Feature selection process. by Balraj Preet Kaur (20370832)

<b>DDS3 - Dataset of mosaic sputum smear microscopy images for evaluation of bacillus detection algorithms</b> by Marly G F Costa (19812192)

Automatic Pre-Processing (APP). by Yuxiang Wu (748543)

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

Signal detection algorithm adapted from [1] yields exponential distributions and unrealistic mean durations of percepts. by Quynh-Anh Nguyen (847240)

Particle tracking validation of the S8 algorithm. by Jennifer M. Knighten (15453354)

<b>DDS1 - Dataset of patch images derived from sputum smear microscopy images </b><b>for the development of bacillus detection methods</b> by Marly G F Costa (19812192)

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

“…</li></ul><h2>Training Process</h2><h3>Pre-workout</h3><ul><li>Batch size: 16</li><li>Epochs: 5</li><li>Learning rate: 2e-5 with 10% warmup steps</li><li>Early stopping with patience=2</li></ul><h3>Fine-tuning</h3><ul><li>Batch size: 128</li><li>Epochs: 5</li><li>Learning rate: 2e-5 with 10% warmup steps</li><li>Early stopping with patience=2</li><li>Custom metrics:</li><li>Recall for non-hate class</li><li>Precision for hate class</li><li>F1-score (weighted)</li><li>AUC-PR</li><li>Recall at precision=0.9 (non-hate)</li><li>Precision at recall=0.9 (hate)</li></ul><h2>Evaluation Metrics</h2><p dir="ltr">The model is evaluated using:</p><ul><li>Macro recall, precision, and F1-score</li><li>One-vs-Rest AUC</li><li>Accuracy</li><li>Metrics by class</li><li>Confusion matrix</li></ul><h2>Requirements</h2><p dir="ltr">The following Python packages are required (see requirements.txt for the full list):</p><ul><li>TensorFlow</li><li>Transformers</li><li>scikit-learn</li><li>pandas</li><li>datasets</li><li>matplotlib</li><li>seaborn</li></ul><h2>Usage</h2><p dir="ltr">The model expects input data with the following specifications:</p><ol><li><b>Data Format</b>:</li></ol><ul><li>CSV file or Pandas DataFrame</li><li>Mandatory column name: <code>text</code> (type string)</li><li>Optional column name: <code>label</code> (type integer, 0 or 1) if available for evaluation</li></ul><ol><li><b>Text Preprocessing</b>:</li></ol><ul><li>Text will be automatically converted to lowercase during processing</li><li>Maximum length: 128 tokens (longer texts will be truncated)</li><li>Special characters, URLs, and emojis must remain in the text (the tokenizer handles these)</li></ul><ol><li><b>Label Encoding</b>:</li></ol><ul><li><code>0</code> = No hateful content (including neutral/positive content)</li><li>1 = Hate speech</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). …”

Optimization of tracking parameters based on <i>P</i>_<i>swap</i>. by Benjamin Gallois (10131683)

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

Image from video analysis. by Yunlin Li (6049214)

Fig 1 - A Discretized Overlap Resolution Algorithm (DORA) for resolving spatial overlaps in individual-based models of microbes by Ihab Hashem (21156806)

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). …”

Experimental setup and trajectory estimation methodology. by Nouman Javed (16624785)

Pre-processing of raw images for pose estimation using physical model-trained convolutional neural network model. by Aniket Ravan (3174171)

Segmentation, trace denoising and spike extraction framework. by Changjia Cai (10647521)

“…(B) Algorithm for fluorescence trace denoising and spike extraction. ①-② Load and high-pass filter the signal in one context region of the movie. …”

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…</p><p><br></p><p dir="ltr">In the fifth measurement technique, the numbers of sharp <b>surface projections/protrusions</b> were calculated by initially applying Canny's edge detection algorithm to generate an edge map of the cell mask image. …”

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

Fig 3 - by Ying Zhou (25031)

“…Help refers to seeking expert guidance and reference laboratory assistance. <b>B. Role of ICT in this algorithm shown diagrammatically.…”

Relative azimuth calculations reveal nanoscale cadherin geometry in the desmosome. by William F. Dean (19370322)