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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F13%3APU104555%21RIV14-MPO-26220___
rdf:type
skos:Concept n11:Vysledek
dcterms:description
Electronic nose (e-nose) is an electronic device which can measure chemical compounds in air and consequently classify different odors. In this paper, an e-nose device consisting of 8 different gas sensors was designed and constructed. Using this device, 104 different experiments involving 11 different odor classes (moth, angelica root, rose, mint, polis, lemon, rotten egg, egg, garlic, grass, and acetone) were performed. The main contribution of this paper is the finding that using the chemical domain knowledge it is possible to train an accurate odor classification system. The domain knowledge about chemical compounds is represented by a decision tree whose nodes are composed of classifiers such as Support Vector Machines and -Nearest Neighbor. The overall accuracy achieved with the proposed algorithm and the constructed e-nose device was 97.18 %. Training and testing data sets used in this paper are published onlin Electronic nose (e-nose) is an electronic device which can measure chemical compounds in air and consequently classify different odors. In this paper, an e-nose device consisting of 8 different gas sensors was designed and constructed. Using this device, 104 different experiments involving 11 different odor classes (moth, angelica root, rose, mint, polis, lemon, rotten egg, egg, garlic, grass, and acetone) were performed. The main contribution of this paper is the finding that using the chemical domain knowledge it is possible to train an accurate odor classification system. The domain knowledge about chemical compounds is represented by a decision tree whose nodes are composed of classifiers such as Support Vector Machines and -Nearest Neighbor. The overall accuracy achieved with the proposed algorithm and the constructed e-nose device was 97.18 %. Training and testing data sets used in this paper are published onlin
dcterms:title
Electronic Nose Odor Classification with Advanced Decision Tree Structures Electronic Nose Odor Classification with Advanced Decision Tree Structures
skos:prefLabel
Electronic Nose Odor Classification with Advanced Decision Tree Structures Electronic Nose Odor Classification with Advanced Decision Tree Structures
skos:notation
RIV/00216305:26220/13:PU104555!RIV14-MPO-26220___
n11:predkladatel
n12:orjk%3A26220
n3:aktivita
n13:P
n3:aktivity
P(FR-TI4/151)
n3:cisloPeriodika
1
n3:dodaniDat
n6:2014
n3:domaciTvurceVysledku
n15:2629291
n3:druhVysledku
n14:J
n3:duvernostUdaju
n7:S
n3:entitaPredkladatele
n9:predkladatel
n3:idSjednocenehoVysledku
72426
n3:idVysledku
RIV/00216305:26220/13:PU104555
n3:jazykVysledku
n16:eng
n3:klicovaSlova
Electronic nose, odor classification, machine learning, data-mining.
n3:klicoveSlovo
n5:odor%20classification n5:machine%20learning n5:data-mining. n5:Electronic%20nose
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[7995C16F7E4B]
n3:nazevZdroje
Radioengineering
n3:obor
n18:BD
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
3
n3:projekt
n17:FR-TI4%2F151
n3:rokUplatneniVysledku
n6:2013
n3:svazekPeriodika
2011
n3:tvurceVysledku
Burget, Radim Atasoy, Ayten Güney, Selda
s:issn
1210-2512
s:numberOfPages
9
n19:organizacniJednotka
26220