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Smell classification using weakly responding data

Published: 24 March 2014 Publication History

Abstract

This paper considers an array sensing system of odors and adopts a layered neural network for classification. In order to classify odors, we use data from all fourteen sensors even if some of them are not sensitive so much. We will propose three methods to use the data by insensitive sensors to find the features of odors.

References

[1]
S. Omatu and M. Yano. Intelligent electronic nose system independent on odorconcentration. In International Symposium on Distributed Computing and Artificial Intelligence, pages 1--9. DCAI, September 2012.
[2]
T. F. Sigeru Omatu, Hideo Araki and M. Yano. Intelligent classification of odor data using neural networks. In International Conference on ADVCOMP, pages 1--7. ADVCOMP, September 2011.

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  1. Smell classification using weakly responding data

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    cover image ACM Conferences
    SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing
    March 2014
    1890 pages
    ISBN:9781450324694
    DOI:10.1145/2554850
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 24 March 2014

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    Author Tags

    1. features of odor
    2. layered neural network
    3. neural networks
    4. odor classification
    5. odor sensors
    6. smell classification

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    SAC 2014: Symposium on Applied Computing
    March 24 - 28, 2014
    Gyeongju, Republic of Korea

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    SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;
    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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