A modeling of animal diseases through using artificial neural network - Alias, Norma; Farid, Fatin Naemah Mohd; Al-Rahmi, Waleed Mugahed; Yahaya, Noraffandy; Al-Maatouk, Qusay (International Journal of Engineering and Technology, 2018)
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.