History of Process Transformation by de Wit Coetsee (21380672)

Overview of the segmentation algorithm. by Michael C. Robitaille (11949174)

A* Path-Finding Algorithm to Determine Cell Connections by Max Weng (22327159)

“…To address this, the research integrates a modified A* pathfinding algorithm with a U-Net convolutional neural network, a custom statistical binary classification method, and a personalized Min-Max connectivity threshold to automate the detection of astrocyte connectivity.…”

VLCC-States: Versioned Lineage-Driven Checkpointing of Composable States - Application to LLMs by Bogdan Nicolae (19418197)

Adjusted LASSO coefficients, Mexico 2022. by Oliver Mendoza-Cano (9148646)

List of data tables. by Mukhtar Ijaiya (18935122)

“…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …”

Flow chart of data source inclusion. by Mukhtar Ijaiya (18935122)

“…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …”

Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

Predictive model-building process. by Mukhtar Ijaiya (18935122)

“…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …”

Comparison of models performance metrics. by Mukhtar Ijaiya (18935122)

“…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …”

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

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). …”

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. …”

Image processing workflow. by Denis Tamiev (7404980)

“…<p>Raw fluorescent microscope images (a) were processed with a binary segmentation algorithm, and clusters of bacterial cells were manually annotated. …”

Validation of image processing steps and metric results using images of a microfluidic device. by Gavrielle R. Untracht (11820311)

Flowchart of software workflow. by Gavrielle R. Untracht (11820311)

Symmetry test result. by Jiyong Moon (16819302)

Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants by Ahmed M. Alaa (5029781)

“…Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-optimal performance across all patient groups. Data-driven techniques based on machine learning (ML) might improve the performance of risk predictions by agnostically discovering novel risk predictors and learning the complex interactions between them. …”

Flowchart scheme of the ML-based model. by Noshaba Qasmi (20405009)

“…<b>I)</b> Testing data consisting of 20% of the entire dataset. <b>J)</b> Optimization of hyperparameter tuning. <b>K)</b> Algorithm selection from all models. …”