Multidimensional Multiconvolution-Based Feature Extraction Approach for Drift Tolerant Robust Classifier for Gases/Odors

Recently, convolutional neural networks (CNNs) have been used for the classification of gases/odors. These methods involve statistical algorithms for drift compensation of sensor characteristics inhibiting its application in real time. In this letter, we have proposed a hybrid CNN model called "drift tolerant robust classifier (DTRC)," which extracts multidimensional features from the raw sensor array responses and automatically compensates for any drift in the sensor response characteristics. The proposed DTRC comprises of 1-D, 2-D, and 3-D convolutional layers in a hybrid manner to compensate for the referred drift without any statistical algorithm. The efficacy of DTRC has been evaluated on a popular dataset and its published results, which comprise of ten batches of sensor characteristics exhibiting drift over a period of three years. Our proposed DTRC outperformed the referred results. In another experiment, DTRC outperformed other state-of-the-art methods. The proposed CNN architecture (DTRC) is a simpler, lightweight CNN with multidimensional multiconvolution end-to-end architecture, suitable for real-time applications.