Machine Learning

As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on recognizing textures and materials in real-world images, which plays an important role in object recognition and scene understanding. Aiming at describing objects or scenes with more detailed information, we explore how to computationally characterize apparent or latent properties (e.g. surface smoothness) of materials, i.e., computational material characterization, which moves a step further beyond material recognition.

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The dataset consists of the following columns:

Data description

ColumnDescriptiongift_idUnique ID of giftgift_typeType of gift (clothes/perfumes/etc.)gift_categoryCategory to which the gift belongs under that gift typegift_clusterType of industry the gift belongsinstock_dateDate of arrival of stockstock_update_dateDate on which the stock was updatedlsg_1 - lsg_6Anonymized variables related to giftuk_date1, uk_date2Buyer related datesis_discountedShows whether the discounted is applicable on the giftvolumesNumber of packages boughtpriceThe total price

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This dataset was used and described in detail in the following publication. The Effects of Different Levels of Realism on the Training of CNNs with only Synthetic Images for the Semantic Segmentation of Robotic Instruments in a Head Phantom. Heredia-Perez, S. A.; Marinho, M. M.; Harada, K.; and Mitsuishi, M. International Journal of Computer Assisted Radiology and Surgery (IJCARS). 2020. https://doi.org/10.1007/s11548-020-02185-0

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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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