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Pathologically-Validated Tumor Prediction Maps In Mri, Alex Barrington Oct 2019

Pathologically-Validated Tumor Prediction Maps In Mri, Alex Barrington

Master's Theses (2009 -)

Glioblastoma (GBM) is an aggressive cancer with an average 5-year survival rate of about 5%. Following treatment with surgery, radiation, and chemotherapy, diagnosing tumor recurrence requires serial magnetic resonance imaging (MRI) scans. Infiltrative tumor cells beyond gadolinium enhancement on T1-weighted MRI are difficult to detect. This study therefore aims to improve tumor detection beyond traditional tumor margins. To accomplish this, a neural network model was trained to classify tissue samples as ‘tumor’ or ‘not tumor’. This model was then used to classify thousands of tiles from histology samples acquired at autopsy with known MRI locations on the patient’s final clinical …

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Artificial Intelligence In Ultrasound Imaging: Current Research And Applications, Shuo Wang, Bs, Ji-Bin Liu, Md, Ziyin Zhu, Md, John Eisenbrey, Phd Sep 2019

Artificial Intelligence In Ultrasound Imaging: Current Research And Applications, Shuo Wang, Bs, Ji-Bin Liu, Md, Ziyin Zhu, Md, John Eisenbrey, Phd

Department of Radiology Faculty Papers

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent software or system based on big data information, machine learning and deep learning technologies. The rapid development of science and technology as well as internet communication has enabled AI and big data to gradually apply to many fields of health care. The modern imaging medicine is one of the first areas that AI can play an important role and applications. As cross-sectional imaging, ultrasound (US) is well suitable for AI technology to standardize imaging protocols and improve diagnostic accuracy. This article reviews current AI technology …

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Machine Learning-Based Models For Assessing Impacts Before, During And After Hurricane Events, Julie L. Harvey Sep 2019

Machine Learning-Based Models For Assessing Impacts Before, During And After Hurricane Events, Julie L. Harvey

Electronic Theses and Dissertations

Social media provides an abundant amount of real-time information that can be used before, during, and after extreme weather events. Government officials, emergency managers, and other decision makers can use social media data for decision-making, preparation, and assistance. Machine learning-based models can be used to analyze data collected from social media. Social media data and cloud cover temperature as physical sensor data was analyzed in this study using machine learning techniques. Data was collected from Twitter regarding Hurricane Florence from September 11, 2018 through September 20, 2018 and Hurricane Michael from October 1, 2018 through October 18, 2018. Natural language …

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Urban Health Related Air Quality Indicators Over The Middle East And North Africa Countries Using Multiple Satellites And Aeronet Data, Maram El-Nadry, Wenzhao Li, Hesham El-Askary, Mohamed A. Awad, Alaa Ramadan Awad Sep 2019

Urban Health Related Air Quality Indicators Over The Middle East And North Africa Countries Using Multiple Satellites And Aeronet Data, Maram El-Nadry, Wenzhao Li, Hesham El-Askary, Mohamed A. Awad, Alaa Ramadan Awad

Mathematics, Physics, and Computer Science Faculty Articles and Research

Air pollution is reported as one of the most severe environmental problems in the Middle East and North Africa (MENA) region. Remotely sensed data from newly available TROPOMI - TROPOspheric Monitoring Instrument on board Sentinel-5 Precursor, shows an annual mean of high-resolution maps of selected air quality indicators (NO2, CO, O3, and UVAI) of the MENA countries for the first time. The correlation analysis among the aforementioned indicators show the coherency of the air pollutants in urban areas. Multi-year data from the Aerosol Robotic Network (AERONET) stations from nine MENA countries are utilized here to study the aerosol optical depth …

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Medical Ai And Contextual Bias, W. Nicholson Price Ii Sep 2019

Medical Ai And Contextual Bias, W. Nicholson Price Ii

Articles

Artificial intelligence will transform medicine. One particularly attractive possibility is the democratization of medical expertise. If black-box medical algorithms can be trained to match the performance of high-level human experts — to identify malignancies as well as trained radiologists, to diagnose diabetic retinopathy as well as board-certified ophthalmologists, or to recommend tumor-specific courses of treatment as well as top-ranked oncologists — then those algorithms could be deployed in medical settings where human experts are not available, and patients could benefit. But there is a problem with this vision. Privacy law, malpractice, insurance reimbursement, and FDA approval standards all encourage developers …

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The Application Of Synthetic Signals For Ecg Beat Classification, Elliot Morgan Brown Sep 2019

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.

