Artificial Intelligence

The dataset encompasses a diverse array of electrical signals representing Power Quality Disturbances (PQD), both in single and combined forms, meticulously generated in adherence to the IEEE 1159 guideline.  Crucially, the dataset includes both raw data and corresponding labels, facilitating supervised learning tasks and enabling the development and evaluation of classification algorithms.

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The safe implementation and adoption of Autonomous Vehicle (AV) vision models on public roads requires not only an understanding of the natural environment comprising pedestrians and other vehicles but also the ability to reason about edge situations such as unpredictable maneuvers by other drivers, impending accidents, erratic movement of pedestrians, cyclists, and motorcyclists, animal crossings, and cyclists using hand signals.

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This dataset contains both the artificial and real flower images of bramble flowers. The real images were taken with a realsense D435 camera inside the West Virginia University greenhouse. All the flowers are annotated in YOLO format with bounding box and class name. The trained weights after training also have been provided. They can be used with the python script provided to detect the bramble flowers. Also the classifier can classify whether the flowers center is visible or hidden which will be helpful in precision pollination projects.

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This is the relevant data in "Monocular Homography Estimation and Positioning Method for the Spatial-Temporal Distribution of Vehicle Loads Identification".

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The public UA-DETRAC data set was used to train the vehicle target detection model in this study. This data set was captured predominantly in the Beijing-Tianjin-Hebei area. Using a Canon EOS 550 camera, 10 hours of video were captured at 25 frames per second, and then the footage was processed. Each frame had a resolution of 960*540 pixels. The UA-DETRAC data set contained 8,259 manually labeled vehicles, with a total of 1,210,000 labeled vehicles. The labeled types included cars, buses, trucks, etc.

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The dataset crafted for this study is intentionally designed to encapsulate instances of cyberbullying across three distinct languages: Urdu, Roman Urdu, and English. This strategic selection aims to mirror the linguistic variations that are prevalent in social media dialogues among Urdu-speaking communities globally. Further, it undergoes meticulous annotation to encapsulate the diverse linguistic nuances characteristic of these languages. This process includes integrating critical aspects of cyberbullying, such as aggression, repetition, and intent to harm.

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This work selects two real datasets, namely Diginetica and Yoochoose. Diginetica is employed in CIKM Cup 2016, which contains user's transaction data. Yoochoose is released in Recsys Challenge 2015 as a public dataset and mainly consists of click data within six months. For better recommendation, we follow previous work and preprocess the real datasets. 

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An experimental study was conducted on a high-voltage glass-type disc (LD-160) to investigate the effect of string arrangements on pollution and icing flashover characteristics. Two Artificial Neural Network (ANN) applications were developed to simulate and calculate the flashover voltage based on the experimental results. The test results showed that the inverted T-type arrangement can improve the pollution flashover voltage and increase the icing flashover voltage of insulator strings compared to the traditional arrangement of the I-string.

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This data repository comprises three distinct datasets tailored for different predictive modeling tasks. The first dataset is a synthetic dataset designed to simulate multivariate time series patterns, incorporating both linear and non-linear dependencies among input and target features. The second dataset, the Beijing Air Quality PM2.5 dataset, consists of PM2.5 measurements alongside meteorological data like temperature, humidity, and wind speed, with the objective of predicting PM2.5 concentrations.

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