As part of the College of Health and Medical Technologies' efforts to support scientific research and promote rigorous academic publishing, Assistant Lecturer Mohammed Ghazi Khassaf, a faculty member in the Department of Anesthesia, has published a research paper titled:

"Scalable Plug-in Electric Vehicle Smart Charging through Tariff-Aware Classification with RUSBoost Ensemble"

The study was published in IOP Science, a journal indexed within the Scopus database. The research focuses on developing a scalable and efficient smart charging management system for plug-in electric vehicles (PEVs). By analyzing electricity tariffs and classifying optimal charging times, the proposed system contributes to reducing energy consumption and lowering operational costs.

The study utilized the RUSBoost algorithm—an ensemble machine learning technique—to improve classification accuracy and support decision-making. This approach aims to achieve a balance between charging efficiency and power grid stability, particularly in light of the continuous increase in electric vehicle adoption.

This scientific achievement contributes to the advancement of smart energy infrastructure and enhances the sustainability of electrical load management, aligning with modern trends toward intelligent and sustainable solutions in the energy sector.