A genetic algorithm for generating fuzzy classification rules
Fuzzy Sets and Systems
Feasibility of impact-acoustic emissions for detection of damaged wheat kernels
Digital Signal Processing
Support vector machines, Decision Trees and Neural Networks for auditor selection
Journal of Computational Methods in Sciences and Engineering - Intelligent Systems and Knowledge Management
Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique
Expert Systems with Applications: An International Journal
Application of an intelligent classification method to mechanical fault diagnosis
Expert Systems with Applications: An International Journal
An intelligent system for sorting pistachio nut varieties
Expert Systems with Applications: An International Journal
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Comparing data mining classifiers for grading raisins based on visual features
Computers and Electronics in Agriculture
A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories
Computers in Biology and Medicine
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper presents an expert system for sorting open and closed shell pistachio nuts. A prototype was set up to detect closed shell pistachio nuts by dropping them onto a steel plate and recording the acoustic signals that was generated when a kernel hit the plate. To determine the important characteristics and to unravel the significance of these signals, further analysis or processing was required. J48 decision tree (DT) is used for both feature selection and classification. Initially, the J48 DT was used for selecting the best statistical features that will discriminate among two classes from impact acoustic signals. The output of J48 DT algorithm was then converted into crisp IF-THEN rules and membership function sets of the fuzzy classifier. Four IF-THEN rules, generated from the extracted features of J48 DT, were required by the fuzzy classifier. To evaluate the performance of the expert system, data on 300 nuts of open and closed shells were used. The data were initially divided into two parts: 210 instances (70%) for training and the remaining 90 instances (30%) for testing the classifier. The correct classification rate and RMSE for the training set were 99.52% and 0.07, and for the test set were 95.56% and 0.21, respectively. These encouraging results as well as the robustness of the FIS based expert system makes the approach ideal for automated inspection systems.