Real-world applications of Bayesian networks
Communications of the ACM
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Soft Computing and Tools of Intelligent Systems Design: Theory and Applications
Soft Computing and Tools of Intelligent Systems Design: Theory and Applications
Support vector machines, Decision Trees and Neural Networks for auditor selection
Journal of Computational Methods in Sciences and Engineering - Intelligent Systems and Knowledge Management
An intelligent system for sorting pistachio nut varieties
Expert Systems with Applications: An International Journal
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Application of a fuzzy classification technique in computer grading of fish products
IEEE Transactions on Fuzzy Systems
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In this study, quality grading of raisins using image processing and data mining based classifiers was investigated. Images from four different classes of raisins (green, green with tail, black, and black with tail) were acquired using a color CCD camera. After pre-processing and segmentation of images, 44 features including 36 color and eight shape features were extracted. Correlation-based feature selection was used to select best features for grading the raisins. Seven features were found superior. To classify raisins, four different data mining-based techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs) and Bayesian networks (BNs) were investigated. Results of validation stage showed ANN with 7-6-4 topology had the highest classification accuracy, 96.33%. After ANN, SVM with polynomial kernel function (95.67%), DT with J48 algorithm (94.67%) and BN with simulated annealing learning (94.33%) had higher accuracy, respectively. Results of this research can be adapted for developing an efficient raisin sorting system.