Hyperparameter settings for the optimization algorithms. by Ahmed M. Elshewey (21463867)

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Descriptive analysis of the outcomes by the optimized LSTM using several optimization algorithms. by Ahmed M. Elshewey (21463867)

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The schematic diagram of benefit-form binary utility measure functions and. by Chao Yu (562804)

Illustration of the transformed binary images of normal, benign, and malignant samples into grayscale. by Tehnan I. A. Mohamed (16846175)

Performance of the bAD-PSO-Guided WOA algorithm compared with another algorithm. by Ahmed M. Elshewey (21463867)

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Performance of the proposed AD-PSO-Guided WOA-LSTM algorithm compared with another algorithm. by Ahmed M. Elshewey (21463867)

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The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms. by Ahmed M. Elshewey (21463867)

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Total computational cost of the proposed AD-PSO-Guided WOA-LSTM algorithm compared with another algorithm. by Ahmed M. Elshewey (21463867)

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Adaptive dynamic algorithm for Australia rainfall. by Ahmed M. Elshewey (21463867)

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Hyperparameters of the LSTM Model. by Ahmed M. Elshewey (21463867)

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Prediction results of individual models. by Ahmed M. Elshewey (21463867)

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Metrics for evaluating the performance of the proposed model. by Ahmed M. Elshewey (21463867)

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The outcomes of the ANOVA results for the proposed AD-PSO-Guided WOA-LSTM algorithm for rainfall prediction. by Ahmed M. Elshewey (21463867)

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Analysis plots of the obtained results using the proposed AD-PSO-Guided WOA LSTM algorithm. by Ahmed M. Elshewey (21463867)

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The AD-PSO-Guided WOA LSTM framework. by Ahmed M. Elshewey (21463867)

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Data_Sheet_1_Crystal Structure Prediction of Binary Alloys via Deep Potential.PDF by Haidi Wang (9694004)

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…Importantly, this strategy locates samples adaptively on the transition between facies which improves the performance of conventional \emph{<i>MPS</i>} algorithms. In conclusion, this work shows that preferential sampling can contribute in \emph{<i>MPS</i>} even at very small sampling regimes and, as a corollary, demonstrates that prior models (obtained form a training image) can be used effectively not only to simulate non-sensed variables of the field, but to decide where to measure next.…”

Bayesian sequential design for sensitivity experiments with hybrid responses by Yuxia Liu (1779592)

“…To deal with the problem of complex computation involved in searching for optimal designs, fast algorithms are presented using two strategies to approximate the optimal criterion, denoted as SI-optimal design and Bayesian D-optimal design, respectively. …”