Digital Commons Network^™

Predicting Sleep And Sleep Stage In Children Using Actigraphy And Heartrate Via A Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation, Robert Weaver Med, Phd, James White, Olivia Finnegan, Hongpeng Yang, Zifei Zhong, Keagan Kiely, Catherine Jones, Yan Tong, Srihari Nelakuditi, Rahul Ghosal, David E. Brown, Russell R. Pate Ph.D., Gregory J. Welk, Massimiliano De Zambotti, Yuan Wang, Sarah Burkart, Elizabeth L. Adams Phd, Bridget Armstrong, Michael Beets Med, Mph, Phd

Faculty Publications

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to predict wake (i.e., specificity, values typically < 70) and cannot predict sleep stages. Long short-term memory (LSTM) is a machine learning algorithm that may address these deficiencies. This study evaluated the agreement of LSTM sleep estimates from actigraphy and heartrate (HR) data with polysomnography (PSG). Children (N = 238, 5–12 years,52.8% male, 50% Black 31.9% White) participated in an overnight laboratory polysomnography. Participants were referred be-cause of suspected sleep disruptions. Children wore an ActiGraph GT9X accelerometer and two of three consumer wearables(i.e., Apple Watch Series 7, Fitbit Sense, Garmin Vivoactive 4) on their non-dominant wrist during the polysomnogram. LSTM estimated sleep versus wake and sleep stage (wake, not-REM, REM) using raw actigraphy and HR data for each 30-s epoch. Logistic regression and random forest were also estimated as a benchmark for performance with which to compare the LSTM results. A 10-fold cross-validation technique was employed, and confusion matrices were constructed. Sensitivity and specificity were calculated to assess the agreement between research-grade and consumer wearables with the criterion polysomnography. For sleep versus wake classification, LSTM outperformed logistic regression and random forest with accuracy ranging from 94.1to 95.1, sensitivity ranging from 94.9 to 95.9 across different devices, and specificity ranging from 84.5 to 89.6. The addition of HR improved the prediction of sleep stages but not binary sleep versus wake. LSTM is promising for predicting sleep and sleep staging from actigraphy data, and HR may improve sleep stage prediction.

Perceptions Of Blind Adults On Non-Visual Mobile Text Entry, Dylan Gaines, Keith Vertanen

Michigan Tech Publications

Text input on mobile devices without physical keys can be challenging for people who are blind or low-vision. We interview 12 blind adults about their experiences with current mobile text input to provide insights into what sorts of interface improvements may be the most beneficial. We identify three primary themes that were experiences or opinions shared by participants: the poor accuracy of dictation, difficulty entering text in noisy environments, and difficulty correcting errors in entered text. We also discuss an experimental non-visual text input method with each participant to solicit opinions on the method and probe their willingness to learn …

Synthesis Of Cancrinite Zeolite From Toraja Natural Bentonite For Heavy Metal Removal From Wastewater, Yuli Astuti, Paulina Taba, Siti Fauziah, Syarifuddin Liong, Yusafir Hala, Nur Umriani Permatasari, Satria Putra Jaya Negara, Fadliah Fadliah

Karbala International Journal of Modern Science

This study explores the production of cancrinite (CAN) zeolite from bentonite found in Toraja (a place in South Sulawesi Province, Indonesia) and its effectiveness in removing Pb²⁺ and Fe³⁺ ions from wastewater. The synthesis was carried out using a hydrothermal method with varying NaOH concentrations, where a single phase of zeolite was formed at 5 M, as confirmed through XRD analysis. FTIR results showed the characteristics of CAN zeolite with typical absorption peaks at 676, 622, and 565 cm^-1, as well as the presence of carbonate groups (CO₃^2-) at 1300-1400 cm^-1. SEM …

Machine Learning Crime Prediction Models And The Gap Between Research And Implementation: A Systematic Review, Ricardo Huamantingo, Miguel Cano-Lengua, Ciro Rodriguez

