An Effective Machine Learning Approach for Explosive Trace Detection

Globally, the proliferation of explosives and terrorist attacks has caused significant harm to public areas and heightened security concerns. The majority of public places, such as trains, airports, and government buildings, are being targeted, endangering people's lives and property. These target sites must be shielded against terrorist attacks and explosives without putting human security workers in jeopardy. Animals have been used as one of various techniques to try and tackle the aforementioned issue. It has been demonstrated that machine learning models, however, offer superior results. Large volumes of data are necessary for machine learning models to be accurate, but certain specialized training methods have drawbacks of their own because they can be difficult to get. It is now essential to create systems that are highly adaptable to real-time data. This work focuses on the essence of deploying an Artificial intelligence model for effective explosive trace detection. The model used was adapted from deep learning technology trained with a large explosive trace data set that was collected from a sensor network. The dataset was converted to 2D data using serial data to an image generator. The model was developed to classify explosive gas based on the concentration of Carbon (C), Hydrogen (H), Oxygen (O), and Nitrogen (N) gases and was able to classify the gas combinations as either explosive or not. The adaptation of CNN was tested and validated using 10% of the explosive trace dataset with an accuracy of 98.2%, and an AUC of 1 was recorded. The result shows that the deep learning concept is a useful tool in explosive trace detection.