Digital Commons Network^™

Automatic Scoring Cornhole Board System, Eric Diffendal, Jonah Harsh, Connor Lengel, Brett Sukie

Williams Honors College, Honors Research Projects

The objective is to create a self-scoring cornhole board that can detect and calculate each team's score based on the bags thrown each round and to be created at a low cost/eventually being sold at the current cost of a normal board. When playing cornhole, the game is simple: throw a bag on the board; however, the scores are variable (deduct and add) across each round. The most common issue when playing cornhole is miscalculations of the scores and forgetting the correct scores. Thus, this invention will make gameplay easy for all to play.

Skinrisk Ai, Spencer Simms

Williams Honors College, Honors Research Projects

SkinRisk AI is an exploration of the opportunities for implementing machine learning (ML) and artificial intelligence (AI) in the medical technology field, specifically in the early detection of skin cancer. This project presents the design, development, and evaluation of a mobile application that allows users to capture images of skin lesions and receive a machine learning assisted risk assessment. The system combines a convolutional neural network (CNN) for image analysis with an intuitive mobile app built using Flutter, FastAPI, and Supabase to deliver real time screening.

Motivated by the rising skin cancer rates and importance of early detection, SkinRisk AI …

Leveraging Large-Language Models As Collaborative Reasoning Partners To Enhance Scientific Workflow, Douglas B. Craig

Wayne State University Dissertations

The rise of Large Language Models (LLMs) has transformed artificial intelligence, offering advanced capabilities in text generation, natural language understanding, and multi-modal interactions. However, their use as standalone tools or as perceived repositories of static knowledge has limited their potential in real-world applications, especially in critical domains like healthcare and scientific research, where transparency, explainability, and accountability are paramount. This research addresses these limitations by conceptualizing LLMs as reasoning engines within a hybrid framework that integrates retrieval-augmented generation (RAG) and case-based reasoning (CBR) within a note-taking application.

The study introduces a novel system, LmRaC, designed to enhance the reliability, explainability, …

Artificial Intelligence And Digital Technologies In Finance: A Comprehensive Review, Soudeh Pazouki, Mohamad Jamshidi, Mirarmia Jalali, Arya Tafreshi

Finance Faculty Publications

This study explores the transformative impact of artificial intelligence (AI) and digital technologies on the financial technology (FinTech) industry, highlighting their role in fostering business growth, operational efficiency, and enhanced customer engagement. AI-driven strategies have unlocked new avenues for streamlining workflows, boosting productivity, and expanding financial inclusion by reaching underrepresented populations. However, these advancements also pose challenges, including navigating complex regulatory frameworks and adapting to the rapidly evolving technological landscape. This paper delves into the macroeconomic effects of AI, examining its influence on labor markets, consumer behavior, and organizational success. Furthermore, the paper discusses blockchain applications and their potential to …

Deep Reinforcement Learning With Explicit Context Representation, Francisco Munguia-Galeano, Ah-Hwee Tan, Ze Ji

Research Collection School Of Computing and Information Systems

Though reinforcement learning (RL) has shown an outstanding capability for solving complex computational problems, most RL algorithms lack an explicit method that would allow learning from contextual information. On the other hand, humans often use context to identify patterns and relations among elements in the environment, along with how to avoid making wrong actions. However, what may seem like an obviously wrong decision from a human perspective could take hundreds of steps for an RL agent to learn to avoid. This article proposes a framework for discrete environments called Iota explicit context representation (IECR). The framework involves representing each state …

Recdreamer: Consistent Text-To-3d Generation Via Uniform Score Distillation, Chenxi Zheng, Yihong Lin, Bangzhen Liu, Xuemiao Xu, Yongwei Nie, Shengfeng He

Research Collection School Of Computing and Information Systems

Current text-to-3D generation methods based on score distillation often suffer from geometric inconsistencies, leading to repeated patterns across different poses of 3D assets. This issue, known as the Multi-Face Janus problem, arises because existing methods struggle to maintain consistency across varying poses and are biased toward a canonical pose. While recent work has improved pose control and approximation, these efforts are still limited by this inherent bias, which skews the guidance during generation. To address this, we propose a solution called RecDreamer, which reshapes the underlying data distribution to achieve more consistent pose representation. The core idea behind our method …

