Search for Correlations Between the Results of the Density Functional Theory and Hartree–Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms حسب Saadiallakh Normatov (20682324)

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

Speech spectrogram extracted from speech signal processed by wavelet transform method (Morlet mother wavelet with center frequency parameter of 50 and bandwidth parameter of 3 was... حسب Li Jiashen (20552690)

Various MFOs from three mechanisms. حسب Jingjing Ma (419752)

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. …"

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)