A deep learning model for odor classification using deep neural network - Grodniyomchai, Boonyawee; Chalapat, Khattiya; Jitkajornwanich, Kulsawasd; Jaiyen, Saichon (My University, 2019)
The odor is an environment that surrounds us. However, to identify the odor by using the human nose in order to prove the odor is very dangerous. Therefore, the artificial intelligent (AI) system should be built based on machine learning in order to achieve more accurate results. This research adopts the Deep Neural Network (DNN) model to identify some types of odor including odorless, beer odor, whisky odor, and wine odor. Each contains 60 instances that are obtained from seven sensors of the electronic nose. The experiments are conducted, and the results are compared to the comparative machine learning methods including Multilayer Perceptron (MLP), Decision Tree and Naïve Bayes (NB). From the experimental results, it can signify that the proposed deep learning model can achieve the best average accuracy.
Keywords: artificial intelligence; classification; deep neural network; electronic nose; machine learning; odor classification