Recent Research Interest
Dr. Hamdy S. Soliman's research is centered on machine learning and neural network modeling, with emphasis on classification, association, and generalization in complex systems. His work includes a wide range of ANN models, such as LVQ, BP, BAM, Hopfield AM, ART, and KSOFM, applied to big data analytics, cloud computing management, intelligent sensor networks, and image processing.
His research further focuses on the development of smart and secure wireless sensor networks (SS-WSNs) for early detection of critical asynchronous events, including forest fires, border intrusion, environmental hazards, and disease progression, enabling real-time prediction and intelligent decision-making.
Small-scale forest-fire emulation field experiment
Dr. Soliman has also contributed to network security, developing novel encryption mechanisms and secure communication protocols, resulting in five U.S. patents. His recent work extends to machine learning applications in healthcare, particularly early cancer detection and subtype identification using high-dimensional data.
Currently, his research group includes two Ph.D. students and multiple undergraduate and graduate researchers, working on interdisciplinary problems spanning machine learning, sensor networks, and security, with outcomes reflected in peer-reviewed publications and patented technologies.
Research Labs
Smart & Secure Sensor Lab
This research lab is used to develop "smart" and "secure" wireless sensor networks to detect asynchronous events such as forest fire, volcanic eruption, border intrusion, seismic activity, etc., with the help of the neural networks security sibling labs.
Security Lab
The major task of this lab is to secure cyber and cellphone communication. Our novel encryption algorithm and security protocol (to be compared to the known CCMP) is more advanced than existing government standards such as AES and CCMP. Most recent activity includes development of cross-Atlantic security communication and secure cellphone apps. The work is managed and supported by NMT Research Foundation.
Neural Networks Lab
This lab focuses on applying neural network models to complex classification, pattern recognition, and data analysis problems across domains including healthcare, security, and environmental monitoring.
Field Experiments
The Smart & Secure Sensor Lab has conducted multiple field experiments to validate sensor-based event detection in realistic outdoor conditions, including small-scale forest-fire emulation using Mica-Z motes and border intrusion detection trials.
Mica-Z mote deployment, forest-fire emulation, and border intrusion detection trials
Igniting the small-scale forest-fire emulation test bed
Selected Past Grants
Combining BIG DATA with Sensor Networks for Healthcare: Early Detection of Chronic Diseases 2014–2016by NASA & NMSGC $24,999 |
Volunteer Cloud Federation to Enable Data- and Compute-Intensive Scientific Applications 2012–2013by NASA & NMSGC $25,000 |
Experimental Network of Sensors Lab for advancing research in sensor-data collection, integration, management, and analysis 2007–2010by National Science Foundation, Computing Research Infrastructure $160,000 |
Equipment for a Computer Networks Laboratory 1992–1995by National Science Foundation, Instrumentation and Laboratory Improvement Program $48,674with Dr. George A. Cunningham, III
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A Task-Grain Very High Level Language for Data-Driven Multiprocessing System 1991–1993by Sandia National Laboratories $60,000 |