The performance of the machine learning algorithms to differentiate between W and other sleep stages during three and five class binary classification tasks on the test set sleep s... by Sowmya M. Ramaswamy (18954755)

The results of 10 performance metrics from the miRNA biomarker study (M0 vs M1), comparing NQE+SVC and NQE+QSVC in the 5 PCs case. by Aninda Astuti (21736334)

The results of 10 performance metrics from the miRNA biomarker study (M0 vs M1), comparing NQE+NN and NQE+QNN in the 5 PCs case. by Aninda Astuti (21736334)

Classification performance results using 5-fold cross-validation for the training datasets after applying the data imbalance tool, SMOTE and NQE for data embedding. The values with... by Aninda Astuti (21736334)

Classification performance results using 5-fold cross-validation for the training datasets after applying the data imbalance tool, SMOTE and NQE for data embedding. The values with... by Aninda Astuti (21736334)

Head/tail classification using a Hungarian algorithm. by David Tadres (9120564)

Explicability results of the maxillary protrusion classification model. by Kengo Oka (1420585)

Cell classification. by Adam L. Tyson (10892822)

<b>BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification</b> by BRISC Dataset (22559540)

“…</p><p dir="ltr">Highlights<br>- 6,000 T1-weighted MRI slices (5,000 train / 1,000 test)<br>- Four classes: Glioma, Meningioma, Pituitary Tumor, No Tumor<br>- Pixel-wise segmentation masks reviewed by radiologists<br>- Slices from three anatomical planes: Axial, Coronal, Sagittal<br>- Clean, stratified train/test splits and aligned image–mask filenames</p><h2> Dataset structure</h2><p dir="ltr"><b>brisc2025/</b><br>├─ classification_task/<br>│ ├─ train/<br>│ │ ├─ glioma/<br>│ │ │ ├─ brisc2025_train_00001_gl_ax_t1.jpg<br>│ │ │ └─ ...…”

Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective by Xiangyang Jia (10026932)

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

The data processing and analysis pipeline. by Xin Zhang (35492)

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

“…<p>A, stroke presence; B, MCA location; C, acuity. These curves represent different combinations of text featurization (BOW, tf-idf, GloVe) and binary classification algorithms (Logistic Regression, RF, RNN). …”

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

“…<p>A, stroke presence; B, MCA location; C, acuity. These curves represent different combinations of text featurization (BOW, tf-idf, GloVe) and binary classification algorithms (Logistic Regression, k-NN, CART, OCT, OCT-H, RF, RNN). …”

Dataset 1: Zip file containing the figures of the presented methods and results in jpeg files by Suchismita Behera (22027316)

Illustration of unprocessed-image analysis and cropped-image analysis by the algorithm. by Seung Seog Han (4782486)

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

“…</li><li>It generates a binary classification (hate vs. non-hate).</li></ul><h2>Datasets</h2><ol><li><b>Pre-training Dataset</b>: Cardiff NLP multilingual tweet sentiment dataset (Spanish part).…”

Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19. by Jiaqing Luo (10975030)

“…(D). The MCDM algorithm-Stage 4. Performance evaluation, this stage is to measure the performance of the binary classification by ACC, TPR, FPR and F1 score.…”

<b>From street view imagery to the countryside: large-scale perception of rural China using deep learning</b> by Yao Yao (7903457)

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). …”