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Semi-Supervised Regression With Generative Adversarial Networks Using Minimal Labeled Data, Greg Olmschenk Sep 2019

Semi-Supervised Regression With Generative Adversarial Networks Using Minimal Labeled Data, Greg Olmschenk

Dissertations, Theses, and Capstone Projects

This work studies the generalization of semi-supervised generative adversarial networks (GANs) to regression tasks. A novel feature layer contrasting optimization function, in conjunction with a feature matching optimization, allows the adversarial network to learn from unannotated data and thereby reduce the number of labels required to train a predictive network. An analysis of simulated training conditions is performed to explore the capabilities and limitations of the method. In concert with the semi-supervised regression GANs, an improved label topology and upsampling technique for multi-target regression tasks are shown to reduce data requirements. Improvements are demonstrated on a wide variety of vision …

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Analyzing Evolution Of Rare Events Through Social Media Data, Xiaoyu Lu Aug 2019

Analyzing Evolution Of Rare Events Through Social Media Data, Xiaoyu Lu

Dissertations

Recently, some researchers have attempted to find a relationship between the evolution of rare events and temporal-spatial patterns of social media activities. Their studies verify that the relationship exists in both time and spatial domains. However, few of those studies can accurately deduce a time point when social media activities are most highly affected by a rare event because producing an accurate temporal pattern of social media during the evolution of a rare event is very difficult. This work expands the current studies along three directions. Firstly, we focus on the intensity of information volume and propose an innovative clustering …

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Machine Learning In Support Of Electric Distribution Asset Failure Prediction, Robert D. Flamenbaum, Thomas Pompo, Christopher Havenstein, Jade Thiemsuwan Aug 2019

Machine Learning In Support Of Electric Distribution Asset Failure Prediction, Robert D. Flamenbaum, Thomas Pompo, Christopher Havenstein, Jade Thiemsuwan

SMU Data Science Review

In this paper, we present novel approaches to predicting as- set failure in the electric distribution system. Failures in overhead power lines and their associated equipment in particular, pose significant finan- cial and environmental threats to electric utilities. Electric device failure furthermore poses a burden on customers and can pose serious risk to life and livelihood. Working with asset data acquired from an electric utility in Southern California, and incorporating environmental and geospatial data from around the region, we applied a Random Forest methodology to predict which overhead distribution lines are most vulnerable to fail- ure. Our results provide evidence …

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Document Images And Machine Learning: A Collaboratory Between The Library Of Congress And The Image Analysis For Archival Discovery (Aida) Lab At The University Of Nebraska, Lincoln, Ne, Yi Liu, Chulwoo Pack, Leen-Kiat Soh, Elizabeth Lorang Aug 2019

Document Images And Machine Learning: A Collaboratory Between The Library Of Congress And The Image Analysis For Archival Discovery (Aida) Lab At The University Of Nebraska, Lincoln, Ne, Yi Liu, Chulwoo Pack, Leen-Kiat Soh, Elizabeth Lorang

School of Computing: Conference and Workshop Papers

This presentation summarized and presented preliminary results from the first weeks of work conducted by the Aida research team in response to Library of Congress funding notice ID 030ADV19Q0274, “The Library of Congress – Pre-processing Pilot.” It includes overviews of projects on historic document segmentation, document classification, document quality assessment, figure and graph extraction from historic documents, text-line extraction from figures, subject and objective quality assesments, and digitization type differentiation.

