Search alternatives:
wolf optimization » whale optimization (Expand Search), swarm optimization (Expand Search), _ optimization (Expand Search)
forest global » forestal global (Expand Search), first global (Expand Search), free global (Expand Search)
binary risk » primary risk (Expand Search), dietary risk (Expand Search)
wolf optimization » whale optimization (Expand Search), swarm optimization (Expand Search), _ optimization (Expand Search)
forest global » forestal global (Expand Search), first global (Expand Search), free global (Expand Search)
binary risk » primary risk (Expand Search), dietary risk (Expand Search)
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Hyperparameters of the LSTM Model.
Published 2025“…The AD-PSO-Guided WOA overcomes limitations of conventional optimization algorithms, such as premature convergence by balancing global search (exploration) and local refinement (exploitation). …”
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The AD-PSO-Guided WOA LSTM framework.
Published 2025“…The AD-PSO-Guided WOA overcomes limitations of conventional optimization algorithms, such as premature convergence by balancing global search (exploration) and local refinement (exploitation). …”
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Prediction results of individual models.
Published 2025“…The AD-PSO-Guided WOA overcomes limitations of conventional optimization algorithms, such as premature convergence by balancing global search (exploration) and local refinement (exploitation). …”
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Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles
Published 2025“…For this purpose, we have developed a general procedure that we use to model an experimentally relevant 270-atom Fe<sub>182</sub>C<sub>88</sub> NP using the neural network-assisted stochastic surface walk global optimization algorithm (SSW-NN). Once generated, the Fe<sub>182</sub>C<sub>88</sub> NP active sites and particle morphology are thoroughly characterized before the effects of syngas adsorbate interactions are explored by using DFT and molecular dynamics simulations. …”
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