Symmetry test result. حسب Jiyong Moon (16819302)

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)

"…</li></ul><p dir="ltr"><b>File Structure</b></p><p dir="ltr">The code generates and saves:</p><ul><li>Weights of the trained model (.h5)</li><li>Configured tokenizer</li><li>Training history in CSV</li><li>Requirements file</li></ul><p dir="ltr"><b>Important Notes</b></p><ul><li>The model excludes category 2 during training</li><li>Implements transfer learning from a pre-trained model for binary hate detection</li><li>Includes early stopping callbacks to prevent overfitting</li><li>Uses class weighting to handle category imbalances</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). …"

Related studies on IDS using deep learning. حسب Arshad Hashmi (13835488)

"…The suggested model’s accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. …"

The architecture of the BI-LSTM model. حسب Arshad Hashmi (13835488)

"…The suggested model’s accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. …"

Comparison of accuracy and DR on UNSW-NB15. حسب Arshad Hashmi (13835488)

"…The suggested model’s accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. …"

Comparison of DR and FPR of UNSW-NB15. حسب Arshad Hashmi (13835488)

"…The suggested model’s accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. …"

Spontaneous ERG-based random forest model discriminates control and disease cases in rodent models of obesity and type 1 diabetes and predicts disease evolution in the type 1 diabe... حسب Ramsés Noguez Imm (6658637)

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

"…</p><p>Result: The developed algorithm was trained and tested on ECG data of nine types of cardiac states, fulfilling a task of multi-label classification. …"

Fig 12 - حسب Nisha Yadav (366131)

Flowchart of software workflow. حسب Gavrielle R. Untracht (11820311)

Recognition of each neuron’s digital identity. حسب Yuxiang Wu (748543)

Schematic overview of SINATRA Pro: A novel framework for discovering biophysical signatures that differentiate classes of proteins. حسب Wai Shing Tang (9541179)

Steps in the extraction of 14 coordinates from the CT slices for the curved MPR. حسب Linus Woitke (22783534)

"…Protruding paths are then eliminated using graph-based optimization algorithms, as demonstrated in f). …"

a-g SHapley Additive exPlanation (SHAP) values for model features by class (MIS-C/Non-MIS-C PASC). حسب Vitaly Lorman (16814000)

Super.Complex identifies likely protein complexes within a PPI network using a distributed supervised AutoML method. حسب Meghana Venkata Palukuri (11890672)

Models and Dataset حسب M RN (9866504)

"…</p><p dir="ltr"><br></p><p dir="ltr"><b>RAO (Rao Optimization Algorithm):</b><br>RAO is a parameter-less optimization algorithm that updates solutions based on simple arithmetic operations involving the best and worst individuals in the population. …"

iNCog-EEG (ideal vs. Noisy Cognitive EEG for Workload Assessment) Dataset حسب Fariya Bintay Shafi (21692408)

"…</li><li><b>Ground Truth Labels:</b> Derived from task difficulty metadata and subjective feedback, yielding a <b>two-level annotation scheme</b>:</li><li><ol><li><b>Binary classification:</b> No Workload (rest) vs. …"

DataSheet1_Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network.PDF حسب Zhen Shen (393133)

"…</p><p>Methods: In this study, based on the deep learning models that implement pixel-level binary classification prediction in computer vision, we viewed the CircRNA-protein binding sites prediction as a nucleotide-level binary classification task, and use a fully convolutional neural networks to identify CircRNA-protein binding motif sites (CPBFCN).…"

Table1_Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network.XLSX حسب Zhen Shen (393133)

"…</p><p>Methods: In this study, based on the deep learning models that implement pixel-level binary classification prediction in computer vision, we viewed the CircRNA-protein binding sites prediction as a nucleotide-level binary classification task, and use a fully convolutional neural networks to identify CircRNA-protein binding motif sites (CPBFCN).…"

Supplementary Material for: Utilizing Deep Learning to Identify Electron-Dense Deposits in Renal Biopsy Electron Microscopy Images حسب figshare admin karger (2628495)

"…The ResNet18 architecture was selected for our task. To evaluate the model's classification capability, we created a binary classification model to identify the presence of deposits in EM images. …"