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Articles 1 - 6 of 6
Full-Text Articles in Entire DC Network
Environmental Regulation Of The Heart: The Role Of Non-Coding Rna And Epigenetics In Influencing Mitochondrial And Cellular Health, Quincy Alexander Hathaway
Environmental Regulation Of The Heart: The Role Of Non-Coding Rna And Epigenetics In Influencing Mitochondrial And Cellular Health, Quincy Alexander Hathaway
Graduate Theses, Dissertations, and Problem Reports
The mitochondrion, a small but ubiquitously distributed organelle in the cell, continues to be the focus of many disease pathogeneses, tissue and organ dysfunctions, and other morbidities that occur throughout the body. The purpose of this work was to understand how cardiac mitochondrion are altered in disease and pathological states, specifically in their adaptation to environmentally stimulated regulatory networks, such as epigenetic modifications and promotion/inhibition of non-coding RNAs. Acute stress to mitochondrial regulation (inhalation toxicology) as well as chronic (type 2 diabetes mellitus) was examined. Using a FVB transgenic microRNA-378a mouse knockout model, the cardiovascular impact derived from altering the …
The Structural Information Filtered Features Potential For Machine Learning Calculations Of Energies And Forces Of Atomic Systems., Jorge Arturo Hernandez Zeledon
The Structural Information Filtered Features Potential For Machine Learning Calculations Of Energies And Forces Of Atomic Systems., Jorge Arturo Hernandez Zeledon
Graduate Theses, Dissertations, and Problem Reports
In the last ten years, machine learning potentials have been successfully applied to the study of crystals, and molecules. However, more complex materials like clusters, macro-molecules, and glasses are out reach of current methods. The input of any machine learning system is a tensor of features (the most universal type are rank 1 tensors or vectors of features), the quality of any machine learning system is directly related to how well the feature space describes the original physical system. So far, the feature engineering process for machine learning potentials can not describe complex material. The current methods are highly inefficient …
Intelligent Malware Detection Using File-To-File Relations And Enhancing Its Security Against Adversarial Attacks, Lingwei Chen
Intelligent Malware Detection Using File-To-File Relations And Enhancing Its Security Against Adversarial Attacks, Lingwei Chen
Graduate Theses, Dissertations, and Problem Reports
With computing devices and the Internet being indispensable in people's everyday life, malware has posed serious threats to their security, making its detection of utmost concern. To protect legitimate users from the evolving malware attacks, machine learning-based systems have been successfully deployed and offer unparalleled flexibility in automatic malware detection. In most of these systems, resting on the analysis of different content-based features either statically or dynamically extracted from the file samples, various kinds of classifiers are constructed to detect malware. However, besides content-based features, file-to-file relations, such as file co-existence, can provide valuable information in malware detection and make …
Quantifying Human Biological Age: A Machine Learning Approach, Syed Ashiqur Rahman
Quantifying Human Biological Age: A Machine Learning Approach, Syed Ashiqur Rahman
Graduate Theses, Dissertations, and Problem Reports
Quantifying human biological age is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age prediction, each with its advantages and limitations. In this work, we first introduce a new anthropometric measure (called Surface-based Body Shape Index, SBSI) that accounts for both body shape and body size, and evaluate its performance as a predictor of all-cause mortality. We analyzed data from the National Health and Human Nutrition Examination Survey (NHANES). Based on the analysis, we introduce a new body shape index constructed from four important anthropometric determinants of body shape and body size: body …
Deformation Correlations And Machine Learning: Microstructural Inference And Crystal Plasticity Predictions, Michail Tzimas
Deformation Correlations And Machine Learning: Microstructural Inference And Crystal Plasticity Predictions, Michail Tzimas
Graduate Theses, Dissertations, and Problem Reports
The present thesis makes a connection between spatially resolved strain correlations and material processing history. Such correlations can be used to infer and classify prior deformation history of a sample at various strain levels with the use of Machine Learning approaches. A simple and concrete example of uniaxially compressed crystalline thin films of various sizes, generated by two-dimensional discrete dislocation plasticity simulations is examined. At the nanoscale, thin films exhibit yield-strength size effects with noisy mechanical responses which create an interesting challenge for the application of Machine Learning techniques. Moreover, this thesis demonstrates the prediction of the average mechanical responses …
Object-Based Supervised Machine Learning Regional-Scale Land-Cover Classification Using High Resolution Remotely Sensed Data, Christopher A. Ramezan
Object-Based Supervised Machine Learning Regional-Scale Land-Cover Classification Using High Resolution Remotely Sensed Data, Christopher A. Ramezan
Graduate Theses, Dissertations, and Problem Reports
High spatial resolution (HR) (1m – 5m) remotely sensed data in conjunction with supervised machine learning classification are commonly used to construct land-cover classifications. Despite the increasing availability of HR data, most studies investigating HR remotely sensed data and associated classification methods employ relatively small study areas. This work therefore drew on a 2,609 km2, regional-scale study in northeastern West Virginia, USA, to investigates a number of core aspects of HR land-cover supervised classification using machine learning. Issues explored include training sample selection, cross-validation parameter tuning, the choice of machine learning algorithm, training sample set size, and feature selection. A …