A Combined Classifier to Detect Landmines Using Rough Set Theory and Hebb Net Learning & Fuzzy Filter as Neural Networks
Shrikant Kumar, Shivam Atri, Hardwari Lal Mandoria · 2009
Landmines are significant barrier to financial, economic & social development in various parts of the world. The demand of dependable, trustworthy, intelligent diagnostic systems in the field of landmines detection has been increasing rapidly. Metal detectors used in mine decontamination, cannot differentiate a mine from metallic debris where the soil contains large quantities of metal scrap & cartridge cases, so a device is required that will reliably confirm that the ground being tested does not contain an explosive device, with almost perfect reliability. Human experts are unable to give belief & plausibility to the rules devised from the huge databases. In this paper two combined classifiers have been discussed. In the first classifier Hebb Net learning is used with rough set theory and in the second one Fuzzy filter neural network is used with the rough set theory. Rough sets have been applied to classify the landmine data because in this theory no prior knowledge of rules are needed, these rules are automatically discovered from the database. The rough logic classifier uses lower & upper approximations for determining the class of the objects. The neural network is for training the data, and has been used especially to avoid the boundary rules given by the rough sets that do not classify the data with cent percentage probability.