Karbala International Journal of Modern Science

A crime is an illegal or violent act committed by one individual against another. The increasing crime rate has become a major concern as it negatively affects people's quality of life and generates significant social and economic costs. This study aims to identify the most widely used machine learning (ML) models for crime prediction, determine evaluation metrics for assessing model performance, and analyze key data characteristics to enhance real-world implementation. The study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. A search string was formulated using the population, intervention, comparison, and outcomes (PICO) framework and applied …

"...Anything My Friend Shares, I Would Want To Support Them By Clicking On It": Co-Designing Story-Based Interventions Against Clickbait For Teenagers, Ankit Shrestha, Audrey Flood, Bryson Hackler, Mahdi Nasrullah Al-Ameen

Computer Science Student Research

Teenagers' lack of digital sophistication and cyber hygiene makes them vulnerable to social engineering attacks, especially as they start using social media. Clickbait, one of such attacks, is primarily performed through social media to trick users into clicking on malicious links. With teenagers' increasing use of social media, clickbait poses a substantial threat to their online safety. The existing online safety measures for teens mainly focus on parental mediation, which can be perceived as restrictive and privacy-invasive. To this end, researchers recommended empowering teens to deal with online risks. In order to design such interventions for clickbait, we conducted co-design …

Research And Education In Robotics: A Comprehensive Review, Trends, Challenges, And Future Directions, Mutaz Ryalat, Natheer Almtireen, Ghaith Al-Refai, Hisham Elmoaqet, Nathir Rawashdeh

Michigan Tech Publications

Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution of robotics, tracing its development from early automation to intelligent, autonomous systems. Key enabling technologies, such as Artificial Intelligence (AI), soft robotics, the Internet of Things (IoT), and swarm intelligence, are examined along with real-world applications in healthcare, manufacturing, agriculture, and sustainable smart cities. A central focus is placed on robotics education, where hands-on, interdisciplinary learning is …

Digital Forensics And Ai: Artifact Analysis And Using Ai In The Forensics Domain, Clinton Joel Walker

LSU Doctoral Dissertations

Digital Forensics (DF) is a field of forensic science focusing on the acquisition, authentication, and analysis of digital evidence while maintaining integrity of that data. DF analysts use forensic tools to parse large volumes of data for investigations and depend on them for identification of pertinent digital evidence in vast amounts of data. Keeping up with innovations and ever-expanding data volumes is a constant challenge for these investigators. The prevalence of Artificial Intelligence (AI) in everyday computing is rapidly expanding, with the use of Machine Learning (ML) and Large Language Models (LLM)s becoming increasingly commonplace. Innovations in technology bring new …

Review Of Averting The Digital Dark Age: The Future Of Digital Literary Heritage, Rebecca L. Hastings

Journal of Contemporary Archival Studies

In Averting the Digital Dark Age: How Archivists, Librarians, and Technologists Built the Web a Memory, Ian Milligan explores the efforts of technologists and information professionals to develop means of preserving information shared on the World Wide Web. The web lacks a built-in archiving function, raising fears of a “digital dark age,” an unprecedented gap in the historical record as information sharing via the web increases. Milligan tracks the development of web preservation debates and activities from the anxiety and turmoil of the early to mid-1990s to the coming of age of web preservation in the wake of the …

Integrating Artificial Intelligence And Machine Learning Technologies Into Common Operating Picture And Course Of Action Development, C. Anthony Pfaff, Christopher John Hickey

Books, Monographs & Collaborative Studies

C. Anthony Pfaff and Christopher John Hickey, Principal Investigators

©2025 C. Anthony Pfaff. All rights reserved.

