Abstract
In this paper, we explored a new approach of odor identification using deep learning. We also verified whether it is possible to classify odors in real time. Attention recurrent neural network (ARNN) is mainly used in the sequence to sequence model in terms of natural language processing field. We applied the simple ARNN embedding part with auto encoder to the deep learning model. This model can visualize the attention part of input data. Six types of aroma oil were measured using five types of metal oxide semiconductor gas sensors. Using the measured data, the accuracy was 96.83 [%] for training data and 96.88 [%] for the verification data.
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Fukuyama, K., Matsui, K., Omatsu, S., Rivas, A., Corchado, J.M. (2020). Feature Extraction and Classification of Odor Using Attention Based Neural Network. In: Herrera, F., Matsui , K., Rodríguez-González, S. (eds) Distributed Computing and Artificial Intelligence, 16th International Conference. DCAI 2019. Advances in Intelligent Systems and Computing, vol 1003 . Springer, Cham. https://doi.org/10.1007/978-3-030-23887-2_17
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DOI: https://doi.org/10.1007/978-3-030-23887-2_17
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