Digital Commons Network^™

Applying Data Science And Machine Learning To Understand Health Care Transition For Adolescents And Emerging Adults With Special Health Care Needs, Lisamarie Turk

Nursing ETDs

A problem of classification places adolescents and emerging adults with special health care needs among the most at risk for poor or life-threatening health outcomes. This preliminary proof-of-concept study was conducted to determine if phenotypes of health care transition (HCT) for this vulnerable population could be established. Such phenotypes could support development of future studies that require data classifications as input. Mining of electronic health record data and cluster analysis were implemented to identify phenotypes. Subsequently, a machine learning concept model was developed for predicting acute care and medical condition severity. Three clusters were identified and described (Cluster 1, n …

Photonic Monitoring Of Atmospheric Fauna, Adrien P. Genoud

Dissertations

Insects play a quintessential role in the Earth’s ecosystems and their recent decline in abundance and diversity is alarming. Monitoring their population is paramount to understand the causes of their decline, as well as to guide and evaluate the efficiency of conservation policies. Monitoring populations of flying insects is generally done using physical traps, but this method requires long and expensive laboratory analysis where each insect must be identified by qualified personnel. Lack of reliable data on insect populations is now considered a significant issue in the field of entomology, often referred to as a “data crisis” in the field. …

A Neural Analysis-Synthesis Approach To Learning Procedural Audio Models, Danzel Serrano

Theses

The effective sound design of environmental sounds is crucial to demonstrating an immersive experience. Classical Procedural Audio (PA) models have been developed to give the sound designer a fast way to synthesize a specific class of environmental sounds in a physically accurate and computationally efficient manner. These models are controllable due to the choice of parameters from analyzing a class of sound. However, the resulting synthesis lacks the fidelity for the preferred immersive experience; thus, the sound designer would rather search through an extensive database for real recordings of a target sound class. This thesis proposes the Procedural audio Variational …

Light-Weight Structural Optimization Through Biomimicry, Machine Learning, And Inverse Design, Adithya Challapalli

LSU Doctoral Dissertations

In load-bearing lightweight architectures, cellular materials were frequently utilized. While octahedron, tetrahedron, and octet truss lattice truss were built for lightweight architectures with stretching and flexural dominance, it can be believed that new cells could easily be designed that might perform much better than the present ones in terms of mechanical and architectural characteristics. Machine learning-based structure scouting and design improvisation for better mechanical performance is a growing field of study. Additionally, biomimicry—the science of imitating nature’s elements—offers people a wealth of resources from which to draw motivation as they work to create a better quality of life.

Here, utilizing …

Narrative Review: Food Image Use For Machine Learnings’ Function In Dietary Assessment And Real Time Nutrition Feedback And Education, Jason Fee

Masters Theses, 2020-current

Technology has played a key role in advancing the health and agriculture sectors to improve obesity rates, diseasecontrol, food waste, and overall health disparities. However, these health and lifestyle determinants continue to plague theUnited States population. While new technologies have been and are currently being developed to address these concerns, they may not be practical for the general population. Utilizing machine learning advancement in food recognition using smartphone technology may be a means to improve the dietary component of nutrition assessments while providing valuable nutrition feedback. This narrative review was conducted to assess the current state of the literature on …

A Machine Learning Approach For Identification Of Low-Head Dams, Salvador Augusto Vinay Mollinedo

Theses and Dissertations

Identifying Low-head dams (LHD) and creating an inventory become a priority as fatalities continue to occur at these structures. Because obstruction inventories do not specifically identify LHDs, and they are not assigned a hazard classification, there is not an official inventory of LHD. However, there is a multi-agency taskforce that is creating an inventory of LHD. All efforts have been performed by manually identifying LHD on Google Earth Pro (GE Pro). The purpose of this paper is to assess whether a machine learning approach can accelerate the national inventory. We used a machine learning approach to implement a high-resolution remote …

Effect On 360 Degree Video Streaming With Caching And Without Caching, Md Milon Uddin

Electrical Engineering Theses

People all around the world are becoming more and more accustomed to watching 360-degree videos, which offer a way to experience virtual reality. While watching videos, it enables users to view video scenes from any perspective. To reduce bandwidth costs and provide the video with less latency, 360-degree video caching at the edge server may be a smart option. A hypothetical 360-degree video streaming system can partition popular video materials into tiles that are cached at the edge server. This study uses the Least Recently Used (LRU) and Least Frequently Used (LFU) algorithms to accomplish video caching and suggest a …

Algorithmic Solutions To Combat Online Fake News, Xinyi Zhou

Dissertations - ALL

The unprecedented growth of new information producing, distributing, and consuming every moment on the Web has fostered the rise of ``fake news.'' Because of its detrimental effect on democracy, global economies, and public health, effectively combating online fake news has become an essential and urgent task.

This dissertation starts with making typological, theoretical, and empirical efforts to promote the public's comprehension of fake news and lay the foundation for algorithmically combating fake news. As there has been no universal definition of fake news, this dissertation discusses the definition of fake news from three dimensions: veracity, intention, and news, comparing it …

Development Of Alternative Air Filtration Materials And Methods Of Analysis, Ivan Philip Beckman

Theses and Dissertations

Clean air is a global health concern. Each year more than seven million people across the globe perish from breathing poor quality air. Development of high efficiency particulate air (HEPA) filters demonstrate an effort to mitigate dangerous aerosol hazards at the point of production. The nuclear power industry installs HEPA filters as a final line of containment of hazardous particles. Advancement air filtration technology is paramount to achieving global clean air. An exploration of analytical, experimental, computational, and machine learning models is presented in this dissertation to advance the science of air filtration technology. This dissertation studies, develops, and analyzes …

Tent Detection In Satellite Imagery: Responding To Natural Disasters With Unet, Zachary Roman Lazzara

Student Theses

The purpose of this research is to create a deep learning tent detection system using UNet, that can be used to guide disaster relief efforts using satellite imagery. If the tent density in a given location can be detected following a natural disaster, this may be indicative of displaced people in need of aid and can guide search and rescue teams. In this paper we produce a tent detection system utilizing UNet, which achieved an overall accuracy of 80% when compared with the ground truth, or accuracies of 86% and 67% on the training and validation subsets respectively. We also …

Generation Of Phase Transitions Boundaries Via Convolutional Neural Networks, Christopher Alexis Ibarra

Open Access Theses & Dissertations

Accurate mapping of phase transitions boundaries is crucial in accurately modeling the equation of state of materials. The phase transitions can be structural (solid-solid) driven by temperature or pressure or a phase change like melting which defines the solid-liquid melt line. There exist many computational methods for evaluating the phase diagram at a particular point in temperature (T) and pressure (P). Most of these methods involve evaluation of a single (P,T) point at a time. The present work partially automates the search for phase boundaries lines utilizing a machine learning method based on convolutional neural networks and an efficient search …

Machine Learning Models For Human Synapse Genomics, Anqi Wei

All Dissertations

In the central nervous system, synapses are essential junctions that connect neurons and play important roles in neurotransmission and synaptic plasticity. While there are many challenges in human synapse genomics, machine learning techniques, which are capable of mining and interpreting large amounts of genomic data, may be utilized to facilitate the functional studies of human synapses. In this study, we have developed machine learning models for human synapse genomics to address several biological problems.

RNA localization plays an important role at the synapse, allowing local protein synthesis required for synaptic plasticity during brain development. Previous studies were conducted in mice …

Iot In Smart Communities, Technologies And Applications., Muhammad Zaigham Abbas Shah Syed

Electronic Theses and Dissertations

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing …

A New Comprehensive And Practical Taxonomy Of Demands Healthcare Professionals Experience: The Development Process And Testing Using Machine Learning, Phoebe Xoxakos

All Dissertations

Given the complex (Ratnapalan & Lang, 2020) and high stress environment of healthcare organizations (Freshwater & Cahill, 2010), a better understanding of the conditions in which healthcare professionals work is important. Although previous research has resulted in somewhat limited categories of the demands on healthcare professionals (Borteyrou et al., 2014; Shanafelt et al., 2020), a comprehensive taxonomy that covers the breadth and depth of demands is lacking. Using longitudinal data collected over 28 measurement waves spanning two years during the COVID-19 pandemic, the present studies outline the development of a taxonomy based on an in-depth literature review of related workplace …

Precision Weed Management Based On Uas Image Streams, Machine Learning, And Pwm Sprayers, Jason Allen Davis

Graduate Theses and Dissertations

Weed populations in agricultural production fields are often scattered and unevenly distributed; however, herbicides are broadcast across fields evenly. Although effective, in the case of post-emergent herbicides, exceedingly more pesticides are used than necessary. A novel weed detection and control workflow was evaluated targeting Palmer amaranth in soybean (Glycine max) fields. High spatial resolution (0.4 cm) unmanned aircraft system (UAS) image streams were collected, annotated, and used to train 16 object detection convolutional neural networks (CNNs; RetinaNet, Faster R-CNN, Single Shot Detector, and YOLO v3) each trained on imagery with 0.4, 0.6, 0.8, and 1.2 cm spatial resolutions. Models were …

Cyber Resilience Analytics For Cyber-Physical Systems, Md Ariful Haque

Electrical & Computer Engineering Theses & Dissertations

Cyber-physical systems (CPSs) are complex systems that evolve from the integrations of components dealing with physical processes and real-time computations, along with networking. CPSs often incorporate approaches merging from different scientific fields such as embedded systems, control systems, operational technology, information technology systems (ITS), and cybernetics. Today critical infrastructures (CIs) (e.g., energy systems, electric grids, etc.) and other CPSs (e.g., manufacturing industries, autonomous transportation systems, etc.) are experiencing challenges in dealing with cyberattacks. Major cybersecurity concerns are rising around CPSs because of their ever-growing use of information technology based automation. Often the security concerns are limited to probability-based possible attack …

Machine Learning-Based Event Generator, Yasir Alanazi

Computer Science Theses & Dissertations

Monte Carlo-based event generators have been the primary source for simulating particle collision experiments for the study of interesting physics scenarios. Monte Carlo generators rely on theoretical assumptions, which limit their ability to capture the full range of possible correlations between particle’s momenta. In addition, the simulations of the complete pipeline often take minutes to generate a single event even with the help of supercomputers.

In recent years, much attention has been devoted to the development of machine learning event generators. They demonstrate attractive advantages, including fast simulations, data compression, and being agnostic of theoretical assumptions. However, most of the …

Divide-And-Conquer Distributed Learning: Privacy-Preserving Offloading Of Neural Network Computations, Lewis C.L. Brown

Graduate Theses and Dissertations

Machine learning has become a highly utilized technology to perform decision making on high dimensional data. As dataset sizes have become increasingly large so too have the neural networks to learn the complex patterns hidden within. This expansion has continued to the degree that it may be infeasible to train a model from a singular device due to computational or memory limitations of underlying hardware. Purpose built computing clusters for training large models are commonplace while access to networks of heterogeneous devices is still typically more accessible. In addition, with the rise of 5G networks, computation at the edge becoming …

Atomlbs: An Atom Based Convolutional Neural Network For Druggable Ligand Binding Site Prediction, Md Ashraful Islam

Theses and Dissertations

Despite advances in drug research and development, there are few and ineffective treatments for a variety of diseases. Virtual screening can drastically reduce costs and accelerate the drug discovery process. Binding site identification is one of the initial and most important steps in structure-based virtual screening. Identifying and defining protein cavities that are likely to bind to a small compound is the objective of this task. In this research, we propose four different convolutional neural networks for predicting ligand-binding sites in proteins. A parallel optimized data pipeline is created to enable faster training of these neural network models on minimal …

House Price Classification Using Clustering Algorithms, Hamad Ahli

Theses

The housing segment is one of the most lucrative industries in almost all parts of the world, and with an emerging place like Dubai with global attraction the real-estate market is set to expand more. With this, there is an importance to be able to understand such markets with in-depth expertise which not only helps to be a subject matter expert, but also provide recommendations and insights to customers and stakeholders. According to Asteco, UAE would witness an addition of 38,500 apartments and 3,800 villas and Dubai is estimated to account the most with 30,000 flats and 3,500 villas in …

Imaging Normal Fluid Flow In He Ii With Neutrons And Lasers — A New Application Of Neutron Beams For Studies Of Turbulence, Xin Wen

Doctoral Dissertations

Turbulence is ubiquitous in life —from biology to astrophysics. The best direct numeric simulations (DNS) have only been benchmarked against low resolution, time-averaged experimental configurations—partly because of limitations in computing power. With time, computing power has greatly increased, so there is need for higher quality data of turbulent flow. In this dissertation, we explore a solution that enables quantitative visualization measurement of the velocity field in liquid helium, which has the potential of breaking new ground for high Reynolds number turbulence research and model testing.

Our technique involves creation of clouds of molecular tracers using 3He-neutron absorption reaction in liquid …

Improved Computational Prediction Of Function And Structural Representation Of Self-Cleaving Ribozymes With Enhanced Parameter Selection And Library Design, James D. Beck

Boise State University Theses and Dissertations

Biomolecules could be engineered to solve many societal challenges, including disease diagnosis and treatment, environmental sustainability, and food security. However, our limited understanding of how mutational variants alter molecular structures and functional performance has constrained the potential of important technological advances, such as high-throughput sequencing and gene editing. Ribonuleic Acid (RNA) sequences are thought to play a central role within many of these challenges. Their continual discovery throughout all domains of life is evidence of their significant biological importance (Weinreb et al., 2016). The self-cleaving ribozyme is a class of noncoding Ribonuleic Acid (ncRNA) that has been useful for …

Probabilistic Forecasting Of Winter Mixed Precipitation Types In New York State Utilizing A Random Forest, Brian Chandler Filipiak

Legacy Theses & Dissertations (2009 - 2024)

Operational forecasters face a plethora of challenges when making a forecast; they must consider multiple data sources ranging from radar and satellites to surface and upper air observations, to numerical weather prediction output. Forecasts must be done in a limited window of time, which adds an additional layer of difficulty to the task. These challenges are exacerbated by winter mixed precipitation events where slight differences in thermodynamic profiles or changes in terrain create different precipitation types across small areas. In addition to being difficult to forecast, mixed precipitation events can have large-scale impacts on our society.

