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Improved Ozone Level Detection through Feature Selection with Modified Whale Optimization Algorithm

This study presents a new approach for ozone level detection through feature selection by the modified Whale Optimization Algorithm (mWOA). This study aims to enhance the accuracy and efficiency of ozone level prediction models by selecting the most informative features from the dataset. As air qual...

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Main Authors: Li Yu Yab, Li Yu Yab, Wahid, Noorhaniza, A. Hamid, Rahayu
Format: Article
Language:English
Published: QAJ 2024
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/11099/1/J17608_ad214b23c8adfc74a66aed41866d6f7d.pdf
http://eprints.uthm.edu.my/11099/
https://doi.org/10.58429/qaj.v4n1a466
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spelling my.uthm.eprints.110992024-06-09T07:36:30Z http://eprints.uthm.edu.my/11099/ Improved Ozone Level Detection through Feature Selection with Modified Whale Optimization Algorithm Li Yu Yab, Li Yu Yab Wahid, Noorhaniza A. Hamid, Rahayu T Technology (General) This study presents a new approach for ozone level detection through feature selection by the modified Whale Optimization Algorithm (mWOA). This study aims to enhance the accuracy and efficiency of ozone level prediction models by selecting the most informative features from the dataset. As air quality deterioration poses significant risks to both human health and ecological equilibrium, pinpointing relevant features becomes essential for boosting prediction accuracy. The scope of the research includes comparing the performance of mWOA with the original WOA in two feature selection techniques: filter-based and wrapper-based. The experiments run proposed approaches on a multivariate time-series dataset with 20 repetitions. The evaluation criteria include processing time, number of features selected, and classification accuracy obtained by the kNN classifier. The statistical results demonstrate the effectiveness of the proposed mWOA approach, outperforming WOA due to the modified control parameter that enables a more precise exploration of the search area. The findings of this study reveal the improved performance of mWOA in selecting informative features, resulting in better prediction on average: 93.75% for filter-based and 94.49% for wrapperbased. In conclusion, the wrapper-based feature selection using the mWOA approach proves to be a valuable asset in enhancing the accuracy and efficiency of ozone level detection models. In the future, the proposed technique can be used for more applications in environmental science and engineering research. QAJ 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/11099/1/J17608_ad214b23c8adfc74a66aed41866d6f7d.pdf Li Yu Yab, Li Yu Yab and Wahid, Noorhaniza and A. Hamid, Rahayu (2024) Improved Ozone Level Detection through Feature Selection with Modified Whale Optimization Algorithm. QUBAHAN ACADEMIC JOURNAL, 4 (1). pp. 265-276. https://doi.org/10.58429/qaj.v4n1a466
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Li Yu Yab, Li Yu Yab
Wahid, Noorhaniza
A. Hamid, Rahayu
Improved Ozone Level Detection through Feature Selection with Modified Whale Optimization Algorithm
description This study presents a new approach for ozone level detection through feature selection by the modified Whale Optimization Algorithm (mWOA). This study aims to enhance the accuracy and efficiency of ozone level prediction models by selecting the most informative features from the dataset. As air quality deterioration poses significant risks to both human health and ecological equilibrium, pinpointing relevant features becomes essential for boosting prediction accuracy. The scope of the research includes comparing the performance of mWOA with the original WOA in two feature selection techniques: filter-based and wrapper-based. The experiments run proposed approaches on a multivariate time-series dataset with 20 repetitions. The evaluation criteria include processing time, number of features selected, and classification accuracy obtained by the kNN classifier. The statistical results demonstrate the effectiveness of the proposed mWOA approach, outperforming WOA due to the modified control parameter that enables a more precise exploration of the search area. The findings of this study reveal the improved performance of mWOA in selecting informative features, resulting in better prediction on average: 93.75% for filter-based and 94.49% for wrapperbased. In conclusion, the wrapper-based feature selection using the mWOA approach proves to be a valuable asset in enhancing the accuracy and efficiency of ozone level detection models. In the future, the proposed technique can be used for more applications in environmental science and engineering research.
format Article
author Li Yu Yab, Li Yu Yab
Wahid, Noorhaniza
A. Hamid, Rahayu
author_facet Li Yu Yab, Li Yu Yab
Wahid, Noorhaniza
A. Hamid, Rahayu
author_sort Li Yu Yab, Li Yu Yab
title Improved Ozone Level Detection through Feature Selection with Modified Whale Optimization Algorithm
title_short Improved Ozone Level Detection through Feature Selection with Modified Whale Optimization Algorithm
title_full Improved Ozone Level Detection through Feature Selection with Modified Whale Optimization Algorithm
title_fullStr Improved Ozone Level Detection through Feature Selection with Modified Whale Optimization Algorithm
title_full_unstemmed Improved Ozone Level Detection through Feature Selection with Modified Whale Optimization Algorithm
title_sort improved ozone level detection through feature selection with modified whale optimization algorithm
publisher QAJ
publishDate 2024
url http://eprints.uthm.edu.my/11099/1/J17608_ad214b23c8adfc74a66aed41866d6f7d.pdf
http://eprints.uthm.edu.my/11099/
https://doi.org/10.58429/qaj.v4n1a466
_version_ 1803337338289389568
score 13.211869

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