A hybrid deep convolutional neural network-based electronic nose for pollution detection purposes

in the electronic nose fields, many types of research have been focused on deep learning for gas classification.
Compared to traditional machine learning algorithms such as K-Nearest Neighbors (KNN) and Support Vector
Machines (SVM), convolutional neural network (CNN) architectures can classify gases, resulting in higher
classification accuracy. In this study, a hybrid convolutional neural network with linear discriminant analysis
(CNN-LDA) was proposed for the classification of pollutant gases. One open-source gas dataset applied the
proposed model. CNN and LDA models were both used for feature extraction and classification. Results showed
the reliability of the hybrid CNN-LDA model, which achieved the highest test accuracy with a score of 93%,
compared to the individual CNN and LDA models with classification accuracies of 90% and 83%, respectively.
Metrics such as accuracy, recall, F1 score, and precision, allowed us to combine the results of the/experiments
used.