Artificial Intelligence

This database contains Synthetic High-Voltage Power Line Insulator Images.

There are two sets of images: one for image segmentation and another for image classification.

The first set contains images with different types of materials and landscapes, including the following landscape types: Mountains, Forest, Desert, City, Stream, Plantation. Each of the above-mentioned landscape types consists of 2,627 images per insulator type, which can be Ceramic, Polymeric or made of Glass, with a total of 47,286 distinct images.

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LGG Segmentation Dataset

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This a Lightning arrester point cloud dataset, using TXT documents to save, each file format is (8192, 7), 8192 means each file has 8192 points, where 1-3 columns are spatial dimensions, 4-6 columns are color information, and the last column is the label information of lightning arrester parts segmentation. It can be used to finished pointcloud segmention task.

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While picking robots aim to address this, the complex growth environment poses challenges in identifying and locating fruits due to factors like light and leaf occlusion. This study focuses on designing a recognition and localization method tailored to the natural growth conditions of melons and fruits, aiming to provide precise positional information for effective harvesting. Leveraging GTR-Net and binocular stereo vision, the proposed technology integrates a lightweight backbone network with Ghost bottleneck and TCSPG modules.

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The following are three publicly available datasets for experiments related to federated learning or machine learning.

Availability of Data and Materials: The datasets used to support the findings of this study are publicly available on Internet as follow:

MNIST: http://yann.lecun.com/exdb/mnist/;

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Popularity of smartphones also popularized, reading content using smartphones. Reading using smartphones quite differs from reading using desktop system. Mouse and Keyboard are the peripherals associated with the reading in desktop systems. Study of the handling of such devices has led to provide implicit feedback of the content read. Similar study in smartphones to get implicit feedback remains to be a huge gap. Reading using smartphones involves screen gestures like pinch to zoom, tap, scroll, orientation change and screen capture.

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The SA dataset is a small- to medium-sized dataset that we collected and produced in 2023 for anemone image generation, primarily from EUVP, Flicker, and YouTube, and we consider the availability of the SA dataset to be of particular significance given the scarcity of marine life image datasets. Currently, we have not publicly released the SA dataset, but will do so in the near future.

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MHS

Existing datasets of infrared and visible images only contain few extreme scenes, we construct a dataset of images with haze based on the M3FD dataset. We pick 450 aligned image pairs from M3FD dataset and synthesize hazy visible images using the ASM. Due to the unique imaging principle of infrared images, rarely affected by haze, there is no need to do additional process for infrared images. Finally, a dataset named MHS has been released, which contains 450 pairs of images in hazy conditions.

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data have 16 features with 1 target value

Scope: Primarily focused on diabetes-related information.

Data Size: Contains a substantial volume of records.

Variables: Likely includes patient demographics, medical history, lab results, medications, treatments, and outcomes.

Temporal Range: Time span covered by the dataset may vary.

Privacy Measures: Anonymized to protect patient identities.

Ethical Considerations: Collected and shared adhering to ethical guidelines.

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The research team conducted logistic and Cox regression according to the behavioral data of gastroesophageal reflux disease patients who had long been drinking caffeinated coffee drinks, and determined the sensitivity and mathematical rationality of AI prediction model in behavioral science, which can support the research team to build a deep learning neural network and complete the prediction of gastrointestinal tract involvement.

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