Expectation of Tourism Demand in Iraq by Using Artificial Neural Network

  • Rashid Anasari Faculty of Humanities and Social Sciences, Department of Administration & Economy, Koya University, Koya, Iraq

Abstract

This study survey and proves this effectiveness connected with artificial neural networks (ANNs) as an alternative approach in the tourism research. The learning utilizes the travel industry in the Japan being a method for estimating need to exhibit the solicitation. The outcome reveals the use of ANNs in tourism research might perhaps result in better quotations when it comes to prediction bias and accuracy. Even more applications of ANNs in the context of tourism demand examination is needed to establish and validate the effects. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it.

References

Boser, B., Guyon, I., & Vapnik, V. (1992). A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory.
Crouch, G. I. (1995). A meta-analysis of tourism demand. Annals of Tourism Research, 22(1), 103–118. http://doi.org/10.1016/0160-7383(94)00054-V
Fyfe, C. (2000). Artificial Neural Networks and Information Theory (1.2 ed.).
G.Dreyfus. (n.d.). Neural Networks Methodology and Applications. Springer-Verlag Berlin Heidelberg 2005.
Gershenson, C. (n.d.). Artificial Neural Networks for Beginners, 1–8.
Johnson, P., & Ashworth, J. (1990). Modelling tourism demand: A summary review. Leisure Studies. Retrieved from http://www.tandfonline.com/doi/pdf/10.1080/02614369000390131
Kecman, V. (2001). Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. MIT press.
Kon, S., & Turner, L. (2005). Neural network forecasting of tourism demand. Tourism Economics. Retrieved from http://www.ingentaconnect.com/content/ip/tec/2005/00000011/00000003/art00001
Law, R. (2001). The impact of the Asian financial crisis on Japanese demand for travel to Hong Kong: A study of various forecasting techniques. Journal of Travel & Tourism Marketing. Retrieved from http://www.tandfonline.com/doi/abs/10.1080/10548400109511558
Lim, C. (1997). Review of international tourism demand models. Annals of Tourism Research, 24(4), 835–849. http://doi.org/10.1016/S0160-7383(97)00049-2
Na, M. G., Kim, J. W., Lim, D. H., & Kang, Y. J. (2008). Residual stress prediction of dissimilar metals welding at NPPs using support vector regression. Nuclear Engineering and Design, 238, 1503–1510.
Schölkopf, B., & Burges, C. (1999). Advances in kernel methods: support vector learning. MIT press.
Singh, S. K., & Gupta, A. K. (2010). Application of support vector regression in predicting thickness strains in hydro-mechanical deep drawing and comparison with ANN and FEM. CIRP Journal of Manufacturing Science and Technology, 3(1), 66–72.
Song, H., & Li, G. (2008). Tourism demand modelling and forecasting—A review of recent research. Tourism Management, 29(2), 203–220. http://doi.org/10.1016/j.tourman.2007.07.016
Vakili, A. H., Davoodi, S., Arab, A., Researcher, Y., Club, E., Branch, E., … Tebal, N. (2015). Use of Artificial Neural Network in Predicting Permeability of Dispersive Clay Treated With Lime and Pozzolan. Ijsres, 3(1), 23–37.
Vapnik, V., Golowich, S. E., & Smola, A. (1997). Support vector method for function approximation, regression estimation, and signal processing, 281–287.
Witt, S. F., & Witt, C. A. (1995). Forecasting tourism demand: A review of empirical research. International Journal of Forecasting, 11(3), 447–475. http://doi.org/10.1016/0169-2070(95)00591-7
Published
2019-06-01
How to Cite
Anasari, R. (2019). Expectation of Tourism Demand in Iraq by Using Artificial Neural Network. International Journal of Social Science Research and Review, 2(2), 1-7. https://doi.org/10.47814/ijssrr.v2i2.19