Llms-Based Augmentation For Domain Adaptation In Long-Tailed Food Datasets, Qing Wang, Chong-Wah Ngo, Ee-Peng Lim, Qianru Sun

Research Collection School Of Computing and Information Systems

Training a model for food recognition is challenging because the training samples, which are typically crawled from the Internet, are visually different from the pictures captured by users in the free-living environment. In addition to this domain-shift problem, the real-world food datasets tend to be long-tailed distributed and some dishes of different categories exhibit subtle variations that are difficult to distinguish visually. In this paper, we present a framework empowered with large language models (LLMs) to address these challenges in food recognition. We first leverage LLMs to parse food images to generate food titles and ingredients. Then, we project the …

Weakly-Supervised Semantic Segmentation With Image-Level Labels: From Traditional Models To Foundation Models, Zhaozheng Chen, Qianru Sun

Research Collection School Of Computing and Information Systems

The rapid development of deep learning has driven significant progress in image semantic segmentation—a fundamental task in computer vision. Semantic segmentation algorithms often depend on the availability of pixel-level labels (i.e., masks of objects), which are expensive, time consuming, and labor intensive. Weakly supervised semantic segmentation (WSSS) is an effective solution to avoid such labeling. It utilizes only partial or incomplete annotations and provides a cost-effective alternative to fully supervised semantic segmentation. In this article, our focus is on the WSSS with image-level labels, which is the most challenging form of WSSS. Our work has two parts. First, we conduct …

Synthesizing Multi-Person And Rare Pose Images For Human Pose Estimation, Liuqing Zhao, Zichen Tian, Zou Peng, Richang Hong, Qianru Sun

Research Collection School Of Computing and Information Systems

Human pose estimation (HPE) models underperform in recognizing rare poses because they suffer from data imbalance problems (i.e., there are few image samples for rare poses) in their training datasets. From a data perspective, the most intuitive solution is to synthesize data for rare poses. Specifically, the rule-based methods apply manual manipulations (such as Cutout and GridMask) to the existing data, so the limited diversity of the data constrains the model. An alternative method is to learn the underlying data distribution via deep generative models (such as ControlNet and HumanSD) and then sample “new data” from the distribution. This works …

Fuzzing Drones For Anomaly Detection: A Systematic Literature Review, Vikas Kumar Malviya, Wei Minn, Lwin Khin Shar, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Drones, also referred to as Unmanned Aerial Vehicles (UAVs), are becoming popular today due to their uses in different fields and recent technological advancements which provide easy control of UAVs via mobile apps. However, UAVs may contain vulnerabilities or software bugs that cause serious safety and security concerns. For example, the communication protocol used by the UAV may contain authentication and authorization vulnerabilities, which may be exploited by attackers to gain remote access over the UAV. Drones must therefore undergo extensive testing before being released or deployed to identify and fix any software bugs or security vulnerabilities. Fuzzing is one …

A Review Of Chinese Sentiment Analysis: Subjects, Methods, And Trends, Zhaoxia Wang, Donghao Huang, Jingfeng Cui, Xinyue Zhang, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Sentiment analysis has emerged as a prominent research domain within the realm of natural language processing, garnering increasing attention and a growing body of literature. While numerous literature reviews have examined sentiment analysis techniques, methods, topics and applications, there remains a gap in the literature concerning thematic trends and research methodologies in sentiment analysis, particularly in the context of Chinese text. This study addresses this gap by presenting a comprehensive survey dedicated to the progression of research subjects, methods and trends in sentiment analysis of Chinese text. Employing a framework that combines keyword co-occurrence analysis with a sophisticated community detection …

Harnessing The Power Of Gradient-Based Simulations For Multi-Objective Optimization In Particle Accelerators, Kishansingh Rajput, Malachi Schram, Auralee Edelen, Jonathan Colen, Armen Kasparian, Ryan Roussel, Adam Carpenter, He Zhang, Jay Benesch

