Publications
Academic publications and research work.
Predicting Emergency Department Patient Arrivals at Hospitals Using Machine Learning Techniques
A. M. Alenezi, M. Sameh, M. Aljohani, H. S. Alharbi
Accurately forecasting emergency department (ED) patient arrivals is critical for hospital resource planning and reducing patient wait times. This study applies and compares multiple machine learning techniques to predict daily and hourly ED arrival volumes, enabling proactive staffing and capacity management.
Efficiently Adapting SAM 2 for Automated Schematic Capture from Hand-Drawn Circuit Diagrams
M. Sameh, A. BenAbdennour, J. K. Ali
This paper proposes an efficient adaptation of Segment Anything Model 2 (SAM 2) for the task of automated schematic capture from hand-drawn electrical circuit diagrams. By fine-tuning SAM 2's video segmentation capabilities on circuit imagery, the system accurately segments and identifies components, enabling downstream netlist extraction and circuit simulation.
HSI Control Suite: An Integrated GUI for Operating and Acquiring Data from DIY Push-Broom Hyperspectral Imaging Systems
M. Sameh, A. Albeladi, A. Fawzy
This paper presents HSI Control Suite, an open-source Python GUI application (PyQt6) for end-to-end operation of DIY push-broom hyperspectral imaging (HSI) systems. The suite integrates camera control, stepper motor synchronization, data acquisition, spectral calibration, and hyperspectral cube assembly into a single accessible interface, lowering the barrier to entry for researchers building custom HSI hardware.
Real-Time Human Fall Detection from Video Using YOLOv11 with Pose Estimation: A Paradigm Shift Towards Efficient Transformer-Based Architectures
A. BenAbdennour, M. Sameh, B. A. Khawaja, A. K. Vallappil, A. M. Alenezi, Q. H. Abbasi, S. Qazi
This paper presents a real-time fall detection system leveraging YOLOv11 with integrated pose estimation. By adopting an efficient transformer-based architecture, the system achieves high detection accuracy and low latency suitable for deployment in clinical and home monitoring environments, representing a significant advance over prior CNN-based approaches.