Intelligent ontological multi-agent for healthy diet planning

Abstract

Good eating habits can make human beings to live in a healthy lifestyle. When a person constantly eats too much or too little, it will have a high risk of causing a disease for him. Therefore, developing healthy and balanced eating habits is important for most people to stay away from diseases. This study proposes an intelligent healthy diet planning multi-agent (IHDPMA), including a personal profile agent, a nutrition facts analysis agent, a knowledge analysis agent, a discovery agent, a fuzzy inference agent, and a semantic generation agent for healthy diet planning. The IHDPMA provides a semantic analysis of healthy diet status for people based on the pre-constructed ontology by domains experts and results of fuzzy inference. With the generated semantic analysis, people can get healthy information about what they eat and make it easier to eat a balanced and healthy diet. The experimental platform has been constructed to test the performance of the IHDPMA. The results indicate that the IHDPMA can effectively work for healthy diet planning.

Interval Type-2 Fuzzy Application for Diet Journaling

“…In (Asghari, Ejtahed, Sarsharzadeh, Nazeri, & Mirmiran, 2013), a Type-1 fuzzy system is used to inform the user in whether the number of servings consumed for each of six food groups are classified as fuzzy values of normal, attention, or danger. The system in (Wang, et al, 2016) implements a very similar system to (Lee, Wang, & Hagras, A Type-2 Fuzzy Ontology and Its Application to Personal Diabetic-Diet Recommendation, 2010), (Lee, Wang, Hsu, & Hagras, 2009), (Wang, Lee, Hsieh, Hsu, & Chang, 2009), and (Wang, et al, 2010). A creative use of fuzzy systems may be found in (Chavan, Sambare, & Joshi, 2016), where a specific cultural evaluation of food is modeled in a fuzzy system.…”

“…When a user enters a food quantity into a diet database, there is a certain level of uncertainty associated with it. To deal with this problem, the Type-2 Fuzzy Ontologies (T2FOs) in (Lee, Wang, & Hagras, 2010) (Lee, Wang, Hsu & Hagras, 2009) (Wang, Lee, Hsieh, Hsu & Chang, 2009), and ) have a certain level of uncertainty based on the measurement the user enters (bowl, glass, plate, etc.) However, none of the popular diet logging systems (listed in Table 2) appear to tackle this problem.…”

Ontology-based multi-agents for intelligent healthcare applications

“…Based on the FML, an FML editor, developed by the LASA Laboratory, University of Salerno, Italy, is used to construct the knowledge base and rule base of the OMAS Wang et al 2009). The knowledge base describes fuzzy concepts of the OMAS, including fuzzy variables, fuzzy terms, and membership functions of fuzzy sets.…”

“…An intelligent agent that automatically provides tips for how to choose foods that improve health and avoid foods that increase risk of illness would be of great assistance for most people, especially those with diabetes or cardiovascular diseases. Wang et al (2009) proposed an intelligent healthy diet planning multi-agent (IHDPMA) for healthy diet planning. Chen and Chen (2008) noted that agent technology is a key area in the field of artificial intelligence research and agents are being used in an increasingly wide area of applications.…”

A Review on Automated Menu Planning Approaches

“…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) …”