The Concept of Neuroagents in Hospitality Industry and Tourism
Abstract
This study is aimed to develop a modern concept for forecasting in hospitality industry and tourism. Methods/Analysis: In this paper it is studied a current researches in neural network’s technology and its implementation in hospitality and tourism. It is important to understand that classical methods of data analysis nowadays are not able to make best results. There is a need to combine them to some elements of artificial intelligence. Hospitality industry and tourism paid great attention to the implementation of neural network applications in the analysis and forecasting. Modern economy is very susceptible to the influence of many factors which complicates the application of classical statistical procedures; neuroagent is a complex method of forecasting and decision-making and can be a good solution in the analysis of hidden relationships and the search for optimal response to emerging threats in hospitality and tourism. This study can be used as a concept for creation of some decision-making systems in hospitality and tourism or early warning systems.
References
[2] Claveria, O., Monte, E., and Torra, S. 2013. Tourism demand forecasting with different neural networks models. Research Institute of Applied Economics. Working Paper 2013/21.
[3] Çuhadar, M., Cogurcu, I., and Kukrer, C. 2014. Modelling and forecasting cruise tourism demand to Izmir by different artificial neural network architectures. International Journal of Business and Social Research (IJBSR), 4(3): 12-28.
[4] Efendigil, T., Onut, S., and Kahraman. C. 2008. A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Systems with Applications, DOI:10.1016/j.eswa.2008.08.058
[5] Fernandes, P.O., Teixeira, J.P., Ferreira, J.M., and Azevedo, S.G. 2011. Forecasting tourism demand with artificial neural networks. Book of proceedings Vol.II – Paper presented at the International conference on tourism & management studies – Algarve, 26-29 October 2011, Faro, Algarve, Portugal.
[6] Kozlov, D.A. 2016. Modelling and forecasting of Russian outbound tourism. Actual Problems of Economics. 181(7): 446-453.
[7] Kozlov, D.A. 2017. Agent technology in hotel business. Journal of Environmental Management and Tourism, Volume VIII, Spring, 2(18): 285-290. DOI:10.14505/jemt.v8.2(18).01
[8] Lin, C.-J., Chen, H.-F., and Lee, T.-S. 2011. Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines: Evidence from Taiwan. International Journal of Business Administration. 2(2): 14-24.
[9] Miloradov, K.A., and Eidlina, G.M. 2016. Tourism market of the Russian Federation: Analysis of interactions between outbound and domestic tourism using neural networks. Indian Journal of Science and Technology, 9(27), DOI: 10.17485/ijst/2016/v9i27/97698
[10] Noersasongko, E. et al. 2016. A tourism arrival forecasting using genetic algorithm based neural network. Indian Journal of Science and Technology, 9(4), DOI: 10.17485/ijst/2016/v9i4/78722
[11] Popîrlan C.I., and Ștefănescu L. 2011. A mobile agents’ system for intelligent data analysis, Proceedings of WSEAS Applied Computing Conference 2009 (ACC 2009), September 28-30, Athens, Greece, pp. 663-668.
[12] Popîrlan, C.I., and Ștefănescu L. 2011. A Multi-agent approach for adaptive virtual organization using JADE, book chapter in Lecture Notes in Computer Science, Volume 6943, Adaptive and Intelligent Systems - Abdelhamid Bouchachia (Ed.) pp. 344 - 355.
[13] Popîrlan, C.I., and Ștefănescu L. 2011. Intelligent software agents for data analysis in knowledge-based systems, in Intelligent Decision Support Systems for Managerial Decision Making, Chapter 2, pp. 25-48, ASERS Publishing.
[14] Song H., and Li, G. 2008. Tourism demand modelling and forecasting – A review of recent research. Tourism Management, 29(2):203-220.
[15] Zhang, H., and Li, J. 2012. Prediction of Tourist Quantity Based on RBF Neural Network. Journal of Computers, 7(4): 965-970.
The Copyright Transfer Form to ASERS Publishing (The Publisher)
This form refers to the manuscript, which an author(s) was accepted for publication and was signed by all the authors.
The undersigned Author(s) of the above-mentioned Paper here transfer any and all copyright-rights in and to The Paper to The Publisher. The Author(s) warrants that The Paper is based on their original work and that the undersigned has the power and authority to make and execute this assignment. It is the author's responsibility to obtain written permission to quote material that has been previously published in any form. The Publisher recognizes the retained rights noted below and grants to the above authors and employers for whom the work performed royalty-free permission to reuse their materials below. Authors may reuse all or portions of the above Paper in other works, excepting the publication of the paper in the same form. Authors may reproduce or authorize others to reproduce the above Paper for the Author's personal use or for internal company use, provided that the source and The Publisher copyright notice are mentioned, that the copies are not used in any way that implies The Publisher endorsement of a product or service of an employer, and that the copies are not offered for sale as such. Authors are permitted to grant third party requests for reprinting, republishing or other types of reuse. The Authors may make limited distribution of all or portions of the above Paper prior to publication if they inform The Publisher of the nature and extent of such limited distribution prior there to. Authors retain all proprietary rights in any process, procedure, or article of manufacture described in The Paper. This agreement becomes null and void if and only if the above paper is not accepted and published by The Publisher, or is with drawn by the author(s) before acceptance by the Publisher.