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@article{177356,
        author = {VANSH PRAVIN YELEKAR and Abhinav Shimpi and Vivek Dabhade and Ajinkya Shelke and Shalini Wankhade},
        title = {Gesture-Powered MathSolver: AIML-Driven Handwriting and Gesture Interface},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1807-1812},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177356},
        abstract = {The research introduces new methods for mathematical problem-solving and real-time interactive drawing to generate graphs. Our system leverages complex hand-tracking algorithms and gesture recognition technology with machine learning models to allow users a natural way of drawing mathematical equations along with shapes on digital systems. The system translates the user input in real-time to display immediate solutions or visual graph output. The new method connects traditional human input methods with contemporary computational systems by presenting users with a flexible user-friendly interface that works well for both educational and professional scenes. The paper demonstrates how the system developed through its creation process and its operational deployment and testing stage resulted in improved mathematical learning and problem-solving abilities.},
        keywords = {Machine learning, HTML, XGBoost, Selenium, Real time tracking, Fake profiles, Behavioral analysis, SHAP values, Fraud detection.},
        month = {May},
        }