T. Poongodi

About

T. Poongodi is a prominent researcher whose work sits at the intersection of artificial intelligence, computer vision, and next-generation digital systems. Their key research areas include deep learning for object detection, the application of AI in Industry 4.0, and the development of digital twin technologies for smart healthcare and autonomous systems. Poongodi’s most impactful contribution is the highly cited study on multi-object detection using the YOLO algorithm, which has garnered 31 citations for its advancement of real-time computer vision in robotics, surveillance, and autonomous vehicles. They have also made notable strides in exploring AI-driven digital twins for Industry 4.0, a work that has attracted 11 citations and underscores their role in bridging virtual and physical systems. Additionally, Poongodi has investigated IoT-based chatbots using NLP and SVM algorithms, contributing to AI-empowered healthcare applications. Their research on digital twin technologies for automated vehicles in smart healthcare systems further highlights their interdisciplinary approach. With a growing citation record and a focus on transformative technologies, Poongodi’s work continues to shape the future of intelligent, connected systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Deep Learning Approach for Multi-Object Detection Using Yolo Algorithm

   31 citations · 2023
2. 2
   Artificial intelligence–driven digital twins in Industry 4.0

   11 citations · 2023
3. 3
   Retracted: IoT based Chatbots using NLP and SVM Algorithms

   9 citations · 2022
4. 4
   Digital twin technologies for automated vehicles in smart healthcare systems

   2 citations · 2023
5. 5
   Historical Perspectives and Introduction to UAV Cellular Communications

   1 citations · 2022

Key Collaborators

Contact & Links