Data_Sheet_1_The Nutri-Score algorithm: Evaluation of its validation process.docx حسب Daphne L. M. van der Bend (13247511)

"…Predictive validity (i.e., ability to predict disease risk when applied to population dietary data) could be re-assessed after adaptations are made to the algorithm. …"

Binary form of Table 1. حسب Muhammad Waqar (500788)

Flowchart of the general algorithm for one trial. حسب Junying Wang (509009)

Characteristics of the data sets used to evaluate Machine Learning and Deep Learning models for arboviral diseases classification. حسب Sebastião Rogério da Silva Neto (11944944)

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

Analysis and design of algorithms for the manufacturing process of integrated circuits حسب Sonia Fleytas (16856403)

Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification حسب Vaibhavi Patel (19843282)

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

Logistic regression permutation evaluation. حسب Elisa Ferrari (7000166)

Min-Cut/Max-Flow Problem Instances for Benchmarking حسب Patrick Møller Jensen (7232042)

Flowchart for classification tasks in automated extinction risk assessment method, using the XGBoost algorithm [29]. حسب Gabriel Henrique de Oliveira Caetano (12647413)

Application of ABC SMC to the fitting of the binary flip with cusp model. حسب Elena Camacho-Aguilar (10906327)

The overview of the proposed method. حسب 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.…"

Pseudocode of NSGAII [23]. حسب Seyed Mahdi Hosseiniyan Khatibi (16791475)

"…Next, to apply feature selection, a t-test and binary Non-Dominated Sorting Genetic Algorithm II (NSGAII) were chosen to be employed. …"

The basic concept of our idea is illustrated graphically. حسب Yi-Wen Hsiao (13767916)

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

"…In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …"

Parameter settings. حسب Olaide N. Oyelade (14047002)

"…In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …"

Data_Sheet_1_Information-theoretic analysis of Hierarchical Temporal Memory-Spatial Pooler algorithm with a new upper bound for the standard information bottleneck method.pdf حسب Shiva Sanati (16035875)

Algoritmo de detección de odio en español (Algorithm for detection of hate speech in Spanish) حسب 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). …"

Data set constituents. حسب Rémy Peyret (14691736)

"…We then performed a calibration step based on transfer learning to maintain the performance when translating on a new target acquisition center by using a limited amount of additional training data. Performance was evaluated using classical binary measures (accuracy, recall, precision) for both centers (referred to as “test reference dataset” and “test target dataset”) and at two levels: patch and slide level. …"