Statistics and Probability Commons^™

A Computerized Mastitis Classification Aid Using A Dairy Herd-Based Records: Multi-Layer Perceptron (Mlp) Neural Network With Backpropagation Approach, Ahmed M. Gad Prof, Dina Faris De, Sherif Ramadan Prof, Ghada Afifi Dr, Eman Manaa Prof, Mahmoud El-Tarabany Prof

Business Administration

The main objective of this study is to develop an efficient machine learning-based model for the early prediction of clinical mastitis in Holstein Friesian dairy cattle where automatic milking system (AMS) data is used. The model aims to offer a costless opportunity for mastitis control and reduce its negative impact on livestock production. Different forward multilayer perceptron (MLP) neural networks with backpropagation (BP) learning algorithms using various numbers of hidden neurons and epochs have been introduced. The results of the established models are evaluated based on different metrics such as the accuracy, the F1 core, the precision, the recall, and …

Understanding Social Dynamics In Toxic Conversations And Public Health Intervention Acceptance On Social Media, Ana Aleksandric

Computer Science and Engineering Dissertations - Archive

Social media is now central to daily life, offering users a space to share content and opinions. However, these platforms also facilitate the spread of hate speech and misinformation, which can negatively impact public health. This dissertation develops methodologies to analyze social media data for insights that could inform health interventions. The research first examines user responses to toxic content, focusing on behavioral and emotional reactions, as well as group dynamics and bystander effects in toxic interactions. Another key focus is public opinion toward health interventions, particularly COVID-19 vaccination, using geolocated posts and analyzing factors such as race, ethnicity, and …

Advancing Deep Learning With Graph-Based Structural Insights: From Graph Classification To Semantic Segmentation, Xin Ma

Computer Science and Engineering Dissertations - Archive

Deep learning has profoundly transformed machine learning by offering sophisticated data representations, yet effectively incorporating structural information remains a challenge. Structural data, whether explicit or implicit, has the potential to significantly enhance the performance of deep learning tasks. This research investigates the benefits of structural information across three crucial tasks: classification, clustering, and segmentation. For explicit structural data, where inputs are directly represented as graphs, we investigate graph-level classification in brain connectivity networks. We introduce the Multi-resolution Edge Network (MENET), a novel framework designed to identify disease-specific connectomic benchmarks with high discriminatory power across diagnostic categories. MENET leverages graph-level representations …

Exact Testing For Heteroscedasticity In A Two-Way Layout In Variety Frost Trials When Incorporating A Covariate, Angelika A. Pilkington, Brenton R. Clarke, Dean A. Diepeveen

Grain and Other Field Crops Research Articles

Two-way layouts are common in grain industry research where it is often the case that there are one or more covariates. It is widely recognised that when estimating fixed effect parameters, one should also examine for possible extra error variance structure. An exact test for heteroscedasticity, when there is a covariate, is illustrated for a data set from frost trials in Western Australia. While the general algebra for the test is known, albeit in past literature, there are computational aspects of implementing the test for the two way when there are covariates. In this scenario the test is shown to …

Predicting Superconducting Critical Temperature Using Regression Analysis, Roland Fiagbe

Data Science and Data Mining

This project estimates a regression model to predict the superconducting critical temperature based on variables extracted from the superconductor’s chemical formula. The regression model along with the stepwise variable selection gives a reasonable and good predictive model with a lower prediction error (MSE). Variables extracted based on atomic radius, valence, atomic mass and thermal conductivity appeared to have the most contribution to the predictive model.

A 3-Step, Open-Data, Ride-Hailing Ridership Model With Pricing Applications, Richard A. Mucci

Theses and Dissertations--Civil Engineering

Researchers and practitioners studied the effects ride-hailing had in cities before the covid-19 pandemic. Previous research found ride-hailing to produce negative externalities, such as reducing transit ridership and increasing congestion in various cities. Since the pandemic, ride-hailing ridership has nearly recovered to pre-pandemic levels in Chicago. Ride-hailing ridership has grown steadily since the pandemic while a rider’s willingness to share their trip stagnated. Ride-hailing ridership nearly recovering to pre-covid levels in Chicago suggests that transportation planners, and policy makers, will need to continue assessing the impacts ride-hailing trips have in their cities.

