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@article{206803,
        author = {Maheshwara K. S. and Dhanush D. and K. Mokshith and Akshith D. K. and Dhanush and Likesh K. and Kiran P. Acharya},
        title = {AI-Powered Rural Student Dropout Early Warning System with Explainable Predictions, SMS Alerts, and Government Scheme Recommendations},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {509-513},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206803},
        abstract = {Student dropout is a major concern in rural education systems. This process is controlled by a variety of known and unknown variables. Financial difficulties, poor academic performance, a lack of family support, health issues, migration, and other unspecified situations are the primary causes for many pupils dropping out of school. The driven method follows some machine learning algorithms to examine a range of data, including attendance trends, academic achievement, demographic information, and financial circumstances. Using the analysis, the algorithm determines which kids are most likely to drop out. It also contains an alarm system to assist and inform the case in order to place it under parental supervision, as well as a government program recommendation system. The model contains explainable AI (XAI) techniques, offering lucid and interpretable reasons behind each prediction.},
        keywords = {AI (XAI) methods, alert system, Demographic details, government scheme recommendations, machine learning algorithms.},
        month = {July},
        }