The Development and Comparing the Performance of Temporal Fuzzy Neural Network Technique and Temporal Fuzzy Decision Trees Case Study of Suitable Thai Elderly Tourists


Tawin Tanawong Siriporn Dachasilaruk


     The purpose of this research is to present a solution to change the value of the temporal fuzzy attribute. A model with data mining technique was developed to solve the problems involved with an effect to the suitability of the decision making of Thai elderly in various tourist destinations in Thailand. This model based on the import factors with the crisp value and fuzzy linguistic term. A temporal fuzzy database system with a design in the Conceptual Meta Schema was applied to collect information in the form of temporal fuzzy attributes. Moreover, this model used a temporal fuzzy attribute matching technique, which consists of the first format of the crisp and fuzzy linguistic term, and the second pattern of the fuzzy linguistic term, and the fuzzy linguistic term expression. The replica models developed between the Temporal Fuzzy Neural Network (TFNN) and the Temporal Fuzzy Decision Tree (TFDT), were compared. The result shown that performance values of the TFNN model was the most valuable, with accuracy value, precision value, recall value and f-measure at 88.9%, 79.0%, 88.9% and 83.7%, respectively. The TFNN model was a structure of 24-3-2, with momentum 0.2, and the learning rate value 0.3. This model provided a form of learning and testing cross-validation folds = 5.

Keywords: Temporal Fuzzy Decision Tree (TFDT), Temporal Fuzzy Neural Network (TFNN), Temporal Fuzzy Attribute Matching, Elderly Tourists


Aghdam, A. R., Hee, J. M., & Sim, A. T. H. (2014). Identifying places of interest for tourists using knowledge discovery techniques. Retrieved from
Chittayasothorn, S. (2009). Toward fuzzy temporal databases with temporal fuzzy linguistic terms. Retrieved from
Guoxia, Z., & Jianqingm, T. (2009). The application of data mining in tourism information. Retrieved from
Hua, J. (2016). Optimization for urban tourism product development system based on data mining. Retrieved from
Huang, Y., & Ling, B. (2015). Using ontologies and formal concept analysis to integrate heterogeneous tourism information. IEEE Transactions on Emerging Topics in Computing, 3(2), 172 – 184. http://
Jieh, R. C., & Betty, C. (2015). The development of a tourism attraction model by using fuzzy theory. Mathematical Problems in Engineering, 2015(2), 1-10.
Nijssen, G. M., & Halpin, T. A. (1989). Conceptual schema and relational database design. USA: Prentice Hall.
Pang, N. T., Michael, S., & Vipin, K. (2006). Introduction to data mining. Boston, MA, USA: Addison-Wesley.
Pasichnyk, V., & Artemenko, O. (2015). Intelligent advisory systems and information technology support for decision making in tourism. Retrieved from
Richard, J. R., Michael, & W. G. (2003). Data mining a tutorial-based primer. Boston: Addison-Wesley /Pearson Education Inc.
Shapoval, V., Wang, M. C., & Shioya, H. (2018). Data mining in tourism data analysis: inbound visitors to Japan. Journal of Travel Research, 57(3), 310–323. 6960
Tanawong, T. (2017). Temporal fuzzy case-based reasoning retrieval. Chiang Mai Journal of Science, 44(1), 267-278.
Zimmermann, H. (2001). Fuzzy set theory and its applications. Boston: Kluwer Academic Publishers.

Research Articles


How to Cite
TANAWONG, Tawin; DACHASILARUK, Siriporn. The Development and Comparing the Performance of Temporal Fuzzy Neural Network Technique and Temporal Fuzzy Decision Trees Case Study of Suitable Thai Elderly Tourists. Naresuan University Journal: Science and Technology (NUJST), [S.l.], v. 27, n. 2, p. 36-47, may 2019. ISSN 2539-553X. Available at: <>. Date accessed: 25 oct. 2021. doi: