Table 1_A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing... by Charmayne Mary Lee Hughes (12959972)

“…</p>Conclusion<p>Machine learning algorithms show promise for the detection of current frailty status, particularly in binary classification. …”

Evaluation of Binary Classifiers for Asymptotically Dependent and Independent Extremes by Juliette Legrand (21771740)

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

“…</p><p dir="ltr">Astrocytes were dissociated from E18 mouse cortical tissue, and image data were processed using a Cellpose 2.0 model to mask nuclei. Pixel paths were classified using a z-score brightness threshold of 1.21, optimized for noise reduction and accuracy. …”

Comparison with previous studies. by Ji Seung Ryu (16187857)

“…Three datasets were used for the experiment: the male, female, and entire dataset. A cutoff for binary classification was defined as the meaningful as a screening test (<132 g/m² vs. ≥132 g/m², <109 g/m² vs. ≥109 g/m²). …”

Dataset characteristics. by Ji Seung Ryu (16187857)

“…Three datasets were used for the experiment: the male, female, and entire dataset. A cutoff for binary classification was defined as the meaningful as a screening test (<132 g/m² vs. ≥132 g/m², <109 g/m² vs. ≥109 g/m²). …”

Acronym table. by Ji Seung Ryu (16187857)

“…Three datasets were used for the experiment: the male, female, and entire dataset. A cutoff for binary classification was defined as the meaningful as a screening test (<132 g/m² vs. ≥132 g/m², <109 g/m² vs. ≥109 g/m²). …”

Random forest model performs better than support vector machine algorithms and when it primarily uses spontaneous photopic ERG of 60-s duration in humans. by Ramsés Noguez Imm (6658637)

Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses by Meichen Liu (8168148)

Fairness in Machine Learning: A Review for Statisticians by Xianwen He (22529252)

“…We organize these fairness-enhancing mechanisms into three categories—pre-processing, in-processing, and post-processing—corresponding to different stages of the machine learning lifecycle and varying levels of access to the underlying algorithm. The discussion focuses on fairness in binary classification models using numerical tabular data, which serve as a foundation for addressing fairness in more complex algorithms. …”

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. …”

Table_10_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.xlsx by Changho Han (1309563)

Table_5_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.xlsx by Changho Han (1309563)

Table_8_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.xlsx by Changho Han (1309563)

Table_6_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.xlsx by Changho Han (1309563)

Table_7_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.xlsx by Changho Han (1309563)

Table_9_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.xlsx by Changho Han (1309563)

Data_Sheet_1_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.DOCX by Changho Han (1309563)

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). …”

An adapted Figure [10] demonstrating the process of injury prediction validation using a pattern recognition approach. by Seren Lois Evans (19942603)

Spontaneous ERG-based random forest model discriminates control and disease cases in rodent models of obesity and type 1 diabetes and predicts disease evolution in the type 1 diabe... by Ramsés Noguez Imm (6658637)