Metadata overview for <i>Mycobacterium bovis</i> samples analyzed for the present study, including Sequence ID, host species, United States Department of Agriculture - National Veterinary Services Laboratory <i>M. bovis</i> worldwide metadata project classification, clonal complex, lineage, and two-letter country code. حسب Hind Yahyaoui Azami (3712459)

Distributions of classification scores from 5-fold cross-validation experiments. حسب Marcos Soares Barbeitos (20416883)

Flowchart of exclusion and inclusion process. حسب Nina Vibeche Skei (20904020)

"…<div><p>Background</p><p>In observational studies that use administrative data, it is essential to report technical details such as the number of International Classification of Disease (ICD) coding fields extracted. …"

Code repository حسب Dimitrios Iason Papadopoulos (21556811)

"…<pre>This repository contains the code that was used to conduct the experiments that are mentioned in the paper.…"

Model code حسب Waltraud Schulze (20695250)

"…<p dir="ltr">Quantification and classification of leaf surface texture complexity: SEM images were used as databases to quantify the texture complexity of leaf abaxial surfaces. …"

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" حسب FirstName1 LastName1 (20547569)

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" حسب FirstName LastName (20554465)

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" حسب FirstName LastName (20554465)

GAM between the biometric (morphological) shell descriptors (Response) and a. Latitude and b. Longitude. Codes of biometric variables are given in the text (Material and Methods se... حسب Ioan Sîrbu (18475797)

IKM Pseudo – code. حسب Yingbo Mao (22522192)

Map displaying the earthworks of the southeastern LM, set against a land cover classification demarcating areas of tropical forest (green), savanna (light green), and water (blue). حسب Joseph Hirst (21453867)

Simple and conditional term effects of climatic predictors on the p-distances between populations, within a db-RDA analysis. p-adj – probabilities adjusted by the false discovery r... حسب Ioan Sîrbu (18475797)

Additional file 4 of Chemical classification program synthesis using generative artificial intelligence حسب Christopher J. Mungall (6793694)

Dataset: OHID-1: A New Large Hyperspectral Image Dataset for Multi-Classification حسب Jianwen Deng (19780538)

"…</p>code<p dir="ltr">The "HSI_Classification" folder contains the code for ID CNN, 2D CNN, 3D CNN, and SVM. …"

Additional file 5 of Chemical classification program synthesis using generative artificial intelligence حسب Christopher J. Mungall (6793694)

"…This is considerably more complex, but it achieves a higher F1 score (c) evolution of F1 scores. …"

S1 Archive - حسب Hootan Kamran (21519846)

Supplementary Data to "Model interpretability enhances domain generalization in the case of textual complexity modeling" حسب Frans Van der Sluis (18365826)

"…</li></ul><h3>Performance Metrics</h3><p dir="ltr">Metrics listed are evaluated during the outer loop of the training process.</p><ul><li><b>Classification Metrics (</b><code><strong>_classif_</strong></code><b>)</b>: Includes accuracy (acc), precision, recall, f1 score, true negatives (tn), true positives (tp), false negatives (fn), and false positives (fp) for task 1 (text classification).…"

SH-DETR architecture. حسب Shouluan Wu (22601074)

"…In this context, we introduce a deep learning framework that combines multi-channel random coding with modules for multi-scale feature fusion to tackle the challenges of low recognition accuracy and insufficient classification power prevalent in conventional models. …"

Experimental hardware and software configuration. حسب Shouluan Wu (22601074)

"…In this context, we introduce a deep learning framework that combines multi-channel random coding with modules for multi-scale feature fusion to tackle the challenges of low recognition accuracy and insufficient classification power prevalent in conventional models. …"

The detection performance for different dataset. حسب Shouluan Wu (22601074)

"…In this context, we introduce a deep learning framework that combines multi-channel random coding with modules for multi-scale feature fusion to tackle the challenges of low recognition accuracy and insufficient classification power prevalent in conventional models. …"