Rosenbrock function losses for . حسب Shikun Chen (14625352)

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

Levy function losses for . حسب Shikun Chen (14625352)

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

Rastrigin function losses for . حسب Shikun Chen (14625352)

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

Levy function losses for . حسب Shikun Chen (14625352)

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

Rastrigin function losses for . حسب Shikun Chen (14625352)

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

Levy function losses for . حسب Shikun Chen (14625352)

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

Levy function losses for . حسب Shikun Chen (14625352)

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

Rastrigin function losses for . حسب Shikun Chen (14625352)

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

Rastrigin function losses for . حسب Shikun Chen (14625352)

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

Rosenbrock function losses for . حسب 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. حسب 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. …"

NRPStransformer, an Accurate Adenylation Domain Specificity Prediction Algorithm for Genome Mining of Nonribosomal Peptides حسب Zhihan Zhang (1403308)

"…Our work lays a foundation to understand the sequence-to-function relationship of the bacterial adenylation domain and will facilitate the exploitation of nonribosomal peptides. …"

Agreements and disagreements between scores of the therapist’s score on the tablet (T-score) and the automatic score (A-score) from the TabMe2 software. حسب Dominique Vincent-Genod (20552594)

Data is generated from ARMA (p, q) models with (p, q) , and the corresponding AIC table is created. حسب Jesse Wheeler (18462457)

Spectrograms extracted from the generalized S-transform corresponding to the adjustment parameters λ_<i>G</i> = 0.05, λ_<i>L</i> = 0.01. حسب Li Jiashen (20552690)

Type-1 membership function for distance. حسب 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. حسب 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. …"

Supplementary Tables from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors حسب Xinyi Liu (20493753)

The convergence curves of the test functions. حسب 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. حسب 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. …"