Physical Sciences and Mathematics Commons^™

Low-Resource Icd Coding Of Discharge Summaries, Ashton Williamson

All Theses

Medical coding is the process by which standardized medical codes are assigned to patient health records. This is a complex and challenging task that typically requires an expert human coder to review health records and assign codes from a classification system based on a standard set of rules. Considering the downstream use of these codes in statistical analysis, billing, and patient care, improving the accuracy and efficiency of the medical coding process through automation could have a far-reaching impact on the healthcare domain. Since health records typically consist of a large proportion of free-text documents, this problem has traditionally been …

Interpreting Shift Encoders As State Space Models For Stationary Time Series, Patrick Donkoh

Electronic Theses and Dissertations

Time series analysis is a statistical technique used to analyze sequential data points collected or recorded over time. While traditional models such as autoregressive models and moving average models have performed sufficiently for time series analysis, the advent of artificial neural networks has provided models that have suggested improved performance. In this research, we provide a custom neural network; a shift encoder that can capture the intricate temporal patterns of time series data. We then compare the sparse matrix of the shift encoder to the parameters of the autoregressive model and observe the similarities. We further explore how we can …

Develop An Interactive Python Dashboard For Analyzing Ezproxy Logs, Andy Huff, Matthew Roth, Weiling Liu

Faculty Scholarship

This paper describes the development of an interactive dashboard in Python with EZproxy log data. Hopefully, this dashboard will help improve the evidence-based decision-making process in electronic resources management and explore the impact of library use.

Cradle Explorer: Casfer Interactive Platform For Data And Model Visualization, Olatunde D. Akanbi, Vibha S. Mandayam, Haiping Ai, Arafath Nihar, Erika I. Barcelos, Laura S. Bruckman, Jeffrey Yarus, Yinghui Wu, Huichun (Judy) Zhang, Roger H. French

Student Scholarship

No abstract provided.

Data Engineering: Building Software Efficiency In Medium To Large Organizations, Alessandro De La Torre

Whittier Scholars Program

The introduction of PoetHQ, a mobile application, offers an economical strategy for colleges, potentially ushering in significant cost savings. These savings could be redirected towards enhancing academic programs and services, enriching the educational landscape for students. PoetHQ aims to democratize access to crucial software, effectively removing financial barriers and facilitating a richer educational experience. By providing an efficient software solution that reduces organizational overhead while maximizing accessibility for students, the project highlights the essential role of equitable education and resource optimization within academic institutions.

Visualizing Nfl Player Metrics, Jayson Rhea

Campus Research Day

This project is dedicated to reshaping the exploration of NFL player data. Tailored for sports analysts and fantasy football managers, the goal is to deliver convenience through seamless data navigation and precise filtering through an interactive dashboard. In contrast to the static formats found on the NFL website and ESPN, this dynamic interface offers interactive visualizations, empowering users to effortlessly compare data. These comparisons can be used draw quick conclusions about player performance.

Dashboard To Quickly Estimate The Cost And Duration Of An Nyc Green Taxi Trip, Isaac Braun

Campus Research Day

Before hailing a New York City (NYC) taxi, residents and tourists do not easily know how much the trip will cost them or how long it may take. Taxis are still heavily used, even with the increase of ride-hailing services like Uber, and a new system has yet to be built to provide customers with these two metrics before taking a trip. This project aims to give riders a quick way to estimate a ride’s cost and duration through an interactive dashboard that allows filtering by pickup and drop-off neighborhoods. This is accomplished by analyzing three years of public data …

Binder, Tyler A. Peaster, Lindsey M. Davenport, Madelyn Little, Alex Bales

ATU Research Symposium

Binder is a mobile application that aims to introduce readers to a book recommendation service that appeals to devoted and casual readers. The main goal of Binder is to enrich book selection and reading experience. This project was created in response to deficiencies in the mobile space for book suggestions, library management, and reading personalization. The tools we used to create the project include Visual Studio, .Net Maui Framework, C#, XAML, CSS, MongoDB, NoSQL, Git, GitHub, and Figma. The project’s selection of books were sourced from the Google Books repository. Binder aims to provide an intuitive interface that allows users …

Techniques To Detect Fake Profiles On Social Media Using The New Age Algorithms – A Survey, A K M Rubaiyat Reza Habib, Edidiong Elijah Akpan

ATU Research Symposium

This research explores the growing issue of fake accounts in Online Social Networks [OSNs]. While platforms like Twitter, Instagram, and Facebook foster connections, their lax authentication measures have attracted many scammers and cybercriminals. Fake profiles conduct malicious activities, such as phishing, spreading misinformation, and inciting social discord. The consequences range from cyberbullying to deceptive commercial practices. Detecting fake profiles manually is often challenging and causes considerable stress and trust issues for the users. Typically, a social media user scrutinizes various elements like the profile picture, bio, and shared posts to identify fake profiles. These evaluations sometimes lead users to conclude …

