Rosenbrock function losses for . by Shikun Chen (14625352)

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

Flow chart diagram of quantum hash function. by Sultan H. Almotiri (14029251)

“…Our study addresses five major components of the quantum method to overcome these challenges: lattice-based cryptography, fully homomorphic algorithms, quantum key distribution, quantum hash functions, and blind quantum algorithms. …”

<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…”

Categories and subcategories defined from the topics of interest of contemplation users. by Diana Ramírez-Cifuentes (20700054)

Type-1 membership function for distance. by Seung-Min Ryu (21463891)

“…<div><p>In this study, we present an algorithm to estimate the distance between a vehicle and a target object using light from headlights captured by a camera. …”

Type-1 membership function for speed. by Seung-Min Ryu (21463891)

“…<div><p>In this study, we present an algorithm to estimate the distance between a vehicle and a target object using light from headlights captured by a camera. …”

P-values from analysis of ABIDE dataset for continuous outcome of VIQ. by Alexandria M. Jensen (6494957)

The convergence curves of the test functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Single-peaked reference functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Integration of three machine learning algorithms to characterize lipid metabolism-associated hub genes in sepsis and atrial fibrillation. by Changze Ou (22806374)

Cross-validated estimate of MSE error for the LASSO, Ridge and Elastic Net penalties (from top to bottom respectively) as a function of <i>log</i>λ. by Omid Chatrabgoun (17960015)

Cross-validated estimate of MSE error for the LASSO, Ridge and Elastic Net penalties (from top to bottom respectively) as a function of <i>log</i>λ. by Omid Chatrabgoun (17960015)

IDRAS algorithm outline. by Ron Teichner (21492434)

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

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

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

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

2D Rastrigin function. by Shikun Chen (14625352)

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

2D Levy function. by Shikun Chen (14625352)

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

2D Rosenbrock function. by Shikun Chen (14625352)

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

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

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

Heatmap results from the global sensitivity analysis for the generation of chorionic vessels. by Diana C. de Oliveira (9220729)