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Media Literacy Education In The Age Of Machine Learning, Teemu Valtonen, Matti Tedre, Kati Mäkitalo, Henriikka Vartiainen Aug 2019

Media Literacy Education In The Age Of Machine Learning, Teemu Valtonen, Matti Tedre, Kati Mäkitalo, Henriikka Vartiainen

Journal of Media Literacy Education

The media environment has radically changed over the past few decades. Transition and transformation of media platforms has enabled algorithms and automation to take over media processes such as production, content generation, curation, delivery, recommendation, and filtering of information. It has also enabled tracking of users’ actions, data mining, profiling, and the use of computational and machine learning techniques for purposes like behavior engineering, targeted advertisement, spread of mis- and disinformation, swaying political moods, and many others. In the field of media literacy education, the need to understand algorithm-driven media requires educators to re-think the connections between media literacy education …

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Proton Exchange Membrane Fuel Cell Stack Design Optimization Using An Improved Jaya Algorithm, Uday K. Chakraborty Aug 2019

Proton Exchange Membrane Fuel Cell Stack Design Optimization Using An Improved Jaya Algorithm, Uday K. Chakraborty

Computer Science Faculty Works

Fuel cell stack configuration optimization is known to be a problem that, in addition to presenting engineering challenges, is computationally hard. This paper presents an improved computational heuristic for solving the problem. The problem addressed in this paper is one of constrained optimization, where the goal is to seek optimal (or near-optimal) values of (i) the number of proton exchange membrane fuel cells (PEMFCs) to be connected in series to form a group, (ii) the number of such groups to be connected in parallel, and (iii) the cell area, such that the PEMFC assembly delivers the rated voltage at the …

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Proton Exchange Membrane Fuel Cell Stack Design Optimization Using An Improved Jaya Algorithm, Uday K. Chakraborty Aug 2019

Proton Exchange Membrane Fuel Cell Stack Design Optimization Using An Improved Jaya Algorithm, Uday K. Chakraborty

Educator Preparation & Leadership Faculty Works

Fuel cell stack configuration optimization is known to be a problem that, in addition to presenting engineering challenges, is computationally hard. This paper presents an improved computational heuristic for solving the problem. The problem addressed in this paper is one of constrained optimization, where the goal is to seek optimal (or near-optimal) values of (i) the number of proton exchange membrane fuel cells (PEMFCs) to be connected in series to form a group, (ii) the number of such groups to be connected in parallel, and (iii) the cell area, such that the PEMFC assembly delivers the rated voltage at the …

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A Machine Learning Approach To Genome Assessment, Charles Adam Thrash Aug 2019

A Machine Learning Approach To Genome Assessment, Charles Adam Thrash

Theses and Dissertations

A key use of high throughput sequencing technology is the sequencing and assembly of full genome sequences. These genome assemblies are commonly assessed using statistics relating to contiguity of the assembly. Measures of contiguity are not strongly correlated with information about the biological completion or correctness of the assembly, and a commonly reported metric, N50, can be misleading. Over the past ten years, multiple research groups have rejected the overuse of N50 and sought to develop more informative metrics. This research seeks to create a ranking method that includes biologically relevant information about the genome, such as completeness and correctness …

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Predicting Vulnerability For Requirements: A Data-Driven Approach, Sayem Mohammad Imtiaz Aug 2019

Predicting Vulnerability For Requirements: A Data-Driven Approach, Sayem Mohammad Imtiaz

Theses and Dissertations

Being software security one of the primary concerns in the software engineering community, researchers are coming up with many preemptive approaches which are primarily designed to detect vulnerabilities in the post-implementation stage of the software development life-cycle (SDLC). While they have been shown to be effective in detecting vulnerabilities, the consequences are often expensive. Accommodating changes after detecting a bug or vulnerability in late stages of the SDLC is costly. On that account, in this thesis, we propose a novel framework to provide an additional measure of predicting vulnerabilities at earlier stages of the SDLC. To that end, we leverage …

