Machine Learning
This dataset contains the trained model that accompanies the publication of the same name:
Anup Tuladhar*, Serena Schimert*, Deepthi Rajashekar, Helge C. Kniep, Jens Fiehler, Nils D. Forkert, "Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 94871-94879, 2020, doi:10.1109/ACCESS.2020.2995632. *: Co-first authors
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The database contains the raw range-azimuth measurements obtained from mmWave MIMO radars (IWR1843BOOST http://www.ti.com/tool/IWR1843BOOST) deployed in different positions around a robotic manipulator.
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This is a preprocessed dataset of 2 companies from Pakistan Stock Exchange.
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In this paper, we present a collaborative recommend system that recommends elective courses for students based on similarities of student’s grades obtained in the last semester. The proposed system employs data mining techniques to discover patterns between grades. Consequently, we have noticed that clustering students into similar groups by performing clustering. The data set is processed for clustering in such a way that it produces optimal number of clusters.
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The data contains files which are unprocessed and signals which pass through alert signal separator.
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This dataset is offered as .csv and is part of 3 files which are:
- File 1: has all 1699 arabic news headlines colllected with the corresponding emotion classification that 3 annotators agreed on with no bias
- File 2: has the dataset with BOW features extracted
- File 3: has the dataset with n-gram features extracted
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The supplementary files of our submitted TIFS paper: "CALPA-NET: Channel-pruning-assisted Deep Residual Network for Steganalysis of Digital Images".
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This aerial image dataset consists of more than 22,000 independent buildings extracted from aerial images with 0.0075 m spatial resolution and 450 km^2 covering in Christchurch, New Zealand. The most parts of aerial images are down-sampled to 0.3 m ground resolution and cropped into 8,189 non-overlapping tiles with 512* 512. These tiles make up the whole dataset. They are split into three parts: 4,736 tiles for training, 1,036 tiles for validation and 2,416 tiles for testing.
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Dataset is intended for studying how student programming styles and usage of IDE differs between students who plagiarise their homework and students who solve them honestly.
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This data includes 350 Korotkoff sounds and their corresponding osciilometric waveforms, cuff pressure and detected systolic and diastolic pressures.
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