Hisham Daoud
Hisham is a Ph.D. candidate in the Computer Engineering program at University of Louisiana at Lafayette. His research interest includes biomedical signal processing, machine learning, deep learning, and neuromorphic computing. In his dissertation, he develops deep learning methods for diagnosis, prediction and localization of epileptic seizures. Before commencing his graduate studies at University of Louisiana, Hisham has held multiple positions in both industry and academia.
Publications:
- H. Daoud and M. Bayoumi, "Deep Learning Approach for Epileptic Focus Localization," in IEEE Transactions on Biomedical Circuits and Systems, vol. 14, no. 2, pp. 209-220, April 2020.
- H. Daoud and M. Bayoumi, "Semi-supervised Learning for Epileptic Focus Localization Using Deep Convolutional Autoencoder," in Proc. of IEEE Biomedical Circuits and Systems Conference (BioCAS), Nara, Japan, 2019.
- H. Daoud and M. A. Bayoumi, “Efficient Epileptic Seizure Prediction based on Deep Learning,” in IEEE Transactions on Biomedical Circuits and Systems, vol. 13, no. 5, pp. 804-813, Oct. 2019.
- H. Daoud and M. A. Bayoumi, “Deep Learning based Reliable Early Epileptic Seizure Predictor,” in Proc. of IEEE Biomedical Circuits and Systems Conference (BioCAS), Cleveland, OH, USA, 2018.
- H. Daoud, A. M. Abdelhameed and M. A. Bayoumi, “FPGA Implementation of High Accuracy Automatic Epileptic Seizure Detection System,” in Proc. of 61st IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), Windsor, ON, Canada, 2018.
- H. Daoud, A. M. Abdelhameed and M. A. Bayoumi, “Automatic epileptic seizure detection based on empirical mode decomposition and deep neural network,” in Proc. of 14th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), Batu Feringghi, Malaysia, 2018.