Best optimizer results of Lightbgm. by Balraj Preet Kaur (20370832)

Best optimizer results of Adaboost. by Balraj Preet Kaur (20370832)

Best optimizer results of Lightbgm. by Balraj Preet Kaur (20370832)

Best optimizer results of KNN. by Balraj Preet Kaur (20370832)

Best optimizer results of KNN. by Balraj Preet Kaur (20370832)

One advantage of our algorithm is that it is not binary in nature. by Yong Kuk Kim (13955214)

Best optimizer results of Decision tree. by Balraj Preet Kaur (20370832)

Best optimizer result for Adaboost classifier. by Balraj Preet Kaur (20370832)

Best optimizer results for random forest. by Balraj Preet Kaur (20370832)

Best optimizer results of Decision tree. by Balraj Preet Kaur (20370832)

Best optimizer results of Extra tree. by Balraj Preet Kaur (20370832)

Best optimizer results of Random Forest. by Balraj Preet Kaur (20370832)

Best optimizer result for Extra tree. by Balraj Preet Kaur (20370832)

The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms. by Ahmed M. Elshewey (21463867)

Joint Detection of Change Points in Multichannel Single-Molecule Measurements by Hugh Wilson (11797444)

Subjects: “…complex biomolecular processes…”

Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by Ashok Kumar K (21441108)

“…In general, BRBPNN does not show any optimization adaption methods to determine the optimal parameter for appropriate detection. Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. …”

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

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

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). …”

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data by Changhun Kim (682542)

“…Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. …”