Osamah Ibrahim Khalaf

About

Osamah Ibrahim Khalaf is a leading researcher in wireless sensor networks (WSNs) and applied machine learning, with a focus on energy efficiency and medical image analysis. His most cited work, "Accurate and Effective Data Collection with Minimum Energy Path Selection in Wireless Sensor Networks using Mobile Sinks" (2021, 40 citations), addresses a critical challenge in WSNs—energy consumption—by optimizing data collection paths for mobile sinks, directly impacting applications in industry, agriculture, and military systems. In deep learning, Khalaf's 2022 study on facial expression recognition using CNNs (12 citations) explores how activation, optimization, and regularization methods enhance human-computer interaction and robotics. He also contributes to biomedical engineering, developing a machine learning model for breast cancer detection (BCD) from thermal mammogram images (2022, 9 citations), offering a non-invasive diagnostic tool for early-stage cancer in both women and men. Khalaf's work bridges theoretical advances with practical, life-saving applications, demonstrating significant impact across energy-constrained networks and healthcare AI.

Research Focus

Key Achievements

Top Papers

1. 1
   Accurate and Effective Data Collection with Minimum Energy Path Selection in Wireless Sensor Networks using Mobile Sinks

   40 citations · 2021
2. 2
   Impact of Activation, Optimization, and Regularization Methods on the Facial Expression Model Using CNN

   12 citations · 2022
3. 3
   CAD of BCD from Thermal Mammogram Images Using Machine Learning

   9 citations · 2022

Key Collaborators

Contact & Links