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Full-Text Articles in Physical Sciences and Mathematics
Materials Prediction Using High-Throughput And Machine Learning Techniques, Chandramouli Nyshadham
Materials Prediction Using High-Throughput And Machine Learning Techniques, Chandramouli Nyshadham
Theses and Dissertations
Predicting new materials through virtually screening a large number of hypothetical materials using supercomputers has enabled materials discovery at an accelerated pace. However, the innumerable number of possible hypothetical materials necessitates the development of faster computational methods for speedier screening of materials reducing the time of discovery. In this thesis, I aim to understand and apply two computational methods for materials prediction. The first method deals with a computational high-throughput study of superalloys. Superalloys are materials which exhibit high-temperature strength. A combinatorial high-throughput search across 2224 ternary alloy systems revealed 102 potential superalloys of which 37 are brand new, all …
The Application Of Synthetic Signals For Ecg Beat Classification, Elliot Morgan Brown
The Application Of Synthetic Signals For Ecg Beat Classification, Elliot Morgan Brown
Theses and Dissertations
A brief overview of electrocardiogram (ECG) properties and the characteristics of various cardiac conditions is given. Two different models are used to generate synthetic ECG signals. Domain knowledge is used to create synthetic examples of 16 different heart beat types with these models. Other techniques for synthesizing ECG signals are explored. Various machine learning models with different combinations of real and synthetic data are used to classify individual heart beats. The performance of the different methods and models are compared, and synthetic data is shown to be useful in beat classification.
Computational Regiospecific Analysis Of Brain Lipidomic Profiles, Austin Ahlstrom
Computational Regiospecific Analysis Of Brain Lipidomic Profiles, Austin Ahlstrom
Undergraduate Honors Theses
Mass spectrometry provides an extensive data set that can prove unwieldy for practical analytical purposes. Applying programming and machine learning methods to automate region analysis in DESI mass spectrometry of mouse brain tissue can help direct and refine such an otherwise unusable data set. The results carry promise of faster, more reliable analysis of this type, and yield interesting insights into molecular characteristics of regions of interest within these brain samples. These results have significant implications in continued investigation of molecular processes in the brain, along with other aspects of mass spectrometry, collective analysis of biological molecules (i.e. omics), and …