Halil İbrahim Sarı

Classification of Synthetic Biosensor Data with Quantum Simulations Using Feature Optimization and Explainable Machine Learning Supported by RFECV

Background/Aim: Accurate detection of trace amounts of heavy metal toxins in environmental samples requires sensitive and reliable diagnostic systems. This study presents a machine learning framework to classify synthetic biosensor signals—generated via quantum simulation—representing five toxin classes: Control, Lead,...