Quantum-Confidence Gated IDS — Suricata + LightGBM + 4-qubit QSVM. Senior project, Lusail University 2026.
Hybrid machine learning models to predict adverse drug reactions (ADRs) using LightGBM, XGBoost, CatBoost, and a Random Forest meta-model. For research and educational purposes only
Hybrid quantum–classical pipeline benchmarking DNA sequence classifiers, contrasting tuned SVM baselines with quantum circuits in reproducible notebooks.
Hybrid SAP ticket routing: Rule Engine → TF-IDF → LLM fallback. Three-layer decision system with zero unnecessary API calls.
Hybrid Classical-Quantum ML system for BRCA1/BRCA2 variant classification using ClinVar data.
An AI-Powered Hybrid Intrusion Detection System (IDS) combining Supervised ML (XGBoost) for signature threats and Unsupervised Deep Learning (PyTorch Autoencoder) for zero-day network anomaly detection. Features a hardware-aware real-time Streamlit dashboard.
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