Integrating Deep Learning Features Extraction and Image Processing for Visual Pattern Analysis in Air Quality Index (AQI) Classification
This paper presents a hybrid approach that combines deep learning with traditional image processing to identify and classify visual patterns related to air quality index (AQI) levels in digital images. This method integrates the VGG16 model, famous for deep visual feature extraction, with classical...
| 出版年: | 2024 IEEE 22nd Student Conference on Research and Development, SCOReD 2024 |
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| 第一著者: | |
| フォーマット: | Conference paper |
| 言語: | English |
| 出版事項: |
Institute of Electrical and Electronics Engineers Inc.
2024
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| オンライン･アクセス: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219525159&doi=10.1109%2fSCOReD64708.2024.10872648&partnerID=40&md5=2f9c260df2aaba7ccd7d93e353f7cde5 |
| id |
Razimi U.N.A.; Ali A.M.; Osman R. |
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| spelling |
Razimi U.N.A.; Ali A.M.; Osman R. 2-s2.0-85219525159 Integrating Deep Learning Features Extraction and Image Processing for Visual Pattern Analysis in Air Quality Index (AQI) Classification 2024 2024 IEEE 22nd Student Conference on Research and Development, SCOReD 2024 10.1109/SCOReD64708.2024.10872648 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219525159&doi=10.1109%2fSCOReD64708.2024.10872648&partnerID=40&md5=2f9c260df2aaba7ccd7d93e353f7cde5 This paper presents a hybrid approach that combines deep learning with traditional image processing to identify and classify visual patterns related to air quality index (AQI) levels in digital images. This method integrates the VGG16 model, famous for deep visual feature extraction, with classical image processing techniques such as color and texture analysis. In this study, a set of outdoor image data representing six different AQI categories ranging from 'Good' to 'Severe' are utilized. The results of the combination of VGG16 features and traditional image processing techniques show an improvement in accuracy and pattern detection ability compared to using only a single method. This finding provides new insights into image-based air quality monitoring. The results of this discovery should encourage a more effective environmental monitoring system as well as facilitate the detection of extreme or abnormal atmospheric conditions © 2024 IEEE. Institute of Electrical and Electronics Engineers Inc. English Conference paper |
| author |
2-s2.0-85219525159 |
| spellingShingle |
2-s2.0-85219525159 Integrating Deep Learning Features Extraction and Image Processing for Visual Pattern Analysis in Air Quality Index (AQI) Classification |
| author_facet |
2-s2.0-85219525159 |
| author_sort |
2-s2.0-85219525159 |
| title |
Integrating Deep Learning Features Extraction and Image Processing for Visual Pattern Analysis in Air Quality Index (AQI) Classification |
| title_short |
Integrating Deep Learning Features Extraction and Image Processing for Visual Pattern Analysis in Air Quality Index (AQI) Classification |
| title_full |
Integrating Deep Learning Features Extraction and Image Processing for Visual Pattern Analysis in Air Quality Index (AQI) Classification |
| title_fullStr |
Integrating Deep Learning Features Extraction and Image Processing for Visual Pattern Analysis in Air Quality Index (AQI) Classification |
| title_full_unstemmed |
Integrating Deep Learning Features Extraction and Image Processing for Visual Pattern Analysis in Air Quality Index (AQI) Classification |
| title_sort |
Integrating Deep Learning Features Extraction and Image Processing for Visual Pattern Analysis in Air Quality Index (AQI) Classification |
| publishDate |
2024 |
| container_title |
2024 IEEE 22nd Student Conference on Research and Development, SCOReD 2024 |
| container_volume |
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| container_issue |
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| doi_str_mv |
10.1109/SCOReD64708.2024.10872648 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219525159&doi=10.1109%2fSCOReD64708.2024.10872648&partnerID=40&md5=2f9c260df2aaba7ccd7d93e353f7cde5 |
| description |
This paper presents a hybrid approach that combines deep learning with traditional image processing to identify and classify visual patterns related to air quality index (AQI) levels in digital images. This method integrates the VGG16 model, famous for deep visual feature extraction, with classical image processing techniques such as color and texture analysis. In this study, a set of outdoor image data representing six different AQI categories ranging from 'Good' to 'Severe' are utilized. The results of the combination of VGG16 features and traditional image processing techniques show an improvement in accuracy and pattern detection ability compared to using only a single method. This finding provides new insights into image-based air quality monitoring. The results of this discovery should encourage a more effective environmental monitoring system as well as facilitate the detection of extreme or abnormal atmospheric conditions © 2024 IEEE. |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| issn |
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| language |
English |
| format |
Conference paper |
| accesstype |
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| record_format |
scopus |
| collection |
Scopus |
| _version_ |
1828987861102231552 |