A modeling of animal diseases through using artificial neural network

  • Authors

    • Norma Alias
    • Fatin Naemah Mohd Farid
    • Waleed Mugahed Al-Rahmi
    • Noraffandy Yahaya
    • Qusay Al-Maatouk
    https://doi.org/10.14419/ijet.v7i4.21574

  • Abstract

    This paper studied the implementation of Artificial Neural Network (ANN) where it well-known recently in veterinary disease research field in Malaysia. The parameter identification under consideration is types of animal disease, types of species and locations of disease based on the Geographical Information System (GIS) data set. There are many types of animal diseases that affect farm animals in Malaysia. In this research, the method of multilayer perceptron neural network is used as main model since it is an effective solving method in predicting the future of veterinary disease. ANN has ability to visual animal diseases involving the computational model. The model is to present the rela-tionship between causes of the species and location and consequence of animal disease without emphasizing the process, considering the initial and boundary condition and considering the nature of the relations. The data collection of animal disease is considered as a large sparse data set. Therefore method of ANN is well suited for optimizing of the data, to train the data operational and to predict the parameter identification of animal disease. The output layers of ANN are plotted in SPSS software for statistical solution and MATLAB programming for sequential ANN implemented. The ANN will be compare to genetic algorithm for the performance and effectiveness of the method. The numerical simulation of ANN helps in future prediction of animal disease based on the species and location parameters.

  • References

    1. [1] Al Shamisi, M. H., Assi, A. H., and Hejase, H. A. (2011). Using MATLAB to Develop Artificial Neural Network Models for Predicting Global Solar Radiation in Al Ain City-UAE. INTECH Open Access Publisher.

      [2] Alias, N., Ghani, A. C. A., Saipan, H. F., Ramli, N., & Palil, S. Q. M. (2012, January). Wave equation for early detection of breast cancer growth using MATLAB Distributed Computing. In Enabling Science and Nanotechnology (ESciNano), 2012 International Conference on (pp. 1-1). IEEE.

      [3] Alias, N., Saipol, H. F. S., & Ghani, A. C. A. (2014). Chronology of DIC technique based on the fundamental mathematical modeling and dehydration impact. Journal of food science and technology, 51(12), 3647-3657. https://doi.org/10.1007/s13197-012-0913-7.

      [4] Gen, M., and Cheng, R. (1997). Genetic algorithms and engineering design. John Wily and Sons, New York.

      [5] Krenker, A., Kos, A., & Bešter, J. (2011). Introduction to the Artificial Neural Networks. INTECH Open Access Publisher.

      [6] Milhajlovic, I., Nikolic, Ã. Å trbac, N.,and Živkovic, Ž. (2010). Statistical modeling in ecological management using the artificial neural networks (ANNs). Serbian Journal of Management. 5(1):39-50. https://doi.org/10.5772/15751.

      [7] Morens DM and Fauci AS (2013) Emerging Infectious Diseases: Threats to Human Health and Global Stability. PLoS Pathog 9(7): e1003467. https://doi.org/10.1371/journal.ppat.1003467.

      [8] Nanda, S. K., Tripathy, D. P., Nayak, S. K., and Mohapatra, S. (2013). Prediction of Rainfall in India using Artificial Neural Network (ANN) Models. International Journal of Intelligent Systems and Applications (IJISA). 5 (12):1. https://doi.org/10.5815/ijisa.2013.12.01.

      [9] Pintér, J. D. (2012). Calibrating Artificial Neural Networks by Global optimization. Expert Systems with Applications. 39(1):25-32. https://doi.org/10.1016/j.eswa.2011.06.050.

      [10] Ramli N.A. (2014), Modeling Corrosion Rate in Gas Pipelines using Artificial Neural Network, Bachelor of Science (Mathematics).

      [11] Rusli N.L. (2014), Mathematical Modeling of Veterinary Disease by using Genetic Algorithm, Bachelor of Science (Industrial Mathematics).

      [12] Safi, Y., & Bouroumi, A. (2011). A neural network approach for predicting forest fires. In Multimedia Computing and Systems (ICMCS), 2011 International Conference on 1-5. IEEE. https://doi.org/10.1109/ICMCS.2011.5945716.

      [13] Sahimi, M. S., Alias, N., & Sundararajan, E. (2001, May). The AGEB algorithm for solving the heat equation in three space dimensions and its parallelization using PVM. In International Conference on Computational Science (pp. 918-927). Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45545-0_104.

      [14] Seyam, M., and Mogheir, Y. (2011). Application of artificial neural networks model as analytical tool for groundwater salinity. Journal of Environmental Protection. 2(1):56. https://doi.org/10.4236/jep.2011.21006.

      [15] Volna, E., Kotyrba, M., Kocian, V., and Janosek, M. (2012,). Cryptography Based On Neural Network. In ECMS. 386-391.

      [16] Wijayasekara, D., Manic, M., Sabharwall, P., & Utgikar, V. (2011). Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique. Nuclear Engineering and Design, 241(7), 2549-2557. https://doi.org/10.1016/j.nucengdes.2011.04.045.

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  • How to Cite

    Alias, N., Farid, F. N. M., Al-Rahmi, W. M., Yahaya, N., & Al-Maatouk, Q. (2018). A modeling of animal diseases through using artificial neural network. International Journal of Engineering & Technology, 7(4), 3255-3262. https://doi.org/10.14419/ijet.v7i4.21574

    Received date: 2018-11-25

    Accepted date: 2018-11-25