Oct 5, 2026

🎉 Research Publication News | Our Manuscript Has Been Published!

📄 Title: A Hybrid Framework for Malware Detection Leveraging Abstract Syntax Trees, L-Moments, and Multi-Dimensional Graphs with Quantum-Inspired Neural Networks 🔬 Research Focus: Cybersecurity • Malware Detection • Graph Learning • Quantum-Inspired AI • Self-Supervised Learning • Machine Learning The study presents a hybrid framework that combines Abstract Syntax Trees (ASTs), L-moments, multi-dimensional graph analysis, Quantum-Inspired Neural Networks (QINNs), Self-Supervised Learning, Meta-Learning, and Reinforcement Learning to improve malware detection and classification. The proposed approach achieved 98.62% accuracy, 96.14% precision, 95.28% recall, and 95.71% F1-score in the reported experiments. The work also explores robustness against adversarial attacks and evasion techniques, aiming toward more adaptive and scalable cybersecurity solutions. We are grateful to all co-authors, collaborators, and institutions whose contributions made this work possible.

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Sep 29, 2026

Accepted! [AI, MDPI]: FedQDFU: Non-IID Federated Quantum Learning for Diabetic Foot Ulcer Classification from Deep Image Features

The first publication on quantum machine learning has been accepted in a peer-reviewed Q1-ranked journal in AI-MDPI. The project was led by one of the doctoral students in our lab. The main outcome of the research is to create a quantum-enhanced secure diagnosis system for diabetic foot ulcer diagnosis.

Sep 28, 2026

Neuronomy Lab Launches New Website

We are pleased to announce the launch of the new Neuronomy Lab website. The platform highlights our research, people, publications, projects, courses, lab activities, and latest updates. It will serve as a central hub for students, researchers, collaborators, and visitors to explore our work in artificial intelligence, machine learning, cybersecurity, autonomous systems, federated and edge intelligence, healthcare AI, and intelligent networked systems.

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