Open AccessNew Classifier Design for Static Security Evaluation Using Artificial In-telligence Techniques
Author(s) -
Ibrahim Saeh,
Wazir Mustafa,
Nasir A. Al-geelani
Publication year - 2016
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v6i2.pp870-876
Subject(s) - computer science , classifier (uml) , decision tree , machine learning , artificial intelligence , decision tree learning , data mining
This paper proposes evaluation and classification classifier for static security evaluation (SSE) and classifica-tion. Data are generated on (30, 57, 118 and 300) bus IEEE test systems used to design the classifiers. The implementation decision tree methods on several IEEE test systems involved appropriateness SSE and classi-fication by using four algorithms of DT’s. Empirically, with the present of FSA, the implementation results indicate that these classifiers have the capability for system security evaluation and classification. Lastly, FSA is efficient and effective approach for real-time evaluation and classification classifier design.