Genetic fuzzy markup language for diet application
Chang-Shing Lee, Mei‐Hui Wang, Zhiwei Chen, Chin-Yuan Hsu, Su-E Kuo, Hui-Ching Kuo, Huihua Cheng, Akio Naito · 2011
In this paper, the genetic fuzzy markup language (GFML) is presented to describe the knowledge base and rule base of the diet domain, including ingredients and the contained servings of six food categories of some common food. The domain experts first define the nutrient facts of the common food to construct the fuzzy food ontology. Meanwhile, the involved Taiwanese students of National University of Tainan (NUTN) record their daily meals for a constant period of time. Then, based on the built fuzzy food ontology, a GFML-based learning mechanism combining the genetic learning mechanism with the fuzzy markup language (FML) is carried out to infer the possibility of dietary healthy level for one-day meals. From the experimental results, it is known that the proposed GFML-based learning mechanism is workable for the diet-domain healthcare applications.