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Ali Al-Ataby
Also published under:Ali A. Al-Ataby
Affiliation
Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK
Topic
Convolutional Neural Network,Deep Learning,Generative Adversarial Networks,Neural Network,Accuracy Of Model,Arduino,Binary Classification,Body Temperature,Body Temperature Measurements,COVID Cases,COVID-19 Cases,COVID-19 Vaccine,Classification Methods,Conditional Generative Adversarial Network,Daily Cases,Data Augmentation,Data Quality,Denial Of Service,Digital Networks,Distance Metrics,Distributed Denial Of Service,Distributed Denial Of Service Detection,District Of Columbia,F1 Score,Face Detection,Face Masks,False Negative,Field Dataset,Flow Duration,Fully-connected Layer,Future Cases,Generative Adversarial Networks Model,Goal Of This Paper,Government Intervention,Graphical User Interface,Human Faces,Internet Of Things,Intrusion Detection,Intrusion Detection System,Learning Algorithms,Logistic Function,Long Short-term Memory,Machine Learning,Machine Learning Methods,March 2020,Mobile Phone,Model Performance,Number Of Cases,Number Of Daily Cases,Object Detection,
Biography
Ali Al-Ataby received the B.Sc. degree in electronic and telecommunications engineering from Nahrain University, Baghdad, Iraq, in 1997, and the M.Sc. degree in electronic circuits and systems engineering and Ph.D. degree in electrical engineering from the University of Liverpool, Liverpool, U.K., in 1999 and 2012.
In 2011, he joined the Department of Electrical Engineering and Electronics, University of Liverpool, as a Lecturer in signal processing. He was in academia from 1997 to 2002 and in industry from 2002 to 2009. His current research interests include devising automatic interpretation algorithms for nondestructive testing data with a particular interest in visual, ultrasonic, and radar data, biomedical signal/data processing (e.g. EEG, ECG, and DNA data) and in driver fatigue detection/management, as well as machine learning, hardware signal processing, and microcontroller/microprocessor-based embedded systems.
In 2011, he joined the Department of Electrical Engineering and Electronics, University of Liverpool, as a Lecturer in signal processing. He was in academia from 1997 to 2002 and in industry from 2002 to 2009. His current research interests include devising automatic interpretation algorithms for nondestructive testing data with a particular interest in visual, ultrasonic, and radar data, biomedical signal/data processing (e.g. EEG, ECG, and DNA data) and in driver fatigue detection/management, as well as machine learning, hardware signal processing, and microcontroller/microprocessor-based embedded systems.