Machine Learning Techniques in Indoor Environmental Quality Assessment
This chapter provides a comprehensive exploration of the evolving role of machine learning in Indoor Environmental Quality (IEQ) assessment. As urban living spaces become increasingly enclosed, the importance of maintaining optimal IEQ for human health and well-being has surged. Traditional methods...
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| Autores principales: | , , , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
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IntechOpen
2023
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| Acceso en línea: | https://www.intechopen.com/chapters/88872 |
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| _version_ | 1879164823724359680 |
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| author | Gajendran, Mohan Kumar Kabir, Ijaz Fazil Syed Ahmed Vadivelu, Sudhakar Ng, Eddie Yin-Kwee Thota, Ravi Chandra |
| author_browse | Gajendran, Mohan Kumar Kabir, Ijaz Fazil Syed Ahmed Ng, Eddie Yin-Kwee Thota, Ravi Chandra Vadivelu, Sudhakar |
| author_facet | Gajendran, Mohan Kumar Kabir, Ijaz Fazil Syed Ahmed Vadivelu, Sudhakar Ng, Eddie Yin-Kwee Thota, Ravi Chandra |
| author_sort | Gajendran, Mohan Kumar |
| building | IntechOpen |
| collection | Acceso abierto |
| description | This chapter provides a comprehensive exploration of the evolving role of machine learning in Indoor Environmental Quality (IEQ) assessment. As urban living spaces become increasingly enclosed, the importance of maintaining optimal IEQ for human health and well-being has surged. Traditional methods for IEQ assessment, while effective, often fail to provide real-time monitoring and control. This gap is increasingly being addressed by the integration of machine learning techniques, allowing for enhanced predictive modeling, real-time optimization, and robust anomaly detection. The chapter delves into a comparative analysis of various machine learning techniques including supervised, unsupervised, and reinforcement learning, demonstrating their unique benefits in IEQ assessment. Practical implementations of these techniques in residential, commercial, and specialized environments are further illustrated through detailed case studies. The chapter also addresses the existing challenges in implementing machine learning for IEQ assessment and provides an outlook on future trends and potential research directions. The comprehensive review offered in this chapter encourages continued innovation and research in leveraging machine learning. for more efficient and effective IEQ assessment. |
| doi_str_mv | 10.5772/intechopen.114012 |
| first_indexed | 2026-10-15T21:40:28Z |
| format | Book Chapter |
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<dc:title>Machine Learning Techniques in Indoor Environmental Quality Assessment</dc:title>
<dc:creator>Mohan Kumar Gajendran</dc:creator>
<dc:creator>Ijaz Fazil Syed Ahmed Kabir</dc:creator>
<dc:creator>Sudhakar Vadivelu</dc:creator>
<dc:creator>Eddie Yin-Kwee Ng</dc:creator>
<dc:creator>Ravi Chandra Thota</dc:creator>
<dc:subject>Life Sciences</dc:subject>
<dc:description>This chapter provides a comprehensive exploration of the evolving role of machine learning in Indoor Environmental Quality (IEQ) assessment. As urban living spaces become increasingly enclosed, the importance of maintaining optimal IEQ for human health and well-being has surged. Traditional methods for IEQ assessment, while effective, often fail to provide real-time monitoring and control. This gap is increasingly being addressed by the integration of machine learning techniques, allowing for enhanced predictive modeling, real-time optimization, and robust anomaly detection. The chapter delves into a comparative analysis of various machine learning techniques including supervised, unsupervised, and reinforcement learning, demonstrating their unique benefits in IEQ assessment. Practical implementations of these techniques in residential, commercial, and specialized environments are further illustrated through detailed case studies. The chapter also addresses the existing challenges in implementing machine learning for IEQ assessment and provides an outlook on future trends and potential research directions. The comprehensive review offered in this chapter encourages continued innovation and research in leveraging machine learning. for more efficient and effective IEQ assessment.</dc:description>
<dc:publisher>IntechOpen</dc:publisher>
<dc:date>2023-12-26</dc:date>
<dc:type>Chapter, Part Of Book</dc:type>
<dc:identifier>https://www.intechopen.com/chapters/88872</dc:identifier>
<dc:identifier>doi:10.5772/intechopen.114012</dc:identifier>
<dc:language>en</dc:language>
<dc:relation>ISBN:978-0-85014-018-7</dc:relation>
<dc:rights>https://creativecommons.org/licenses/by/3.0/</dc:rights>
<dc:source>https://www.intechopen.com/books/13021 ; Advancements in Indoor Environmental Quality and Health</dc:source>
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| id | intech-88872 |
| institution | IntechOpen |
| isbn | 978-0-85014-018-7 |
| language | English |
| last_indexed | 2026-10-15T21:40:28Z |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | oai_dc |
| spelling | intech-888722023-12-26 Machine Learning Techniques in Indoor Environmental Quality Assessment Mohan Kumar Gajendran Ijaz Fazil Syed Ahmed Kabir Sudhakar Vadivelu Eddie Yin-Kwee Ng Ravi Chandra Thota Life Sciences This chapter provides a comprehensive exploration of the evolving role of machine learning in Indoor Environmental Quality (IEQ) assessment. As urban living spaces become increasingly enclosed, the importance of maintaining optimal IEQ for human health and well-being has surged. Traditional methods for IEQ assessment, while effective, often fail to provide real-time monitoring and control. This gap is increasingly being addressed by the integration of machine learning techniques, allowing for enhanced predictive modeling, real-time optimization, and robust anomaly detection. The chapter delves into a comparative analysis of various machine learning techniques including supervised, unsupervised, and reinforcement learning, demonstrating their unique benefits in IEQ assessment. Practical implementations of these techniques in residential, commercial, and specialized environments are further illustrated through detailed case studies. The chapter also addresses the existing challenges in implementing machine learning for IEQ assessment and provides an outlook on future trends and potential research directions. The comprehensive review offered in this chapter encourages continued innovation and research in leveraging machine learning. for more efficient and effective IEQ assessment. IntechOpen 2023-12-26 Chapter, Part Of Book https://www.intechopen.com/chapters/88872 doi:10.5772/intechopen.114012 en ISBN:978-0-85014-018-7 https://creativecommons.org/licenses/by/3.0/ https://www.intechopen.com/books/13021 ; Advancements in Indoor Environmental Quality and Health |
| spellingShingle | Life Sciences Gajendran, Mohan Kumar Kabir, Ijaz Fazil Syed Ahmed Vadivelu, Sudhakar Ng, Eddie Yin-Kwee Thota, Ravi Chandra Machine Learning Techniques in Indoor Environmental Quality Assessment |
| title | Machine Learning Techniques in Indoor Environmental Quality Assessment |
| title_full | Machine Learning Techniques in Indoor Environmental Quality Assessment |
| title_fullStr | Machine Learning Techniques in Indoor Environmental Quality Assessment |
| title_full_unstemmed | Machine Learning Techniques in Indoor Environmental Quality Assessment |
| title_short | Machine Learning Techniques in Indoor Environmental Quality Assessment |
| title_sort | machine learning techniques in indoor environmental quality assessment |
| topic | Life Sciences |
| url | https://www.intechopen.com/chapters/88872 |
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