Artificial Intelligence
This dataset contains different intestinal polyp datasets, in which the method of dividing test set and training set is the same as that mentioned in most intestinal polyp segmentation methods, where the training set consists of pictures from Kvasir and CVC-ClinicDB, and pictures from the two datasets are mixed into the same training set. The test set clearly indicates the division of the test set from different data sets in the form of folder names, and all images are unified as 352*352.
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3D Gaussian Splatting performs 3D reconstruction by densifying sparse point clouds into Gaussian ellipsoids of the order of 100,000, and the reconstruction results show excellent visual effects. However, the point cloud data derived from 3D Gaussian Splatting is not fully utilized in the reconstruction process. To this end, this paper proposes to optimize the point cloud data derived from 3D Gaussian Splatting to improve the rendering quality of 3D Gaussian Splatting. First, the sparse point cloud is input into 3D Gaussian Splatting for reconstruction.
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Modern automotive embedded systems include a large number of electronic control units (ECU) responsible for managing sophisticated systems such as engine control, ABS brake systems, traction control, and power steering systems. To ensure the reliability and effectiveness of these functions, it is essential to apply rigorous test approaches and standards. The integration of diagnostic functions in automotive embedded systems demands consistent tests and a detailed analysis of data.
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Animal habitat surveys play a critical role in preserving the biodiversity of the land. One of the effective ways to gain insights into animal habitats involves identifying animal footprints, which offers valuable information about species distribution, abundance, and behavior.
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Data sources of MKG with structured medical knowledge database and unstructured scientific publications
Source Type
Name
Related researches
Structured medical knowledge database
KEGG
[20]
SIDER
[21]
ICD-10
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The dataset aims to compile images of buildings with structural damage for analysis. The images can be classified by the severity of damage to building facades after seismic events using deep learning techniques, particularly pre-trained convolutional neural networks and transfer learning. The analysis can precisely identify structural damage levels, aiding in effective evaluation and response strategies.
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The Defect Tracking dataset provides a comprehensive resource for software maintenance and defect prediction research. This dataset, downloaded from the Jira Spring website, includes detailed defect data from a variety of Spring application projects such as Spring Framework, Spring Boot, Spring Security, Spring Data, and others. It encompasses numerous attributes, including issue summaries, types, statuses, priorities, resolution details, and additional relevant information.
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This is the MRI scan database used in the research work of classifying Meningioma Tumor in humans by using hybrid Ensemble Deep Learning Network AlGoRes. It consist of two sets; one for training and another one for testing the Deep Learning Network AlGoRes.
Training data set consist of 822 imagers with meningioma_tumor and 395 images without tumor.
Testing data set consist of 115 imagers with meningioma_tumor and 104 images without tumor.
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