Accessing Advanced National Supercomputing And Storage Resources For Computational Research, Ramazan Aygun

All Things Open

This presentation will cover ACCESS (Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support), and Kennesaw State University's involvement in Open Science Data Federation program as a data origin to help researchers and educators with or without supporting grants to utilize the nation’s advanced computing systems and services. ACCESS, a program established and funded by the National Science Foundation, is an ecosystem with capabilities for new modes of research and further democratizing participation. The presentation covers how to apply for allocations on ACCESS. The last part of the presentation will briefly explain Open Science Data Federation and Kennesaw State University's involvement as …

The Vulnerabilities Of Artificial Intelligence Models And Potential Defenses, Felix Iov

Cybersecurity Undergraduate Research Showcase

The rapid integration of artificial intelligence (AI) into various commercial products has raised concerns about the security risks posed by adversarial attacks. These attacks manipulate input data to disrupt the functioning of AI models, potentially leading to severe consequences such as self-driving car crashes, financial losses, or data breaches. We will explore neural networks, their weaknesses, and potential defenses. We will discuss adversarial attacks including data poisoning, backdoor attacks, evasion attacks, and prompt injection. Then, we will explore defense strategies such as data protection, input sanitization, and adversarial training. By understanding how adversarial attacks work and the defenses against them, …

Urinalysis Test Data Analysis And Prediction, Nikhil Mhatre

2024 Datathon Challenges

OUTLIERS Team submission to the Urinalysis Test Results Timed Challenge

Researched various algorithms like boosting and random forest. We learned a lot about their strength and weaknesses, and used these algorithms accordingly to solve the issues faced in the dataset.

Gender Detection In Facial Images: A Comprehensive Cnn Analysis, Jose N T Ambrosio, Anas Hourani, Magdalene Moy

SACAD: John Heinrichs Scholarly and Creative Activity Days

This research investigates the construction of a robust gender detection system using facial features and Convolutional Neural Networks (CNNs), exploring the impact of different layer configurations on accuracy and computational efficiency. With a validation accuracy of 91%, findings illuminate the nuanced relationship between precision and computational resources, enriching discussions on facial recognition technologies.

Artificial Intelligence Could Probably Write This Essay Better Than Me, Claire Martino

Augustana Center for the Study of Ethics Essay Contest

No abstract provided.

Localized Collocation Meshless Method For Modeling Transdermal Pharmacokinetics In Multiphase Skin Structures, Eduardo Divo

Math Department Colloquium Series

The human skin has a complicated structure with many multi-scale, biophysical effects impacting the propagation of skin-injected substances, such as partitioning, metabolic reactions, adsorption and elimination. An extended version of Fick’s second law governing the process of the compound diffusion in various skin layer is employed in the current work by considering the conservation of mass of the substance and the metabolic reaction of the substance in viable skin. Additionally, a model assuming linear coupling between the substance concentrations that are bound and unbound with blood was developed. Using such a model, a set of coupled partial differential equations are …

Transcriptional Dynamics During Rhodococcus Erythropolis Infection With Phage Wc1, Dana Willner, Sudip Paudel, Andrew D. Halleran, Grace E. Solini, Veronica Gray, Margaret Saha

Arts & Sciences Articles

Background

Belonging to the Actinobacteria phylum, members of the Rhodococcus genus thrive in soil, water, and even intracellularly. While most species are non-pathogenic, several cause respiratory disease in animals and, more rarely, in humans. Over 100 phages that infect Rhodococcus species have been isolated but despite their importance for Rhodococcus ecology and biotechnology applications, little is known regarding the molecular genetic interactions between phage and host during infection. To address this need, we report RNA-Seq analysis of a novel Rhodococcus erythopolis phage, WC1, analyzing both the phage and host transcriptome at various stages throughout the infection process.

Results

By five …

Health And Healthcare: Designing For The Social Determinants Of Health And Blue Zones In North Nashville, Rebecca Tonguis, Honor Thomas, Olivia Hobbs

Belmont University Research Symposium (BURS)

Owned by North Nashville’s First Community Church, a now empty site in the Osage-North Fisk neighborhood of North Nashville has been identified as a potential site for a new location of The Store, in addition to a community-centric architectural development based on the social determinants of health and informed by the principles behind Blue Zones, the locations with the highest lifespans in the world. Opened by Brad Paisley and Kimberly Williams-Paisley, The Store is a free grocery store that “allow[s] people to shop for their basic needs in a way that protects dignity and fosters hope”, for which North Nashville …

Demographic Data Analysis For Measuring Economic Impact Of The Branch Of Nashville, Tessa Pendleton, Annie Wardroup, Nicole Speyrer, Kimberly Amaya Hernandez

Belmont University Research Symposium (BURS)

As part of the Global Honors Scholars Collaborative, researchers aggregated data from The Belmont Data Collaborative to analyze the three primary ZIP codes (37211, 37013, 37217) served by The Branch of Nashville. These communities include immigrant and refugee populations, whom The Branch supports through its food bank, English classes, and further comprehensive care. Future program development will rely on the analysis of the current client base and eventual assessment of The Branch’s economic impact on the surrounding community. The goal of this research for The Branch of Nashville is twofold: (1) analyze the existing demographics within the above ZIP codes …

Elevating Academic Administration: A Comprehensive Faculty Dashboard For Tracking Student Evaluations And Research, Musa M. Azeem

Senior Theses

The USC Faculty Dashboard is a web application designed to revolutionize how department heads, professors, and instructors monitor progress and make decisions, providing a centralized hub for efficient data storage and analysis. Currently, there’s a gap in tools tailored for department heads to concisely manage the performance of their department, which our platform aims to fill. The USC Faculty Dashboard offers easy access to upload and view student evaluation and research information, empowering department heads to evaluate the performance of faculty members and seamlessly track their research grants, publications, and expenditures. Furthermore, professors and instructors gain personalized performance analysis tools, …

Combating Financial Crimes With Unsupervised Learning Techniques: Clustering And Dimensionality Reduction For Anti-Money Laundering, Ahmed N. Bakry, Almohammady S. Alsharkawy, Mohamed S. Farag, Kamal R. Raslan

Al-Azhar Bulletin of Science

Anti-Money Laundering (AML) is a crucial task in ensuring the integrity of financial systems. One keychallenge in AML is identifying high-risk groups based on their behavior. Unsupervised learning, particularly clustering, is a promising solution for this task. However, the use of hundreds of features todescribe behavior results in a highdimensional dataset that negatively impacts clustering performance.In this paper, we investigate the effectiveness of combining clustering method agglomerative hierarchicalclustering with four dimensionality reduction techniques -Independent Component Analysis (ICA), andKernel Principal Component Analysis (KPCA), Singular Value Decomposition (SVD), Locality Preserving Projections (LPP)- to overcome the issue of high-dimensionality in AML data and …

Graph Neural Network Guided By Feature Selection And Centrality Measures For Node Classification On Homophilic And Heterophily Graphs, Asmaa M. Mahmoud, Heba F. Eid, Abeer S. Desuky, Hoda A. Ali

Al-Azhar Bulletin of Science

One of the most recent developments in the fields of deep learning and machine learning is Graph Neural Networks (GNNs). GNNs core task is the feature aggregation stage, which is carried out over the node's neighbours without taking into account whether the features are relevant or not. Additionally, the majority of these existing node representation techniques only consider the network's topology structure while completely ignoring the centrality information. In this paper, a new technique for explaining graph features depending on four different feature selection approaches and centrality measures in order to identify the important nodes and relevant node features is …

Improving Educational Delivery And Content In Juvenile Detention Centers, Yomna Elmousalami

Undergraduate Research Symposium

Students in juvenile detention centers have the greatest need to receive improvements in educational delivery and content; however, they are one of the “truly disadvantaged” populations in terms of receiving those improvements. This work presents a qualitative data analysis based on a focus group meeting with stakeholders at a local Juvenile Detention Center. The current educational system in juvenile detention centers is based on paper worksheets, single-room style teaching methods, outdated technology, and a shortage of textbooks and teachers. In addition, detained students typically have behavioral challenges that are deemed "undesired" in society. As a result, many students miss classes …

Accurate Estimation Of Ethanol Content In Fruit Juices Using Cielab Color Space And Chemometrics Via Smartphone-Based Digital Image Colorimetry, Chairul Ichsan, Yasir Amrulloh, Desti Erviana

Makara Journal of Science

This study aims to investigate the optimal color space and chemometric technique for digital image colorimetry to determine ethanol content (% v/v) in apple, orange, and grape juices, using potassium dichromate (K₂Cr₂O₇) under acidic conditions. The accuracy of colorimetric–chemometric integration across various color spaces (RGB, HSV, CIELab, CMYK, CIELuv, CIEXYZ, and CIELch) was benchmarked against UV–Vis spectrophotometry using metrics such as coefficient of determination (R²), mean absolute percentage error (MAPE), and root–mean–squared error (RMSE). Various chemometric techniques (PLS, PCR, MLR, multivariable–SVR, and multivariable NN regression) were evaluated. Results demonstrate that combining the CIELab color …

