Adaptive Personalized Diet Linguistic Recommendation Mechanism Based on Type-2 Fuzzy Sets and Genetic Fuzzy Markup Language

Abstract

Many different real-world applications with a highlevel of uncertainty proved the good performance of the type-2 fuzzy sets (T2 FSs). Balanced diet means that the intake of each necessary nutrient meets its adequate demand and actual caloric intake balances with calories burned. Additionally, making a diversity of choice from various types of food is also essential to reduce the risk of developing various chronic diseases. Different people have a different goal and it is hard to measure how healthy the eaten meal is for those who are not the domain experts on the diet. This paper presents an adaptive personalized diet linguistic recommendation mechanism based on type-2 fuzzy logic system (T2 FLS) and genetic fuzzy markup language (GFML). First, an adaptive dietary assessment and recommendation ontology is constructed by domain experts, and then a T2 FS-based GFML, describing the fuzzy knowledge base and the fuzzy rule base of the proposed mechanism, is evolved by using genetic algorithms. Next, a T2 FS-based fuzzy inference mechanism infers the result of the dietary health level based on the evolved type-2 GFML (T2GFML). In addition, the balanced computation mechanism is also proposed to reduce the computational complexity of the T2 FLS for the diet domain knowledge. Finally, the linguistic knowledge discovery mechanism presents the discovered linguistic meaning about the meal's health level to show the involved subjects how to make a personalized diet linguistic recommendation. This type of information about the eaten meal can provide the subjects with a reference to gradually improve their unhealthy eating habit and then become healthier and healthier. Experimental results show that the results of the proposed mechanism for the T2 FLS are better than those for the type-1 fuzzy logic system (T1 FLS).Index Terms-Adaptive ontology, genetic fuzzy markup language (GFML), genetic learning, personalized diet linguistic recommendation, Type-2 fuzzy set. 1063-6706

FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go

“…[21][22][23][24] The W3C XML Schema Definition is used to define the syntax and semantics of FML-based programs. [21][22][23][24] A considerable number of research applications have been based on FML and genetic FML such as an ambient intelligence environment, 24 a diet linguistic recommendation mechanism, 25 and an emotional expression mechanism for games of computer Go. 26 Table 1 provides a brief introduction to FML (for further details, refer to the IEEE Standard for FML 21 ).…”

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“…Table 2 summarizes the previous work covered on Fuzzy Diet Model. Lee et al (2015;2010;Wang et al, 2009;2010) GA+ Fuzzy/ FML Wang et al (2016;Lee et al, 2011) HCA + Ontology Naming Tree ) Fuzzy Interval Buisson and Garel (2003) Neural Network (ANN) Sandham et al (1998) Fuzzy Sets Wirsam andUthus (1996) …”

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“…Chang-Shing Lee, Mei-Hui Wang, and Shun-Teng Lan et al [9]. Proposed an Adaptive Personalized Diet Linguistic Recommendation Mechanism based on Type-2 Fuzzy Logic System (T2FLS) and Genetic Fuzzy Markup language (GFML).…”