A deep learning model for odor classification using deep neural network

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dc.contributor.authorGrodniyomchai, Boonyawee
dc.contributor.authorChalapat, Khattiya
dc.contributor.authorJitkajornwanich, Kulsawasd
dc.contributor.authorJaiyen, Saichon
dc.date.accessioned2026-08-06T10:25:16Z
dc.date.available2026-08-06T10:25:16Z
dc.date.issued2019-07-01
dc.description.abstractThe 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.
dc.identifier.citationProceeding 5th International Conference on Engineering Applied Sciences and Technology Iceast 2019, 2019
dc.identifier.doi10.1109/ICEAST.2019.8802538
dc.identifier.other2-s2.0-85071725260
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/10045
dc.sourceProceeding 5th International Conference on Engineering Applied Sciences and Technology Iceast 2019
dc.subjectartificial intelligence
dc.subjectclassification
dc.subjectdeep neural network
dc.subjectelectronic nose
dc.subjectmachine learning
dc.subjectodor classification
dc.titleA deep learning model for odor classification using deep neural network
dc.typeConference Paper

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