Meer Shadman Saeed

American International University-Bangladesh

Papers

Total Citations

H-Index

About

Meer Shadman Saeed is an emerging researcher in autonomous systems and applied machine learning, with a focus on developing practical, safety-critical technologies. His work bridges robotics and deep learning to address real-world hazards, from industrial gas leaks to public health threats. In his highly cited 2019 paper, "Design and Implementation of a Dual Mode Autonomous Gas Leakage Detecting Robot" (8 citations), Saeed engineered a robotic platform capable of autonomously detecting flammable gas leaks in industrial settings, tunnels, and pipelines—offering a proactive solution to prevent catastrophic explosions. More recently, his 2021 study, "Detection of Mosquito Larvae Using Convolutional Neural Network" (5 citations), introduces a novel, AI-driven approach to vector control. Instead of targeting adult mosquitoes, Saeed’s work uses convolutional neural networks to identify mosquito larvae in breeding sites, enabling early intervention to curb the spread of deadly diseases like dengue and malaria. Though early in his career, Saeed’s contributions demonstrate a clear commitment to deploying intelligent systems for environmental monitoring and public safety, with his work laying the groundwork for scalable, autonomous solutions to pressing global challenges.