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

Latent state estimations for experimental data with interruptions for subject 19. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 3. حسب Samiul Alam (18563323)

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

Latent state estimations for experimental data with interruptions for subject 16. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 17. حسب Samiul Alam (18563323)

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

Latent state estimations for experimental data with interruptions for subject 1. 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 interruptions for subject 10. 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)

Solubility Prediction of Different Forms of Pharmaceuticals in Single and Mixed Solvents Using Symmetric Electrolyte Nonrandom Two-Liquid Segment Activity Coefficient Model حسب Getachew S. Molla (6416744)

"…Because of the semipredictive nature of the symmetric eNRTL-SAC model, the segment parameter regression is a critical step for solubility prediction accuracy. A particle swarm optimization algorithm is incorporated to preregress conceptual segment parameters of solutes. …"

Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity حسب George S. Watts (7962206)

"…We tested the accuracy, sensitivity, and resource requirements of three top metagenomic taxonomic classifiers that use fast k-mer based algorithms: Centrifuge, CLARK, and KrakenUniq. Binary mixtures of bacteria showed all three reliably identified organisms down to 1% relative abundance, while only the relative abundance estimates of Centrifuge and CLARK were accurate. …"

Experimental observation of gastruloids and shape quantification using LOCO-EFA. حسب Martijn A. de Jong (17896732)

Natural language processing for automated quantification of bone metastases reported in free-text bone scintigraphy reports حسب Olivier Q. Groot (9370461)

"…Pending external validation, the NLP algorithm developed in this study may be implemented as a means to aid researchers in tackling large amounts of data.…"

tUbenet Foundation Model Weights حسب Natalie Holroyd (7099391)

Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx حسب Veera Narayana Balabathina (22518524)

"…</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …"