This study proposes a CNN-based machine learning framework for early detection of dysgraphia in children aged 8–15, using handwriting data collected via a WACOM tablet. By integrating static and dynamic handwriting features with data augmentation, the model achieved 89% classification accuracy, demonstrating its potential for robust dysgraphia diagnosis.
A console-based Trade Market Simulator featuring a CSV reader for market data, an order book for managing trades, a wallet to track balances and transactions, and an interactive command interface for user interaction.
We have balanced a two-wheeled robot using Arduino and MATLAB-based control design
We designed and built a sumo robot to compete in a university robotics competition, integrating advanced sensors and mechanical systems.