Pickup and drop off locations in the Chicago …

Differential Impacts Of Weather Anomalies On Household Energy Expenditure Shares: A Comparison Of Clustered Panel Analysis Methods, Jordan Champion

Theses and Dissertations--Agricultural Economics

Recent emphasis on environmental justice has highlighted deficiencies in our energy system that produce disparities in accessibility and affordability for the most vulnerable. Meanwhile, the realities of a gradually warming climate and the onset of a global energy crisis (IEA 2022) have coincidently contributed to spikes in both energy prices and demand. These implications threaten to further exacerbate existing disparities for income-constrained and vulnerable populations, enhancing their risk of falling into prolonged insecurity. To ensure our transition to a just, sustainable future, we must first ensure equitable access to affordable and reliable energy for everyone. Combining household-level panel and state-level …

Outpatient Fall Prevention In Ambulatory Adults 65 Years Old And Over, Dorothy L. Osborne-White

Doctor of Nursing Practice (DNP) Scholarly Projects - Archive

Background: In the United States (U.S.), falls are the leading cause of injury among adults 65 and over, resulting in 36 million falls yearly (Moreland et al., 2020). According to the Centers for Disease Control and Prevention (CDC, 2023), one in four older adults experiences a fall each year. Falls are the world's second most prominent cause of accidental deaths (World Health Organization [WHO], 2021). Falls are the leading cause of both fatal and non-fatal injuries among older adults (Moreland et al., 2020).

Methods: A quality improvement project that included a fall bundle was implemented in a primary clinic. A …

Cryptographic Algorithms, Cryptocurrencies, And A Predictive Model Of Bitcoin Value By Pls Regression, Paul Kenneth O'Connor

Masters Theses

"With the invention of Bitcoin in 2009, as a seemingly timed response to the ongoing financial crisis, the popularity of the cryptocurrency has since continued to grow. Just this year, the Security Exchange Commission approved Bitcoin for exchange traded funds, allowing major investment firms to begin product trading. With this approval, and during this very moment of writing, Bitcoin has entered a bull market and reached a record value of over 72,000 USD. In addition, the Bitcoin halving event in April of 2024 is expected to increase demand even further. It has been anticipated that Bitcoin and other cryptocurrencies will …

Coral Scar Investigation: An Application Of Machine Learning And Computational Biology Methods To Understand Coral Holobiont Response To Various Tissue Loss Diseases, Emily W. Van Buren

Biology Dissertations - Archive

Coral disease is one of the biggest challenges facing coral reefs that actively changes biodiversity resulting in coral decline. With the rising threat of diseases, corals require biomarkers that reflect the immune systems and differences between common coral tissue loss diseases to best assist in coral restoration efforts. To obtain these biomarkers, my dissertation leverages two previously published datasets from two tissue loss disease exposure studies to investigate genes that are relevant for coral immune pathways, disease susceptibility, and classification between the diseases. In Chapter 2, I use comparative computational biology tools and protein assays to identify the melanin cascade …

Reinforcement Learning: Applying Low Discrepancy Action Selection To Deep Deterministic Policy Gradient, Aleksandr Svishchev

College of Graduate Studies: Theses & Dissertations

Reinforcement learning (RL) is a subfield of machine learning concerned with agents learning to behave optimally by interacting with an environment. One of the most important topics in RL is how the agent should explore, that is, how to choose actions in order to rate their impact on long-term reward. For example, a simple baseline strategy might be uniformly random action selection. This thesis investigates the heuristic idea that agents will learn faster if they explore by factoring the environment’s state into their decision and intentionally choose actions which are as different as possible from what they have previously observed. …

Performing Holt-Winters Time Series Forecasting Using Neural Network Based Models, Kazeem Olanrewaju Bankole

College of Graduate Studies: Theses & Dissertations

We show how to create Artificial Neural Network based models for performing the well- known Holt-Winters time series analysis. Our work fares well compared to the well-known Holt-Winter time series prediction method while avoiding the burden of searching for the parameters of the model. We present the theoretical justification of the connection between the two models and experimental results showing the similarities of these models

Examining Stigma In Rural Mental Health Care Settings: A Mixed Methods Approach, Lainie Krumenacker

Murray State Theses and Dissertations

More than half of Americans will be diagnosed with a mental illness in their lifetime (CDC, 2021), yet stigma towards mental health affects both patients and providers. Although programs exist to address stigma, improve cultural competency among providers, and educate families on the importance of support, facilities are often limited on programs they provide due to allocation of resources and funds. Without a shift in treatment and programing, stigma will continue to impact patient care and outcome.