Integrating Artificial Intelligence and Machine Learning Technologies into Common Operating Picture and Course of Action Development explores the potential of artificial intelligence (AI) and machine learning to revolutionize military planning processes by enhancing situational awareness and expediting course of action development within the Joint planning process. The study delves into technical, organizational, and resource considerations that are critical for AI integration. In addition, the study highlights the importance of clean, structured data in training AI systems, addresses challenges in data collection across varying formats …

"Chatgpt Told Me To Say It": Ai Chatbots And Class Participation Apprehension In University Students, Daisuke Akiba

Publications and Research

The growing prevalence of AI chatbots in everyday life has prompted educators to explore their potential applications in promoting student success, including support for classroom engagement and communication. This exploratory study emerged from semester-long observations of class participation apprehensions in an introductory educational psychology course, examining how chatbots might scaffold students toward active and independent classroom contribution. Four students experiencing situational participation anxiety voluntarily participated in a pilot intervention using AI chatbots as virtual peer partners. Following comprehensive training in AI use and prompt design given to the entire class, participants employed systematic consultation frameworks for managing classroom discourse trepidations. …

Seeking Structure In Complex Systems: From Feature Analysis To Space-Time Causal Discovery With Earth Science Applications, Jeffrey J. Nichol

Computer Science ETDs

Complex systems are difficult to study because of their many interacting parts, emergent phenomena, and feedback loops. These systems underpin all life on Earth. We need improved tools for seeking an understanding of them. This body of research presents my investigations into data-driven methods for understanding complex systems, including my invention of a novel causal discovery meta-algorithm for space-time gridded data. I demonstrated machine learning feature importance and causal discovery capabilities for comparing simulated and observed climate data. I developed a new benchmark for modeling space-time dynamics of locally driven phenomena and examined a prominent causal discovery algorithm. Finding that …

Creativity And Curb Cuts: Experiences In Our First Offering Of A Front End Development And Accessibility Focused Cs Course, Briana C. Bettin, Tony Garnett, Alex Gore, Andrea Llanas

Michigan Tech Publications

Students learn an abundance of technical skills while obtaining a computer science degree. The ability to develop meaningful front end user interfaces is often considered the domain of only ''more artistic'' CS students. However, for users to effectively engage with any piece of software, functional user interfaces are critical. Moreover, even among students who have front end skills, semantic and accessible design is all too often less considered. The first author piloted a ''Front End Development and Accessibility'' course this past Fall. This course teaches basic skills of front end with web and leverages key accessibility standards via WCAG. This …

Understanding The Roots Of Swarm Intelligence In Defence To Find The Path Forward: A Scientometric Study Of Autonomous Systems, Anton Klarin, Pi-Shen Seet, Janice Jones, Michael N. Johnstone, Helen Cripps, Jalleh Sharafizad, Tony Marceddo

Research outputs 2022 to 2026

Swarm intelligence, inspired by the decentralised, adaptive and self-synchronising behaviours of natural swarms, is a pivotal component of autonomous systems, enhancing efficiency, robustness and scalability. The research in this area is nascent and interdisciplinary. To drive this important research forward, it is necessary to adopt a systems perspective on what is available in the current literature. This chapter offers a comprehensive systems perspective of the integration of swarm intelligence within the broader domain of automation, emphasising its application in the defence sector. A systems perspective of an interdisciplinary field is afforded through scientometrics. Using VOSviewer algorithms, we analysed 1706 publications …

Neurophysiology And Endocrine Responses To Hunger And Satiety Mechanisms: The Brain-Gut Crosstalk, Nour Shakir Rezaieg, Muthanna M. Awad

Karbala International Journal of Modern Science

Background: Obesity is a main public health problem which substantially increases the risk of many diseases. The complex neural circuitry controls energy homeostasis and food consumption by the incorporation of hormonal and neural signals. Circulating hormones, in specific the gut hormones, have been found to be very important in appetite regulation. These hormones transfer energy situation signs to the brain throughout three principle paths: the circulation system, activation of the vagus nerve, and direct modification of main brain regions such as the hypothalamus and brainstem. The control of food eating is not exclusively dependent on the homeostatic processes, rather it …

