Abstract
Air pollution demonstrates the appearance of toxins into the air which is blocking human prosperity and the earth. It will portray as potentially the riskiest threats that humanity anytime faced. It makes hurt animals, harvests to thwart these issues in transportation territories need to expect air quality from pollutions utilizing AI systems and IoT. Along these lines, air quality evaluation and assumption has become a huge target for human health factors and also affect internal organs related to respiratory. The accuracy of Air Pollution prediction has been involved with the machine learning techniques and the best accuracy model is identified. The air quality prediction dataset is used for identifying the meteorology air pollution data while the predicted model is involved the decision tree computation for predicting the toxin contents in the region, the Air quality indicator is used to assess the pollution level and monitoring the air quality. The performance analysis shows that the decision tree technique has produced the better results in the performance metrics of Accuracy, precision, recall, and F1-score with the minimized error values while the comparative evaluation of Attribute-enabled classification has identified the best technique for predicting the air quality.
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12 February 2024
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s41324-024-00575-z
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Subbulakshmi, P., Vimal, S., Robinson, Y.H. et al. RETRACTED ARTICLE: Comparative Evaluation of Attribute-Enabled Supervised Classification in Predicting the Air Quality. Spat. Inf. Res. 31, 399–407 (2023). https://doi.org/10.1007/s41324-023-00507-3
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DOI: https://doi.org/10.1007/s41324-023-00507-3