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Prediction Enhancement Through Machine Learning Of North Atlantic Tropical Cyclone Rapid Intensification: Diagnostics, Model Development, And Independent Verification, Alexandria Grimes Aug 2019

Prediction Enhancement Through Machine Learning Of North Atlantic Tropical Cyclone Rapid Intensification: Diagnostics, Model Development, And Independent Verification, Alexandria Grimes

Theses and Dissertations

Forecasting rapid intensification (RI) of tropical cyclones (TCs) is considered one of the most challenging problems for the TC operational and research communities and remains a top priority for the National Hurricane Center. Upon landfall, these systems can have detrimental impacts to life and property. To support continued improvement of TC RI forecasts, this study investigated large-scale TC environments undergoing RI in the North Atlantic basin, specifically identifying important diagnostic variables in three-dimensional space. These results were subsequently used in the development of prognostic machine learning algorithms designed to predict RI 24 hours prior to occurrence. Using three RI definitions, …

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Fast And Effective Techniques For Lwir Radiative Transfer Modeling: A Dimension-Reduction Approach, Nicholas M. Westing, Brett J. Borghetti, Kevin C. Gross Aug 2019

Fast And Effective Techniques For Lwir Radiative Transfer Modeling: A Dimension-Reduction Approach, Nicholas M. Westing, Brett J. Borghetti, Kevin C. Gross

Faculty Publications

The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms. These detailed measurements allow for material classification, with many recent advancements from the fields of machine learning and deep learning. In many scenarios, the hyperspectral image must first be corrected or compensated for atmospheric effects. Radiative Transfer (RT) computations can provide look up tables (LUTs) to support these corrections. This research investigates a dimension-reduction approach using machine learning methods to create an effective sensor-specific long-wave infrared (LWIR) RT model.

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Sensory Relevance Models, Walt Woods Aug 2019

Sensory Relevance Models, Walt Woods

Dissertations and Theses

This dissertation concerns methods for improving the reliability and quality of explanations for decisions based on Neural Networks (NNs). NNs are increasingly part of state-of-the-art solutions for a broad range of fields, including biomedical, logistics, user-recommendation engines, defense, and self-driving vehicles. While NNs form the backbone of these solutions, they are often viewed as "black box" solutions, meaning the only output offered is a final decision, with no insight into how or why that particular decision was made. For high-stakes fields, such as biomedical, where lives are at risk, it is often more important to be able to explain a …

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Reliability Of Technical Stock Price Pattern Predictability, Matthew Lutey Aug 2019

Reliability Of Technical Stock Price Pattern Predictability, Matthew Lutey

LSU New Orleans Theses and Dissertations

Academic research has shown throughout the years the ability of technical indicators to convey predictive value, informational content, and practical use. The popularity of such studies goes in and out over the years and today is being recognized widely by behavioral economists. Automated technical analysis is said to detect geometric and nonlinear shapes in prices which ordinary time series methods would be unable to detect. Previous papers use smoothing estimators to detect such patterns. Our paper uses local polynomial regressions, digital image processing, and state of the art machine learning tools to detect the patterns. Our results show that they …

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The Importance Of Landscape Position Information And Elevation Uncertainty For Barrier Island Habitat Mapping And Modeling, Nicholas Matthew Enwright Aug 2019

The Importance Of Landscape Position Information And Elevation Uncertainty For Barrier Island Habitat Mapping And Modeling, Nicholas Matthew Enwright

LSU Doctoral Dissertations

Barrier islands provide important ecosystem services, including storm protection and erosion control to the mainland, habitat for fish and wildlife, and tourism. As a result, natural resource managers are concerned with monitoring changes to these islands and modeling future states of these environments. Landscape position, such as elevation and distance from shore, influences habitat coverage on barrier islands by regulating exposure to abiotic factors, including waves, tides, and salt spray. Geographers commonly use aerial topographic lidar data for extracting landscape position information. However, researchers rarely consider lidar elevation uncertainty when using automated processes for extracting elevation-dependent habitats from lidar data. …

