Application of Dimensionality Reduction and Machine Learning Methods for the Interpretation of Gas Sensor Array Readouts from Mold-Threatened Buildings

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F23%3A00367152" target="_blank" >RIV/68407700:21110/23:00367152 - isvavai.cz</a>

Alternative codes found

RIV/75081431:_____/23:00002581

Result on the web

<a href="https://doi.org/10.3390/app13158588" target="_blank" >https://doi.org/10.3390/app13158588</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app13158588" target="_blank" >10.3390/app13158588</a>

Result language

angličtina

Original language name

Application of Dimensionality Reduction and Machine Learning Methods for the Interpretation of Gas Sensor Array Readouts from Mold-Threatened Buildings

Original language description

Paper is in the scope of moisture-related problems which are connected with mold threat in buildings, sick building syndrome (SBS) as well as application of electronic nose for evaluation of different building envelopes and building materials. The machine learning methods used to analyze multidimensional signals are important components of the e-nose system. These multidimensional signals are derived from a gas sensor array, which, together with instrumentation, constitute the hardware of this system. The accuracy of the classification and the correctness of the classification of mold threat in buildings largely depend on the appropriate selection of the data analysis methods used. This paper proposes a method of data analysis using Principal Component Analysis, metric multidimensional scaling and Kohonen self-organizing map, which are unsupervised machine learning methods, to visualize and reduce the dimensionality of the data. For the final classification of observations and the identification of datasets from gas sensor arrays analyzing air from buildings threatened by mold, as well as from other reference materials, supervised learning methods such as hierarchical cluster analysis, MLP neural network and the random forest method were used.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20501 - Materials engineering

Project

<a href="/en/project/GA22-00420S" target="_blank" >GA22-00420S: Functional characteristics and environmental impact of lime plasters with natural additives for historical building renovation</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Applied Sciences

ISSN

2076-3417

e-ISSN

2076-3417

Volume of the periodical

13

Issue of the periodical within the volume

July

Country of publishing house

CH - SWITZERLAND

Number of pages

19

Pages from-to

—

UT code for WoS article

001046173800001

EID of the result in the Scopus database

2-s2.0-85167901347