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 OverView
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My research focuses on unlocking the potential of sensors through advanced data analysis. I leverage deep learning, particularly techniques like feature extraction and convolutional neural networks, to classify various sensory data. This allows for accurate prediction and decision-making in diverse applications.
One area of interest is sensor fusion, where I explore combining data from multiple sensors to gain a richer understanding of the environment. This can be crucial for environmental monitoring, where I aim to develop intelligent systems for anomaly detection in sensor networks used for tasks like pollution control.
Explainable AI is another key focus. By understanding how deep learning models make decisions based on sensor data, I strive to build trust and transparency in these systems.
Finally, I'm interested in the future of sensor-based control systems. My research delves into sensorimotor control, where deep learning could enable robots or prosthetics to react intelligently based on real-time sensory feedback.
Overall, my research bridges the gap between sensor data and real-world applications, aiming to create intelligent systems that can learn, predict, and ultimately, make a positive impact.
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 Research Intersets:
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Sensory data classification, Deep learning, Feature extraction and prediction, Sensor Fusion, Sensorimotor Control, Explainable AI, Environmental Monitoring, and Anomaly Detection in Sensor Networks
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 Qualifications
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Degree
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University
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Specialization
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Graduation year
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| 1 | PHD | University of Debrecen | Engineering and technological informatics | 2021 | | 2 | MASTER DEGREE | Al-Balqa Applied University | Computer science | 2014 | | 3 | BACHELOR'S DEGREE | Al-Balqa Applied University | Software engineering | 2006 |
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