Submitted By:
Ashish Kumar Agrawal

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Abstract

This project, being developed as a part of MHRD research project (Designing an Intelligent Robot for Explosive Detection and Decontamination funded by MHRD, Govt. of India), explores the design and development of classifier based on statistical methods and soft computing based approaches which is capable of identifying the mines and non mines using various clustering, classification and rules establishment algorithms as to compare the algorithm on the basis of complexity and accuracy. Designing such a classifier is a big challenge because data is not linearly separable and since it has overlapping features, it is not possible to design a classifier with 100% accuracy .This project deals with PVC tubes, wood piece and copper cylinders as non mine data in addition to data of various mines. The basic idea of the classification is based on a fact that it is safe if the non-mines data is predicted as mine but it is not the case when we predict mines data as non-mines. So the unsupervised learning based ART algorithm divides the data into several clusters which are merged on the basis of above fact. Genetic algorithm is enhancing the results to establish the results having negation in the antecedent part. In addition to these approaches, fuzzy approaches also give the membership values corresponding to each class to visual the class of data in better way.