Khan Muhammad

Papers

Total Citations

H-Index

About

Khan Muhammad is a researcher at the intersection of computer vision, deep learning, and intelligent robotic systems. His work focuses on developing autonomous solutions for real-world challenges, from sports analytics to safety and domestic robotics. His most cited paper, "Video-Based Table Tennis Tracking and Trajectory Prediction Using Convolutional Neural Networks" (2022, 25 citations), introduces fractal AI-driven methods for capturing and analyzing dynamic game events, pushing the boundaries of computer-aided sports analysis. Muhammad also addresses critical safety needs with "Automated Fire Extinguishing System Using a Deep Learning Based Framework" (2023, 14 citations), proposing a robotic system capable of detecting and extinguishing fires autonomously, reducing human risk. His more recent work, "MoMo: Mouse-Based Motion Planning for Optimized Grasping to Declutter Objects Using a Mobile Robotic Manipulator" (2023), demonstrates a cost-effective, deep learning-powered robot for decluttering in homes and industries. With a growing citation record, Muhammad’s contributions highlight a commitment to practical, AI-driven automation that enhances human capability and safety across diverse domains.