Risk Management Of The Open Data Services Industry In Digital Transformation Under Neutrosophic Sets, Fadhl Ehsan Hadi, Mohammed Musa Mohammed, Noorhan Waleed Abdullah, Baraa Hasan Hadi

Neutrosophic Systems with Applications

The open data services industry is very important for artificial intelligence and digital transformations. The open data services industry has different risks and challenges, so this study proposed a multi-criteria decision making (MCDM) approach for risk management in open data services industry. This study uses the average method to compute the criteria weights and the WASPAS method to rank the alternatives. The triangular neutrosophic set (TNS) is used in this study to overcome uncertainty and vague information. It has three membership functions such as truth, indeterminacy, and falsity. This study uses nine criteria and 18 risks to be evaluated. The …

Type-2 Neutrosophic Numbers For Artificial Intelligence Software Choice For Cybersecurity Testing, O.M. Akash, We’Am Adel Talafha, Mamdouh Gomaa

Neutrosophic Systems with Applications

The choice of artificial intelligence (AI) software for cybersecurity testing is a multi-criteria decision-making approach (MCDM) due to it including different criteria. Evaluation decision making problems include uncertainty and vague information. So, the neutrosophic set is used in this study to overcome this uncertainty and vague information. It has three functions such as truth, indeterminacy, and falsity functions. Type-2 neutrosophic numbers is a type of neutrosophic set that includes nine membership functions. This study uses the average method of computing the criteria weights. The CoCoSo method is used to rank alternatives. Six experts and decision makers created the decision makers …

Neutrosophic Numbers For Selection Best Strategy For Dual Supply Chains With Green And Non-Green Products, Nada A. Nabeeh, Waleed Abd Elkhalik, Gawaher Soliman Hussein

Neutrosophic Systems with Applications

Dual supply chains with green and non-green products are an important aspect of supply chain management, which enable companies to balance traditional operations with ethical and eco-friendly practices to reduce carbon emissions. This study proposed a novel approach that combines the Probabilistic Simplified Neutrosophic Set (PSNS) with the Ranking of Alternatives Method (RAM) for the selection of best strategy selection for dual supply chains with green and non-green products. The novel approach uses PSNS as a representation of uncertainty, by incorporating probabilistic degrees of truth, indeterminacy, and falsity, which occurred in real life situations. Furthermore, RAM illustrates efficient ranking and …

Location Selection Of Migrating Beetles Under Neutrosophic Model With Sensitivity And Comparative Analysis, Rabih Sbera, Ahmed A El-Douh, Darin Shafek, Tareef S. Alkellezli

Neutrosophic Systems with Applications

Location Selection of Migrating Beetles has different criteria to select the best location. So, multi-criteria decision making (MCDM) is used to deal with different and numerous criteria in this study. This study proposes an MCDM methodology to rank the locations and select the best criterion. The average method is used to compute the criteria weights. The locations are ranked using the root assessment method (RAM). This study uses eight criteria and 20 locations. We use the single valued neutrosophic numbers (SVNNs) to overcome uncertainty and vague information. The RAM methodology is used under the SVNNs. The results show that Availability …

Detecting Misuse Of Security Apis: A Systematic Review, Zahra Mousavi, Chadni Islam, Muhammad Ali Babar, Alsharif Abuadbba, Kristen Moore

Research outputs 2022 to 2026

Security Application Programming Interfaces (APIs) are crucial for ensuring software security. However, their misuse introduces vulnerabilities, potentially leading to severe data breaches and substantial financial loss. Complex API design, inadequate documentation, and insufficient security training often lead to unintentional misuse by developers. The software security community has devised and evaluated several approaches to detecting security API misuse to help developers and organizations. This study rigorously reviews the literature on detecting misuse of security APIs to gain a comprehensive understanding of this critical domain. Our goal is to identify and analyze security API misuses, the detection approaches developed, and the evaluation …

Discrete Time Series Forecasting In Non-Invasive Monitoring Of Managed Honey Bee Colonies: Part Ii: Are Hive Weight And In-Hive Temperature Seasonal And Colony-Specific, Vladimir A. Kulyukin, Aleksey V. Kulyukin, William G. Meikle

Computer Science Faculty and Staff Publications

We explored the stationarity, trend, and seasonality of the hive weight and in-hive temperature of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, Arizona, USA. The hives were monitored with electronic scales and in-hive temperature sensors from June to October 2022. The weight and temperature were recorded every five minutes around the clock. The collected data were curated into 2160 timestamped weight and 2160 timestamped temperature observations. We performed a systematic autoregressive integrated moving average (ARIMA) time series analysis to answer three fundamental questions: (a) Does …

A Participatory Approach To Deploy Responsible Artificial Intelligence For Diabetes Prediction And Prevention, Laura C. Rosella, James Shaw, Shion Guha, Ibukun-Oluwa Omolade Abejirinde, Jennifer L. Gibson, Lorraine Lipscombe, Kathy Kornas, Remziye Zaim, Victoria Chui, Ijeoma Uchenna Itanyi

Health Services and Informatics Research

Artificial intelligence (AI) technologies have the potential to improve healthcare and public health. Although there has been success in AI for research uses, little progress has been made in implementing health-related AI technologies in health systems. Responsible AI for health systems requires engagement and co-design with health system partners, policymakers, and the community. Deploying responsible AI requires engaging stakeholders, particularly those affected by the technology. This commentary presents the importance of participatory approaches for responsible AI implementation. In this commentary, we discuss the planned use of participatory approaches to responsibly deploying validated machine learning models, with a specific case example …

In The Shadow Of Prompts: Adversarial Attacks And Model Cloning In Large Language Models, Kanchon Gharami

Doctoral Dissertations and Master's Theses

Large-language models (LLMs) already power mission critical tasks such as command-and-control chat, satellite ground-station automation, military analytics, and cyber-defense. Since most of these services are offered through application programming interfaces (APIs) that still expose full or top-k logits and lack mature safeguards, they present a serious, often overlooked attack surface. Earlier work has shown how to rebuild the output projection layer or distill surface behavior, but no attack has produced a deployable clone within a tight query budget. In this thesis, we address this problem by presenting a practical pipeline for cloning LLMs under constrained settings. The approach first estimates …

The Impact Of Entrepreneurial Orientation On Innovation Performance: The Role Of Knowledge Sharing As A Mediating Factor, Dhia Qasim, Ahmed Shuhaiber, Zainab Rawshdeh

All Works

Innovation is critical for enhancing business products and processes, leading to improved overall performance for firms. Knowledge sharing (KS) plays a crucial role in fostering innovation within firms. Literature has addressed entrepreneurial orientation (EO) and innovation performance (IP) in firms; however, extant literature has not considered the influence of EO on IP in emerging economies. Thus, based on the EO theory, this paper develops a theoretical framework to investigate the influence of EO antecedents on IP within the mediating role of KS. Data were collected from three national telecom companies in Jordan, and 215 responses were analyzed using partial least …

Benford's Law In Basic Rnn And Long Short-Term Memory And Their Associations, Farshad Ghassemi Toosi

Department of Computer Science Publications

Benford's Law describes the distribution of numerical patterns, specifically focusing on the frequency of the leading digit in a set of natural numbers. It divides these numbers into nine groups based on their first digit, with the largest category comprising numbers beginning with 1, followed by those starting with 2, and so on. Each neuron within a neural network (NN) is associated with a numerical value called a weight, which is updated according to specific functions. This research examines the Degree of Benford's Law Existence (DBLE) across two language model methodologies: (1) recurrent neural networks (RNNs) and (2) long short-term …

Bridging Classical Rhetoric And Ai: A Systematic Framework For Developing Authorial Voice Through Large Language Models, Daniel Plate