Extracting Dnn Architectures Via Runtime Profiling On Mobile Gpus, Dong Hyub Kim

Masters Theses

Due to significant investment, research, and development efforts over the past decade, deep neural networks (DNNs) have achieved notable advancements in classification and regression domains. As a result, DNNs are considered valuable intellectual property for artificial intelligence providers. Prior work has demonstrated highly effective model extraction attacks which steal a DNN, dismantling the provider’s business model and paving the way for unethical or malicious activities, such as misuse of personal data, safety risks in critical systems, or spreading misinformation. This thesis explores the feasibility of model extraction attacks on mobile devices using aggregated runtime profiles as a side-channel to leak …

Investigation Of Gas Dynamics In Water And Oil-Based Muds Using Das, Dts, And Dss Measurements, Temitayo S. Adeyemi

LSU Master's Theses

Reliable prediction of gas migration velocity, void fraction, and length of gas-affected region in water and oil-based muds is essential for effective planning, control, and optimization of drilling operations. However, there is a gap in our understanding of gas behavior and dynamics in water and oil-based muds. This is a consequence of the use of experimental systems that are not representative of field-scale conditions. This study seeks to bridge the gap via the well-scale deployment of distributed fiber-optic sensors for real-time monitoring of gas behavior and dynamics in water and oil-based mud. The aforementioned parameters were estimated in real-time using …

Automated Identification And Mapping Of Interesting Mineral Spectra In Crism Images, Arun M. Saranathan

Doctoral Dissertations

The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has proven to be an invaluable tool for the mineralogical analysis of the Martian surface. It has been crucial in identifying and mapping the spatial extents of various minerals. Primarily, the identification and mapping of these mineral spectral-shapes have been performed manually. Given the size of the CRISM image dataset, manual analysis of the full dataset would be arduous/infeasible. This dissertation attempts to address this issue by describing an (machine learning based) automated processing pipeline for CRISM data that can be used to identify and map the unique mineral signatures present in …

Data To Science With Ai And Human-In-The-Loop, Gustavo Perez Sarabia

Doctoral Dissertations

AI has the potential to accelerate scientific discovery by enabling scientists to analyze vast datasets more efficiently than traditional methods. For example, this thesis considers the detection of star clusters in high-resolution images of galaxies taken from space telescopes, as well as studying bird migration from RADAR images. In these applications, the goal is to make measurements to answer scientific questions, such as how the star formation rate is affected by mass, or how the phenology of bird migration is influenced by climate change. However, current computer vision systems are far from perfect for conducting these measurements directly. They may …

Historical Perspectives In Volatility Forecasting Methods With Machine Learning, Zhiang Qiu, Clemens Kownatzki, Fabien Scalzo, Eun Sang Cha

Seaver College Research And Scholarly Achievement Symposium

Volatility forecasting in the financial market plays a pivotal role across a spectrum of disciplines, such as risk management, option pricing, and market making. However, volatility forecasting is challenging because volatility can only be estimated, and different factors influence volatility, ranging from macroeconomic indicators to investor sentiments. While recent works suggest advances in machine learning and artificial intelligence for volatility forecasting, a comprehensive benchmark of current statistical and learning-based methods for such purposes is lacking. Thus, this paper aims to provide a comprehensive survey of the historical evolution of volatility forecasting with a comparative benchmark of key landmark models. We …

Deep Learning Can Be Used To Classify And Segment Plant Cell Types In Xylem Tissue, Reem Al Dabagh, Benjamin Shin, Sean Wu, Fabien Scalzo, Helen Holmlund, Jessica Lee, Chris Ghim, Samuel Fitzgerald, Marinna Grijalva

Seaver College Research And Scholarly Achievement Symposium

Studies of plant anatomical traits are essential for understanding plant physiological adaptations to stressful environments. For example, shrubs in the chaparral ecosystem of southern California have adapted various xylem anatomical traits that help them survive drought and freezing. Previous studies have shown that xylem conduits with a narrow diameter allows certain chaparral shrub species to survive temperatures as low as -12 C. Other studies have shown that increased cell wall thickness of fibers surrounding xylem vessels improves resistance to water stress-induced embolism formation. Historically, these studies on xylem anatomical traits have relied on hand measurements of cells in light micrographs, …

Mechanistic Investigation Of C—C Bond Activation Of Phosphaalkynes With Pt(0) Complexes, Roberto M. Escobar, Abdurrahman C. Ateşin, Christian Müller, William D. Jones, Tülay Ateşin

Research Symposium

Carbon–carbon (C–C) bond activation has gained increased attention as a direct method for the synthesis of pharmaceuticals. Due to the thermodynamic stability and kinetic inaccessibility of the C–C bonds, however, activation of C–C bonds by homogeneous transition-metal catalysts under mild homogeneous conditions is still a challenge. Most of the systems in which the activation occurs either have aromatization or relief of ring strain as the primary driving force. The activation of unstrained C–C bonds of phosphaalkynes does not have this advantage. This study employs Density Functional Theory (DFT) calculations to elucidate Pt(0)-mediated C–CP bond activation mechanisms in phosphaalkynes. Investigating the …