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@article{191041,
        author = {I. Arun and A. Anandhi},
        title = {AURORA AIR IQ : AN INTELLIGENT MACHINE LEARNING FRAMEWORK FOR REAL-TIME AIR QUALITY INDEX PREDICTION AND ANALYSIS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {5702-5705},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191041},
        abstract = {Air pollution has become a critical global concern due to its severe impact on human health, environmental stability, and urban living conditions. The increasing need for reliable forecasting systems has encouraged the adoption of data-driven approaches to assess and predict fluctuating pollution levels. This paper presents AuroraAir IQ, an intelligent and deployable machine learning system designed to generate accurate Air Quality Index predictions using optimized models such as Linear Regression, Random Forest, and XGBoost.
The system integrates automated preprocessing, secure user authentication, real-time prediction, pollutant visualization, and SQL-based data management to ensure high performance and smooth deployment. Experimental studies demonstrate that machine learning–based forecasting significantly improves accuracy compared to traditional statistical methods, offering valuable insights for public health protection and decision-making [1][2].},
        keywords = {Air Pollution, Machine Learning, AQI Prediction, Random Forest, XGBoost, Linear Regression, Flask Web Framework, PostgreSQL, Data Visualization, Environmental Analytics.},
        month = {January},
        }