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@article{165076,
        author = {Aasavari Harshal Madiwale and Dr. Nandkumar Kulkarni},
        title = {Revolutionizing Air Quality Prediction with Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {11},
        number = {1},
        pages = {25-28},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165076},
        abstract = {This paper looks at how we can predict air quality better using fancy computer methods called machine learning. Predicting air quality is really important for keeping our environment clean and making sure people stay healthy. Machine learning can help us make these predictions more accurate and faster.First, the paper talks about why it's important to predict air quality well and why the old ways aren't always good enough. Then, it explains some different computer methods we can use, like artificial neural networks and random forests, to make better predictions .It also looks at recent studies that have used these computer methods to predict air quality. These studies found some important things and used different types of data, like weather info and pictures from satellites, to make their predictions better.
The paper also talks about some problems we still need to solve when using these computer methods, like getting the data ready and making sure the computer programs are easy to understand. It also suggests some cool new ways we can make air quality predictions even better in the future. Overall, this paper helps us understand how computers can help predict air quality, which can help scientists, people who work with the environment, and government officials make better decisions about keeping our air clean.
},
        keywords = {air quality monitoring; machine learning; air quality index, Linear Regression, Support Vector Machine},
        month = {},
        }