a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset. by Nebojsa Bacanin (13932723)

Correlation heatmap of performance scores among different feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO. by Doaa Sami Khafaga (21463870)

Density distribution plot of performance metrics for various feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO. by Doaa Sami Khafaga (21463870)

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

<i>Patient 1</i> GA-optimized model error. by Dmitrii Smirnov (8822324)

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<i>Patient 8</i> GA-optimized model error. by Dmitrii Smirnov (8822324)

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Optimal Drug-Target Docking Visualization. by Qijun Wo (12992781)

Algorithm verification. by Dmitrii Smirnov (8822324)

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Feature selection results. by Doaa Sami Khafaga (21463870)

“…Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”

Genetic Algorithm (GA) and CAGE-based personalization block diagrams. by Dmitrii Smirnov (8822324)

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Screening MMRG gene variables associated with UC based on the optimal machine learning model. by Li Li (14993)

<i>Patient 2</i> CAGE-based model error. by Dmitrii Smirnov (8822324)

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<i>Patient 9–11</i> RNA-seq based models error. by Dmitrii Smirnov (8822324)

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Comparison of presented algorithm precision to Bot et al. by Dmitrii Smirnov (8822324)

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Model convergence to steady state. by Dmitrii Smirnov (8822324)

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Key gene identification based on two machine learning algorithms. by Lijun Guo (605276)

Distributions of model parameters mirror differentially expressed ion channel genes. by Daniele Linaro (227790)

<i>Patient 8</i> GA output model. by Dmitrii Smirnov (8822324)

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The flow chart of harmony search-based OED using a double nested loops. by Wei Zhang (405)

Table 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf by Valentina Di Salvatore (6296114)

“…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …”