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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 ...
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)], ...
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 ...
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. ...
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 ...
SUB-SEISMIC REEF CHARACTERIZATION USING MACHINE LEARNING AND MULTI-ATTRIBUTE ANALYSIS
(2020)
Historically, Silurian reef complexes in the Michigan Basin have been largely identified using 2D seismic surveys with very little research focusing on characterizing these reefs (morphology, internal architecture, reservoir ...
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 ...
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 ...
Enhancing Performance and Reducing Emissions in Natural Gas Aspirated Engines through Machine Learning Algorithm
(2023-12-15)
In an era where the global energy landscape is increasingly defined by the dual imperatives of efficiency and sustainability, the natural gas sector stands at a crucial juncture. The engines powering this sector, especially ...
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 ...