UAV paths for Coverage-Driven scenario (blue circles: sensors, red squares: relays, black lines: UAV trajectories). by Bashir Olaniyi Sadiq (22478011)

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RosettaAMRLD: A Reaction-Driven Approach for Structure-Based Drug Design from Combinatorial Libraries with Monte Carlo Metropolis Algorithms by Yidan Tang (6623693)

“…The Rosetta automated Monte Carlo reaction-based ligand design (RosettaAMRLD) integrates a Monte Carlo Metropolis (MCM) algorithm and reaction-driven molecule proposal to enhance structure-based <i>de novo</i> drug discovery. …”

Comparison of parameter ranges used for Latin hypercube sampling (LHS) in [29] and genetic algorithms (GA) in the present work. by Jyotika Bahuguna (729510)

Comparison of Target SC and Optimized SC. by Filipe Menezes (13205305)

“…For the optimal adjustment of the electrical circuit model parameters, a Genetic Algorithm (GA) is used. …”

Optimization outcome for the Rosenbrock function. by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Optimization outcome for the Rastrigin function. by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Optimization outcome for the elongation problem. by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Optimization outcome for the Levy function. by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Machine Learning-Driven Methods for Nanobody Affinity Prediction by Hua Feng (234718)

VLCC-States: Versioned Lineage-Driven Checkpointing of Composable States - Application to LLMs by Bogdan Nicolae (19418197)

a–b. Percentage increase in mean squared error (%IMSE) (a) and the increase in node purity (IncNP) (b) by the Random Forest algorithm, Mexico 2022. by Oliver Mendoza-Cano (9148646)

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Sensor value scenario for fuzzy logic algorithm. by Gourab Saha (8987405)

Image 1_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.tif by Xuehui Fan (10762662)

Supplementary file 1_Enhancing dissolved oxygen prediction in lake-reservoirs via a hybrid BO+SSA-driven backpropagation neural network.docx by Hanyi Liu (10227785)

Dynamic game equilibrium convergence performance. by Dandan Wang (286632)

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Prediction Accuracy of Vessel Arrival at Ports Using the Yuan Gradient Model. by Dandan Wang (286632)

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Equilibrium income distribution in multi-agent dynamic game. by Dandan Wang (286632)

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Robustness Test under Disturbance Events. by Dandan Wang (286632)

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Basic data of nodes in the Beijing-Shanghai Corridor (2023). by Dandan Wang (286632)

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Model framework. by Dandan Wang (286632)

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