Browsing by Subject "machine learning"
Now showing items 1-20 of 53
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A Comprehensive Framework for Adaptive Optics Scanning Light Ophthalmoscope Image Analysis
(2019)Diagnosis, prognosis, and treatment of many ocular and neurodegenerative diseases, including achromatopsia (ACHM), require the visualization of microscopic structures in the eye. The development of adaptive optics ophthalmic ... -
A High-Tech Solution for the Low Resource Setting: A Tool to Support Decision Making for Patients with Traumatic Brain Injury
(2019)Background. The confluence of a capacity-exceeding disease burden and persistent resource shortages have resulted in traumatic brain injury’s (TBI) devastating impact in low and middle income countries (LMIC). Lifesaving ... -
Accelerating Probabilistic Computing with a Stochastic Processing Unit
(2020)Statistical machine learning becomes a more important workload for computing systems than ever before. Probabilistic computing is a popular approach in statistical machine learning, which solves problems by iteratively generating ... -
Adaptive Methods for Machine Learning-Based Testing of Integrated Circuits and Boards
(2020)The relentless growth in information technology and artificial intelligence (AI) is placing demands on integrated circuits and boards for high performance, added functionality, and low power consumption. As a result, design ... -
Advancements in Probabilistic Machine Learning and Causal Inference for Personalized Medicine
(2019)In this dissertation, we present four novel contributions to the field of statistics with the shared goal of personalizing medicine to individual patients. These methods are developed to directly address problems in health ... -
Algorithmic handwriting analysis of Judah's military correspondence sheds light on composition of biblical texts.
(Proceedings of the National Academy of Sciences of the United States of America, 2016-04)The relationship between the expansion of literacy in Judah and composition of biblical texts has attracted scholarly attention for over a century. Information on this issue can be deduced from Hebrew inscriptions from the ... -
An Ensemble Approach to Knowledge-Based Intensity-Modulated Radiation Therapy Planning.
(Frontiers in oncology, 2018-01)Knowledge-based planning (KBP) utilizes experienced planners' knowledge embedded in prior plans to estimate optimal achievable dose volume histogram (DVH) of new cases. In the regression-based KBP framework, previously planned ... -
An Investigation of Machine Learning Methods for Delta-radiomic Feature Analysis
(2018)Background: Radiomics is a process of converting medical images into high-dimensional quantitative features and the subsequent mining these features for providing decision support. It is conducted as a potential noninvasive, ... -
Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?
(Frontiers in physiology, 2021-01)Analysis of pulmonary function tests (PFTs) is an area where machine learning (ML) may benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes, and carbon monoxide diffusion capacity of the ... -
Applications of Deep Learning, Machine Learning, and Remote Sensing to Improving Air Quality and Solar Energy Production
(2021)Exposure to higher PM2.5 can lead to increased risks of mortality; however, the spatial concentrations of PM2.5 are not well characterized, even in megacities, due to the sparseness of regulatory air quality monitoring (AQM) ... -
Applying machine learning to investigate long-term insect-plant interactions preserved on digitized herbarium specimens.
(Applications in plant sciences, 2020-06)Premise:Despite the economic significance of insect damage to plants (i.e., herbivory), long-term data documenting changes in herbivory are limited. Millions of pressed plant specimens are now available online and can be ... -
Approximate Inference for High-Dimensional Latent Variable Models
(2018)Latent variable models are widely used in applications ranging fromnatural language processing to recommender systems. Exact inferenceusing maximum likelihood for these models is generally NP-hard, andcomputationally prohibitive ... -
Automated Generation of Radiotherapy Treatment Plans Using Machine Learning Methods
(2021)With the development of medical linear accelerator technologies, the precision and complexity of external beam radiation therapy have increased tremendously over the years. The goal of radiation therapy has always been to ... -
Automatic Identification of Training & Testing Data for Buried Threat Detection using Ground Penetrating Radar
(2017)Ground penetrating radar (GPR) is one of the most popular and successful sensing modalities that has been investigated for landmine and subsurface threat detection. The radar is attached to front of a vehicle and collects ... -
Automatic Planning of Whole Breast Radiation Therapy Using Machine Learning Models.
(Frontiers in Oncology, 2019-01)Purpose: To develop an automatic treatment planning system for whole breast radiation therapy (WBRT) based on two intensity-modulated tangential fields, enabling near-real-time planning. Methods and Materials: A total of ... -
Characterizing antipsychotic behavioral and corticostriatal neurophysiological effects to psychotomimetic challenge
(2022)Schizophrenia is marked by significant disruptions to dopaminergic signaling across the mesolimbic and mesocortical circuits. Antipsychotic drugs have been largely unsuccessfully treating cognitive symptoms that debilitate ... -
Data-driven Decision Making with Dynamic Learning under Uncertainty: Theory and Applications
(2022)Digital transformation is changing the landscape of business and sparking waves of innovation, calling for advanced big data analytics and artificial intelligence techniques. To survive from intensified and rapidly changing ... -
Deep Generative Models for Image Representation Learning
(2018)Recently there has been increasing interest in developing generative models of data, offering the promise of learning based on the often vast quantity of unlabeled data. With such learning, one typically seeks to build rich, ... -
Deep Generative Models for Vision and Language Intelligence
(2018)Deep generative models have achieved tremendous success in recent years, with applications in various tasks involving vision and language intelligence. In this dissertation, I will mainly discuss the contributions that I ... -
Development and Evaluation of a Small Airway Disease Index Derived From Modeling the Late-Expiratory Flattening of the Flow-Volume Loop.
(Frontiers in physiology, 2022-01)Excessive decrease in the flow of the late expiratory portion of a flow volume loop (FVL) or "flattening", reflects small airway dysfunction. The assessment of the flattening is currently determined by visual inspection ...
