Adaptive Neuro-Fuzzy Inference System (ANFIS) For Classification of Iron Ore Using Vision Based System

Abstract

The quality control in metal mines is always a challenging task due to complex nature of ore reserves. The present study attempts to develop adaptive neuro-fuzzy inference system (ANFIS) for classification of iron ores. The ANFIS system was developed using the optimised image feature set as input and the ore classes as the output. The study used 812 image samples for feature extractions. The sample data were partitioned for training and testing in the ratio of 70:30 respectively. The performance of the ANFIS system was evaluated using four confusion matrix parameters viz., sensitivity, specificity, misclassification, and accuracy, which were found to be 0.8750, 0.9681, 0.0510, and 0.9490 respectively. The high value of sensitivity, specificity, and accuracy, and the low value of the misclassification indicate a good performance of the model. It was observed that 13 % of the total testing image samples was misclassified by the model. Thus, the proposed model can be used satisfactorily for classification of iron ores.