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- W4313444630 abstract "Dealing with air contamination presents a significant environmental hazard in the urban environment. Constant monitoring of contamination data empowers local authorities to analyse the current traffic circumstance of the city and settle on choices likewise. Arrangement of the Internet of Things-based sensors has extensively changed the elements of foreseeing air quality. Existing exploration has utilized distinctive artificial intelligence learning tools for contamination prediction; however, near examination of these procedures is needed to have a superior comprehension of their handling time for numerous datasets. This paper tends to the test of foreseeing the air quality index (AQI), with the aim to predict the contamination on different studies, utilizing two machine learning algorithms: neural networks and support vector machines. The air contamination datasets downloaded from the Central Pollution Control Board (CPCB). The proposed machine learning (ML) model is used to predict the Delhi Air Quality Index (AQI) data and compare it with the actual and predicted data." @default.
- W4313444630 created "2023-01-06" @default.
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- W4313444630 date "2023-01-01" @default.
- W4313444630 modified "2023-09-25" @default.
- W4313444630 title "Air Contamination Prediction and Comparison Using Machine Learning Algorithms" @default.
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- W4313444630 doi "https://doi.org/10.1007/978-981-19-2358-6_60" @default.
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