Data Science Faculty Publications

Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-objective optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithms (GAs), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems in particle accelerators using a deep differentiable reinforcement learning (DDRL) algorithm. We compare the DDRL algorithm with model-free reinforcement learning (MFRL), GA, and Bayesian optimization (BO) for simultaneous optimization of heat load and trip rates in the continuous electron beam accelerator facility. The …

Fares On Fairness: Using A Total Error Framework To Examine The Role Of Measurement And Representation In Training Data On Model Fairness And Bias, Patrick Oliver Schenk, Christoph Kern, Trent D. Buskirk

Data Science Faculty Publications

Data-driven decisions, often based on predictions from machine learning (ML) models are becoming ubiquitous. For these decisions to be just, the underlying ML models must be fair, i.e., work equally well for all parts of the population such as groups defined by gender or age. What are the logical next steps if, however, a trained model is accurate but not fair? How can we guide the whole data pipeline such that we avoid training unfair models based on inadequate data, recognizing possible sources of unfairness early on? How can the concepts of data-based sources of unfairness that exist in the …

Key Brain Region Identification In Obesity Prediction With Structural Mri And Probabilistic Uncertainty Aware Model, Walia Farzana, Megan A. Witherow, Ahmed Temtam, Liangsuo Ma, Melanie Bean, F. Gerry Moeller, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Objectives/Goals: Predictive performance alone may not determine a model’s clinical utility. Neurobiological changes in obesity alter brain structures, but traditional voxel-based morphometry is limited to group-level analysis. We propose a probabilistic model with uncertainty heatmaps to improve interpretability and personalized prediction. Methods/Study Population: The data for this study are sourced from the Human Connectome Project (HCP), with approval from the Washington University in St. Louis Institutional Review Board. We preprocessed raw T1-weighted structural MRI scans from 525 patients using an automated pipeline. The dataset is divided into training (357 cases), calibration (63 cases), and testing (105 cases). Our probabilistic model …

A Fast Framework For Generating Radioactive Mixture Spectra And Its Application To Remote High-Performance Mixture Identification, Chiman Kwan, Bulent Ayhan, Adam Stavola, Kazi Aminul Islam, Hongfang Zhang, Jiang Li

Electrical & Computer Engineering Faculty Publications

Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework.

Pca Text Sentiment Analysis Tool, Luke Gegick

Williams Honors College, Honors Research Projects

This project applies principal component analysis (PCA) to sentiment analysis of text to identify complex emotional responses from plain text. Existing sentiment analysis tools often rely on large language models or struggle to achieve high accuracy when processing large collections of short inputs, such as social media comments. By contrast, this project uses PCA as a lightweight, mathematically grounded alternative that can scale efficiently while still capturing meaningful emotional structure in text data.

PCA has shown strong effectiveness in text analysis, particularly when supported by a sufficiently large dataset and a robust preprocessing pipeline. To create consistent, information-rich input vectors, …

Personalized Physics Learning Through Ai: Insights From Problem Generation, Chatbot Dialogues, And Intelligent Tutoring Systems, Atharva Dange

Physics Dissertations - Archive

Artificial intelligence (AI) is poised to transform science education, yet questions remain on how best to integrate these technologies into teaching and learning. This dissertation investigates the use of AI-driven tools in university physics courses through three complementary studies. In the first study, a generative language model (ChatGPT) was used to create novel physics homework problems aligned with course objectives. Analysis showed that, after expert vetting, AI-generated questions can foster higher-order problem-solving and reduce student reliance on solution memorization, though careful instructor oversight is required to ensure accuracy. The second study embedded an AI chatbot as a learning aid in …

Generating Negotiations For Iago, Kylee R. Weener

Honors Undergraduate Theses

Negotiation is a complex field that can benefit from introducing artificial intelligence (AI); doing so would benefit researchers as they try to deepen their understanding of human-human and human-agent negotiation. Investigating how large language models (LLMs) can generate negotiation dialogue with emotional context would bring agents closer to acting more human. This study explores how fine-tuning and prompt engineering can achieve this goal and the possibilities for an AI that fills these criteria to be included in the Interactive Arbitration Guide Online platform (IAGO). Doing so will make the negotiation interactions in IAGO feel more complex and natural, allowing researchers …

