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Found 10 papers, 1 papers with code
Multimodal Wildland Fire Smoke Detection
Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States.
Towards a Dynamic Composability Approach for using Heterogeneous Systems in Remote Sensing
In this paper, we present a novel approach for using composable systems in the intersection between scientific computing, artificial intelligence (AI), and remote sensing domain.
FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection
Early detection of fire ignitions from initial smoke can assist the response to such fires before they become difficult to manage.
Cardiac MRI Image Segmentation for Left Ventricle and Right Ventricle using Deep Learning
The goal of this project is to use magnetic resonance imaging (MRI) data to provide an end-to-end analytics pipeline for left and right ventricle (LV and RV) segmentation.
Workflow-Driven Distributed Machine Learning in CHASE-CI: A Cognitive Hardware and Software Ecosystem Community Infrastructure
The advances in data, computing and networking over the last two decades led to a shift in many application domains that includes machine learning on big data as a part of the scientific process, requiring new capabilities for integrated and distributed hardware and software infrastructure.
Ten Simple Rules for Reproducible Research in Jupyter Notebooks
For example, what are the technical and non-technical barriers to reproducible computational studies?
Other Computer Science Computers and Society
Enabling FAIR Research in Earth Science through Research Objects
Data-intensive science communities are progressively adopting FAIR practices that enhance the visibility of scientific breakthroughs and enable reuse.
Left Ventricle Segmentation and Volume Estimation on Cardiac MRI using Deep Learning
An end-to-end analytics pipeline with multiple stages is provided for automated LV segmentation and volume estimation.
Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows
In this vision paper, we outline our approach to leveraging Deep Learning algorithms to discover solutions to unique problems that arise in a system with computational infrastructure that is spread over a wide area.
Modular Resource Centric Learning for Workflow Performance Prediction
The central idea of this work is to train resource-centric machine learning agents to capture complex relationships between a set of program instructions and their performance metrics when executed on a specific resource.
