Megha Nain

About

Megha Nain is an Assistant Professor at Poornima University, Jaipur, and a researcher specializing in computer applications, artificial intelligence, and deep learning. With a strong academic foundation from Jamia Millia Islamia, New Delhi, and the University of Delhi, she has developed expertise in computer vision, AI-driven safety solutions, and machine learning applications in industrial environments.
Her research primarily focuses on real-time safety monitoring, particularly automated Personal Protective Equipment (PPE) detection using deep learning architectures like Detectron2, YOLO, and Mask R-CNN. She has published research in reputed journals and conferences, contributing significantly to the domain of workplace safety automation.
Alongside her academic role, Megha has hands-on experience in software development and AI model deployment, working on projects that integrate machine learning for industrial safety compliance and automation. She actively collaborates with researchers and industry professionals to bridge the gap between theoretical advancements and real-world implementations.
As an Assistant Professor, Megha is committed to mentorship and knowledge dissemination, guiding students in AI, deep learning, and computer vision projects. Her long-term goal is to enhance AI-powered compliance monitoring systems, ensuring that high-risk industries like construction and manufacturing benefit from intelligent automation and real-time safety enforcement.

Employment

• Poornima University Assistant Professor
  2021 - Present
• NIMS University Assistant Professor
  ? - 2019
• HCL Technologies Ltd Software tester
  ? - 2017

Education

• Jamia Millia Islamia MSc Tech Industrial Mathematics and Computer Applications
  ? - 2015
• University of Delhi BSc (Honours) Mathematics
  ? - 2011

Projects & Funding

Projects & funding information is unavailable.

Publications (8)

• A deep learning based dual-verification framework for ensuring construction workers safety
  Discover Applied Sciences 2026 DOI: 10.1007/s42452-026-09123-w
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• Optimized multi-modal deep learning for integrated safety compliance monitoring : PPE detection, face recognition, and trade classification in industrial environments
  Journal of Information & Optimization Sciences 2026 DOI: 10.47974/jios-2252
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• Computer vision based human action recognition of construction workers
  AIP Conference Proceedings 2025 DOI: 10.1063/5.0247833
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• Deep Learning Oriented Smart Constructional Material and Face Recognition System for the Site Safety
  2024 DOI: 10.1109/UPCON62832.2024.10983026
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• Deep Learning-Based Safety Assurance of Construction Workers: Real-Time Safety Kit Detection
  2023 DOI: 10.1007/978-981-99-2602-2_22
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• Pandemic Management Using Artificial Intelligence-Based Safety Measures
  Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease 2021 DOI: 10.4018/978-1-7998-7188-0.ch007
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• Safety and Compliance Management System Using Computer Vision and Deep Learning
  IOP Conference Series: Materials Science and Engineering 2021 DOI: 10.1088/1757-899x/1099/1/012013
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• Authentication control system for the efficient detection of hard-hats using deep learning algorithms
  Journal of Discrete Mathematical Sciences and Cryptography 2021 DOI: 10.1080/09720529.2021.2011109
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