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Asap: A Source Code Authorship Program, Matthew F. Tennyson Phd Aug 2019

Asap: A Source Code Authorship Program, Matthew F. Tennyson Phd

Faculty & Staff Research and Creative Activity

Source code authorship attribution is the task of determining who wrote a computer program, based on its source code, usually when the author is either unknown or under dispute. Areas where this can be applied include software forensics, cases of software copyright infringement, and detecting plagiarism. Numerous methods of source code authorship attribution have been proposed and studied. However, there are no known easily accessible and user-friendly programs that perform this task. Instead, researchers typically develop software in an ad hoc manner for use in their studies, and the software is rarely made publicly available. In this paper, we present …

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Convex And Non-Convex Optimization Methods For Machine Learning, Fariba Zohrizadeh Aug 2019

Convex And Non-Convex Optimization Methods For Machine Learning, Fariba Zohrizadeh

Computer Science and Engineering Dissertations - Archive

This dissertation is concerned with modeling fundamental and challenging machine learning tasks as convex/non-convex optimization problems and designing a mechanism that could solve them in a cost and time-effective manner. Extensive theoretical and practical studies are carried out to give deeper insights into the robustness and effectiveness of the formulated problems. In what follows, we investigate some well-known tasks that frequently arise in machine learning applications. Image Segmentation: Image segmentation is a fundamental and challenging task in computer vision with diverse applications in various areas. One of the major challenges in image segmentation is to determine the optimal number of …

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Pose Based Human Activity Recognition, Wenbo Li Aug 2019

Pose Based Human Activity Recognition, Wenbo Li

Legacy Theses & Dissertations (2009 - 2024)

Pose based human activity recognition is an important step towards video understanding. The last decade has witnessed the great progress in this field which is driven by multiple technical innovations, i.e., kinect, pose estimation techniques, deep learning, etc.

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Bias And Productivity In Humans And Machines, Bo Cowgill Aug 2019

Bias And Productivity In Humans And Machines, Bo Cowgill

Upjohn Institute Working Papers

Where should better learning technology (such as machine learning or AI) improve decisions? I develop a model of decision-making in which better learning technology is complementary with experimentation. Noisy, inconsistent decision-making introduces quasi-experimental variation into training datasets, which complements learning. The model makes heterogeneous predictions about when machine learning algorithms can improve human biases. These algorithms can remove human biases exhibited in historical training data, but only if the human training decisions are sufficiently noisy; otherwise, the algorithms will codify or exacerbate existing biases. Algorithms need only a small amount of noise to correct biases that cause large productivity distortions. …

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Understanding Self-Assembly And Charge Transport In Organic Solar Cells Through Efficient Computation, Evan Miller Aug 2019

Understanding Self-Assembly And Charge Transport In Organic Solar Cells Through Efficient Computation, Evan Miller

Boise State University Theses and Dissertations

Organic solar cells capable of sustainably generating electricity are possible if: (1) The structures assembled by photoactive molecules can be controlled, and (2) The structures favorable for charge transport can be determined. In this dissertation we conduct computational studies to understand relationships between organic solar cell compounds, processing, structure and charge transport. We advance tools for encapsulating computational workflows so that simulations are more reproducible and transferable. We find that molecular dynamic simulations using simplified models efficiently predict experimental structures. We find that the mobilities of charges through these structures—as determined by kinetic Monte Carlo simulations—match qualitative trends expected with …

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Advancing Molecular Simulations Of Crystal Nucleation: Applications To Clathrate Hydrates, Ryan Scott Defever Aug 2019

Advancing Molecular Simulations Of Crystal Nucleation: Applications To Clathrate Hydrates, Ryan Scott Defever

