Object-oriented knowledge framework for modelling human mastication of foods
| Autor: | Kylie D. Foster, Weiliang Xu, John E. Bronlund, Dong Xie |
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| Rok vydání: | 2009 |
| Předmět: |
Object-oriented programming
Class (computer programming) Hierarchy business.industry Computer science General Engineering Decision tree Context (language use) Object (computer science) computer.software_genre Computer Science Applications Knowledge-based systems Knowledge extraction Artificial Intelligence Artificial intelligence business computer Mastication Natural language processing |
| Zdroj: | Expert Systems with Applications. 36:4810-4821 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2008.05.045 |
| Popis: | To model human mastication of foods, an object-oriented knowledge framework is developed that consists of three class objects, one for the physiology related to the mastication, one for the masticatory measurements, and the other for the factors affecting mastication. Each class object is structured in a hierarchy of sub-objects according to the domain or literature knowledge. The knowledge about the relationships among the attributes of objects is represented by IF-THEN rules. These rules can be discovered from the experimental database following the knowledge discovery in database. A case study is presented where a foods chewing database involving EMG mandibular movement measurements is used, two decision trees are discovered with respect to the type of rheological properties and hardness, and the rules derived are expressed in the context of the knowledge framework. |
| Databáze: | OpenAIRE |
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