Reinforcement Learning Automates Multi-Fault Self-Healing Across Software-Defined Networks
Researchers have developed a reinforcement learning framework that autonomously diagnoses and remediates multiple SDN fault types within tens of seconds.
Researchers have developed a reinforcement learning framework that autonomously diagnoses and remediates multiple SDN fault types within tens of seconds.
An explainable machine learning pipeline combining XGBoost and SHAP revealed that specific patient knowledge deficits were strongly associated with tuberculosis ...
A study published in Disciplinary and Interdisciplinary Science Education Research demonstrates that artificial intelligence and network analysis can uncover distinct, ...
A hydrological modeling study in Ethiopia's Meki watershed revealed that three decades of agricultural expansion and deforestation drove a 35.58 ...
A narrative review of 23 studies finds that AI and machine learning tools show promise for early eating disorder detection, ...
A new national-scale study published in Geoenvironmental Disasters demonstrates that climate change is primarily redistributing landslide susceptibility across the Nepal ...
The study, published in Discover Electronics, presents a practical framework that achieves a 98.87% reduction in cumulative communication compared to ...
A cross-sectional study of 189 hospital nurses in Iran revealed that positive attitudes toward professional competence did not correlate with ...
A cross-sectional study in Montreal demonstrates that machine learning models cannot reliably predict antiretroviral therapy adherence barriers from sociodemographic characteristics ...
Machine learning models trained on X-chromosomal gene expression classify female systemic lupus erythematosus in T cells with performance comparable to ...
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© 2026 Scienmag - Science Magazine