Application of Dimensionality Reduction and Machine Learning Methods for the Interpretation of Gas Sensor Array Readouts from Mold-Threatened Buildings
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 multidimens...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/15/8588 |
| _version_ | 1797587125422522368 |
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| author | Grzegorz Łagód Magdalena Piłat-Rożek Dariusz Majerek Ewa Łazuka Zbigniew Suchorab Łukasz Guz Václav Kočí Robert Černý |
| author_facet | Grzegorz Łagód Magdalena Piłat-Rożek Dariusz Majerek Ewa Łazuka Zbigniew Suchorab Łukasz Guz Václav Kočí Robert Černý |
| author_sort | Grzegorz Łagód |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T00:32:48Z |
| format | Article |
| id | doaj.art-f17372e89a5549ee9c3a4eafdaf5349b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T00:32:48Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f17372e89a5549ee9c3a4eafdaf5349b2023-11-18T22:34:54ZengMDPI AGApplied Sciences2076-34172023-07-011315858810.3390/app13158588Application of Dimensionality Reduction and Machine Learning Methods for the Interpretation of Gas Sensor Array Readouts from Mold-Threatened BuildingsGrzegorz Łagód0Magdalena Piłat-Rożek1Dariusz Majerek2Ewa Łazuka3Zbigniew Suchorab4Łukasz Guz5Václav Kočí6Robert Černý7Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, PolandFaculty of Technology Fundamentals, Lublin University of Technology, 20-618 Lublin, PolandFaculty of Technology Fundamentals, Lublin University of Technology, 20-618 Lublin, PolandFaculty of Technology Fundamentals, Lublin University of Technology, 20-618 Lublin, PolandFaculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, PolandFaculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, PolandFaculty of Civil Engineering, Czech Technical University in Prague, 166 29 Prague, Czech RepublicFaculty of Civil Engineering, Czech Technical University in Prague, 166 29 Prague, Czech RepublicPaper 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.https://www.mdpi.com/2076-3417/13/15/8588multidimensional signals analysisdimensionality reductionmachine learning methodsgas sensors arrayelectronic nosemold-threatened buildings |
| spellingShingle | Grzegorz Łagód Magdalena Piłat-Rożek Dariusz Majerek Ewa Łazuka Zbigniew Suchorab Łukasz Guz Václav Kočí Robert Černý Application of Dimensionality Reduction and Machine Learning Methods for the Interpretation of Gas Sensor Array Readouts from Mold-Threatened Buildings Applied Sciences multidimensional signals analysis dimensionality reduction machine learning methods gas sensors array electronic nose mold-threatened buildings |
| title | Application of Dimensionality Reduction and Machine Learning Methods for the Interpretation of Gas Sensor Array Readouts from Mold-Threatened Buildings |
| title_full | Application of Dimensionality Reduction and Machine Learning Methods for the Interpretation of Gas Sensor Array Readouts from Mold-Threatened Buildings |
| title_fullStr | Application of Dimensionality Reduction and Machine Learning Methods for the Interpretation of Gas Sensor Array Readouts from Mold-Threatened Buildings |
| title_full_unstemmed | Application of Dimensionality Reduction and Machine Learning Methods for the Interpretation of Gas Sensor Array Readouts from Mold-Threatened Buildings |
| title_short | Application of Dimensionality Reduction and Machine Learning Methods for the Interpretation of Gas Sensor Array Readouts from Mold-Threatened Buildings |
| title_sort | application of dimensionality reduction and machine learning methods for the interpretation of gas sensor array readouts from mold threatened buildings |
| topic | multidimensional signals analysis dimensionality reduction machine learning methods gas sensors array electronic nose mold-threatened buildings |
| url | https://www.mdpi.com/2076-3417/13/15/8588 |
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