Digital Commons Network^™

Early Detection Of Fake News On Social Media, Yang Liu

Dissertations

The ever-increasing popularity and convenience of social media enable the rapid widespread of fake news, which can cause a series of negative impacts both on individuals and society. Early detection of fake news is essential to minimize its social harm. Existing machine learning approaches are incapable of detecting a fake news story soon after it starts to spread, because they require certain amounts of data to reach decent effectiveness which take time to accumulate. To solve this problem, this research first analyzes and finds that, on social media, the user characteristics of fake news spreaders distribute significantly differently from those …

Cancer Risk Prediction With Whole Exome Sequencing And Machine Learning, Abdulrhman Fahad M Aljouie

Dissertations

Accurate cancer risk and survival time prediction are important problems in personalized medicine, where disease diagnosis and prognosis are tuned to individuals based on their genetic material. Cancer risk prediction provides an informed decision about making regular screening that helps to detect disease at the early stage and therefore increases the probability of successful treatments. Cancer risk prediction is a challenging problem. Lifestyle, environment, family history, and genetic predisposition are some factors that influence the disease onset. Cancer risk prediction based on predisposing genetic variants has been studied extensively. Most studies have examined the predictive ability of variants in known …

Predictive Modeling Of Influenza In New England Using A Recurrent Deep Neural Network, Alfred Amendolara

Theses

Predicting seasonal variation in influenza epidemics is an ongoing challenge. To better predict seasonal influenza and provide early warning of pandemics, a novel approach to Influenza-Like-Illness (ILI) prediction was developed. This approach combined a deep neural network with ILI, climate, and population data. A predictive model was created using a deep neural network based on TensorFlow 2.0 Beta. The model used Long-Short Term Memory (LSTM) nodes. Data was collected from the Center for Disease Control, the National Center for Environmental Information (NCEI) and the United States Census Bureau. These parameters were temperature, precipitation, wind speed, population size, vaccination rate and …

Non-Invasive Hyperglycemia Detection Using Ecg And Deep Learning, Renato Silveira Cordeiro

Master's Theses

Hyperglycemia is characterized by an elevated level of glucose in the blood. It is normally asymptomatic, except for an extremely high level, and thus a person can live in that state for years before the negative - sometimes irreversible - health impacts appear. Unexpected hyperglycemia can also be an indication of diabetes, a chronic disease that, when not treated, can lead to serious consequences, including limb amputations and even death. Therefore, identifying hyperglycemic state is important. The most common and direct way to measure a person’s glucose level is by directly assessing it from a blood sample by pricking a …

Detecting Myocardial Infarctions Using Machine Learning Methods, Aniruddh Mathur

Master's Projects

Myocardial Infarction (MI), commonly known as a heart attack, occurs when one of the three major blood vessels carrying blood to the heart get blocked, causing the death of myocardial (heart) cells. If not treated immediately, MI may cause cardiac arrest, which can ultimately cause death. Risk factors for MI include diabetes, family history, unhealthy diet and lifestyle. Medical treatments include various types of drugs and surgeries which can prove very expensive for patients due to high healthcare costs. Therefore, it is imperative that MI is diagnosed at the right time. Electrocardiography (ECG) is commonly used to detect MI. ECG …

Smartphone-Based Human Fatigue Detection In An Industrial Environment Using Gait Analysis, Swapnali Babasaheb Karvekar

Theses

Human fatigue due to repetitive and physically challenging jobs may result in poor performance and a Work-related Musculoskeletal Disorder (WMSD). Thus, the importance of being able to monitor fatigue to implement preventative interventions cannot be overstated. This study was designed to monitor fatigue through the development of a methodology that objectively classifies an individual’s level of fatigue in the workplace by utilizing the motion sensors embedded in smartphones. An experiment consisting of squatting tasks, primarily involving the lower extremity musculature, was conducted with 24 participants using a smartphone attached to their right shank. Using Borg’s Ratings of Perceived Exertion (RPE) …

Assessing Wildfire Damage From High Resolution Satellite Imagery Using Classification Algorithms, Ai-Linh Alten

Master's Projects

Wildfire damage assessments are important information for first responders, govern- ment agencies, and insurance companies to estimate the cost of damages and to help provide relief to those affected by a wildfire. With the help of Earth Observation satellite technology, determining the burn area extent of a fire can be done with traditional remote sensing methods like Normalized Burn Ratio. Using Very High Resolution satellites can help give even more accurate damage assessments but will come with some tradeoffs; these satellites can provide higher spatial and temporal resolution at the expense of better spectral resolution. As a wildfire burn area …

Deep Reinforcement Learning For Real-Time Residential Hvac Control, Evan Mckee

Masters Theses

The model-free Deep Reinforcement Learning (DRL) environment developed for this work attempts to minimize energy cost during residential heating, ventilation, and air conditioning (HVAC) operation. The HVAC load associated with heating and cooling is an ideal candidate for price optimization through automation for two reasons: Its power footprint in a typical home is sizeable, and the required level of participation from an inhabitant is passive. HVAC is difficult to accurately model and unique for every home, so online machine learning is used to allow for real-time readjustment in performance. Energy cost for the cooling unit shown in this work is …

Improving Manufacturing Data Quality With Data Fusion And Advanced Algorithms For Improved Total Data Quality Management, David Juriga

Masters Theses

Data mining and predictive analytics in the sustainable-biomaterials industries is currently not feasible given the lack of organization and management of the database structures. The advent of artificial intelligence, data mining, robotics, etc., has become a standard for successful business endeavors and is known as the ‘Fourth Industrial Revolution’ or ‘Industry 4.0’ in Europe. Data quality improvement through real-time multi-layer data fusion across interconnected networks and statistical quality assessment may improve the usefulness of databases maintained by these industries. Relational databases with a high degree of quality may be the gateway for predictive modeling and enhanced business analytics. Data quality …

On Action Quality Assessment, Paritosh Parmar

UNLV Theses, Dissertations, Professional Papers, and Capstones

In this dissertation, we tackle the task of quantifying the quality of actions, i.e., how well an

action was performed using computer vision. Existing methods used human body pose-based features to express the quality contained in an action sample. Human body pose estimation in actions such as sports actions, like diving and gymnastic vault, is particularly challenging, since the athletes undergo convoluted transformations while performing their routines. Moreover, pose-based features do not take into account visual cues such as water splash in diving. Visual cues are taken into account by human judges. In our first work, we show that using …

Neural Network In Hardware, Jiong Si

UNLV Theses, Dissertations, Professional Papers, and Capstones

This dissertation describes the implementation of several neural networks built on a field programmable gate array (FPGA) and used to recognize a handwritten digit dataset – the Modified National Institute of Standards and Technology (MNIST) database. A novel hardwarefriendly activation function called the dynamic ReLU (D-ReLU) function is proposed. This activation function can decrease chip area and power of neural networks when compared to traditional activation functions at no cost to prediction accuracy.

The implementations of three neural networks on FPGA are presented: 2-layer online training fully-connected neural network, 3-layer offline training fully-connected neural network, and two solutions of Super-Skinny …

An Application Of Deep Learning Models To Automate Food Waste Classification, Alejandro Zachary Espinoza

Dissertations and Theses

Food wastage is a problem that affects all demographics and regions of the world. Each year, approximately one-third of food produced for human consumption is thrown away. In an effort to track and reduce food waste in the commercial sector, some companies utilize third party devices which collect data to analyze individual contributions to the global problem. These devices track the type of food wasted (such as vegetables, fruit, boneless chicken, pasta) along with the weight. Some devices also allow the user to leave the food in a kitchen container while it is weighed, so the container weight must also …

Machine Learning On Acoustic Signals Applied To High-Speed Bridge Deck Defect Detection, Yao Chou

Theses and Dissertations

Machine learning techniques are being applied to many data-intensive problems because they can accurately provide classification of complex data using appropriate training. Often, the performance of machine learning can exceed the performance of traditional techniques because machine learning can take advantage of higher dimensionality than traditional algorithms. In this work, acoustic data sets taken using a rapid scanning technique on concrete bridge decks provided an opportunity to both apply machine learning algorithms to improve detection performance and also to investigate the ways that training of neural networks can be aided by data augmentation approaches. Early detection and repair can enhance …

Truck Activity Pattern Classification Using Anonymous Mobile Sensor Data, Taslima Akter

Graduate Theses and Dissertations

To construct, operate, and maintain a transportation system that supports the efficient movement of freight, transportation agencies must understand economic drivers of freight flow. This is a challenge since freight movement data available to transportation agencies is typically void of commodity and industry information, factors that tie freight movements to underlying economic conditions. With recent advances in the resolution and availability of big data from Global Positioning Systems (GPS), it may be possible to fill this critical freight data gap. However, there is a need for methodological approaches to enable usage of this data for freight planning and operations.