Ascat Wind Estimation At 2.5 Km Resolution Supported By Machine Learning Rain Detection, Joshua Benjamin Kjar

Theses and Dissertations

The Advanced Scatterometer (ASCAT) is a C-band scatterometer designed to be less sensitive to rain contamination than other higher frequency scatterometers. However, the radar backscatter is still affected by rain which increases error during wind estimation. The error can be reduced in rainy conditions by combining a rain backscatter model with the existing wind only (WO) backscatter model to perform simultaneous wind and rain (SWR) estimation. I derive and test several 2.5 km resolution rain backscatter models for ASCAT data which are used with the WO model to estimate the near surface winds. Various rain models optimal for different purposes …

Quantifying Floral Resource Availability Using Unmanned Aerial Systems And Machine Learning Classifications To Predict Bee Community Structure, Jesse Anjin Tabor

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Bees are important for agricultural and non-agricultural ecosystems because they pollinate both wild plants and commercial crops. Flowers provide pollen and nectar resources that bees use to survive and reproduce. Measuring the relationship between the floral community and bee community may help apiarists and land managers to make informed decisions in managing wild and domesticated bee species. Manual methods to describe and count flowering vegetation is costly in time and personnel. Unmanned aerial vehicle (UAV) technology may be an efficient way to describe and count flowering vegetation on a large scale. UAVs with classification analysis and ground transect surveys were …

Transient Sources And How To Study Them: Selected Topics In Multi-Messenger Astronomy, Jiawei Luo

UNLV Theses, Dissertations, Professional Papers, and Capstones

The discovery of cosmic neutrino flux by IceCube, and the multi-messenger observations of gravitational event GW170817 ushered in the era of multi-messenger astronomy. Since the Universe itself is a natural laboratory, multi-messenger astronomy can help us study the most extreme physics processes in great detail. In this dissertation, we touch on some of the currently unanswered questions involving different types of transient sources and different “messengers” of multi-messenger astronomy. We employ a variety of analysis methods, including machine learning, a method that has not yet been widely adopted in astronomy but is rapidly gaining momentum.We start this dissertation with Chapter …

Automated Approach For The Enhancement Of Scaffolding Structure Monitoring With Strain Sensor Data, Sayan Sakhakarmi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Construction researchers have made a significant effort to improve the safety of scaffolding structures, as a large proportion of workers are involved in construction activities requiring scaffolds. However, most past studies focused on design and planning aspects of scaffolds. While limited studies investigated scaffolding safety during construction, they are limited to simple cases only with limited failure modes and simple scaffolds. In response to this limitation, this study aims to develop an automated scaffold monitoring approach capable of monitoring large scaffolds. Accordingly, this study developed an automated scaffold safety monitoring framework that leverages sensor data collected from a scaffold, scaffold …

Cyber-Attack Detection In Network Traffic Using Machine Learning, Khalid Almulla

Theses

Rapid shifting by government sectors and companies to provide their services and products over the internet, has immensely increased internet usage by individuals. Through extranets to network services or corporate networks used for personal purposes, computer hackers can lead to financial losses and manpower/time consumption. Therefore, it is vital to take all necessary measures to minimize losses by detecting attacks preemptively. Due to learning algorithms in cyberspace security challenges, deep learning-based cyber defense has lately become a hot topic. Penetration testing, malware categorization and identification, spam filtering, and spoofing detection are just a few of the key concerns in cyber …

Predicting Failure Rate Of Oil & Gas Equipment Using Ml, Shaima Alblooshi

Theses

Value of time has become an important perspective in business application ranging from day to day working to big businesses. The value of time is more important in the case of refinery business which has become of paramount importance with increasing energy needs. The main point of contention in refinery operations is the periodic maintenance of the pipelines which consumes of valuable time and resources. With a proper solution which can cater the time requirements of the lead time. The fact is that time consumption is extremely critical for the operations of refinery. Therefore, the application of machine learning is …

Predicting & Optimizing Airlines Customer Satisfaction Using Classification, Mhd Ridwan Alhabbal

Theses

This research is going to be a machine learning project that aims to study the various factors that may play a role in forming customer satisfaction response and tries to figure out which attributes or combination of them are the driver of positive customer satisfaction. The research is going to use initially some dataset from Kaggle (explained in the section of data source) in order to run machine learning algorithms and creating a predictor that would help airlines in predicting which customers are satisfied and trying to have a proactive reaction in case of negative feedback, so we can make …