All Dissertations

Crystallization is a fundamental physical phenomenon with broad impacts in science and engineering. Nonetheless, mechanisms of crystallization in many systems remain incompletely understood. Molecular dynamics (MD) simulations are a powerful computational technique that, in principle, are well-suited to offer insights into the mechanisms of crystallization. Unfortunately, the waiting time required to observe crystal nucleation in simulated systems often falls far beyond the limits of modern MD simulations. This rare-event problem is the primary barrier to simulation studies of crystallization in complex systems. This dissertation takes a combined approach to advance simulation studies of nucleation in complex systems. First, we apply …

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Evaluating Machine Learning Performance In Predicting Injury Severity In Agribusiness Industries, Fatemeh Davoudi Kakhki, Steven Freeman, Gretchen Mosher Aug 2019

Evaluating Machine Learning Performance In Predicting Injury Severity In Agribusiness Industries, Fatemeh Davoudi Kakhki, Steven Freeman, Gretchen Mosher

Faculty Publications

Although machine learning methods have been used as an outcome prediction tool in many fields, their utilization in predicting incident outcome in occupational safety is relatively new. This study tests the performance of machine learning techniques in modeling and predicting occupational incidents severity with respect to accessible information of injured workers in agribusiness industries using workers’ compensation claims. More than 33,000 incidents within agribusiness industries in the Midwest of the United States for 2008–2016 were analyzed. The total cost of incidents was extracted and classified from workers’ compensation claims. Supervised machine learning algorithms for classification (support vector machines with linear, …

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An Atomistic Approach For The Survey Of Dislocation-Grain Boundary Interactions In Fcc Nickel, Devin William Adams Aug 2019

An Atomistic Approach For The Survey Of Dislocation-Grain Boundary Interactions In Fcc Nickel, Devin William Adams

Theses and Dissertations

It is well known that grain boundaries (GBs) have a strong influence on mechanical properties of polycrystalline materials. Not as well-known is how different GBs interact with dislocations to influence dislocation movement. This work presents a molecular dynamics study of 33 different FCC Ni bicrystals subjected to mechanical loading to induce incident dislocation-GB interactions. The resulting simulations are analyzed to determine properties of the interaction that affect the likelihood of transmission of the dislocation through the GB in an effort to better inform mesoscale models of dislocation movement within polycrystals. It is found that the ability to predict the slip …

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How Does Machine Learning Change Software Development Practices?, Zhiyuan Wan, Xin Xia, David Lo, Gail C. Murphy Aug 2019

How Does Machine Learning Change Software Development Practices?, Zhiyuan Wan, Xin Xia, David Lo, Gail C. Murphy

Research Collection School Of Computing and Information Systems

Adding an ability for a system to learn inherently adds uncertainty into the system. Given the rising popularity of incorporating machine learning into systems, we wondered how the addition alters software development practices. We performed a mixture of qualitative and quantitative studies with 14 interviewees and 342 survey respondents from 26 countries across four continents to elicit significant differences between the development of machine learning systems and the development of non-machine-learning systems. Our study uncovers significant differences in various aspects of software engineering (e.g., requirements, design, testing, and process) and work characteristics (e.g., skill variety, problem solving and task identity). …

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Computational Studies Of Thermal Properties And Desalination Performance Of Low-Dimensional Materials, Yang Hong Aug 2019

Computational Studies Of Thermal Properties And Desalination Performance Of Low-Dimensional Materials, Yang Hong

Department of Chemistry: Dissertations, Theses, and Student Research

During the last 30 years, microelectronic devices have been continuously designed and developed with smaller size and yet more functionalities. Today, hundreds of millions of transistors and complementary metal-oxide-semiconductor cells can be designed and integrated on a single microchip through 3D packaging and chip stacking technology. A large amount of heat will be generated in a limited space during the operation of microchips. Moreover, there is a high possibility of hot spots due to non-uniform integrated circuit design patterns as some core parts of a microchip work harder than other memory parts. This issue becomes acute as stacked microchips get …

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