Scholar
Vasileios Argyriou
Google Scholar ID: GkQOPTUAAAAJ
Kingston University
Machine learning
Scene understanding
Digital Twins
XR
IoT
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Citations & Impact
All-time
Citations
3,556
H-index
29
i10-index
66
Publications
20
Co-authors
0
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Publications
18 items
Open-Vocabulary vs Supervised Learning Methods for Post-Disaster Visual Scene Understanding
2026
Cited
0
Interactive Augmented Reality-enabled Outdoor Scene Visualization For Enhanced Real-time Disaster Response
2026
Cited
0
FR-GESTURE: An RGBD Dataset For Gesture-based Human-Robot Interaction In First Responder Operations
2026
Cited
0
Illicit object detection in X-ray imaging using deep learning techniques: A comparative evaluation
2025
Cited
0
Visual Hand Gesture Recognition with Deep Learning: A Comprehensive Review of Methods, Datasets, Challenges and Future Research Directions
2025
Cited
0
Textile Analysis for Recycling Automation using Transfer Learning and Zero-Shot Foundation Models
2025
Cited
0
A Deep Learning Approach for Facial Attribute Manipulation and Reconstruction in Surveillance and Reconnaissance
2025
Cited
0
X-ray illicit object detection using hybrid CNN-transformer neural network architectures
2025
Cited
0
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Resume (English only)
Background
Focuses on interdisciplinary research in AI, robotics, and immersive technologies (e.g., Extended Reality/XR)
Dedicated to developing impactful, ethical, and sustainable intelligent systems for industry, healthcare, and society
Research interests include Green AI, synthetic data generation, model distillation, and energy-efficient optimization of large AI models
Develops AI and computer vision-powered XR platforms for training and remote maintenance in Industry 5.0
Explores humanoid robots and agentic AI for textile sorting and recycling
Works on drone-based search-and-rescue technologies and real-time XR command centers for disaster response
Creates AI-enhanced dermatological tools for skincare and melanoma detection, integrated into XR environments
Investigates XR-based cognitive and physiotherapy interventions for conditions like Parkinson’s disease
Co-authors
0 total
Co-authors: 0 (list not available)
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