Datasets

Subject
Creator
Type
Date

subject: Machine Learning

Total is 16 Results
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, 0000-0003-4638-3345

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, 0000-0001-5630-6824, 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, 0000-0001-6826-5214, 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, 0000-0002-5327-8431, Venkatesh Mohan Merwade, 0000-0001-5518-2890

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

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

10.4231/B783-2C47

Pin-ching Li, Sayan Dey, 0000-0002-5327-8431, Venkatesh Mohan Merwade, 0000-0001-5518-2890

01/23/2023

This resource contains codes 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) Machine Learning Random Forest streamflow prediction

Clustering in Machine Learning Pattern Formation of VO2

10.4231/966Q-6F95

Alexandre Zimmers, Dmitri Basov, Erica Carlson, 0000-0003-2162-5301, Forrest Simmons, Ivan K. Schuller, Lionel Aigouy, Lukasz Burzawa, Melissa Alzate Banguero, Mumtaz Qazilbash, Pavel Salev, Sayan Basak, 0000-0002-6388-3598

04/07/2023

Codes used in "Deep Learning Hamiltonians form Disordered Image Data in Quantum Materials" https://arxiv.org/abs/2211.01490 and the resulting visualizations.

Clustering Machine Learning Physics quantum materials Scientific visualization symmetry Vanadium dioxide VO2

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

10.4231/FPHP-0153

Meng-yang Lin, Mitchell R Tuinstra, 0000-0002-5322-6519

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

Evaluating Economic Opportunities for Product Recycling via the Sherwood Principle and Machine Learning - Supporting Information

10.4231/E80W-7941

Aihua Huang, John W. Sutherland, 0000-0002-2118-0907, Sidi Deng, 0009-0003-9234-1952, Xiaoyu Zhou, 0000-0002-5083-1713, Yuehwern Yih, 0000-0003-2087-7718

09/03/2020

This repository contains the supporting information for the manuscript regarding Sherwood principle and Machine learning. All critical underlying data files, along with a flow chart that describes the methodologies applied in the paper are enclosed.

Circular Economy Empirical Models Environmental and Ecological Engineering Machine Learning Sherwood Principle

Structural attributes derived from Google Street View imagery, Louisiana coastal zone

10.4231/1TZX-BR43

David R Johnson, 0000-0002-2364-340X, Ed Delp, 0000-0002-2909-7323, Fu-Chen Chen, 0000-0002-6396-2798, Mohammad Jahanshahi

07/28/2020

Structural attributes relevant to flood risk (foundation height/type, square footage, number of stories, building type), produced using machine learning for automated image analysis of GSV images.

flood risk Industrial Engineering Machine Learning structure attributes

Genome-wide, Organ-delimited gene regulatory networks (OD-GRN) provide high accuracy in candidate Transcription Factor (TF) selection across diverse processes

10.4231/50R5-EM83

Karen Hudson, Kranthi K Varala, 0000-0003-1051-6636, Ying Li

01/04/2024

Organ-specific gene expression datasets that include hundreds to thousands of experiments allow reconstruction of gene regulatory networks and discovery of transcriptional regulators various pathways and processes.

Arabidopsis thaliana Gene regulatory networks k-nearest neighbor (kNN) linear support vector machines (SVM) Machine Learning Systems biology

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