General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • Average Days from Submission to Acceptance: 192 days
    • E-mail: ijcte@iacsitp.com
    • Journal Metrics:

Editor-in-chief
Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2018 Vol.10(6): 194-200 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2018.V10.1225

Development of a Decision Support Framework for Health Beverage Flavouring for the Ageing Society Using Artificial Neural Network

Athakorn Kengpol, Jakkarin Klunngien, and Sopida Tuammee

Abstract—The objective of this research is to select health beverage flavour appropriate for the ageing people with the aid of a decision support framework by using the artificial neural network. The decision support framework’s role is to gather information between consumers and manufacturers. The framework has the capability to compile the collected data and form the suitable model for selecting beverage flavouring of the products. In order to identify the preference of consumer, the artificial neural network has been applied to classify the beverage preference, i.e. taste, colour and odour of health beverages as well as the consumer groups. The questionnaire is used to gather the preference for taste, colour and odour from consumer groups which are separated into four groups such as the gender (male or female), age (60-65 years or over 65 years) health condition (healthy or unhealthy) and symptoms. The results of this research can benefit to consumers and manufacturers. The consumers can know the most preferred health beverage. In addition, the manufacturers can produce products that can match the consumer’s preference.

Index Terms—Decision support system, artificial intelligence, artificial neural network, ageing society.

Athakorn Kengpol and Jakkarin Klunngien are with Advanced Industrial Engineering Management Systems Research Center, Department of Industrial Engineering, Faculty of Engineering, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand (e-mail: athakorn@kmutnb.ac.th, jakkarinkl@gmail.com). Sopida Tuammee is with Department of Information and Production Technology Management, College of Industrial Technology, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand (e-mail: sopida.t@cit.kmutnb.ac.th).

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Cite:Athakorn Kengpol, Jakkarin Klunngien, and Sopida Tuammee, "Development of a Decision Support Framework for Health Beverage Flavouring for the Ageing Society Using Artificial Neural Network," International Journal of Computer Theory and Engineering vol. 10, no. 6, pp. 194-200, 2018.


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