Subject
- Biomedical Engineering 2
- Earth and Atmospheric Sciences 2
- Physics 2
- Agronomy 1
- Aviation Technology 1
- Computer Science 1
- Industrial Engineering 1
- Interdisciplinary Research 1
- Life Sciences 1
- Mechanical Engineering 1
Creator
- Pin-ching Li 2
- Sayan Dey, 0000-0002-5327-8431 2
- Venkatesh Mohan Merwade, 0000-0001-5518-2890 2
- Ahmed Khaled Soliman 1
- Aihua Huang 1
- Alexandre Zimmers 1
- Andres Torres 1
- Avnika Manaktala, 0000-0003-4470-4037 1
- Baichuan Zhang 1
- Bartlomiej P. Rajwa 1
Type
- Dataset 18
Date
Total is 18 Results
Clustering in Machine Learning Pattern Formation of VO2
10.4231/966Q-6F95
Alexandre Zimmers
,
Dmitri Basov
,
Erica Carlson
,
Forrest Simmons
,
Ivan K. Schuller
,
Lionel Aigouy
,
Lukasz Burzawa
,
Melissa Alzate Banguero
,
Mumtaz Qazilbash
,
Pavel Salev
,
Sayan Basak
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
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
,
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
Structural attributes derived from Google Street View imagery, Louisiana coastal zone
10.4231/1TZX-BR43
David R Johnson
,
Ed Delp
,
Fu-Chen Chen
,
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
Evaluating Economic Opportunities for Product Recycling via the Sherwood Principle and Machine Learning - Supporting Information
10.4231/E80W-7941
Aihua Huang
,
John W. Sutherland
,
Sidi Deng
,
Xiaoyu Zhou
,
Yuehwern Yih
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
Modeling the Sea Level Changes in Guam
10.4231/0A0F-7A84
10/14/2019
This project works on understanding the different statistical models that are available to analyze and predict mean sea level changes in Guam.
Climate Change Data Education Earth and Atmospheric Sciences FAIR Data Machine Learning Sea level rise Statistical analysis Statistical Methods
Data of global wetland methane emissions from artificial neural network modeling v1.0
10.4231/3YX4-EY30
01/21/2020
Source code of an ANN model, site level data, input data, output data and visualization results, which are presented in the manuscript "Inventorying Global Wetland Methane Emissions Based on In Situ Data and an Artificial Neural Network...
Artificial Neural Network (ANN) Atmospheric Chemistry Modeling Biogeochemistry Earth and Atmospheric Sciences Interactive Data Language (IDL) Machine Learning Matlab Methane Dynamics Model (MDM) Methane Emission
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
Code and Dataset for TARP Detection Benchmarks
10.4231/R7ST7MVC
Kelsie Larson
,
Mireille Boutin
05/16/2017
The TARP method uses random projections, followed by threshold classifications, to construct receiver-operating characteristic curves and uncover underlying structure in the given data.
Electrical and Computer Engineering Machine Learning Receiver Operating Characteristics ROC curve Signal Processing Target Detection
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