Browsing OU - Dissertations by Subject "Machine learning"
Now showing items 1-15 of 15
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Applying novel machine learning technology to optimize computer-aided detection and diagnosis of medical images
(2021-05-14)The purpose of developing Computer-Aided Detection (CAD) schemes is to assist physicians (i.e., radiologists) in interpreting medical imaging findings and reducing inter-reader variability more accurately. In developing ... -
Combining seismic attributes and machine learning for seismic facies analysis
(2022-05-13)Understanding how to correctly select a group of input seismic attributes is critical to perform a robust machine learning (ML)-based seismic facies analysis. However, due to the large number of seismic attributes enhancing ... -
Developing and Applying CAD-generated Image Markers to Assist Disease Diagnosis and Prognosis Prediction
(2022-05-13)Developing computer-aided detection and/or diagnosis (CAD) schemes has been an active research topic in medical imaging informatics (MII) with promising results in assisting clinicians in making better diagnostic and/or ... -
Developing novel quantitative imaging analysis schemes based machine learning for cancer research
(2021-05-14)The computer-aided detection (CAD) scheme is a developing technology in the medical imaging field, and it attracted extensive research interest in recent years. In this dissertation, I investigated the feasibility of ... -
Development of polymer gel systems for lost circulation treatment and wellbore strengthening
(2021-05-07)Lost circulation is a frequent problem and a significant contributor to the non-productive time (NPT) in the drilling operation. Field reports and experimental studies have revealed that conventional solutions are doomed ... -
Integrated characterization of tight siliciclastic reservoirs: examples from the Cretaceous Burro Canyon Formation, Colorado, and Mississippian Meramec Strata, Oklahoma
(2021-05-14)Integration of multiscale data sources for reservoir characterization becomes problematic and challenging due to the collected information variable resolution. Core and well data provide high vertical resolution to evaluate ... -
Machine learning assisted molecular simulations / mass spectrometry data analysis and rational design of antibacterial Co3O4 nanowires flagella
(2023-05-12)In chapter one, inspired by the recent work from Noé and coworkers on the development of machine learning based implicit solvent model for the simulation of solvated peptides [Chen et al., J. Chem. Phys. 155, 084101 (2021)], ... -
Machine learning for the subsurface characterization at core, well, and reservoir scales
(2020-05-08)The development of machine learning techniques and the digitization of the subsurface geophysical/petrophysical measurements provides a new opportunity for the industries focusing on exploration and extraction of subsurface ... -
Novel Computer-Aided Diagnosis Schemes for Radiological Image Analysis
(2022-05-13)The computer-aided diagnosis (CAD) scheme is a powerful tool in assisting clinicians (e.g., radiologists) to interpret medical images more accurately and efficiently. In developing high-performing CAD schemes, classic ... -
Optimization of deepwater channel seismic reservoir characterization using seismic attributes and machine learning
(2023-12-15)Accurate subsurface reservoir mapping is essential for resource exploration. In uncalibrated basins, seismic data, often limited by resolution, frequency, quality, etc., algorithms become the primary information source due ... -
Stratigraphic and diagenetic controls on petrofacies and reservoir-quality variability using semi-supervised and supervised machine learning methods: Sycamore Formation, Sho-Vel-Tum Field, Oklahoma, USA
(2021-12-18)Diagenetic processes in sedimentary rocks have intrigued geologists, but they are poorly understood. In sedimentary fine-grained rocks, there is even less information, due to the complexity and lack of interest despite the ... -
TOWARD ENHANCED WIRELESS COEXISTENCE IN THE 2.4GHZ ISM BAND VIA TEMPORAL CHARACTERIZATION AND EMPIRICAL MODELING OF 802.11B/G/N NETWORKS A DISSERTATION
(2016)This dissertation presents an extensive experimental characterization and empirical modelling of 802.11 temporal behavior. A detailed characterization of 802.11b/g/n homogeneous and heterogeneous network traffic patterns ... -
Unconstrained Learning Machines
(2010)With the use of information technology in industries, a new need has arisen in analyzing large scale data sets and automating data analysis that was once performed by human intuition and simple analog processing machines. ... -
Uncovering the Potential of Federated Learning: Addressing Algorithmic and Data-driven Challenges under Privacy Restrictions
(2023-12-15)Federated learning is a groundbreaking distributed machine learning paradigm that allows for the collaborative training of models across various entities without directly sharing sensitive data, ensuring privacy and ... -
Woodford Shale enclosed mini-basin fill on the Hunton Paleo Shelf. A depositional model for unconventional resource shales
(2020-05-08)The exploration of unconventional hydrocarbon resources of the Woodford Shale in Oklahoma (USA) has focused on characterizing this formation as an entirely open marine deposit. The impact of recognizing the enclosed ...