Philip

Philip

🧠 Check out CNN architectures!

🧠 CNN architectures like ResNet, VGG19, and DenseNet power our AI-driven monitoring of deep-sea ecosystems. These tools help us protect oceans through automated, precise debris detection.  

‼️ Photonic AI for Real-time Threat Mitigation

The future of 6G networks demands intelligent and sustainable security solutions. Our latest research, “Photonic-accelerated AI for cybersecurity in sustainable 6G networks,” introduces a novel approach using photonic-based Convolutional Neural Networks (CNNs) to detect Denial of Service (DoS) Hulk attacks…

🌊 Tackling marine debris with AI!

🌊 Tackling marine debris with AI! Our research compares machine learning models (NN, SVM, XGB, LR, RF) for underwater debris classification. Neural Networks and SVM lead with up to 84% accuracy.  

🌐 Future networks with CLEVER!

🌐 Future networks demand security and efficiency. Our work on Photonic-accelerated AI shows how next-gen photonic hardware can detect cyberattacks in real-time, with 99.7% accuracy.  

💡 Prototype in action!

💡 Prototype in action: CATER seamlessly integrates with Apache Ozone, using modular APIs for intelligent node selection. This ensures flexibility across diverse edge environments like smart factories and hospitals. 

🔍 Optimizing edge storage!

🔍 Optimizing edge storage isn’t just about speed — it’s about sustainability. Our CATER framework strikes the balance between optimal performance and real-world deployment, reducing costly data movement while improving efficiency.