Design and Implementation of an Expert clinical System for Initial Detection of Blood Fat Disease
Journal Title: International Research Journal of Applied and Basic Sciences - Year 2014, Vol 8, Issue 9
Abstract
An improved extended classification system (XCS) is proposed in this paper to initially diagnose diseases. Extended classification systems are known as one of the most successful learning agents in the field of artificial intelligence. They are comprised of a set of simple rules with “condition-action” format, which predict a special reaction (disease in this case) according to information acquired from the environment. This set of rules is completed during time in an interaction with data from the real world, with their prediction accuracy being improved gradually. This evolution is often done using patterns that are inspired from the nature, like genetic algorithm. The method suggested here is based on dual application of real (and usually limited) data within a supervised learning process of the set of rules. Having provided the training data to the system and having updated specifications of its rules, these data are transformed into new data using crossover operator which give new information about the environment and the existing patterns in it. This techniques needs shorter process time, so it will increase convergence rate of the training process for the system under study.
Authors and Affiliations
Ehsan Sadeghi poor| Master of Science StudentArtificial intelligence inDepartment ofElectricaland Computer HormozganUniversityBandarabbas, IRAN, Ahmad Hatam| Assistant ProfessorDepartment of Electricaland ComputerHormozgan UniversityBandar abbas, IRAN, Zeinab bahmanzadeh| HormozganUniversity , Bandar abbass , IRAN, email: z.bahmanzadeh@gmail.com
Keywords
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