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Full-Text Articles in Physical Sciences and Mathematics
Vertical Federated Learning Using Autoencoders With Applications In Electrocardiograms, Wesley William Chorney
Vertical Federated Learning Using Autoencoders With Applications In Electrocardiograms, Wesley William Chorney
Theses and Dissertations
Federated learning is a framework in machine learning that allows for training a model while maintaining data privacy. Moreover, it allows clients with their own data to collaborate in order to build a stronger, shared model. Federated learning is of particular interest to healthcare data, since it is of the utmost importance to respect patient privacy while still building useful diagnostic tools. However, healthcare data can be complicated — data format might differ across providers, leading to unexpected inputs and incompatibility between different providers. For example, electrocardiograms might differ in sampling rate or number of leads used, meaning that a …
Secure And Efficient Federated Learning, Xingyu Li
Secure And Efficient Federated Learning, Xingyu Li
Theses and Dissertations
In the past 10 years, the growth of machine learning technology has been significant, largely due to the availability of large datasets for training. However, gathering a sufficient amount of data on a central server can be challenging. Additionally, with the rise of mobile networking and the large amounts of data generated by IoT devices, privacy and security issues have become a concern, resulting in government regulations such as GDPR, HIPAA, CCPA, and ADPPA. Under these circumstances, traditional centralized machine learning methods face a problem in that sensitive data must be kept locally for privacy reasons, making it difficult to …
Eddy Current Defect Response Analysis Using Sum Of Gaussian Methods, James William Earnest
Eddy Current Defect Response Analysis Using Sum Of Gaussian Methods, James William Earnest
Theses and Dissertations
This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics.