Copyright © 2018 Norma Alias et. al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. International Journal of Engineering & Technology, 7 (4) (2018) 3255-3262 International Journal of Engineering & Technology Website: www.sciencepubco.com/index.php/IJET doi: 10.14419/ijet.v7i4.15154 Research paper A modeling of animal diseases through using artificial neural network Norma Alias 1 *, Fatin Naemah Mohd Farid 1 , Waleed Mugahed Al-Rahmi 2 , Noraffandy Yahaya 2 , Qusay Al-Maatouk 3 1 Ibnu Sina Institute for Scientific &Industrial Research (ISI-SIR), Faculty of Science, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor, Malaysia 2 Faculty of Education, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor, Malaysia 3 Faculty of Engineering, Computing and Technology, Asia Pacific University of Technology & Innovation (APU), Technology Park Malay-sia, Bukit Jalil-57000 Kuala Lumpur, Malaysia *Corresponding author E-mail: norm@ibnusina.utm.my 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 Ma- laysia. 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 pro- cess, 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. Keywords: Mathematical Modeling; Artificial Neural Network (ANN); Geographical Information System (GIS). 1. Introduction Neural network consist a number of simple neuron like processing elements and a number of weighted connections between the ele- ments. Those weights are on connections encode the network (Volna, 2012). In this research, ANN is used to give an output to predict animal disease, hence, it will help to predict the disease based on location and species happen for future study. The basic definition or concept can be seen through paper as written by Krenker et al. in 2011 says that Artificial neural network is a com- putational model that tries to account for the parallel nature of human brain. Besides ANN is one of the most accurate and widely used forecasting model applied in forecasting social, and econom- ic (Al Shamisi et al., 2011, Alias et al., 2012). The characteristics of ANN are fault tolerance, distributed memory, learning ability, collective solution, weighted parameter and network structures (Pintér, 2012). In machine learning and related fields, artificial neural network (ANN) is a computational model inspired by a central nervous system (neuron(s) in brain), and are used to esti- mate functions that can depend on a large number of inputs and are generally unknown (Martínez-Morales et al., 2014). The de- velopment of neurobiology study enables researchers to create mathematical models of neurons to stimulate neural behavior. Safi, (2011) stated that, to train the network, we used the well-back propagation algorithm (BP) of MLP, which consists of an optimi- zation procedure aimed at minimizing the RMSE error observed at the output layer. This algorithm uses a supervised learning mode, meaning that the output corresponding to each input is a priori known, which makes it possible to compute signal errors and try to reduce them through epochs. 2. Multilayer perceptron (MLP) model For multilayer perceptron, it contains several single layer percep- tron. Those single layer perceptron were arranged according some hierarchy. The hierarchy should follow the characteristics. Some of the characteristics were inputs of first layer is taking with the number of perceptron same the number of vectors X of the prob- lem. Then, the output layer generate outputs with the number of neuron equal to the desired number of quantities computed from the inputs as well as there is one layer between single perceptron with another (Morens and Fauci, 2013). Detailed description of MLP architecture of ANN is published previously by Seyam and Mogheir, (2011) where he was used identical approach to ANN construction as well as its training or testing ratio. Neural Net- works are able to learn because they can change the connection weights between units. After learning, the knowledge is stored in the weights. For the purpose of training, 70% of the staring dataset is usually used. Training of the neural network is terminated when the network has learned to generalize the underlying trends of