Hmood Al-Dossari

TL;DR: The results show that the ensemble oversampling technique can more greatly reduce the false negative rate compared to the standard classification techniques and identify the faulty components more accurately resulting in a less expensive detection system (lowering the rate of non-faulty predictions of faulty modules).

TL;DR: The proposed framework demonstrates that the hybrid heuristic can guide the metric selection process in a computationally efficient way by integrating the intrinsic characteristics from the filters into the wrapper and using the advantages of both the filter and wrapper approaches.

TL;DR: Novelty of the proposed adaptive threat detection model for industrial cloud of things (CoT) based on deep learning is that the models are adaptive where training procedures is simpler than earlier work and can adapt new malware behaviors from already available and cheap unlabeled data at the same time.

TL;DR: A comprehensive analysis of the important research works in the field of Arabic sentiment analysis using smoothness analysis to evaluate the percentage error in the performance scores reported in the studies from their linearly-projected values (smoothness).

TL;DR: This paper proposes a framework to address above challenging issues of reliable transmission of vital sign data to cloud, dynamic resource allocation to facilitate seamless access and processing of IoT data, and effective data mining techniques.