To …

Extracting Patterns In Medical Claims Data For Predicting Opioid Overdose, Ryan Sanders

Graduate Theses and Dissertations

The goal of this project is to develop an efficient methodology for extracting features from time-dependent variables in transaction data. Transaction data is collected at varying time intervals making feature extraction more difficult. Unsupervised representational learning techniques are investigated, and the results compared with those from other feature engineering techniques. A successful methodology provides features that improve the accuracy of any machine learning technique. This methodology is then applied to insurance claims data in order to find features to predict whether a patient is at risk of overdosing on opioids. This data covers prescription, inpatient, and outpatient transactions. Features created …

Characterizing Dryland Ecosystems Using Remote Sensing And Dynamic Global Vegetation Modeling, Abdolhamid Dashtiahangar

Boise State University Theses and Dissertations

Drylands include all terrestrial regions where the production of crops, forage, wood and other ecosystem services are limited by water. These ecosystems cover approximately 40% of the earth terrestrial surface and accommodate more than 2 billion people (Millennium Ecosystem Assessment, 2005). Moreover, the interannual variability of the global carbon budget is strongly regulated by vegetation dynamics in drylands. Understanding the dynamics of such ecosystems is significant for assessing the potential for and impacts of natural or anthropogenic disturbances and mitigation planning, and a necessary step toward enhancing the economic and social well-being of dryland communities in a sustainable manner (Global …

Spatially-Explicit Snap Bean Flowering And Disease Prediction Using Imaging Spectroscopy From Unmanned Aerial Systems, Ethan W. Hughes

Theses

Sclerotinia sclerotiorum, or white mold, is a fungus that infects the flowers of snap bean plants and causes a subsequent reduction in snap bean pods, which adversely impacts yield. Timing the application of white mold fungicide thus is essential to preventing the disease, and is most effective when applied during the flowering stage. However, most of the flowers are located beneath the canopy, i.e., hidden by foliage, which makes spectral detection of flowering via the leaf/canopy spectra paramount. The overarching objectives of this research therefore are to i) identify spectral signatures for the onset of flowering to optimally time the …

Machine Learning For Robust Understanding Of Scene Materials In Hyperspectral Images, Utsav B. Gewali

Theses

The major challenges in hyperspectral (HS) imaging and data analysis are expensive sensors, high dimensionality of the signal, limited ground truth, and spectral variability. This dissertation develops and analyzes machine learning based methods to address these problems. In the first part, we examine one of the most important HS data analysis tasks-vegetation parameter estimation. We present two Gaussian processes based approaches for improving the accuracy of vegetation parameter retrieval when ground truth is limited and/or spectral variability is high. The first is the adoption of covariance functions based on well-established metrics, such as, spectral angle and spectral correlation, which are …

Poly-Gan: A Multi-Conditioned Gan For Multiple Tasks, Nilesh Pandey

Theses

We present Poly-GAN, a novel conditional GAN architecture that is motivated by different Image generation and manipulation applications like Fashion Synthesis, an application where garments are automatically placed on images of human models at an arbitrary pose, image inpainting, an application where we try to recover a damaged image using the edges or a rough sketch of the image. While different applications use different GAN setup for image generation, we propose only one architecture for multiple applications with little to no change in the pipeline. Poly-GAN allows conditioning on multiple inputs and is suitable for many different tasks. Our novel …

Toward Self-Reconfigurable Parametric Systems: Reinforcement Learning Approach, Ting-Yu Mu

Dissertations

For the ongoing advancement of the fields of Information Technology (IT) and Computer Science, machine learning-based approaches are utilized in different ways in order to solve the problems that belong to the Nondeterministic Polynomial time (NP)-hard complexity class or to approximate the problems if there is no known efficient way to find a solution. Problems that determine the proper set of reconfigurable parameters of parametric systems to obtain the near optimal performance are typically classified as NP-hard problems with no efficient mathematical models to obtain the best solutions. This body of work aims to advance the knowledge of machine learning …

Comparison Of Rl Algorithms For Learning To Learn Problems, Adolfo Gonzalez Iii

Theses and Dissertations

Machine learning has been applied to many different problems successfully due to the expressiveness of neural networks and simplicity of first order optimization algorithms. The latter being a vital piece needed for training large neural networks efficiently. Many of these algorithms were produced with behavior produced by experiments and intuition. An interesting question that comes to mind is that rather than observing and then designing algorithms with beneficial behaviors, can these algorithms be learned through a reinforcement learning by modeling optimization as a game. This paper explores several reinforcement learning algorithms which are applied to learn policies suited for optimization.