This study explored stigma among mental health providers in rural communities, while exploring potential differences in treatment among patients due to race. Mental …

L∞ Bounds For Transient Growth In Repetitive And Iterative Learning Control Systems, Douglas A. Bristow, John R. Singler

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This paper revisits the problem of large transient growth in Iterative Learning Control (ILC) and Repetitive Process Control (RPC) systems. In ILC and RPC problems a process is repeated iteratively, with new control calculations occurring in between each iteration. Large transient growth refers to the propensity of some control algorithms to grow error exponentially before eventually converging. While robust monotonic convergence algorithms (in which monotonic convergence is guaranteed usually in exchange for a small loss in performance) have largely eliminated the concern for large transient growth in ILC, similar results cannot always be obtained in RPC. The emergence of additive …

ผลกระทบของฟังก์ชันกระตุ้นต่อประสิทธิภาพการใช้ข้อมูลของเครือข่ายประสาทเทียมแบบหลายผลลัพธ์, จินจุ เจริญยิ่งไพศาล

Chulalongkorn University Theses and Dissertations (Chula ETD)

เครือข่ายประสาทเทียมแบบหลายผลลัพธ์เป็นหนึ่งในแนวทางที่มีศักยภาพในการเพิ่มประสิทธิภาพข้อมูล อย่างไรก็ตาม ผลการศึกษาก่อนหน้านี้พบว่า ผลลัพธ์ทางทฤษฎีจากการเปรียบเทียบประสิทธิภาพของเครือข่ายประสาทเทียมแบบหลายผลลัพธ์ที่มีข้อจำกัด กับ เครือข่ายประสาทเทียมแบบผลลัพธ์เดียว และผลลัพธ์เชิงปฏิบัติจากการเปรียบเทียบเครือข่ายประสาทเทียมแบบหลายผลลัพธ์ กับเครือข่ายประสาทเทียมแบบผลลัพธ์เดียวยังคงมีความแตกต่างกัน งานวิจัยนี้มุ่งศึกษาผลกระทบของฟังก์ชันกระตุ้นในชั้นซ่อนที่มีต่อประสิทธิภาพการใช้ข้อมูลของเครือข่ายประสาทเทียมในการจำแนกประเภทแบบไบนารี โดยทำการศึกษาในฟังก์ชันกระตุ้น 4 ชนิด ได้แก่ ฟังก์ชัน Sigmoid, ReLU, Leaky ReLU (LReLUs) และ Exponential Linear Units (ELUs) ภายใต้โครงสร้างประสาทเทียม 2 ประเภทที่ใช้เกณฑ์ในการกำหนดโหนดซ่อนแตกต่างกัน โดยจากผลการวิจัยพบว่า ฟังก์ชัน sigmoid เป็นฟังก์ชันกระตุ้นที่สามารถทำให้ประสิทธิภาพเชิงปฏิบัติมีค่าใกล้เคียงกับผลลัพธ์เชิงทฤษฎีมากที่สุดในโครงสร้างทั้ง 2 ประเภท

Evaluating The Trojan Y Chromosome Strategy For The Removal Of Invasive Sacramento Pikeminnow From The Eel River, Ca, Alexander W. Juan

Cal Poly Humboldt theses and projects

The recent introduction and spread of Sacramento Pikeminnow (Ptychocheilus grandis) in Northern California’s Eel River Basin represents a significant threat and impediment to the recovery of several threatened native fish species. This study was undertaken to evaluate the Trojan Y Chromosome Strategy (TYC) for the extirpation of pikeminnow from the basin. TYC is a genetic biocontrol method that relies on the production and stocking of fish with YY sex chromosomes, which may be phenotypically male (YY male) or female (YY female). These YY fish produce all-male offspring when mating with their wild conspecifics and TYC can lead to …

ประสิทธิภาพของการทำนายความเสี่ยงการเกิดโรคเบาหวานร่วมกับความเสี่ยงการเกิดโรคความดันโลหิตสูงด้วยวิธีการเรียนรู้เชิงลึกสำหรับการจำแนกประเภทหลายเลเบล, วรรษา สุดใจ

