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Mustafa Musa Jaber
Researcher at Dijlah University College
Publications - 104
Citations - 1124
Mustafa Musa Jaber is an academic researcher from Dijlah University College. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 12, co-authored 25 publications receiving 573 citations. Previous affiliations of Mustafa Musa Jaber include Universiti Tun Hussein Onn Malaysia & Universiti Teknikal Malaysia Melaka.
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Journal ArticleDOI
Maintaining Security and Privacy in Health Care System Using Learning Based Deep-Q-Networks.
TL;DR: Learning based Deep-Q-Networks has been introduced for reducing the malware attacks while managing the health information and helps to minimize the intermediate attacks with less complexity.
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Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease
Salama A. Mostafa,Aida Mustapha,Mazin Abed Mohammed,Raed I. Hamed,N. Arunkumar,Mohd Khanapi Abd Ghani,Mustafa Musa Jaber,Shihab Hamad Khaleefah +7 more
TL;DR: Results show that the MFEA of the multi-agent system finds the best set of features and improves the performance of the classifiers’ diagnosis results.
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Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networks
TL;DR: The effective and optimized neural computing and soft computing techniques to minimize the difficulties and issues in the feature set of lung cancer features are introduced.
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Cloud based framework for diagnosis of diabetes mellitus using K-means clustering
TL;DR: The predicted result is used to diagnose which age group and gender are mostly affected by diabetes, and the efficiency of two different clustering techniques suitable for the environment are compared.
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An IoMT cloud-based real time sleep apnea detection scheme by using the SpO2 estimation supported by heart rate variability
TL;DR: The basis of the proposed analysis method is the connection between heart rate variability and oxygen saturation with d apnea events, which was transferred to a cloud-based system architecture to diagnose and warn the remote patients.