Papers
2
Total Citations
30
H-Index
2
About
Sanjay Saxena is a researcher at the forefront of applied deep learning and intelligent IoT systems, whose work bridges foundational computer vision techniques with life-saving robotics. His most influential contribution, the 2019 study "Validation of Random Dataset Using an Efficient CNN Model Trained on MNIST Handwritten Dataset" (28 citations), established a rigorous framework for validating deep learning architectures on benchmark datasets, providing a critical methodology for researchers in image processing, security, and medical imaging. This work underscores his expertise in ensuring model reliability—a cornerstone for real-world deployment. More recently, Saxena has advanced into emergency robotics with his 2025 project, "AEGIS FLARE: IoT-Enabled Robotic Firefighter for Advanced Fire Detection and Suppression." This autonomous system, integrating OpenCV for visual recognition with IoT connectivity, represents a significant leap in rapid hazard response, designed to protect human life and property by detecting and suppressing fires without human intervention. By combining robust CNN validation with practical, deployable robotics, Saxena’s research demonstrates a clear trajectory from foundational AI reliability to tangible, high-impact engineering solutions that address critical societal needs.
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