Symp25s: Cactas-Ai: Automatic Segmentaion Of Calcified Plaque In Carotid Arteries, Jiehyun Kim, Kevin Wang, Yu Sakai, Youxiang Zhu, Andrew C. Hu, Huy Q. Phi, Nathan Arnett, Grace J. Wang, Brett L. Cucchiara, Jae W. Song, Daniel Haehn

Paul English Applied Artificial Intelligence (AI) Institute Publications

Manual segmentation of calcified plaque, essential for assessing stroke risk, is time-consuming, and conventional methods like 2D and 3D UNet often struggle with the small size. We developed CACTAS-AI, a two-step segmentation process. This approach outperforms baseline methods in plaque segmentation.

The Reliability Gap: How Traditional Search Engines Outperform Artificial Intelligence (Ai) Chatbots In Rosacea Public Health Information Quality, Houston C. Nelson, Morgan T. Beauchamp, April A. Pace

Department of Medicine Faculty Publications

Background: The internet has become a primary source of health information for the public, with important implications for patient decision-making and public health outcomes. However, the quality and readability of this content vary widely. With the rise of generative artificial intelligence (AI) tools such as ChatGPT and Gemini, new challenges and opportunities have emerged in how patients access and interpret medical information.

Objective: To evaluate and compare the quality, credibility, and readability of consumer health information provided by traditional search engines (Google, Bing) and generative AI platforms (ChatGPT, Gemini) using three validated instruments: DISCERN, JAMA Benchmark Criteria, and Flesch-Kincaid Readability …

Applications Of Deep Learning For Optimizing Fingerphoto And Latent Fingerprint Biometrics, Amol Sanjay Joshi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Fingerprint-based biometric recognition remains one of the most dependable and widely adopted approaches for identity verification due to its permanence and distinctiveness. Recent advancements in mobile and contactless imaging have extended fingerprint acquisition beyond controlled environments into unconstrained, real-world conditions through fingerphotos, contactless fingerprint images captured by digital or smartphone cameras. While this paradigm shift enhances accessibility and user convenience, it introduces significant technical challenges. Variations in illumination, focus, and motion blur often degrade ridge patterns, making accurate feature extraction and matching more difficult. Similarly, in forensic applications, latent fingerprints, incomplete or smudged prints lifted from surfaces, pose unique challenges …

The Effect Of Facial Phenotypes On Differential Performance Of Facial Recognition, Evan R. Garrett

Graduate Theses, Dissertations, and Problem Reports (ETD)

Facial recognition technology is utilized in many facets of life. As the use has become more widespread these systems have improved in reliability and performance approaching the level of human accuracy. With these improvements the problem of bias still remains as a persistent problem. Efforts have been made to minimize the bias prevalent in the systems via studies into various demographic factors, creating training datasets that have a more uniform distribution of subjects, and other methods. As facial recognition is one of the most utilized forms of biometric recognition it is vital to analyze potential causes of bias to help …

Relationship Extraction Using Retrieval Augmented Generation For Biomedical Dataset, Jannat Jannat

Theses and Dissertations

With the increasing number of structured and unstructured data, obtaining reliable information effectively has become crucial. In the biomedical domain, extracting information from the scientific papers is crucial in order to stay up-to-date with accurate information, given the increased pace by which new research studies are published. This work focuses on identifying relationships between entities that are extracted from the abstracts and titles of biomedical research papers. In this work, we developed a Retrieval Augmented Generation (RAG) based system to automatically identify relations between biomedical entities. We evaluate multiple open source Large Language Models (LLMs) and the number of examples …

Application Of Machine Learning And Large Language Models In Healthcare For Data Prediction And Summarization, Chiazam Chisom Izuchukwu

College of Graduate Studies: Theses & Dissertations

This study aims to examine the use of machine learning (ML) and large language models (LLMs) in healthcare to enhance disease prediction, clinical decision-making, and information management. Five supervised ML models—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT), and Naïve Bayes (NB)—on three different computing platforms—Google Colab, Databricks, and Snowflake—were employed for disease classification. Data preprocessing included treating missing values, encoding categorical variables utilizing one-hot-encoding, feature scaling when needed, and tackling class imbalance with Synthetic Minority Over-sampling Technique (SMOTE) before an 80-20 train-test separation. Models were created with Scikit-learn (Google Collab), Spark MLlib (Databricks), and …

Machine Learning Methods For Intrusion Detection And Response In Network Security, Ayomide Oyemaja

College of Graduate Studies: Theses & Dissertations

Intrusion Detection Systems (IDS) play a crucial role in computer network security by identifying malicious activities and potential cyberattacks. This thesis combines machine learning and cybersecurity by applying Reinforcement Learning (RL) in intrusion detection and response using the NSL-KDD dataset.

