Air-Quality Index Prediction Using Auto Ml Library, TPOT

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@InProceedings{Saxena:2023:ICCCNT,

• author = "Riya Saxena and Komal Jindal and Nidhi Malik and Anshul Bhatia",
• booktitle = "2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)",
• title = "Air-Quality Index Prediction Using Auto Ml Library, {TPOT}",
• year = "2023",
• abstract = "Air pollution nowadays is one of the most important causes of severe diseases. Early analysis of air pollution helps us control pollution levels and keep human health at utmost importance. To solve this problem, a real-time model is required to be constructed that can help us solve this air pollution problem. We're studying how we can use a variety of machine learning models for forecasting air quality index 'AQI' in this paper. The data collected by the air quality sensor are taken and averaged to obtain an AQI. The gathered metrics are analysed using automated library TPOT.",
• keywords = "genetic algorithms, genetic programming, Analytical models, Atmospheric modelling, Computational modelling, Predictive models, Air pollution, Data models, Libraries, Air quality prediction, XG Boost, Random Forest, Auto ML",
• DOI = "doi:10.1109/ICCCNT56998.2023.10307166",
• ISSN = "2473-7674",
• month = jul,
• notes = "Also known as \cite{10307166}",

}

Genetic Programming entries for Riya Saxena Komal Jindal Nidhi Malik Anshul Bhatia

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