Masoud Sistaninezhad | Human–Computer Interaction | Research Excellence Award 

Iran University of Science and Technology (IUST) | Iran

Masoud Sistaninezhad is a researcher from Iran whose work focuses on the application of machine learning and deep learning techniques in biomedical signal processing, medical image analysis, and intelligent healthcare systems. His research portfolio demonstrates a strong emphasis on using data-driven approaches to improve health monitoring, diagnosis, and safety-critical applications. Among his recent journal contributions is a 2025 article in Computers that presents a comprehensive comparative analysis of machine learning and ensemble techniques for classifying drowsiness and alertness states using EEG signals, aiming to enhance road safety through reliable fatigue detection systems. His conference work at the 10th International Conference on Artificial Intelligence and Robotics (QICAR 2024) explores optimized machine learning models for recognizing heart disease severity using spectrophotometric analysis, highlighting his interest in clinical decision-support systems. In addition, his 2024 book chapter published by Springer investigates morning anxiety detection using smartphone-based photoplethysmography signals, reflecting his engagement with wearable and mobile health technologies. Sistaninezhad has also contributed to neuroengineering research through a journal article in the Journal of Information Systems and Telecommunication, where he evaluates Xception networks combined with short-time Fourier transform spectrograms for motor imagery classification, relevant to brain–computer interface applications. Earlier, his 2023 review article in Computational and Mathematical Methods in Medicine provides a structured overview of deep learning methods for medical image analysis, summarizing advances, challenges, and future research directions in the field. Collectively, his publications illustrate an interdisciplinary research trajectory that integrates artificial intelligence, signal processing, and healthcare, with practical implications for medical diagnosis, mental health assessment, human–machine interaction, and safety enhancement.

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