Theses

This project addresses critical gaps in AI-assisted writing by developing the first systematic framework that integrates classical rhetorical principles with modern large language model capabilities for authorial voice development. The primary focus is on creating reliable methods for stylistic control through strategic AI collaboration rather than ad hoc prompting approaches. The project develops a comprehensive coding system for analyzing prose style, creates ten distinct authorial personas, and establishes a dual curation methodology that structures both stylistic analysis and content preparation. Implementation through the AI Writing Guide website provides practical tools including prompt templates, annotated examples, and instructional materials that demonstrate …

Graph Traverse Reference Network For Sign Language Corpus Retrieval In The Wild, Kun Hu, Fengxiang He, Adam Schembri, Zhiyong Wang

Research outputs 2022 to 2026

Sign languages are the primary languages of the deaf community as well as hearing individuals who are unable to speak, which engage the visual-manual modality to convey meanings. In recent years, there has been an explosive growth of sign language videos available from video streaming and social media service platforms. Given the size of these corpora, sign language users often face significant challenges in effectively acquiring the information they need. Therefore, we propose a novel deep learning architecture, namely Graph Traverse Reference Network (GTRN), allowing visual signing queries to retrieve relevant sign language videos (documents) from a large corpus. GTRN …

Domain-Adaptive Diagnosis Of Lewy Body Disease With Transferability Aware Transformer, Xiaowei Yu, Jing Zhang, Tong Chen, Yan Zhuang, Minheng Chen, Chao Cao, Yanjun Lyu, Lu Zhang, Li Su, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Lewy Body Disease (LBD) is a common yet understudied form of dementia that imposes a significant burden on public health. It shares clinical similarities with Alzheimer’s disease (AD), as both progress through stages of normal cognition, mild cognitive impairment, and dementia. A major obstacle in LBD diagnosis is data scarcity, which limits the effectiveness of deep learning. In contrast, AD datasets are more abundant, offering potential for knowledge transfer. However, LBD and AD data are typically collected from different sites using different machines and protocols, resulting in a distinct domain shift. To effectively leverage AD data while mitigating domain shift, …

