BombNose: A Multiple Bomb-Related Gas Prediction Model Using Machine Learning with Electronic Nose Sensor Substitution Technique

Abstract

The safety and security of an individual is important in our society. Bombing attacks can cause significant destruction and death. Energy efficient and compact bomb removal robots are challenging to develop because these typically involved a large array of sensors individually acquiring gas data. This study addresses this challenge by developing a multiple bomb-related gas prediction model using machine learning and the electronic nose sensor substitution technique. Three models can predict gasses such as ammonia, ethanol, and isobutylene using only carbon monoxide, toluene, and methane sensors. The feedforward artificial neural network (FFNN) with three hidden layers was optimized for the regression of each target gas. Consequently, ammonia, ethanol, and isobutylene predictions achieved R 2 values of 1, 1, and 1 as well as MSE values of 0.35696, 0.052995, and 0.0022953, respectively. This study demonstrates that the sensor substitution model (BombNose) is highly reliable and appropriately sensitive in the field of bomb detection.