The research team in the Department of Intelligent Medical Systems has published a scientific research paper titled "Processing Biomedical Signals by Neural Networks Using Hardware-Constrained System" in the Scopus-indexed journals.

The team comprises Dr. Maytham Nabeel Meqdad, Asst. Lect. Mustafa Mohammed Youssef, and Asst. Lect. Reyam Thair Ahmed.

The research paper has been published in the journal "Revue d'Intelligence Artificielle" by IIETA publishing house.

This research aims to detect online heart rate irregularities through machine learning to provide effective clinical care. It demonstrates that using deep learning models can be challenging due to data volume and hardware requirements. The paper introduces a new technique for classifying these irregularities using a lightweight learning approach that can be run on limited-capacity devices. The results show that this model achieves competitive accuracy of up to 83.45%, making it a promising option compared to alternative models used.

Research Link: https://www.iieta.org/journals/ria/paper/10.18280/ria.380238