessons from the past informing the future. Epidemiol. Infect. 147, 578 (2019). https://doi.org/10.1017/S0950268819000578

    Article  Google Scholar 

  24. Hong, J., et al.: Changing epidemiology of hand, foot, and mouth disease in China, 2013–2019: a population-based study. Lancet Reg. Heal. West. Pacif 20, 370 (2022). https://doi.org/10.1016/j.lanwpc.2021.100370

    Article  Google Scholar 

  25. Cho, S., et al.: Prevalence and Characterization of Escherichia coli Isolated from the Upper Oconee Watershed in Northeast Georgia, pp. 1–15 (2018)

    Google Scholar 

  26. Stein, R.A., Katz, D.E.: Escherichia coli, cattle and the propagation of disease. FEMS Microbiol. Lett. 364(6), 1–11 (2017). https://doi.org/10.1093/femsle/fnx050

    Article  Google Scholar 

  27. Salmonella: Symptoms, Diagnosis, Treatment and Prevention. https://my.clevelandclinic.org/health/diseases/15697-salmonella. Accessed 17 Nov 2021

  28. APHA: Salmonella information for sheep buyers. Anim. Plant Heal. Agency 128, 2017–2020 (2019)

    Google Scholar 

  29. APHA: Salmonella in Livestock Production in GB (2021)

    Google Scholar 

  30. Mathew, A., Amudha, P., Sivakumari, S.: Deep learning techniques: an overview. Adv. Intell. Syst. Comput. 1141, 599–608 (2021). https://doi.org/10.1007/978-981-15-3383-9_54

    Article  Google Scholar 

  31. Kota, V.M., Manoj Kumar, V., Bharatiraja, C.: Deep learning: a review. IOP Conf. Ser. Mater. Sci. Eng. 912(3), 68 (2020). https://doi.org/10.1088/1757-899X/912/3/032068

    Article  Google Scholar 

  32. Boucher, P.: Artificial Intelligence: How Does it Work, Why Does it Matter, and What Can We do About It? (2020)

    Google Scholar 

  33. Davenport, T.H.: AI in the enterprise. AI Advant. (2019). https://doi.org/10.7551/mitpress/11781.003.0004

  34. Muniasamy, A.: Machine learning for smart farming: a focus on desert agriculture. In: Proceedings of the 2020 International Conference on Computer Information Technology ICCIT 2020, pp. 438–442 (2020). https://doi.org/10.1109/ICCIT-144147971.2020.9213759

  35. Kroese, R., Dirk, P., Botev, Z.I., Taimre, T., Vaisman, S.: Data science and machine learning at scale. Lect. Notes Comput. Sci. 6911, 10 (2020). https://doi.org/10.1007/978-3-642-23780-5_9

    Article  Google Scholar 

  36. Kumar, T., et al.: Factors impacting the seasonality of sheep breeding: a review. Pharma Innov. J. (2022)

    Google Scholar 

  37. Ajafar, T.M., Hameed, M., Kadhim, A.H., Al-Thuwaini, S.: Reproductive Traits of Sheep and Their Influencing Factors.pdf. Rev. Agricult. Sci. (2022)

    Google Scholar 

  38. Aspers, P., Corte, U.: What is Qualitative in Qualitative Research Content courtesy of Springer Nature. Springer, New York (2019). https://doi.org/10.1007/s11133-019-9413-7%0AWhat

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Kamil, M.F.I., Jamaludin, N.A.A., Isa, M.R.M. (2024). The Importance of Feedforward Neural Network in Developing Small Ruminant Breed Lineage Prediction System. In: Alareeni, B., Elgedawy, I. (eds) AI and Business, and Innovation Research: Understanding the Potential and Risks of AI for Modern Enterprises. Studies in Systems, Decision and Control, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-031-42085-6_7

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  • DOI: https://doi.org/10.1007/978-3-031-42085-6_7

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