Du Undergraduate Showcase Abstracts: Research, Scholarship, And Creative Works, Kabe Aberle, Nadia Kako, Kateri Mcrae, Brooke Agulnek, Sky Palmon, Yasmine Ramirez, Francisca Aguirre Beltran, Ashley Juarez, Bridget Kim, Tessa Appel, Sterling Kerr, Spencer Ingley, Gabe Meyer, Robin Tinghitella, Dale Broder, Lily Baeza, Chloe Beers, Julia Coakley, Whitney Kelsey, Sydney Gainforth, Gabi Wing, Audrey Martin, Aaliyah Amore Berry, Brooke Watley, Kiruthika Venkatesan, Rachel Bienstock, Annabella Brotherston, Madison Bryant, Mia Burgener, Emma P. Lieb, Rachel A. Johnson, Jennifer L. Hoffman, Kania Campbell, Kiena Campbell, Courtney Cassidy, Sage Krzyzkowski, Maddox Jones, Skylar Abookire, Luke Hawkins, Sunnah Yoon, Andrea Chu, Yan Qin, Nyah Cubbison, Brian Gearity, Daniel Mcintosh, Mariely Cruz, Edward Garrido, Grady Dionne, Nicole Doris, Lyndsie Salvagio, Ann-Charlotte Granholm-Bentley, Anna Dymov, Hannah Eckert, Gabrielle Welsh, Erica Larson, Charlie Ernst, Anna Zhou, Sarah Watamura, Larissa Fedorovich-Klein, Georgie Fields, Kimberly A. Guevara, Aven Mccall, Ben Peltier, Feruz Yahia, Patrick Flores, Jadyn Floyd, Sophia Forcier, J. Von R. Monteza, Peter Sokol-Hessner, Gwendolyn Geiger, Scott Nichols, Camryn Gunter, Kendal Hengst, Charlie Bednarz, Issy Garside, Addison Baker, Rachel Mina, Brooke Hermanson, Amanda Klingler, William Highfill, Sydney Jaques, Kerstin Lewey, Allison Grossery, Daniel Linseman, Ethan Lim, Jagger Livengood, Owen Mantelli, Gabby Pappas, Abby Mcdonald, Madeleine Dierking, Eve Miller, Emma Loeber, Anna Marlow, Michael Kerwin, Ella Mathews, Hillary Hamann, Khadija Mohamed, Vivian Nguyen, Gabri Notov, Ifunayachi Ogbonna-Ukuku, Sunil Kumar, Charles Baysah, Sarah Olson, Don Sullivan, Anna Paradiso, Jay Parrish, Mira Pronobis, Alisha Pravasi, Kerstin Haring, Diego Ramirez, Christopher Reardon, Juliana Ramirez, Casey Doherty, Ella Kestner, Teagan Weindel, Cate Billings, Pablo Torre-Walter, Lucy Rand, Samantha Reynolds, Mark Siemens, Khadeeja Rashid, Laine Satterlee, Piper Heilbronner, Lily Pound, Ben Whitehurst, Anna Respet, Lizzie Lesoing, Sydney Hertel, Aya Saad-Masri, Brooke Ballenger, Max Proske, Hannah Rosenberg, Ellia Nakahara, Sophia Espinoza, Ivan Woolhouse, Simon Ruland, Gorkem Er, Timothy Sweeny, Melaku Saketa, Michela Schenk, Maren Lynch, Madi Hamm, Grace Schroeder, Michelle Rozenman, Rana Seif, Jackson Hall, Marisela Simental, Daniel Paredes, Aaron Mena, Preston Spaan, Evelyn Stovin, David Andrew Swartz, Anh Tran, Daniel Pittman, Luke Farchione, Emily Boyer, Ukari Verner, Lacey Conrad, Jonathan Velotta, James Weiner, Jagger Gossett, Noah Sherry, Sam Proud, Ben Block, Avi Narayana, Zoey Weiss, Alyssa Wilson, Gabrielle Walsh, David Zonana, Keely Wright, Kena Riveria, Lillybelle Deer, Jena Doom, Elysia Davis, Isabelle Yaremenko, Caitlyn Young

DU Undergraduate Research Journal Archive

Abstracts from the DU Undergraduate Research Showcase.

An Action Research Study On Ai Video Vs. Written Feedback: Enhancing Undergraduate Academic Writing And Critical Thinking, Sandra Baroudi, Nida Mubeen, Suha Karaki

All Works

This action research explores how AI-assisted feedback formats influence students’ academic writing and critical thinking, a gap particularly relevant in the digital learning landscape. This study involved 40 undergraduate Emirati students divided into two groups, receiving either AI-generated video or written feedback across three assignments. Using a quantitative design, data were gathered using standardized critical thinking and academic writing skills rubrics. Additionally, students completed post-surveys to capture their perceptions, engagement levels and use of AI tools in the learning process. Results showed written feedback significantly improvemed critical thinking (M = 2.80, SD = 0.75) compared to video feedback (M = …

Adapting A Segmentation Foundation Model For Medical Image Classification, Pengfei Gu, Haoteng Tang, Islam A. Ebeid, Jose Angel Nunez, Fabian Vazquez Jr., Diego Adame

Computer Science Faculty Publications

Recent advancements in foundation models, such as the Segment Anything Model (SAM), have shown strong performance in various vision tasks, particularly image segmentation, due to their impressive zero-shot segmentation capabilities. However, effectively adapting such models for medical image classification is still a less explored topic. In this paper, we introduce a new framework to adapt SAM for medical image classification. First, we utilize the SAM image encoder as a feature extractor to capture segmentation-based features that convey important spatial and contextual details of the image, while freezing its weights to avoid unnecessary overhead during training. Next, we propose a novel …