Indian Journal of Science and Technology
DOI: 10.17485/ijst/2016/v9i44/98964
Year: 2016, Volume: 9, Issue: 44, Pages: 1-9
Original Article
P. K. Srimani and Vaddin Prathiba*
Department of Computer Science and Mathematics, Bangalore University, Bangalore - 560056, Karnataka, India; [email protected], [email protected]
*Author for correspondence
Vaddin Prathiba
Department of Computer Science and Mathematics, Bangalore University, Bangalore - 560056, Karnataka, India;
Email: [email protected]
Objective: To develop a model for PCB defect detection and classification with the help of soft computing technique. Methodology: To improve the performance of the prediction and classification we propose a hybrid approach for feature reduction and classification. The proposed approach is divided into three main stages: (i) data pre-processing (ii) feature selection and reduction and (iii) Classification. In this approach, pre-processing, feature selection and reduction is carried out by measuring of confidence with the adaptive genetic algorithm. Prediction and classification is carried out by using neural network classifier. A genetic algorithm is used for data preprocessing to achieve the feature reduction and confidence measurement. Findings: The system is implemented using MatLab 2013b. The resulting analysis shows that the proposed approach is capable of detecting and classifying defects in PCB board.
Keywords: Classification, Data Mining, Feature Selection, PCB Defect
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