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

"…<p>The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms.…"

Algorithm. حسب Daniel Sanchez-Taltavull (11654630)

A hybrid algorithm based on improved threshold function and wavelet transform. حسب Bingbing Li (461702)

"…<p>A hybrid algorithm based on improved threshold function and wavelet transform.…"

Performance profile of the four algorithms based on the function value of iteration number. حسب Sulaiman M. Ibrahim (20614376)

"…<p>Performance profile of the four algorithms based on the function value of iteration number.…"

Benchmark functions T-test for the proposed and compared algorithms. حسب El-Sayed M. El-kenawy (14581088)

Average function evaluation times of the three optimization algorithms. حسب Shuhui Yu (1535500)

EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit حسب Gonzalo Colmenarejo (650249)

"…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …"

Proposed algorithm convergence curves versus other competing algorithms for the seven benchmark functions. حسب El-Sayed M. El-kenawy (14581088)

Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish) حسب Daniel Pérez Palau (11097348)

"…</p><h2>Model Architecture</h2><p dir="ltr">The model is based on <code>pysentimiento/robertuito-base-uncased</code> with the following modifications:</p><ul><li>A dense classification layer was added over the base model</li><li>Uses input IDs and attention masks as inputs</li><li>Generates a multi-class classification with 5 hate categories</li></ul><h2>Dataset</h2><p dir="ltr"><b>HATEMEDIA Dataset</b>: Custom hate speech dataset with categorization by type:</p><ul><li><b>Labels</b>: 5 hate type categories (0-4)</li><li><b>Preprocessing</b>:</li><li>Null values ​​removed from text and labels</li><li>Reindexing and relabeling (original labels are adjusted by subtracting 1)</li><li>Exclusion of category 2 during training</li><li>Conversion of category 5 to category 2</li></ul><h2>Training Process</h2><h3>Configuration</h3><ul><li><b>Batch size</b>: 128</li><li><b>Epoches</b>: 5</li><li><b>Learning rate</b>: 2e-5 with 10% warmup steps</li><li><b>Early stopping</b> with patience=2</li><li><b>Class weights</b>: Balanced to handle class imbalance</li></ul><h3>Custom Metrics</h3><ul><li>Recall for specific classes (focus on class 2)</li><li>Precision for specific classes (focus on class 3)</li><li>F1-score (weighted)</li><li>AUC-PR</li><li>Recall at precision=0.6 (class 3)</li><li>Precision at recall=0.6 (class 2)</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>Per-class metrics</li><li>Confusion matrix</li><li>Full classification report</li></ul><h2>Technical Features</h2><h3>Data Preprocessing</h3><ul><li><b>Tokenization</b>: Maximum length of 128 tokens (truncation and padding)</li><li><b>Encoding of labels</b>: One-hot encoding for multi-class classification</li><li><b>Data split</b>: 80% training, 10% validation, 10% testing</li></ul><h3>Optimization</h3><ul><li><b>Optimizer</b>: Adam with linear warmup scheduling</li><li><b>Loss function</b>: Categorical Crossentropy (from_logits=True)</li><li><b>Imbalance handling</b>: Class weights computed automatically</li></ul><h2>Requirements</h2><p dir="ltr">The following Python packages are required:</p><ul><li>TensorFlow</li><li>Transformers</li><li>scikit-learn</li><li>pandas</li><li>datasets</li><li>matplotlib</li><li>seaborn</li><li>numpy</li></ul><h2>Usage</h2><ol><li><b>Data format</b>:</li></ol><ul><li>CSV file or Pandas DataFrame</li><li>Required column name: <code>text</code> (string type)</li><li>Required column name: Data type label (integer type, 0-4) - optional for evaluation</li></ul><ol><li><b>Text preprocessing</b>:</li></ol><ul><li>Automatic tokenization with a maximum length of 128 tokens</li><li>Long texts will be automatically truncated</li><li>Handling of special characters, URLs, and emojis included</li></ul><ol><li><b>Label encoding</b>:</li></ol><ul><li>The model classifies hate speech into 5 categories (0-4)</li><li><code>0</code>: Political hatred: Expressions directed against individuals or groups based on political orientation.…"

Elapsed time of algorithms on benchmark functions. حسب Muhammad Zubair Rehman (11243478)

الموضوعات:

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

S1 File - حسب Yuh-Chin T. Huang (17867207)

الموضوعات:

S1 Dataset - حسب Yuh-Chin T. Huang (17867207)

الموضوعات:

Prediction performance of different optimization algorithms. حسب Ali-Kemal Aydin (10968731)

The optimal solution set of NYN by using different algorithms. حسب Yi Tao (178829)

الموضوعات: "…evolutionary genetic algorithm…"

The optimal solution set of HN by using different algorithms. حسب Yi Tao (178829)

الموضوعات: "…evolutionary genetic algorithm…"

DataSheet2_Benefits and Challenges of Pre-clustered Network-Based Pathway Analysis.PDF حسب Miguel Castresana-Aguirre (12521683)

"…We explored whether network-based pre-clustering of a query gene set can improve pathway analysis. …"

DataSheet1_Benefits and Challenges of Pre-clustered Network-Based Pathway Analysis.CSV حسب Miguel Castresana-Aguirre (12521683)

"…We explored whether network-based pre-clustering of a query gene set can improve pathway analysis. …"

FAR-1: A Fast Integer Reduction Algorithm Compared to Collatz and Half-Collatz حسب Faizan Ali (21689861)

الموضوعات:

Proposed algorithm mean and standard deviation (StDev) versus compared algorithms for the benchmark functions. حسب El-Sayed M. El-kenawy (14581088)