Classification of Human Body Smell by Learning Vector Quantization

2019
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Author: Sigeru Omatu
Published in: Distributed Computing and Artificial Intelligence, 15th International Conference
Publisher: Springer International Publishing

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Abstract

In this paper we consider classification of human body smell using learning vector quantization (LVQ). Smells of human body are classified as sweaty lockerroom smell, middle-aged smell, and age-of-smell. The first one is mainly detected for persons from teenagers to twenties, the second one is for persons from thirties to fifties, and the third one is for persons over fifties. The aim of this paper is to classify smells into three smalles stated above. The sweaty smell is a smell similar to ammonia and isovaleric acid, middle-aged smell is similar to diacetyl, and the age-of-smell is similar to nonenaar. Using a special sampling box, we train the smell sensing data such that each of those smells could be classified into true smell using LVQ. After that, we develop a hardware (Kunkun body) to classify various smell data into each smell.

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Metadata
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Title: Classification of Human Body Smell by Learning Vector Quantization
Author: Sigeru Omatu
Publisher: Springer International Publishing
Book: Distributed Computing and Artificial Intelligence, 15th International Conference
Print ISBN: 978-3-319-94648-1

Electronic ISBN: 978-3-319-94649-8

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-319-94649-8_11

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