@inproceedings{scholars1251,
         address = {Singapore},
           title = {Forecasting of Salmonellosis incidence in human using artificial neural network (ANN)},
            note = {cited By 7; Conference of 2nd International Conference on Computer and Automation Engineering, ICCAE 2010 ; Conference Date: 26 February 2010 Through 28 February 2010; Conference Code:80373},
          volume = {1},
             doi = {10.1109/ICCAE.2010.5451981},
         journal = {2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010},
           pages = {136--139},
            year = {2010},
        abstract = {Salmonellosis is one of the most common seasonal zoonosis. As from the definition, zoonosis refers to the transmission of infectious diseases from animal to human. This paper presents the prediction of Salmonellosis incidence using Artificial Neural Network (ANN) on the basis of monthly data. A series of Salmonellosis incidence in US from 1993 to 2006, published by Centers for Disease Control and Prevention (CDC), was collected for technical analysis. Multi Layer Perceptron (MLP) has been chosen for the ANN design. The model consists of three layers: input layer, hidden layer, and output layer. Number of nodes in hidden layer was varied in order to find the most accurate forecasting result. The comparisons of models were justified by using Mean Absolute Percentage Error (MAPE). Furthermore, MAPE and Theil's U were used to measure the result accuracy. The least MAPE derived from the best model was 10.761 and Theil's U value was 0.209. It implied that the model was highly accurate and a close fit. It was also indicated the capability of final model to closely represent and made prediction based on the tuberculosis historical dataset. {\^A}{\copyright}2010 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952664058&doi=10.1109\%2fICCAE.2010.5451981&partnerID=40&md5=99f211b092d605d8dade00203b5f0202},
        keywords = {Artificial Neural Network; Artificial neural network (ANN); Best model; Centers for disease control and preventions; Data sets; Hidden layers; Infectious disease; Input layers; Mean absolute percentage error; Multi layer perceptron; Output layer; Technical analysis; Three-layer, Disease control; Forecasting, Neural networks},
            isbn = {9781424455850},
          author = {Permanasari, A. E. and Rambli, D. R. A. and Dominic, P. D. D.}
}