PURR Registry

Simplicity of K-means versus deepness of Deep Learning. A Case of Unsupervised Feature Learning with Limited Data

10.4231/R7N58J9Z

Baichuan Zhang , Bartlomiej P. Rajwa , Murat Dundar , Qiang Kou , Yicheng He

10/01/2015

A study contrasting K-means-based unsupervised feature learning and deep learning techniques for small data sets with limited intra- as well as inter-class diversity

bacterial colonies BARDOT Biomedical Engineering Computer Science deep learning Elastic light scattering Interdisciplinary Research K-Means Clustering Life Sciences Machine Learning representation learning

A deep learning neural network to extract of P- and S-wave transit times from Vertical Seismic Profile (VSP)

10.4231/TT0F-KH40

Douglas R Schmitt , Oumeng Zhang

07/23/2024

This archive contains the training dataset and the Python code to train a deep learning neural net that aims to extract separately P and S wave arrival transit times from synthetic common shot gathers (CSG) in a deviated borehole geometry.

deep learning Machine Learning Machine Learning and Geophysical Signals seismic behavior

Deep Learning of CYP450 Binding of Small Molecules by Quantum Information

10.4231/SCF2-QJ02

Koushiki Basu , Nicholas Huls , Shan Lu , Tonglei Li

11/06/2024

We implemented the Manifold Embedding of Molecular Surface approach, which retains the quantum mechanical characteristics of molecules, to predict a drug's likelihood of binding to cytochrome P450 enzymes by deep learning.

deep learning Informatics Machine Learning Molecular Pharmacology

General Aviation Flight Phase Identification using Automatic Dependent Surveillance-Broadcast (ADS-B)

10.4231/X0HZ-MF23

John H Mott , Qilei Zhang

08/23/2021

Dataset including KLAF ADS-B data and synthetic flight data for clustering validation purposes.

ADS-B Aviation Technology Clustering Machine Learning

RoboMal Malware Detection Dataset

10.4231/YN7G-H807

Byung-cheol Min , Haozhe Zhou , Richard Voyles , Upinder Kaur , Xiaxin Shen

10/15/2021

The RoboMal Dataset consists of 450 binary executables designed to facilitate malware detection in robotic software.

Engineering Technology Machine Learning malware Python Robotics security

Multi-Species Prediction of Physiological Traits with Hyper-Spectral Modeling

10.4231/FPHP-0153

Meng-yang Lin , Mitchell R Tuinstra

02/11/2022

High-throughput hyperspectral imaging in corn and sorghum can be used in multi-species models to predict water and nitrogen status of plants within and across these crop species.

Abiotic stress Agronomy Corn Ecophysiology High-throughput Phenotyping Machine Learning nitrogen content partial least square regression relative water content Remote Sensing Sorghum

Gait phase estimation using wearable sensors and PCA based linear regression model

10.4231/Q0HY-AT09

Ahmed Khaled Soliman , Andres Torres , Chang Heon Lee , Guilherme A. Ribeiro , Li-fan Wu , Mo Rastgaar

05/16/2022

Estimating gait realtime through 2 wearable sensors and a PCA based linear regression model.

Acceleration Biomedical Engineering Machine Learning Mechanical Engineering PCA Robotics Stepwise Multiple linear Regression Wearable Device

A Machine Learning Approach to Design of Aperiodic, Clustered-Dot Halftone Screens via Direct Binary Search

10.4231/AMGQ-0T59

Itamar Roth , Jan Allebach , Jiayin Liu , Orel Bat Mor , Oren Haik , Shani Gat , Tal Frank , Yitzhak Yitzhaky

06/01/2022

This dataset contains two parts: one has halftone patches that were used to predicts the quality level and scale using machine learning methods. The second part contains full versions of halftone images so viewers can zoom in to see the details.

direct binary search Electrical and Computer Engineering Halftone screen Machine Learning

Data for Characterization of Acoustic Emissions from Analogue Rocks using Sparse Regression-DMDc

10.4231/4K64-4818

Charles Fieseler , Chven Mitchell , Laura J Pyrak-Nolte , Nathan Kutz

06/14/2022

Acoustic waveforms collected during the monitoring of moisture loss in synthetic rock samples composed of mortar, and mortar with either distributed clay or localized clay, under ambient laboratory conditions.

acoustic signals clay cracking Fractures Machine Learning Physics

Data for Analyzing the Effect of Data Splitting and Covariate Shift on Machine Leaning Based Streamflow Prediction in Ungauged Basins

10.4231/0PG5-KC30

Pin-ching Li , Sayan Dey , Venkatesh Mohan Merwade

01/23/2023

This resource contains the data used in the study "Analyzing the Effect of Data Splitting and Covariate Shift on Machine Leaning Based Streamflow Prediction in Ungauged Basins" published in Water Resources Research (doi: 10.1029/2023WR034464)

Artificial Neural Network (ANN) covariate shift Hydrology Machine Learning prediction in ungauged basin Random Forest streamflow prediction

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Total is 18 Results