Machine Learning

CoMMonS: Challenging Microscopic Material Surface Dataset

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.

Categories:: Artificial Intelligence
Computer Vision
Image Processing
Machine Learning
Signal Processing

267 Views

HackerEarth Machine Learning challenge: Predict the price for Good Friday gifts

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

Categories:: Artificial Intelligence
Machine Learning

264 Views

Synthetic Images for the Semantic Segmentation of Robotic Instruments in a Head Phantom

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

Categories:: Artificial Intelligence
Machine Learning

311 Views

Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks

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

Categories:: Artificial Intelligence
Image Processing
Machine Learning
Biomedical and Health Sciences
Medical Imaging

3781 Views

Federated Learning: mmWave MIMO radar dataset for testing

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.

Categories:: IoT
Machine Learning
Communications

2850 Views

Pakistan Stock Exchange Datasets

This is a preprocessed dataset of 2 companies from Pakistan Stock Exchange.

Categories:: Machine Learning

328 Views

Grade Sheet With Elective Course

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.