Latent state estimations for experimental data with time limit for subject 6. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 21. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 6. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 8. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 12. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 22. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 4. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 15. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 10. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 10. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 2. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 9. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 12. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 2. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 12. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 20. حسب Samiul Alam (18563323)

An exemplar procedure for activity flow modeling based on the direct and shortest paths at a network density of 15%. حسب Zhengdong Wang (35299)

Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods حسب Jiacong Du (12035845)

"…Building on existing work, we (i) derive and implement efficient cyclic coordinate descent and majorization-minimization optimization algorithms for continuous and binary outcome data, (ii) incorporate adaptive shrinkage penalties, (iii) compare these methods through simulation, and (iv) develop an R package <i>miselect</i>. …"

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles حسب Soham Savarkar (21811825)

"…</p><p dir="ltr">These biological metrics were used to define a binary toxicity label: entries were classified as toxic (1) or non-toxic (0) based on thresholds from standardized guidelines (e.g., ISO 10993-5:2009) and literature consensus. …"