Key steps in neurodegeneration detection algorithm. by Andrew S. Clark (16510496)

Algoritmo de detección de odio en español (Algorithm for detection of hate speech in Spanish) by Elias Said-Hung (10790310)

“…</p><p dir="ltr">Más información:</p><ul><li><a href="https://www.hatemedia.es/" rel="nofollow" target="_blank">https://www.hatemedia.es/</a> o contactar con: <a href="mailto:elias.said@unir.net" target="_blank">elias.said@unir.net</a></li><li>Este algoritmo está relacionado con el algoritmo de clasificación de expresiones de odio por intensidad en español, desarrollado también por los autores: <a href="https://github.com/esaidh266/Algorithm-for-classifying-hate-expressions-by-intensities-in-Spanish" target="_blank">https://github.com/esaidh266/Algorithm-for-classifying-hate-expressions-by-intensities-in-Spanish</a></li><li>Este algoritmo está relacionado con el algoritmo de clasificación de expresiones de odio por tipo en español, desarrollado también por los autores: <a href="https://github.com/esaidh266/Algorithm-for-classifying-hate-expressions-by-type-in-Spanish" target="_blank">https://github.com/esaidh266/Algorithm-for-classifying-hate-expressions-by-type-in-Spanish</a></li></ul><p>----</p>Hate Speech Detection Model<p dir="ltr">This code implements a hate speech classification system using the RoBERTuito model (a Spanish version of RoBERTa) to detect hate speech in tweets.…”

Surround Suppression mechanism. by Elnaz Nemati (21402730)

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_Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases.P... by Jianfeng Wu (360829)

“…Here we attempt to use MRI-MMS to make inferences regarding brain Aβ burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse-coding and Max-Pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each individual subject. …”

WEISS Catheter Segmentation in Fluoroscopy Dataset by Evangelos Mazomenos (6761597)

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

Behavioral and Eye-tracking Data for Adaptive Circuit Dynamics Across Human Cortex During Evidence Accumulation in Changing Environments by Peter Murphy (10141523)

“…</p><p><br></p><p>For example code for analyzing behaviour, fitting behavioural models, and analyzing pupil data, see <a href="https://github.com/DonnerLab/2021_Murphy_Adaptive-Circuit-Dynamics-Across-Human-Cortex">https://github.com/DonnerLab/2021_Murphy_Adaptive-Circuit-Dynamics-Across-Human-Cortex</a>.…”