Timely and accurate classification of lung diseases from chest X-ray images remains an important healthcare challenge. Machine-learning and deep-learning methods can support automated classification, but their evaluation may be affected by class imbalance, feature redundancy, dataset leakage, and computational cost. Th...
Abdulaziz A. Alsulami, Q. A. Al-Haija, Ahmad J. Tayeb et al.· Electronics· 0 citations
LightGBM demonstrates a slight edge in overall detection performance compared to other models, which highlights its effectiveness in detecting diverse and complex attack patterns across multiple traffic directions.
Rania A. Al-Ali, Mohammad M. Alnabhan, Q. A. Al-Haija· Journal of Advances in Infor...· 0 citations
An explainable machine learning framework for network intrusion detection using the CICIDS2017 dataset is proposed, which improves detection accuracy, reduces false positives, and supports informed decision-making, thereby enhancing the transparency, trustworthiness, and practical applicability of intrusion detection s...
Moa’ath Sa’ad Al-A’athal, Q. A. Al-Haija· Recent Progress in Science a...· 0 citations
An Adaptive Action-Based Federated Learning (AA-FL) framework for decentralized intrusion detection in heterogeneous IoT environments is proposed, based on XGBoost boosters and uses quality-weighted server-side ensemble aggregation.
Abdulaziz A. Alsulami, Q. A. Al-Haija, Rayed A. Alakhtar et al.· Computers, Materials & C...· 0 citations
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