UPSI Digital Repository (UDRep)
Start | FAQ | About

QR Code Link :

Type :article
Subject :H Social Sciences
ISSN :21460353
Main Author :Nurul Hila Zainuddin
Additional Authors :Shazlyn Milleana Shaharudin
Title :Bootstrapping the multilayer feedforward propagation system for predicting the arrival guest in Malaysia
Place of Production :Tanjung Malim
Publisher :Fakulti Sains dan Matematik
Year of Publication :2021
Notes :Review of International Geographical Education Online
Corporate Name :Universiti Pendidikan Sultan Idris
Web Link :Click to view web link
PDF Full Text :Login required to access this item.

Abstract : Universiti Pendidikan Sultan Idris
Predicting arrival guest is an essential step in estimating the Malaysia economic impact, particularly in short and long-term COVID-19 crisis. Neural Network family of models has been widely used in economic and tourism. Most of the study used single layer of fed forward propagation system, it is because of less sampling variation in Neural Network. However, apparently the multilayer provides a better predicting result but its disadvantage is to deal with high sampling variation. The motivation of this study is to enhance the ability of multilayer Neural Network in predicting the arrival guest. In this study, a hybrid model based on Bootstrapping the Neural Network proposed to predict the arrival guest of Singapore in Malaysia. The weights of variables to the first hidden layer nodes are bootstrapped. The subsequent hidden layer takes the bootstrapped weights in order to obtain the Neural Network output. The prediction obtained by hybrid model has been compared with conventional multilayer Neural Network in terms of small variation. The computational results shows that the hybrid model provides better performance in predicting the arrival guest. ? 2021 RIGEO. All Rights Reserved.

References

Alameer, Z., Elaziz, M. A., Ewees, A. A., Ye, H., & Jianhua, Z. (2019). Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm. Resources Policy, 61, 250-260. doi:10.1016/j.resourpol.2019.02.014

Alamsyah, A., & Friscintia, P. B. A. (2019). Artificial neural network for indonesian tourism demand forecasting. Paper presented at the 2019 7th International Conference on Information and Communication Technology, ICoICT 2019, doi:10.1109/ICoICT.2019.8835382 Retrieved from www.scopus.com

Azari, E. Z. A. (2019). VM2020: Tourism malaysia sasar ketibaan lebih 12 juta pelancong dari singapura. Retrieved from www.scopus.com

Berrar, D. (2018). Introduction to the non-parametric bootstrap. Encyclopedia of bioinformatics and computational biology: ABC of bioinformatics (pp. 766-773) doi:10.1016/B978-0-12-809633-8.20350-6 Retrieved from www.scopus.com

Chandra, S., & Kumari, K. (2018). Forecasting foreign tourist arrivals in india using time series models. International Journal of Statistics and Applied Mathematics, 3(2), 338-342. Retrieved from www.scopus.com

Chu, F. -. (1998). Forecasting tourism demand in asian-pacific countries. Annals of Tourism Research, 25(3), 597-615. doi:10.1016/S0160-7383(98)00012-7

Chu, Y., Fei, J., & Hou, S. (2019). Adaptive Global Sliding-Mode Control for Dynamic Systems using Double Hidden Layer Recurrent Neural Network Structure, Retrieved from www.scopus.com

Constantino, H. A., Fernandes, P. O., & Teixeira, J. P. (2016). Tourism demand modelling and forecasting with artificial neural network models: The mozambique case study. Tékhne, 14(2), 113-124. Retrieved from www.scopus.com

Efron, B. (1979). Bootstrap methods: Another look at the jackknife. Annals of Statistics, 7, 1-26. Retrieved from www.scopus.com

Hajizadeh, E., Seifi, A., Fazel Zarandi, M. H., & Turksen, I. B. (2012). A hybrid modeling approach for forecasting the volatility of S&P 500 index return. Expert Systems with Applications, 39(1), 431-436. doi:10.1016/j.eswa.2011.07.033

Hila, N. Z., Muhamad Safiih, L., Shaharudin, S. M., & Mohamed, N. A. (2019). A hybrid neural network model to forecast arrival guest in malaysia. Paper presented at the Proceedings - 2019 1st International Conference on Artificial Intelligence and Data Sciences, AiDAS 2019, 70-75. doi:10.1109/AiDAS47888.2019.8970778 Retrieved from www.scopus.com

Höpken, W., Eberle, T., Fuchs, M., & Lexhagen, M. (2020). Improving tourist arrival prediction: A big data and artificial neural network approach. Journal of Travel Research, 4, 1-20. Retrieved from www.scopus.com

Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366. doi:10.1016/0893-6080(89)90020-8

Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18(3), 439-454. doi:10.1016/S0169-2070(01)00110-8

Kim, K. -. (2006). Artificial neural networks with evolutionary instance selection for financial forecasting. Expert Systems with Applications, 30(3), 519-526. doi:10.1016/j.eswa.2005.10.007

