New Classifier Design for Static Security Evaluation Using Artificial In-telligence Techniques

2016 ◽  
Vol 6 (2) ◽  
pp. 870
Author(s):  
Ibrahim Saeh ◽  
Wazir Mustafa ◽  
Nasir Al-geelani
Keyword(s):  
Decision Tree ◽  
Real Time ◽  
Security Evaluation ◽  
Classifier Design ◽  
Test Systems ◽  
Tree Methods ◽  
Static Security ◽  
Classi Fication ◽  
Decision Tree Methods ◽  
Time Evaluation

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.

scholarly journals New Classifier Design for Static Security Evaluation Using Artificial In-telligence Techniques

2016 ◽  
Vol 6 (2) ◽  
pp. 870
Author(s):  
Ibrahim Saeh ◽  
Wazir Mustafa ◽  
Nasir Al-geelani
Keyword(s):  
Decision Tree ◽  
Real Time ◽  
Security Evaluation ◽  
Classifier Design ◽  
Test Systems ◽  
Tree Methods ◽  
Static Security ◽  
Classi Fication ◽  
Decision Tree Methods ◽  
Time Evaluation

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.

Comparisons of decision tree methods using water data

2016 ◽  
Vol 46 (4) ◽  
pp. 2924-2934 ◽  
Author(s):  
Muhammad Azam ◽  
Muhammad Aslam ◽  
Khushnoor Khan ◽  
Anwar Mughal ◽  
Awais Inayat
Keyword(s):  
Decision Tree ◽  
Tree Methods ◽  
Decision Tree Methods

An Approach for ECG Characterization and Classification Using the Combination of Wavelet Transform and Decision Tree Methods

2012 ◽  
Vol 1 (3) ◽  
pp. 72-81
Author(s):  
Faiza Charfi ◽  
Ali Kraiem
Keyword(s):  
Wavelet Transform ◽  
Decision Tree ◽  
High Reliability ◽  
Principal Component ◽  
Component Analysis ◽  
Ecg Signals ◽  
Interaction Detection ◽  
Fast Independent Component Analysis ◽  
Tree Methods ◽  
Decision Tree Methods

A new automated approach for Electrocardiogram (ECG) arrhythmias characterization and classification with the combination of Wavelet transform and Decision tree classification is presented. The approach is based on two key steps. In the first step, the authors adopt the wavelet transform to extract the ECG signals wavelet coefficients as first features and utilize the combination of Principal Component Analysis (PCA) and Fast Independent Component Analysis (FastICA) to transform the first features into uncorrelated and mutually independent new features. In the second step, they utilize some decision tree methods currently in use: C4.5, Improved C4.5, CHAID (Chi - Square Automatic Interaction Detection) and Improved CHAID for the classification of ECG signals, which are taken, from the MIT-BIH database, including normal subjects and subjects affected by arrhythmia. The authors’ results suggest the high reliability and high classification accuracy of C4.5 algorithm with the bootstrap aggregation.

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