Artificial Intelligence

This dataset contains high-resolution retinal fundus images collected from 495 unique subjects from Eye Care hospital in Aizawl, Mizoram, for diabetic retinopathy (DR) detection and classification. The images were captured over five years using the OCT RS 330 device, which features a 45° field of view (33° for small-pupil imaging), a focal length of 45.7 mm, and a 6.25 mm sensor width. Each image was acquired at a resolution of 3000x3000 pixels, ensuring high diagnostic quality and the visibility of subtle features like microaneurysms, exudates, and hemorrhages.

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This dataset was produced as part of the NANCY project (https://nancy-project.eu/), with the aim of using it in the fields of communication and

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Sign Language Recognition integrates computer vision and natural language processing to automatically interpret hand gestures and translate them into spoken or written Bengali. The primary goal is to bridge the communication gap between sign language users and non-users by recognizing gestures, movements, postures, and facial expressions that correspond to spoken language elements. Since hand gestures are the cornerstone of sign language communication, they play a pivotal role in improving the accuracy of sign language recognition systems.

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SUNBURST Attack Dataset for Network Attack Detection

Overview:
The SUNBURST dataset is a unique and valuable resource for researchers studying network intrusion detection and prevention. This dataset provides real-world network traffic data related to SUNBURST, a sophisticated supply chain attack that exploited the SolarWinds Orion software. It focuses on the behavioral characteristics of the SUNBURST malware, enabling the development and evaluation of security mechanisms.

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Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but dropout events, where gene expression is undetected in individual cells, present a significant challenge. We propose \textbf{scMASKGAN}, which transforms matrix imputation into a pixel restoration task to improve the recovery of missing gene expression data.  Specifically, we integrate masking, convolutional neural networks (CNNs), attention mechanisms, and residual networks (ResNets) to effectively address dropout events in scRNA-seq data.

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Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but dropout events, where gene expression is undetected in individual cells, present a significant challenge. We propose \textbf{scMASKGAN}, which transforms matrix imputation into a pixel restoration task to improve the recovery of missing gene expression data.  Specifically, we integrate masking, convolutional neural networks (CNNs), attention mechanisms, and residual networks (ResNets) to effectively address dropout events in scRNA-seq data.

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Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but dropout events, where gene expression is undetected in individual cells, present a significant challenge. We propose \textbf{scMASKGAN}, which transforms matrix imputation into a pixel restoration task to improve the recovery of missing gene expression data.  Specifically, we integrate masking, convolutional neural networks (CNNs), attention mechanisms, and residual networks (ResNets) to effectively address dropout events in scRNA-seq data.

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The application of large language models (LLMs) in urban planning has gained momentum, with prior research demonstrating their value in participatory planning, process streamlining, and event forecasting. This study focuses on further enhancing urban planning through the integration of more comprehensive datasets. We introduce a newly developed instruction dataset that amalgamates crucial information from several prominent urban datasets, including highD, NGSIM, the Road Networks dataset, TLC Trip data, and the Urban Flow Prediction Survey dataset.

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The Left Atrium2018 dataset was used in the 2018 Left Atrium Segmentation Challenge and has the following characteristics:

Data Content

Image Type: It consists of 154 three-dimensional gadolinium-enhanced magnetic resonance imaging (LGE-MRI) images, which is currently the largest cardiac LGE-MRI dataset in the world.

Label Information: It contains the relevant labels of the left atrium segmented by three medical experts.

Application Fields

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