Calibration curves for NLP classification. by Charlene Jennifer Ong (8997278)

Hair removal preprocessing using the DullRazor algorithm before image generation. by Mujung Kim (22367223)

Dataset selection process and exclusion criteria. by Seung Seog Han (4782486)

Receiver operating curves for NLP classification. by Charlene Jennifer Ong (8997278)

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

“…<b>(B)</b> Focusing on the positive class only, the algorithm exploits FIM (<i>apriori</i> algorithm) to build a list of candidate interactions of any desired order, retaining those that have an empirical frequency above a given threshold <i>δ</i>. …”

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). …”

Datasets and their properties. by Olaide N. Oyelade (14047002)

“…The approach used in this study designed a sub-population selective mechanism that dynamically assigns individuals to a 2-level optimization process. …”

Parameter settings. by Olaide N. Oyelade (14047002)

“…The approach used in this study designed a sub-population selective mechanism that dynamically assigns individuals to a 2-level optimization process. …”

Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf by Cecilia Lindig-León (7889777)

Classification result on the ModelNet40 dataset. by Lei Wang (6656)

Classification results on the FG3D dataset. by Lei Wang (6656)

Example depiction of the autoML process. by Cynthia Lokker (3813832)

The overview of the proposed method. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

“…<p>Five main steps, including reading, preprocessing, feature selection, classification, and association rule mining were applied to the mRNA expression data. 1) Required data was collected from the TCGA repository and got organized during the reading step. 2) The pre-processing step includes two sub-steps, nested cross-validation and data normalization. 3) The feature-selection step contains two parts: the filter method based on a t-test and the wrapper method based on binary Non-Dominated Sorting Genetic Algorithm II (NSGAII) for mRNA data, in which candidate mRNAs with more relevance to early-stage and late-stage Papillary Thyroid Cancer (PTC) were selected. 4) Multi-classifier models were utilized to evaluate the discrimination power of the selected mRNAs. 5) The Association Rule Mining method was employed to discover the possible hidden relationship between the selected mRNAs and early and late stages of PTC firstly, and the complex relationship among the selected mRNAs secondly.…”

An Example of a WPT-MEC Network. by Hend Bayoumi (22693738)

Related Work Summary. by Hend Bayoumi (22693738)

Simulation parameters. by Hend Bayoumi (22693738)

Training losses for N = 10. by Hend Bayoumi (22693738)

Normalized computation rate for N = 10. by Hend Bayoumi (22693738)

Summary of Notations Used in this paper. by Hend Bayoumi (22693738)

Creation of top projections from coronal images of gene expression. by Natalie Weed (7345124)