Md Amiruzzaman

Vaughn College of Aeronautics and Technology, West Chester University

Papers

Total Citations

H-Index

About

Md. Amiruzzaman is a researcher whose work lies at the intersection of human activity recognition and applied deep learning for real-world sensing. His most cited paper, "A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data" (2020, 48 citations), addresses a critical challenge in health monitoring and security: accurately characterizing human behavior from smartphone and smartwatch sensors. By developing a robust feature extraction model, he has contributed to making activity recognition more reliable for applications in elderly care, robotics, and industrial monitoring. More recently, Amiruzzaman has ventured into agricultural technology with "Revolutionizing Rose Grading: Real-Time Detection and Accurate Assessment with YOLOv8 and Deep Learning Models" (2024, 6 citations), demonstrating his versatility by applying state-of-the-art object detection to automate quality assessment in floriculture. This work highlights his ability to translate complex deep learning architectures into practical, deployable solutions. With a growing citation record and a focus on sensor-based and vision-based AI, Amiruzzaman’s research continues to bridge the gap between algorithmic innovation and tangible impact in healthcare, security, and agriculture.