Artificial Intelligence

The following is the data set for dissolved gas analysis method, it has been collected from literatures and it contains a total of 177 samples.

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We obtained 6 million instances to be used as an analysis for modelling CO2 behavior. The Data Logging and sensors nodes acquisition are every 1 second.

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It includes 312 ROIs. An ROI is a rectangular BMP image region. A rectangular image region  is located within a PDAC tumor region or within a HP region of a slice CT image. ROIs of 1-153 are PDAC, ROIs of 154:312 are HP.

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Endoscopy is a widely used clinical procedure for the early detection of cancers in hollow-organs such as oesophagus, stomach, and colon. Computer-assisted methods for accurate and temporally consistent localisation and segmentation of diseased region-of-interests enable precise quantification and mapping of lesions from clinical endoscopy videos which is critical for monitoring and surgical planning. Innovations have the potential to improve current medical practices and refine healthcare systems worldwide.

Last Updated On: 
Sat, 02/27/2021 - 05:11

The data set includes three sub-data sets, namely the DAGM2007 data set, the ground crack data set, and the Yibao bottle cap defect data set, which are divided into a training set and a test set, in which the positive and negative samples are unbalanced.

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2193 Views

Nextmed project is a software platform for the segmentation and visualization of medical images. It consist on a series of different automatic segmentation algorithms for different anatomical structures and  a platform for the visualization of the results as 3D models.

This dataset contains the .obj and .nrrd files that correspond to the results of applying our automatic lung segmentation algorithm to the LIDC-IDRI dataset.

This dataset relates to 718 of the 1012 LIDC-IDRI scans.

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Computer vision can be used for environment-adaptive control of robotic leg prostheses and exoskeletons. However, small-scale and private training datasets have impeded the development and dissemination of image classification algorithms (e.g., convolutional neural networks) to recognize the walking environment. To address these limitations, we developed ExoNet, a large-scale hierarchical dataset of wearable camera images (i.e., egocentric perception) of real-world walking environments.

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The dataset is used to detect essential protein in uncertain PPI network.

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The PPI datasets were collected from four different sources: DIP, MIPS, Gavin, and Krogan. All self-interactions and repeated interactions were filtered.  The essential proteins were collected from the following four different databases: MIPS,SGD,DEGand SGDP (http://www.sequence.stanford.edu/group/). Gene expression data were downloaded from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) with accession number GSE3431.

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A Chinese dataset for table-to-text generation named WIKIBIOCN which inculeds 33,244 biography sentences with related tables from Chinese Wikipedia (July 2018).

The dataset is divided into training set (30,000), verification set (1000) and test set (2,244).

 

 

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