We designed and implemented a Q-learning framework where an agent learns to classify network traffic over time by interacting with the environment and receiving rewards based on detection accuracy. We also look at the importance of feature selection and classification techniques and how effective they are in improving model performance, reducing the complexity of computation, and producing more desirable results. …

Leveraging Distributed Semantics From Deep Learning Architectures For Literature-Based Discovery, Clint A. Cuffy

Theses and Dissertations

Literature-based discovery (LBD) is a scientific process that introduces methods to automatically identify novel insights between non-interacting sets of literature. To date, numerous statistical and machine learning-based methods have been applied in the biomedical domain to find treatments for diseases such as Raynaud's disease, Parkinson's disease, and Multiple Sclerosis. However, the lack of standardized practices and creation of bespoke methodologies produces a scenario where the adoption of LBD remains challenging in real-world systems. Our work addresses these concerns through the improvement of five critical areas: 1) error propagation within LBD's a priori dependent tasks, 2) exploring the integration of modern …

Editorial: Ai's Impact On Higher Education: Transforming Research, Teaching, And Learning, Alyse Jordan, Ashley L. Dockens, Natalia Anastasia Pierson, Xinyue Ren

STEMPS Faculty Publications

[Introduction] This Research Topic provides a comprehensive examination of how artificial intelligence (AI) is transforming higher education. The collected studies reveal several interconnected themes that illuminate both the opportunities and challenges of AI integration in academic settings. This editorial summarizes these themes and articulates their significance for the future of higher education.

Cognitive Map Generation For Vision And Language Navigation, Alexander Sandoval Mesa

Dissertations and Theses

Visual-Language Navigation (VLN) presents significant challenges for autonomous agents, such as robots and virtual assistants, particularly in complex, dynamic environments where the seamless integration of visual perception and natural language understanding is critical. Traditional VLN systems often struggle with effectively aligning language instructions and visual scene understanding, limiting their adaptability and navigation efficiency.

This thesis proposes a novel Cognitive Map-based framework that addresses these challenges by transforming natural language navigation instructions into structured graph representations. The Cognitive Map consists of nodes representing waypoints, landmarks, decision points, and edges encoding spatial relationships and navigational actions. These maps are generated using Large …

Using Quanser Platform To Introduce Engineering Technology Students To Autonomous Vehicles, Otilia Popescu, Logan Beaver, Murat Kuzlu, Krishnanand Kaipa

Engineering Technology Faculty Publications

The area of autonomous vehicles is not new, but the latest advances in various technologies gave it a new boost in the last decade and it keeps growing in interest. However, undergraduate curricula rarely include courses specific to this area, which is considered mostly an interdisciplinary graduate field. While various programs introduce students to the background needed to understand and approach the field, specific work on autonomous vehicle projects is left for extra curriculum activities or student clubs, and eventually for senior (capstone) projects. This paper presents the work of a team of electrical engineering technology students on an autonomous …

Large Scale Machine Learning Over Knowledge Graphs, Bedirhan Gergin

Electronic Theses & Dissertations (2024 - present)

Knowledge graphs (KGs) have become popular across various fields, providing convenient access to web-based knowledge while storing and formalizing domain-specific information. By analyzing KGs, patterns, connections, and dependencies can be identified across different data sources, enabling the inference of new knowledge from given facts. As the use of KGs expands, the size of modern KGs has grown significantly, making them impossible to process within the main memory of a single computer. Distributed computing offers a viable solution to this challenge by leveraging the combined capabilities of multiple servers within a cluster. This thesis explores how distributed computing can be effectively …