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Researcher at Research Department
Ask questions about Khlood Shinan's research, publications, and ongoing work
Developed a machine learning-based approach for botnet detection in Software-Defined Networks, contributing to improved network security.
Utilized machine learning to identify potential novel drugs against Keap1 in Alzheimer's disease, advancing drug discovery efforts.
Proposed a graph-based bot detection method using centrality measures and machine learning, enhancing bot detection capabilities.
Developed a machine learning-based approach for the early screening of Autism Spectrum Disorders, contributing to early diagnosis.
Developed a neural network-based ensemble approach for multi-view facial expression recognition, improving emotion recognition accuracy.
Investigated the usability and optimization of online applications in the user's context, improving user experience.
Developed a novel neuroimaging-based early detection framework for Alzheimer's disease using deep learning.
Explored the use of Low-resource MobileBERT for emotion recognition in imbalanced text datasets, addressing challenges with limited resources.
Proposed a blockchain-enabled privacy provisioning scheme for location-based services in VANETs, enhancing privacy.
Khlood Shinan is a researcher specializing in machine learning and AI applications, with a focus on healthcare, cybersecurity, and user experience optimization. Their work significantly contributes to botnet detection, Alzheimer's disease research, and early detection of autism spectrum disorders.
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