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Application of seismic attributes and unsupervised machine learning methods for identification of hidden faults in basement and carbonate rocks
(2023-12-15)
Seismic fault interpretation is a critical task for any type of energy industry and correct fault mapping can be crucial for the success of a project. Common geometric seismic attributes such as coherence and curvature are ...
Exploring Drug-Use Progression Through Stability Enhanced Clustering
(2022-05)
Background and aims: Drug use initiation sequences have been the subject of
much research, and theories such as the Gateway Hypothesis have been created
to explain patterns of progression from common to dangerous drugs. ...
Varying dataset resolution alters predictive accuracy of spatially explicit ensemble models for avian species distribution
(2018-12-06)
Species distribution models can be made more accurate by use of new “Spatiotemporal Exploratory Models” (STEMs), a type of spatially explicit ensemble model (SEEM) developed at the continental scale that averages regional ...
General supervised learning framework for open world classification
(2020-12-18)
In machine learning, the most common scenario for classification modeling is when the training set contains all possible classes and the algorithm learns to identify these classes. The problem setting in which the training ...
Intelligent Condition Monitoring and Prognostic Methods with Applications to Dynamic Seals in the Oil & Gas Industry
(2019)
The capital-intensive oil & gas industry invests billions of dollars in equipment annually and it is important to keep the equipment in top operating condition to help maintain efficient process operations and improve the ...
Seismic-based characterization of a carbonate gas storage reservoir assisted by machine learning techniques
(2021)
Silurian pinnacle reefs found within the Michigan Basin were prolific hydrocarbon producers in the mid-to-late twentieth century. During production, studies over these complex reservoirs were primarily focused on facies ...
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 ...