Künsch, H. R. (1989). The jackknife and the bootstrap for general stationary observations. Ann.Statist., 17(3), 1217-1241. Retrieved from www.scopus.com

Law, R., & Au, N. (1999). A neural network model to forecast japanese demand for travel to hong kong. Tourism Management, 20(1), 89-97. doi:10.1016/S0261-5177(98)00094-6

Lesnussa, Y. A., Melsasail, N. A., & Leleury, Z. A. (2016). Application of principal component analysis for face recognition based on weighting matrix using GUI matlab. Educ.JSMT, 3(2), 1-7. Retrieved from www.scopus.com

Lola, M. S., Hila, N. Z., Ramlee, M. N. A., Abdul Rahman, M. N., & Abdullah, M. T. (2017). Improvement of estimation based on small number of events per variable (EPV) using bootstrap logistics regression model. Malaysian Journal of Fundamental and Applied Sciences, 13(4), 693704. Retrieved from www.scopus.com

Makkar, A., & Kumar, N. (2018). Cognitive spammer: A framework for pagerank analysis with split by over-sampling and train by under-fitting. Future Gener.Comput.Syst., Retrieved from www.scopus.com

Mohamed, Z., & Rosli, R. (2014). Development of A structural model with multicollinearity and outliers problems. EDUCATUM Journal of Science, Mathematics and Technology, 1(1), 38-52. Retrieved from www.scopus.com

Nooripoor, M., Khosrowjerdi, M., Rastegari, H., Sharifi, Z., & Bijani, M. (2020). The role of tourism in rural development: Evidence from iran. GeoJournal, , 1-15. Retrieved from www.scopus.com

Nurul Hila, Z., & Muhamad Safiih, L. (2016). The accuracy of two-sided confidence interval algorithm: An alternative of double bootstrap approach. International Mathematical Forum, 11(170), 845-851. Retrieved from www.scopus.com

Palmer, A., José Montaño, J., & Sesé, A. (2006). Designing an artificial neural network for forecasting tourism time series. Tourism Management, 27(5), 781-790. doi:10.1016/j.tourman.2005.05.006

Pham, B. T., Nguyen, M. D., Bui, K. -. T., Prakash, I., Chapi, K., & Bui, D. T. (2019). A novel artificial intelligence approach based on multi-layer perceptron neural network and biogeography-based optimization for predicting coefficient of consolidation of soil. Catena, 173, 302-311. doi:10.1016/j.catena.2018.10.004

Shukri, N. (2020). Semarakkan Semula Industri Pelancongan, Seni Dan Budaya Negara, Retrieved from www.scopus.com

Singh, U. P., Chouhan, S. S., Jain, S., & Jain, S. (2019). Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access, 7, 43721-43729. doi:10.1109/ACCESS.2019.2907383

Syed Ahmad, S. S., Mohd Mushar, S. H., Zamah Shari, N. H., & Kasmin, F. (2020). A comparative study of log volume estimation by using statistical method. EDUCATUM Journal of Science, Mathematics and Technology, 7(1), 22-28. Retrieved from www.scopus.com

Tahir, M. F., Tehzeeb-Ul-Hassan, & Saqib, M. A. (2016). Optimal scheduling of electrical power in energy-deficient scenarios using artificial neural network and bootstrap aggregating. International Journal of Electrical Power and Energy Systems, 83, 49-57. doi:10.1016/j.ijepes.2016.03.046

Yao, Y., & Cao, Y. (2020). A neural network enhanced hidden markov model for tourism demand forecasting. Applied Soft Computing Journal, 94 doi:10.1016/j.asoc.2020.106465

Yu, L., Wang, S., & Lai, K. K. (2009). A neural-network-based nonlinear metamodeling approach to financial time series forecasting. Applied Soft Computing Journal, 9(2), 563-574. doi:10.1016/j.asoc.2008.08.001

Yusoff, N. I. M., Ibrahim Alhamali, D., Ibrahim, A. N. H., Rosyidi, S. A. P., & Abdul Hassan, N. (2019). Engineering characteristics of nanosilica/polymer-modified bitumen and predicting their rheological properties using multilayer perceptron neural network model. Construction and Building Materials, 204, 781-799. doi:10.1016/j.conbuildmat.2019.01.203

Zhang, H., Zhang, L., & Jiang, Y. (2019). Overfitting and underfitting analysis for deep learning based end-to-end communication systems. Paper presented at the 2019 11th International Conference on Wireless Communications and Signal Processing, WCSP 2019, doi:10.1109/WCSP.2019.8927876 Retrieved from www.scopus.com

Zheng, M., Tang, W., & Zhao, X. (2018). Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing. International Journal of Geographical Information Science, , 1-32. Retrieved from www.scopus.com


This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to previous page

Installed and configured by Bahagian Automasi, Perpustakaan Tuanku Bainun, Universiti Pendidikan Sultan Idris
If you have enquiries with this repository, kindly contact us at pustakasys@upsi.edu.my or Whatsapp +60163630263 (Office hours only)