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในทางการแพทย์ การเรียนรู้เชิงลึกนิยมนำมาใช้ในการสร้างตัวแบบพยากรณ์ซึ่งค่อนข้างให้ผลที่ดีกว่าเมื่อเทียบกับตัวแบบดั้งเดิมแต่บางครั้งผู้ป่วยสามารถเป็นโรคพร้อมกันได้มากกว่าหนึ่งโรค การเรียนรู้เชิงลึกจึงถูกพัฒนาให้สามารถทำนายพร้อมกันได้หลายโรค เรียกว่าโครงข่ายประสาทเทียมประเภทจำแนกหลายเลเบล ถึงแม้โครงข่ายประสาทเทียมจะมีความสามารถที่ดีสำหรับการพยากรณ์แต่ยังมีความท้าทายในข้อมูลบางกลุ่มที่ข้อมูลมีจำกัด วิทยานิพนธ์ฉบับนี้จึงมีความสนใจที่จะทำการศึกษาโดยต้องการเพิ่มประสิทธิภาพของตัวแบบโดยรวมด้วยการใช้เลเบลผลลัพธ์ที่เกี่ยวข้องกันมาศึกษาผ่านข้อมูลโรคเบาหวานและโรคความดันโลหิตสูงซึ่งเป็นโรคที่มักเกิดร่วมกัน แบ่งการทดลองเป็นสองส่วนคือส่วนข้อมูลจำลองและข้อมูลจริงเพื่อเปรียบเทียบระหว่างโครงข่ายประสาทเทียมแบบป้อนไปข้างหน้าหลายเลเบลกับหนึ่งเลเบล ผลการศึกษาพบว่าในโครงข่ายประสาทเทียมหลายเลเบลให้ผลที่ดีในทางทฤษฎีที่ทดสอบกับข้อมูลจำลอง แต่ในข้อมูลจริงผลลัพธ์ของการใช้เลเบลที่มีความเกี่ยวข้องกันไม่สามารถลดค่าฟังชันการสูญเสียได้อย่างมีนัยสำคัญ แต่มีข้อดีคือช่วยลดความรุนแรงของปัญหา overfit ได้และสามารถให้ประสิทธิภาพการพยากรณ์ยังคงเทียบเท่าการใช้หนึ่งเลเบล

Developing Machine Learning And Time-Series Analysis Methods With Applications In Diverse Fields, Muhammed Aljifri

Theses and Dissertations

This dissertation introduces methodologies that combine machine learning models with time-series analysis to tackle data analysis challenges in varied fields. The first study enhances the traditional cumulative sum control charts with machine learning models to leverage their predictive power for better detection of process shifts, applying this advanced control chart to monitor hospital readmission rates. The second project develops multi-layer models for predicting chemical concentrations from ultraviolet-visible spectroscopy data, specifically addressing the challenge of analyzing chemicals with a wide range of concentrations. The third study presents a new method for detecting multiple changepoints in autocorrelated ordinal time series, using the …

The Effect Of Social Determinants Of Health On End-Stage Kidney Disease Mortality Across Diverse Adult Populations: Systematic Review And Meta-Analysis, Prince Agyapong

Electronic Theses and Dissertations

Background: This systematic review and meta-analysis aimed to examine the influence of social determinants of health (SDOH) on End-Stage Kidney Disease (ESKD) mortality among diverse racial populations. Given the high morbidity and mortality associated with ESKD, understanding the impact of various SDOH factors across different racial groups is crucial for improving patient outcomes.
Methods: A comprehensive literature search was conducted to identify studies reporting on the relationship between SDOH and ESKD mortality using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) format. Citations were collated in EndNote 21 and screened in Covidence by two independent reviewers, with inter-rater …

Principal Component Analysis With Application To Credit Card Data, Elenor Cain

Schultz-Werth Award Papers

Principal Component Analysis (PCA) is a type of dimension reduction technique used in data analysis to process the data before making a model. In general, dimension reduction allows analysts to make conclusions about large data sets by reducing the number of variables while retaining as much information as possible. Using the numerical variables from a data set, PCA aims to compute a smaller set of uncorrelated variables, called principal components, that account for a majority of the variability from the data. The purpose of this paper is to understand PCA and determine which principal components should be kept from a …