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

Raw Data and Results from Automatic Murine Cardiac Ultrasound and Photoacoustic Image Segmentations

10.4231/9C8X-H052

Craig J Goergen , Hayley Chan , Katherine Leyba , Olivia Claire Loesch , Pierre Sicard

05/25/2023

Accuracy and dice scores from cross-validation reported in the cross-validation results spreadsheet. Radial strain raw data and results reported in strain results spreadsheet. Oxygen saturation values reported in sO2 results spreadsheet.

cardiovascular deep learning Imaging photoacoustic imaging ultrasound

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

River Obstacle Segmentation En-route By USV Dataset (ROSEBUD)

10.4231/MMJ2-NH88

Jalil Francisco Chavez Galaviz , Jianwen Li , Nina Mahmoudian , Reeve David Lambert , Zihan Wang

06/12/2022

This dataset contains stills from video taken on Sugar Creek and the Wabash River in the US state of Indiana. Images are hand annotated to provide training and testing data for semantic segmentation networks.

autonomous and connected vehicles deep learning Mechanical Engineering River Robotics semantic segmentation

Aerial Fluvial Image Dataset (AFID) for Semantic Segmentation

10.4231/B129-XD47

Li-Fan Wu , Nina Mahmoudian , Zihan Wang

07/20/2022

816 2K/2.7K per-pixel annotated images with 8 classes: River, Boat, Bridge, Sky, Forest vegetation, Dry sediment, Drone self and Obstacle in river. Fluvial scenes are from Wabash River and Wildcat Creek in Indiana, USA.

Artificial Neural Network (ANN) autonomos vehicles Collision Avoidance deep learning drone Image Dataset Mechanical Engineering navigation RGB image dataset River Robotics semantic segmentation Unmanned Aerial Vehicle Wabash River

Raman spectra-based deep learning – A tool to identify microbial contamination

10.4231/HQGB-K827

Amir Hossein Raffiee , Arezoo M. Ardekani , Denilson Mendes De Oliveira , Mohit S. Verma , Murali K. Maruthamuthu

09/03/2020

Raman spectroscopic dataset for the potential microbes contaminating the pharmaceutical industry.

Agricultural and Biological Engineering deep learning microbial contamination Raman

Wheat spike blast image classification using deep convolutional neural networks

10.4231/P0Y7-3428

Carlos Gongora , Christian D Cruz , Darcy Telenko , Jian Jin , Mariela Fernandez-Campos , Mohammad Jahanshahi , Tao Wang , Yuting Huang

04/30/2021

The folder includes i) a wheat spike blast image classification CNN model trained to automatically quantify and classify disease severity, ii) the generated datasets that include images of wheat spike blast severity levels under controlled...

Blast Botany and Plant Pathology CNN deep learning disease Magnaporthe oryzae plant disease phenotyping Plant phenotyping Python Wheat wheat blast

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

Source code for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision

10.4231/R7N58JJ3

Haiguang Wen , Jiayue Cao , Junxing Shi , Kun-Han Lu , Yizhen Zhang , Zhongming Liu

11/17/2017

This document includes the main source code (Matlab or Python) related to our study.

Biomedical Engineering deep learning FMR1 LIBI Matlab natural vision neural decoding Neural encoding Neuroscience Python Code Source Code

Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 1

10.4231/R7X63K3M

Haiguang Wen , Jiayue Cao , Junxing Shi , Kun-Han Lu , Yizhen Zhang , Zhongming Liu

09/15/2017

This is a video-fMRI dataset for subject 1 (out of three) acquired by the Laboratory of Integrated Brain Imaging (LIBI),

Biomedical Engineering deep learning fMRI LIBI natural vision neural decoding Neural encoding Neuroscience

Datasets

Subject

Creator

Type

Date

Total is 15 Results