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 …

Mdt Geolocation Through Machine Learning: Evaluation Of Supervised Regression Ml Algorithms, Aria Canadell Solana

Theses and Dissertations

Minimizing Drive Test is a statistical protocol used to evaluate the network performance. It provides several benefits with respect to traditional drive test analysis; however, multiple inconveniences exist that prevent cell companies from precisely retrieving most of the locations of these reports. . MATLAB and Jupyter Notebook were used to prepare the data and create the models. Multiple supervised regression algorithms were tested and evaluated. The best predictions were obtained from the K-Nearest Neighbor algorithm with one ‘k’ and distance-weighted predictions. The UE geolocation was predicted with a median accuracy of 5.42 meters, a mean error of 61.62 meters, and …

Countering Cybersecurity Vulnerabilities In The Power System, Fengli Zhang

Graduate Theses and Dissertations

Security vulnerabilities in software pose an important threat to power grid security, which can be exploited by attackers if not properly addressed. Every month, many vulnerabilities are discovered and all the vulnerabilities must be remediated in a timely manner to reduce the chance of being exploited by attackers. In current practice, security operators have to manually analyze each vulnerability present in their assets and determine the remediation actions in a short time period, which involves a tremendous amount of human resources for electric utilities. To solve this problem, we propose a machine learning-based automation framework to automate vulnerability analysis and …

Critiquing The New Autonomy Of Immaterial Labour: An Analysis Of Work In The Artificial Intelligence Industry, James Steinhoff

Electronic Thesis and Dissertation Repository

Karl Marx theorized capitalism as a relation between labour, capital and machines. For Marx, capital, the process of self-augmenting value appropriated from human labour, is inherently driven by competition to replace labour in production with machines. Marx goes as far as to describe machines as capital’s “most powerful weapon” for suppressing working class revolt. Marx, however, could not have predicted the computing machines – such as artificial intelligence – which now form the basis for an increasingly cybernetic capital. Since Marx’s time, many Marxist thinkers have sought to apply or update his approach to the cybernetic era. The influential post-operaismo …

Algorithms For Multi-Objective Mixed Integer Programming Problems, Alvaro Miguel Sierra Altamiranda

USF Tampa Graduate Theses and Dissertations

This thesis presents a total of 3 groups of contributions related to multi-objective optimization. The first group includes the development of a new algorithm and an open-source user-friendly package for optimization over the efficient set for bi-objective mixed integer linear programs. The second group includes an application of a special case of optimization over the efficient on conservation planning problems modeled with modern portfolio theory. Finally, the third group presents a machine learning framework to enhance criterion space search algorithms for multi-objective binary linear programming.

In the first group of contributions, this thesis presents the first (criterion space search) algorithm …

Habitat Associations And Reproduction Of Fishes On The Northwestern Gulf Of Mexico Shelf Edge, Elizabeth Marie Keller

LSU Doctoral Dissertations

Several of the northwestern Gulf of Mexico (GOM) shelf-edge banks provide critical hard bottom habitat for coral and fish communities, supporting a wide diversity of ecologically and economically important species. These sites may be fish aggregation and spawning sites and provide important habitat for fish growth and reproduction. Already designated as habitat areas of particular concern, many of these banks are also under consideration for inclusion in the expansion of the Flower Garden Banks National Marine Sanctuary. This project aimed to gain a more comprehensive understanding of the communities and fish species on shelf-edge banks by way of gonad histology, …

Using Uncertainty To Interpret Supervised Machine Learning Predictions, Michael C. Darling

Electrical and Computer Engineering ETDs

Traditionally, machine learning models are assessed using methods that estimate an average performance against samples drawn from a particular distribution. Examples include the use of cross-validation or hold0out to estimate classification error, F-score, precision, and recall.

While these measures provide valuable information, they do not tell us a model's certainty relative to particular regions of the input space. Typically there are regions where the model can differentiate the classes with certainty, and regions where the model is much less certain about its predictions.

In this dissertation we explore numerous approaches for quantifying uncertainty in the individual predictions made by supervised …

Word Importance Modeling To Enhance Captions Generated By Automatic Speech Recognition For Deaf And Hard Of Hearing Users, Sushant Kafle

Theses

People who are deaf or hard-of-hearing (DHH) benefit from sign-language interpreting or live-captioning (with a human transcriptionist), to access spoken information. However, such services are not legally required, affordable, nor available in many settings, e.g., impromptu small-group meetings in the workplace or online video content that has not been professionally captioned. As Automatic Speech Recognition (ASR) systems improve in accuracy and speed, it is natural to investigate the use of these systems to assist DHH users in a variety of tasks. But, ASR systems are still not perfect, especially in realistic conversational settings, leading to the issue of trust and …

Groundwater Level Mapping Tool: Development Of A Web Application To Effectively Characterize Groundwater Resources, Steven William Evans

Theses and Dissertations

Groundwater is used worldwide as a major source for agricultural irrigation, industrial processes, mining, and drinking water. An accurate understanding of groundwater levels and trends is essential for decision makers to effectively manage groundwater resources throughout an aquifer, ensuring its sustainable development and usage. Unfortunately, groundwater is one of the most challenging and expensive water resources to characterize, quantify, and monitor on a regional basis. Data, though present, are often limited or sporadic, and are generally not used to their full potential to aid decision makers in their groundwater management.This thesis presents a solution to this under-utilization of available data …