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Using Deep Learning to Improve Prediction and Understanding of High-impact Weather
(2020-05)
This dissertation describes the application of convolutional neural networks (CNN), a type of deep-learning method, to high-impact weather. CNNs are specially designed to learn directly from spatial grids, which improves ...
An Experimental, Modeling and Machine Learning Based Investigation of Stick-Slip Vibrations
(2022-08-04)
Drilling technologies have improved considerably since the first well drilled by Colonel E.L Drake in 1859. Drilling technologies enable us to safely drill complex well profiles with advanced downhole tools and sensors to ...
Kidney OCT 3D images classification using machine learning
(2023-12-15)
The goal of this research is to make a classification program for 3D images by using a CNN model. The images to classify are kidney images that have 3 different classes: Pelvis, Medulla and Cortex.
To do so, a data ...
Relevance and Privacy Improvements to the YaCy Decentralized Web Search Engine
(2018)
The YaCy decentralized web search engine carries significant potential advantages in censorship resistance over centralized search engines such as Google. However, YaCy currently suffers from deficiencies in relevance of ...
Nurturing as Safe Exploration Promotes the Evolution of Generalized Supervised Learning
(2017-08-01)
The ability to learn is often a desirable property of intelligent systems which can make them more adaptive. However, it is difficult to develop sophisticated learning algorithms that are effective. One approach to the ...
Energy Efficient Machine Learning-Based Classification of ECG Heartbeat Types
(2018-12-05)
To meet the accuracy, latency and energy efficiency requirements during real-time collection and analysis of health data, a distributed edge computing environment is the answer, combined with 5G speeds and modern computing ...
APPLICATIONS OF MACHINE LEARNING METHODS IN THE GENERATION OF SUBSURFACE MEASUREMENTS
(2018-05-11)
Machine learning methods have been used in the Oil and Gas industry for about thirty years. Applications range from interpretations of geophysical, well and seismic responses, identification of minerals, analysis of rock ...
Spatiotemporal gap-filling of NASA Deep Blue aerosol optical depth over CONUS using the UNet 3+ architecture
(2024-05-10)
Due to sensor and algorithmic constraints, satellite aerosol optical depth (AOD) retrievals are spatially incomplete over clouds, deserts, and other bright surfaces. These gaps in satellite AOD datasets represent a significant ...
Wildfire Occurrence Prediction for CONUS with the UNet3+ Deep Learning Model
(2024-12-13)
Wildfire represents a risk to life and property in many areas of the United States and is of growing concern to insurance companies, legislative bodies, and the public. Accurate wildfire forecasting could allow for earlier ...