Khalil El Hindi is a professor, at the Department of Computer Science, King Saud University. His main research interests include machine learning, classification algorithms, outlier detection, instance weighing, ensembles of classifiers, similarity distance metrics, and Neural Networks.
The problem of dealing with noisy data in neural network-based models has been receiving more attention by researchers with the aim of mitigating possible consequences on learning.
Text classification has many applications in text processing and information retrieval. Instance-based learning (IBL) is among the top-performing text classification methods. However, its…
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Bayan Abu Shawar, Reem Aljulaidan,1 and Hussien Alsalamn, Khalil-El-Hindi
Analyzing social data as a participatory sensing system (PSS) provides a deep understanding of city dynamics, such as people’s mobility patterns, social patterns, and events detection. In a PSS,…
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Khalil El Hindi Salaha Alzahrani Khulud-Alharthy
This course focuses on the Advanced Software Engineering concepts that are needed to develop software systems that can meet basic functional requirements within a well-defined problem domain.…
Introduction to inductive learning and decision trees, Neural Networks, Backpropagation algorithm, Deep Neural Networks (Recurrent Networks and Deep Belief Networks), Self-Organizing Maps, Rule…