BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data حسب Jean-Christophe Lachance (6619307)

"…Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a <b>B</b>iomass <b>O</b>bjective <b>F</b>unction from experimental <b>dat</b>a. …"

Schematic showcasing main steps involved in the generation of a new daughter node <i>C</i>_<i>i</i> and subsequently a new branch <i>B</i>_<i>i</i>. حسب Diana C. de Oliveira (9220729)

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.…"

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. …"

Bespoke software controls the sorter GUI and reports on instrument status to the user. حسب Diane M. Wiener (1956583)

الموضوعات:

TITR Algorithm Overview. The workflow of the TITR algorithm is shown describing each step, the tools used, specific conditions, and the output. حسب Bradley Crone (19346873)

Flow chart of the analysis steps in this study. حسب Junjie Su (368807)

الموضوعات:

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. حسب Yuanchen Zhao (12905580)

"…More specifically, we plot the <i>R</i>² of the best linear model minus the <i>R</i>² of the best quadratic, where “best” refers to the model identified by the corresponding Metropolis algorithm over its finite runtime (10000 steps). (B) Nevertheless, even when the linear algorithm loses in <i>R</i>², the grouping it identifies can be a better representation of the underlying ground truth. …"

The schematic view of the Radial Basis Function (RBF) algorithm. حسب Mohsen Yoosefzadeh-Najafabadi (6917399)

Reconstruction errors and their variability due to stochastic steps in the algorithms. حسب Hanane Omichessan (4991552)

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…"

Search Algorithms and Loss Functions for Bayesian Clustering حسب David B. Dahl (11761055)

Graphical summary and pseudo code of our gait signatures framework and algorithm. حسب Taniel S. Winner (17296029)

The performance comparison of the base (CNN+BiLSTM+MHSA) model with different algorithms for solving the problem of imbalanced data classification on the test set. حسب Wenhui Yan (825510)

Convergence of the Cut Value of QFMA as a function of steps for the G1 problem. حسب Máté Tibor Veszeli (12160786)

Convergence curves of the voltage profile improvement by using (a) the HFPSO algorithm and (b) the PSO algorithm. حسب Abdullah Khan (377292)

Convergence curves of voltage stability enhancement based on (a) the HFPSO algorithm and (b) the PSO algorithm. حسب Abdullah Khan (377292)

SP results of algorithms in the CEC 2009 test functions. حسب Vahid Goodarzimehr (21343124)

MS results of algorithms in the CEC 2009 test functions. حسب Vahid Goodarzimehr (21343124)