94 citations · 166 across the 23 of their papers we have counts for
7 papers · 1 filter
Machine Learning Based IoT Adaptive Architecture for Epilepsy Seizure Detection: Anatomy and Analysis
Zag ElSayed, Murat Ozer, Nelly Elsayed +1
A seizure tracking system is crucial for monitoring and evaluating epilepsy treatments. Caretaker seizure diaries are used in epilepsy care today, but clinical seizure monitoring m…
A Convolutional-based Model for Early Prediction of Alzheimer's based on the Dementia Stage in the MRI Brain Images
Shrish Pellakur, Nelly Elsayed, Zag ElSayed +1
Alzheimer's disease is a degenerative brain disease. Being the primary cause of Dementia in adults and progressively destroys brain memory. Though Alzheimer's disease does not have…
LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks
Nelly Elsayed, Zag ElSayed, Anthony S. Maida
Long short-term memory (LSTM) is one of the robust recurrent neural network architectures for learning sequential data. However, it requires considerable computational power to lea…
Early Stage Diabetes Prediction via Extreme Learning Machine
Nelly Elsayed, Zag ElSayed, Murat Ozer
Diabetes is one of the chronic diseases that has been discovered for decades. However, several cases are diagnosed in their late stages. Every one in eleven of the world's adult po…
LiteLSTM Architecture for Deep Recurrent Neural Networks
Nelly Elsayed, Zag ElSayed, Anthony S. Maida
Long short-term memory (LSTM) is a robust recurrent neural network architecture for learning spatiotemporal sequential data. However, it requires significant computational power fo…
Intrusion Detection System in Smart Home Network Using Bidirectional LSTM and Convolutional Neural Networks Hybrid Model
Nelly Elsayed, Zaghloul Saad Zaghloul, Sylvia Worlali Azumah +1
Internet of Things (IoT) allowed smart homes to improve the quality and the comfort of our daily lives. However, these conveniences introduced several security concerns that increa…
Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification
Nelly Elsayed, Anthony S. Maida, Magdy Bayoumi
Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series classification have produced state-of-the-art classification results on univariate time series. We show that rep…