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Machine Learning

Drug Target Negative Interactions Dataset

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The dataset represents the negative interaction dataset of the Drugbank that has been generated from our proposed machine learning method based on drug similarity, which achieved an average accuracy of 95% compared to the randomly generated negative datasets in the literature. Drugbank was used as the drug target interaction dataset from https://go.drugbank.com/. It consists of 1,264 interactions among 504 drugs and 507 proteins. The dataset includes drugs names, their accession numbers, proteins names, their UniproteId on Uniprot at https://www.uniprot.org/.

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SiCWell Dataset

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The SiCWell Dataset contains data of battery electric vehicle lithium-ion batteries for modeling and diagnosis purposes. In this experiment, automotive-grade lithium-ion pouch bag cells are cycled with current profiles plausible for electric vehicles. 

The analysis of current ripples in electric vehicles and the corresponding aging experiments of the battery cells result in a dataset, which is composed of the following parts: 

 

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Equidistant and Uniform Data Augmentation for 3D Objects

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Data augmentation is commonly used to increase the size and diversity of the datasets in machine learning. It is of particular importance to evaluate the robustness of the existing machine learning methods. With progress in geometrical and 3D machine learning, many methods exist to augment a 3D object, from the generation of random orientations to exploring different perspectives of an object. In high-precision applications, the machine learning model must be robust with respect to the small perturbations of the input object.

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Fractures in UAV Imagery for Segmentation

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The dataset contains UAV imagery and fracture interpretation of rock outcrops acquired in Praia das Conchas, Cabo Frio, Rio de Janeiro, Brazil. Along with georeferenced .geotiff images, the dataset contains filtered 500 x 500 .png tiles containing only scenes with fracture data, along with .png binary masks for semantic segmentation and original georeferenced shapefile annotations. This data can be useful for segmentation and extraction of geological structures from UAV imagery, for evaluating computer vision methodologies or machine learning techniques.

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