Fuzzy K-Nearest Neighbour (FkNN) Based Early Stage Fire Source Classification in Building

Abstract

Assessing the smell of burning is vital, as it can help to further detect and prevent early fire. In this paper, an early stage fire detection algorithm has been introduced using Fuzzy k- Nearest Neighbour (FkNN). The tests were made on normally available seven fire sources and three building structure resources. All the test samples were scorched in a vacuum oven at various temperature points, pushed out using vacuum pumps to be sniffed by the electronic nose. The experiments were done in a confined room with monitored temperature and humidity level. Time domain data were sampled. Prior to be given to the classifier, the smellprints were normalised and the features were extracted. Experimental classification results show that the integration of fuzzy logic into conventional kNN has enhanced the accuracy of the classifier and gave excellent consistency, nonetheless of humidity and temperature disparity, baseline sensor errors, the diverse emission concentration range and varied scorching temperature levels. The average classification precision for the classification system is 96.15%.