Pollutant Gases Detection using the Machine learning on Benchmark Research Datasets

摘要

In real time environment, different varieties of sensor devices are deployed to collect and transmit the periodic environment data to the base station to monitor the specific tasks. The pollutant gases like methanol, LPG, ammonia are harmful to human beings, hence such vulnerabilities should automatically be detected and safety alarm generated in a particular area. Such systems are often called as Electronic Nose (E-nose) systems which are an automated system that analyze continues periodic data and detect any harmful situations based on various approaches such as threshold-based or machine learning based. In this Paper, we have evaluated the performance metrics Sensitivity, Specificity, and Accuracy for GSAD dataset and Air Quality datasets.