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

Data_Sheet_1_Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM).pdf by Sai Sakunthala Guddanti (17739363)

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

“…These emergent modalities provide more holistic observations of complex biomolecular processes and call for new analysis methods to detect state changes in multichannel data. Here we develop an algorithm called MULLR (MUlti-channel Log-Likelihood Ratio test) to <i>jointly</i> identify change points in multichannel single-molecule measurements. …”

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. …”

A classification scheme of bipartite randomization algorithms, based on whether the matrix row and columns sums are preserved exactly (Fixed, <i>F</i>), preserved on average (Propo... by Zachary P. Neal (9946331)

Table 1_A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing... by Charmayne Mary Lee Hughes (12959972)

“…</p>Conclusion<p>Machine learning algorithms show promise for the detection of current frailty status, particularly in binary classification. …”

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). …”

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

“…<p dir="ltr"><b>DDS3 - Dataset of mosaic sputum smear microscopy images for evaluation of bacillus detection algorithms</b></p><p dir="ltr">This data set corresponds to mosaic images that are composed of a 10x10 arrangement of patches (negatives and positives) from the DDS1 dataset, resulting in a 400x400 pixel image. …”

Community Detection with Heterogeneous Block Covariance Model by Xiang Li (114679)

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

Joint Network Reconstruction and Community Detection from Rich but Noisy Data by Jie Hu (130301)

Data_Sheet_1_Automatic Detection for Multi-Labeled Cardiac Arrhythmia Based on Frame Blocking Preprocessing and Residual Networks.PDF by Zicong Li (228040)

“…</p><p>Method: The proposed algorithm consists of the following components: (i) a preprocessing component that utilizes the frame blocking method to split an ECG recording into frames with a uniform length for all considered ECG recordings; and (ii) a binary classifier based on ResNet, which is combined with the attention-based bidirectional long-short term memory model.…”

Association between maxillary protrusion and oral habits. by Kengo Oka (1420585)

Subjects:

Association between crowding and oral habits. by Kengo Oka (1420585)

Subjects:

Guide frame that appears on the smartphone application. by Kengo Oka (1420585)

Subjects:

Association between deep bite and oral habits. by Kengo Oka (1420585)

Subjects:

Performance metrics of the logistic regression models on the test dataset. by Kengo Oka (1420585)

Subjects:

Explicability results of the deep bite classification model. by Kengo Oka (1420585)

Subjects:

Representative photographic image. by Kengo Oka (1420585)

Subjects:

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

Subjects: