Rokaya, M. (2014). DEVELOPMENT OF AUTOMATED EXPERT SYSTEM FOR A NUTRITIONAL GUIDANCE APPLICATION BASED ON ROUGH SETS. International Journal of Intelligent Computing and Information Sciences, 14(2), 39-52. doi: 10.21608/ijicis.2014.15772
M Rokaya. "DEVELOPMENT OF AUTOMATED EXPERT SYSTEM FOR A NUTRITIONAL GUIDANCE APPLICATION BASED ON ROUGH SETS". International Journal of Intelligent Computing and Information Sciences, 14, 2, 2014, 39-52. doi: 10.21608/ijicis.2014.15772
Rokaya, M. (2014). 'DEVELOPMENT OF AUTOMATED EXPERT SYSTEM FOR A NUTRITIONAL GUIDANCE APPLICATION BASED ON ROUGH SETS', International Journal of Intelligent Computing and Information Sciences, 14(2), pp. 39-52. doi: 10.21608/ijicis.2014.15772
Rokaya, M. DEVELOPMENT OF AUTOMATED EXPERT SYSTEM FOR A NUTRITIONAL GUIDANCE APPLICATION BASED ON ROUGH SETS. International Journal of Intelligent Computing and Information Sciences, 2014; 14(2): 39-52. doi: 10.21608/ijicis.2014.15772
DEVELOPMENT OF AUTOMATED EXPERT SYSTEM FOR A NUTRITIONAL GUIDANCE APPLICATION BASED ON ROUGH SETS
Information Technology Department, College of Computer & IT, Taif University- Saudi Arabia Mathematics Department, Faculty of Science, Tanta University- Egypt
Abstract
Rough set theory methodology is concerned with the classification and analysis of imprecise, uncertain or incomplete information and knowledge, and is considered one of the first non-statistical approaches in data analysis. An expert system provides advice derived from its knowledge base, using a reasoning process embedded in its inference engine. Expert system seeks to embed the knowledge of a human expert in a computerized consulting service. Nutritional guidance importance increases as well as nutritional problems increase. In this paper an expert system that based on rough theory is presented and tested. In this paper, a rule induction method is introduced, which extracts not only classification rules but also other knowledge needed for guidance. This system is evaluated on a nutritional database. Results show that our proposed method correctly induces guidance rules and estimates the statistical measures of rules.
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