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@article{178220,
        author = {Saravan A and Muneeswaran A and C. Pravinkumar and Dr.J.Hemalatha and P.Murugan},
        title = {Ai Based Petition Monitoring System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4041-4045},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178220},
        abstract = {Efficiently managing citizen petitions is essential for promoting transparency and accountability in public administration. Yet, traditional petition processing methods are often hampered by delays, manual workload, and subjective decision-making. To address these limitations, this paper presents a smart Petition Monitoring System powered by artificial intelligence. Leveraging machine learning algorithms such as Random Forest and XGBoost, the system automates the classification and prioritization of petitions. Built as a full-stack web application, it uses FastAPI for backend services, React for the user interface, and MongoDB for storing data. Additional features like sentiment analysis and optical character recognition (OCR) further enhance the system’s capability to understand and sort large volumes of incoming petitions. Evaluation on a dataset of 10,000 petitions showed a high classification accuracy of 95% and a 60% reduction in manual processing efforts. These outcomes demonstrate the system's promise in making grievance redressal more efficient, impartial, and transparent.},
        keywords = {Petition Monitoring, Machine Learning, Random Forest, XGBoost, FastAPI, Sentiment Analysis, OCR, E-Governance, Grievance Redressal},
        month = {May},
        }