Digital Commons Network^™

Anomaly Detection On Complex Health Information Technology Systems, Haoran Niu

Doctoral Dissertations

While modern complex computer systems provide enormous benefits to our daily lives, the increasing complexity of these large-scale systems also makes them more susceptible to unexpected software malfunctions and malicious attacks. This is especially true for Health Information Technology (HIT), which has revolutionized healthcare delivery by making it more efficient, effective, and accessible. Nevertheless, the widespread adoption of HIT has introduced new challenges related to ensuring system reliability and security. As a result, the development of novel algorithms and frameworks to detect anomalies in such systems has become increasingly important for enhancing patient safety and improving the efficiency and effectiveness …

Quantifying And Reversing Compensatory Movements By Persons Post-Stroke In The Ambient Setting, Aaron Miller

Doctoral Dissertations

Nearly 800,000 people in the United States suffer stroke annually. Following the onset of stroke, survivors will exhibit deficits, such as hemiplegia, which will limit their function and ability to perform activities of daily living (ADLs). In order to regain independence, many stroke survivors will employ maladaptive compensatory strategies to help with the completion of tasks. Compensation is generally defined as any performance of a task that is different than the way it may have been performed before the onset of a neurodegenerative disorder. While for some severely impaired individuals, compensation may be necessary, for most these maladaptive strategies ultimately …

Machine Learning With Topological Data Analysis, Ephraim Robert Love

Doctoral Dissertations

Topological Data Analysis (TDA) is a relatively new focus in the fields of statistics and machine learning. Methods of exploiting the geometry of data, such as clustering, have proven theoretically and empirically invaluable. TDA provides a general framework within which to study topological invariants (shapes) of data, which are more robust to noise and can recover information on higher dimensional features than immediately apparent in the data. A common tool for conducting TDA is persistence homology, which measures the significance of these invariants. Persistence homology has prominent realizations in methods of data visualization, statistics and machine learning. Extending ML with …

Analysis And Enhancement Of Human Cognitive Control Using Noninvasive Brain-Computer Interfaces, Soheil Borhani

Doctoral Dissertations

Cognitive control including attention and working memory are crucial to human daily life. Whether a civilian who walks across a street or a military service member who is responsible for navigating a mission, cognitive control is involved, entirely. This ability is subject to impairment. People with attention disorder are easily disposed to distraction and lacks the ability to maintain the focus to a task. Multiple treatment strategies have been suggested which most of them has been pharmaceutical. Evidently, the medical treatment has side effects for long-term use. Moreover, it has a risk of drug misuse. Another line of treatment is …

Neuron Clustering For Mitigating Catastrophic Forgetting In Supervised And Reinforcement Learning, Benjamin Frederick Goodrich

Doctoral Dissertations

Neural networks have had many great successes in recent years, particularly with the advent of deep learning and many novel training techniques. One issue that has affected neural networks and prevented them from performing well in more realistic online environments is that of catastrophic forgetting. Catastrophic forgetting affects supervised learning systems when input samples are temporally correlated or are non-stationary. However, most real-world problems are non-stationary in nature, resulting in prolonged periods of time separating inputs drawn from different regions of the input space.

Reinforcement learning represents a worst-case scenario when it comes to precipitating catastrophic forgetting in neural networks. …

Computational Analysis Of Neutron Scattering Data, Benjamin Walter Martin

Doctoral Dissertations

This work explores potential methods for use in the detection and classification of defects within crystal structures via analysis of diffuse scattering data generated by single crystal neutron scattering experiments. The proposed defect detection methodology uses machine learning and image processing techniques to perform image texture analysis on neutron diffraction patterns generated by neutron scattering simulations. Once the methodology is presented, it is tested via a series of defect detection problems of increasing difficulty which utilize neutron scattering data simulated by a number of simulation techniques. As the problem difficulty is increased, the defect detection methodology is refined in order …

Deep Machine Learning With Spatio-Temporal Inference, Thomas Paul Karnowski

Doctoral Dissertations

Deep Machine Learning (DML) refers to methods which utilize hierarchies of more than one or two layers of computational elements to achieve learning. DML may draw upon biomemetic models, or may be simply biologically-inspired. Regardless, these architectures seek to employ hierarchical processing as means of mimicking the ability of the human brain to process a myriad of sensory data and make meaningful decisions based on this data. In this dissertation we present a novel DML architecture which is biologically-inspired in that (1) all processing is performed hierarchically; (2) all processing units are identical; and (3) processing captures both spatial and …