Reinforcement learning (RL) is a branch of machine learning that studies how an agent should act within an environment to maximize cumulative reward. Supervised learning fits a function to labeled examples; RL instead learns through the consequences of trial-and-error interaction. It also differs from unsupervised lear...
Mohammad Hatim Eliwi, Waleed A. Hammod, O. A. Hammood et al.· EDRAAK· 0 citations
Data centers are now the largest users of electricity, carbon, and water, driving rapid cloud computing growth and a global shift to renewable-powered, sustainable cloud infrastructure. The large-scale integration of solar and wind energy creates fundamental operational challenges due to their intermittency, uncertaint...
R. Hasan, O. A. Hammood, M. A. Mohammed et al.· ESTIDAMAA· 0 citations
Continuum soft robots have attracted significant attention due to their high flexibility, compliance, and ability to perform complex tasks in unstructured environments. However, their inherent nonlinear, distributed-parameter dynamics pose major challenges for accurate modeling and real-time control. Existing approache...
R. Hasan, Munif Ahmed Abdullah, Mehdi Qahraman Fakhruldin et al.· EDRAAK· 0 citations
Key findings indicate that algorithm choice should be tailored to the prediction target, with temperature variables favoring LSTM, precipitation benefiting from GRU's efficiency, and humidity showing comparable performance across all architectures.
M. M. Akawee, R. Hasan, Munif Ahmed Abdullah et al.· EDRAAK· 0 citations
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