Model outputs compared against calibration targets of TB in Viet Nam. by Alvaro Schwalb (8282517)

Robustness of the optimization process in a real dataset. by Neta Ravid Tannenbaum (2940351)

Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish) by Daniel Pérez Palau (11097348)

“…</li></ul><p dir="ltr"><b>File Structure</b></p><p dir="ltr">The code generates and saves:</p><ul><li>Weights of the trained model (.h5)</li><li>Configured tokenizer</li><li>Training history in CSV</li><li>Requirements file</li></ul><p dir="ltr"><b>Important Notes</b></p><ul><li>The model excludes category 2 during training</li><li>Implements transfer learning from a pre-trained model for binary hate detection</li><li>Includes early stopping callbacks to prevent overfitting</li><li>Uses class weighting to handle category imbalances</li></ul><p dir="ltr">The process of creating this algorithm is explained in the technical report located at: Blanco-Valencia, X., De Gregorio-Vicente, O., Ruiz Iniesta, A., & Said-Hung, E. (2025). …”

Data_Sheet_1_Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN.PDF by Salah Daddinounou (18565915)

Data_Sheet_1_Validation of a Triplex Quantitative Polymerase Chain Reaction Assay for Detection and Quantification of Traditional Protein Sources, Pisum sativum L. and Glycine max... by Adam C. Faller (10865430)

“…Ratios based on mass of protein powder were also tested, resulting in non-linear patterns in data that suggested the requirement of further sample preparation optimization or algorithmic correction. …”

Image 1_Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics.jpeg by Zhenze Zhang (22011422)

“…Predictors included demographics, comorbidities, laboratory parameters, vital signs, and disease severity scores. Missing data (<30%) were imputed using random forest. The cohort was split into training (75%) and internal testing (25%) sets, with hyperparameter optimization via 5-fold cross-validation. …”

Table 1_Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics.docx by Zhenze Zhang (22011422)

“…Predictors included demographics, comorbidities, laboratory parameters, vital signs, and disease severity scores. Missing data (<30%) were imputed using random forest. The cohort was split into training (75%) and internal testing (25%) sets, with hyperparameter optimization via 5-fold cross-validation. …”

SBDI Sativa curated 16S GTDB database by Daniel Lundin (11036598)

“…They will be made available in nf-core/ampliseq for phylogenetic placement.</p><p dir="ltr">The data will be updated circa yearly, after the GTDB database is updated.…”