Deep learning-based gas identification and quantification with auto-tuning of hyper-parameters

In this work, we propose two deep learning-based architectures tailored for gas identification and quantification, which automatically tune hyper-parameters of the network for optimal performance. The immense success of deep learning in the field of computer vision and natural language processing inspired us to design deep learning-based gas identification and quantification network. The first architecture is proposed for gas quantification, which is based on 1D-CNN. It makes use of raw time-series gas sensor array data and provides the concentration of each gas in a mixture of gases. The second architecture is presented for gas quantification, which is based on a deep belief network combined with drift-aware feature adaptation strategy. The proposed models identify and quantify the gases with improved accuracy despite the presence of sensor drift. Additionally, hyper-parameters of both the networks are automatically tuned for optimal performance. Although several pattern recognition methods related to machine learning, fuzzy logic and hybrid models have been used to identify gas and quantify the gases in the mixture, the performances of these techniques enormously depend on the feature engineering and selection of hyper-parameters. Experimental results show that the proposed methods are an effective technique for identifying gases and quantifying the mixture of gases for e-nose data. We also present that the proposed methods outperforms various other methods and can provide higher identification and quantification accuracy in the pres-ence of sensor drift.