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

الموضوعات:

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

الموضوعات:

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

الموضوعات:

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

الموضوعات:

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

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

الموضوعات:

<i>De Novo</i> Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization حسب Alberga Domenico (9356272)

"…In the present study, we conceived a novel pair-based multiobjective approach implemented in an adapted SMILES generative algorithm based on recurrent neural networks for the automated <i>de novo</i> design of new molecules whose overall features are optimized by finding the best trade-offs among relevant physicochemical properties (MW, logP, HBA, HBD) and additional similarity-based constraints biasing specific biological targets. …"

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

الموضوعات:

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

الموضوعات:

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

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

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

الموضوعات:

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

الموضوعات:

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

الموضوعات:

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

"…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …"

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

"…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …"

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

"…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …"

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

"…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …"

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

"…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …"

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

"…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …"