Artificial Intelligence and Robotics Commons^™

Development Of An Ecg-Based Deep Learning Model For Pediatric Congenital Heart Disease (Chd) Diagnosis, Annbar Mekouar

Selected Full-Text Master Theses 2021-

Congenital heart disease (CHD) stands as the leading congenital anomaly which affects pediatric populations throughout the world. The effectiveness of treatment depends on both early diagnosis and accurate identification but echocardiography requires manual interpretation which proves time-consuming and inconsistent especially when examining pediatric patients with their distinct cardiac systems. The research aims to create a deep learning-based diagnostic framework which uses ECG data to identify coronary artery disease subtypes in pediatric patients. The model uses high-quality datasets from Dr. Ignacio Lugones to extract R-R intervals and QRS durations through convolutional neural networks (CNNs). The system addresses pediatric-specific challenges while enhancing …

Strategic Identification Of Prognostic Biomarkers For Knee Osteoarthritis Via Optimized Regression Techniques, Varun Sri Sai Vemuri

Selected Full-Text Master Theses 2021-

Knee Osteoarthritis (KOA) is a progressive musculoskeletal disease involving cartilage matrix degradation, subchondral bone remodeling, and systemic inflammation, significantly impairing joint function and mobility. Existing KOA prediction models are not designed to account for nonlinear multimodal biomarker interactions or to integrate biochemical and imaging data, thus limiting their clinical utility. The current method for early detection and prediction of KOA disease progression is primarily based on machine learning-based approaches using radiographic imaging data, static feature selection, and deterministic outputs. These machine learning approaches often fail to capture the pathophysiology of KOA disease progression, which involves a complex cascade of processes, …

Exploring The Impact Of Generative Ai Chatgpt On Critical Thinking In Higher Education: Passive Ai-Directed Use Or Human-Ai Supported Collaboration?, Nesma Ragab Nasr, Chih-Hsiung Tu, Jennifer Werner, Tonia Bauer, Cherng-Jyh Yen, Laura Sujo-Montes

STEMPS Faculty Publications

Generative AI is weaving into the fabric of many human aspects through its transformative power to mimic human-generated content. It is not a mere technology; it functions as a generative virtual assistant, raising concerns about its impact on cognition and critical thinking. This mixed-methods study investigates how GenAI ChatGPT affects critical thinking across cognitive presence (CP) phases. Forty students from a four-year university in the southwestern United States completed a survey; six provided their ChatGPT scripts, and two engaged in semi-structured interviews. Students’ self-reported survey responses suggested that GenAI ChatGPT improved triggering events (M = 3.60), exploration (M = 3.70), …

Applying Transfer Learning For Street-Scale Nuisance Flood Forecasting In Coastal-Urban Cities, Binata Roy, Jonathan L. Goodall, Diana Mcspadden, Chetan Kumar, Steven Goldenberg, Yidi Wang, Malachi Schram

VMASC Publications

An important challenge with Machine Learning (ML) is its transferability; i.e., whether a ML model trained on one set of data can be applied to a second set of data without requiring full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained for one …

Faithful Narratives From Complex Conceptual Models: Should Modelers Or Large Language Models Simplify Causal Maps, Tyler J. Gandee, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Comprehensive conceptual models can result in complex artifacts, consisting of many concepts that interact through multiple mechanisms. This complexity can be acceptable and even expected when generating rich models, for instance to support ensuing analyses that find central concepts or decompose models into parts that can be managed by different actors. However, complexity can become a barrier when the conceptual model is used directly by individuals. A ‘transparent’ model can support learning among stakeholders (e.g., in group model building) and it can motivate the adoption of specific interventions (i.e., using a model as evidence base). Although advances in …

Examining Teaching Competencies And Challenges While Integrating Artificial Intelligence In Higher Education, Xinyue Ren, Min Lun Wu

STEMPS Faculty Publications

The rapid development of artificial intelligence (AI) technologies has demonstrated their affordances and limitations in revolutionizing pedagogical strategies in higher education. Given the lack of guidelines, policies, and resources to assist instructors in efficiently and ethically integrating AI into teaching and learning practices, this systematic review aimed to investigate AI integration competencies and challenges in higher education from the intelligent Technological Pedagogical Content Knowledge (TPACK) perspective. We first applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify 23 studies published between 2019 and 2023 that met the inclusion and exclusion criteria. After conducting open coding and …

Advancing Pedagogical And Instructional Design Through Artificial Intelligence (Ai) In Education And Training Contexts, Mohan Yang, Jewoong Moon, Tian Luo, Jinhee Kim

STEMPS Faculty Publications

[Introduction] "The only way to discover the limits of the possible is to go beyond them into the impossible." - Arthur C. Clarke

It is our pleasure and honor as guest editors for this special issue of the Journal of Applied Instructional Design (JAID) to present "Advancing Pedagogical and Instructional Design through Artificial Intelligence (AI) in Education and Training Contexts." This issue arrives at a truly timely moment, as the rapid and continuous development of generative artificial intelligence (GenAI) is fundamentally reshaping the traditional paradigms of teaching, learning, and training.

The Importance Of Atomic Charges For Predicting Site-Selective Ir-, Ru-, And Rh-Catalyzed C-H Borylations, Shannon M. Stephens, Kyle M. Lambert

Chemistry & Biochemistry Faculty Publications

A supervised machine learning model has been developed that allows for the prediction of site selectivity in late-stage C-H borylations. Model development was accomplished using literature data for the site-selective (≥95%) C-H borylation of 189 unique arene, heteroarene, and aliphatic substrates that feature a total of 971 possible sp² or sp³ C-H borylation sites. The reported experimental data was supplemented with additional chemoinformatic descriptors, computed atomic charges at the C-H borylation sites, and data from parameterization of catalytically active tris-boryl complexes resulting from the combination of seven different Ir-, Ru-, and Rh-based precatalysts with eight different ligands. Of the over …

Overcoming Resistance To Ai In Higher Education: A Case Study, C. Tomovic, M. Tomovic

Educational Leadership & Workforce Development Faculty Publications

While the deployment of AI in business and industry is widely acknowledged for its potential to improve efficiencies, decision-making, and automation, its implementation often encounters several challenges. These include integrating AI with existing systems, ensuring data quality and availability, bridging the employee skills gap, overcoming resistance to change, addressing ethical concerns, managing costs, complying with regulations, and handling ongoing maintenance and updates. In the context of higher education, however, the challenges take a different shape. Faculty members may express concerns about how AI could disrupt traditional teaching methods, necessitate changes in course delivery, and raise issues related to job security …

Physics-Informed Deep Learning With Kalman Filter Mixture For Traffic State Prediction, Niharika Deshpande, Hyoshin (John) Park

Engineering Management & Systems Engineering Faculty Publications

Accurate traffic forecasting is crucial for understanding and managing congestion for efficient transportation planning. However, conventional approaches often neglect epistemic uncertainty, which arises from incomplete knowledge across different spatiotemporal scales. This study addresses this challenge by introducing a novel methodology to establish dynamic spatiotemporal correlations that captures the unobserved heterogeneity in travel time through distinct peaks in probability density functions, guided by physics-based principles. We propose an innovative approach to modifying both prediction and correction steps of the Kalman Filter (KF) algorithm by leveraging established spatiotemporal correlations. Central to our approach is the development of a novel deep learning model …

Predictive Maintenance In Naval Vessel Propulsion Systems For Enhanced Marine Operations Using A Bigmm-Hmm Framework With Divergence-Based Clustering, Farshid Javadnejad, Hyoshin John Park, Samuel Kovacic, Andres Sousa-Poza

Engineering Management & Systems Engineering Faculty Publications

This study introduces a BiGMM-HMM Integration Framework designed to improve predictive maintenance strategies for naval vessel propulsion systems, addressing the need for efficient and reliable operation in marine engineering applications. The framework effectively manages multimodal sensor data by leveraging a unique combination of Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) in a bidirectional architecture. It analyses the dynamic interactions between sensors and subsystems. Two preprocessing methods are evaluated: Method 1 focuses on subsystem interactions, employing divergence-based root cause analysis to identify key sensor variables by clustering of sensors and subsystems. In contrast, Method 2 processes the entire dataset …

Optimizing Port Logistics Through Generative Ai: Revolutionizing Efficiency And Resilience In The Maritime Industry, Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Gabriel Raicu

Engineering Management & Systems Engineering Faculty Publications

The maritime industry faces growing challenges in optimizing port logistics due to increasing trade volumes, environmental regulations, and supply chain disruptions. This comprehensive literature review examines the transformative role of artificial intelligence (AI), with particular focus on generative AI, in enhancing efficiency and resilience in port operations. Through systematic analysis of 23 peer-reviewed studies published between 2021-2025, this review synthesizes advancements in real-time data integration, machine learning, digital twins, IoT, and autonomous systems that collectively improve operational decision-making, risk management, and environmental sustainability. Key findings reveal that machine learning applications achieve 90% effectiveness ratings in operational optimization, while predictive analytics …

A Bibliometric Analysis Of Ai-Driven Healthcare Literature Containing Kos Keywords: Trends, Themes, And Gaps, Julaine Clunis, Eric Asare

STEMPS Faculty Publications

As artificial intelligence (AI) becomes increasingly embedded in healthcare applications, concerns have emerged around the trustworthiness, interpretability, and context-awareness of these systems. Knowledge Organization Systems (KOS) hold considerable potential to address these challenges by supporting semantic standardization, explainability, and domain alignment. This study presents a bibliometric analysis of scholarly publications referencing both AI and healthcare concepts to examine how KOS are positioned within this evolving discourse. The findings indicate that while early literature frequently and explicitly referenced KOS—such as ontologies, controlled vocabularies, and classification systems—their visibility has declined relative to newer paradigms such as machine learning and large language models. …

Comprehensive Benchmarking Of Several Machine Learning And Bayesian Models For Early-Stage Diabetes Risk Prediction: A Large-Scale Comparative Study, Md. Iqbal Hossain, Najila Alam Porno

Mathematics & Statistics Faculty Publications

Diabetes remains a critical global health challenge, with early detection is crucial for effective management. This study presents a comprehensive benchmarking analysis of 14 diverse machine learning and Bayesian models for early-stage diabetes risk prediction using clinical data [2] from Sylhet, Bangladesh. This research evaluated traditional methods (Logistic Regression, Decision Trees), ensemble techniques (Random Forest, XGBoost, LightGBM), Bayesian approaches (BART, Bayesian Logistic Regression), and advanced neural architectures (Deep Belief Networks) using both 70-30 train-test splits and 10-fold cross-validation. The results demonstrate that ensemble methods consistently outperformed other approaches, with Random Forest(RF) achieving the highest cross-validated AUC (0.9951) and accuracy (0.9699). …

Is It Getting Better? An Evaluation Of Two Successive Generations Of Chatgpt In Answering Specialized Vascular Surgery Questions, Dongjin Suh, Quang Le, Leana Dogbe, Kedar Lavingia, Michael Amendola

Department Surgery Faculty Publications

Purpose: Large language models (LLMs) can generate clinically relevant text; however, their performance in highly specialized medical domains remains uncertain. This study evaluated ChatGPT-3.5 and ChatGPT-4 (OpenAI) using vascular surgery board–style questions from the Vascular Education and Self-Assessment Program, version 4 (VESAP4) and compared the two public model versions (June and November 2023).

Materials and Methods: All non-image VESAP4 questions (n=384) were presented independently three times to each model version (ChatGPT-3.5 June/November; ChatGPT-4, June/November). Outcomes included accuracy (proportion correct), consistency (same option letter across all three attempts and “consistently correct”), explanation length (word count), and modes of failure classified for …

The Influence Of Shopping Context And Anthropocentric Bias On Consumer Preferences For Human And Ai Designers In Fashion, Dooyoung Choi, Ha Kyung Lee, Christina Soyoung Song, Ji Young Lee

Educational Leadership & Workforce Development Faculty Publications

This study explores how shopping contexts (hedonic vs. utilitarian) influence consumer preferences for AI- vs. human-designed fashion products. In Study 1, participants with hedonic motivations preferred human-designed items, while those with utilitarian motivations preferred AI-designed products, supporting the hypothesis that shopping context affects designer preferences. Study 2 further investigates the mediation of competence-warmth traits in designer preferences and the moderation of anthropocentric bias. It finds that hedonic shopping contexts increase the desire for warmth-related traits, leading to a preference for human designers, while utilitarian contexts favor competence-related traits and AI designs. Additionally, individuals with higher anthropocentric biases showed stronger preferences …

Examining Non-Traditional Online Learners' Ownership Of Learning In The Context Of Chatgpt-Facilitated Design, Mohan Yang, Tian Luo, Kristin Herman, Belle Li, Shanan Chappell Moots, Noah Glaser, Shiyan Jiang

STEMPS Faculty Publications

Generative artificial intelligence (GenAI) offers potential solutions to educational challenges by personalizing learning experiences for diverse learners. However, it also introduces complexities around issues of ownership of learning. As an indicator of empowered learning, ownership is a multifaceted but underexplored concept. In this study, the authors adopted a mixed-methods multiple-case study design to explore non-traditional online students’ perceived ownership of learning when using ChatGPT for instructional design. The authors adopted the psychology ownership construct consisting of self-efficacy, accountability, belongingness, and self-identity as the theoretical framework. Findings revealed students’ prior AI experiences and educational levels played a significant role in their …

The Impact Of Loss Function Topology On Gradient Descent, Robert B. Skudnig Jr.

Theses and Dissertations

Gradient descent is a popular optimization method that utilizes a model’s prediction error to iteratively improve its parameters for a given task. The functions that measure this error can be defined to align with the user’s goals and sometimes satisfy metric or norm properties. It is common for these functions to measure over Rn, but any differentiable space allows for gradient descent to occur. There has been some research investigating the influence of topological spaces on optimization methods, but it is a limited field of study. This thesis further explores this phenomenon by applying a transformation prediction model to multiple …

Novel Generative And Language Model Architectures With Applications, Edison Mucllari

Theses and Dissertations--Mathematics

This dissertation investigates novel architectures to address fundamental challenges in machine learning, particularly focusing on transformer models, recurrent neural networks, GAN and continual learning and their applications in natural language processing and computer vision. We propose the Neumann-Cayley Gated Recurrent Unit (NC-GRU), which leverages a Neumann series-based Scaled Cayley transformation to maintain orthogonal weight matrices, effectively mitigating exploding gradients problems while improving long-term memory retention across prediction tasks. We demonstrate the practical applications of NC-GRU by implementing our proposed architecture into an autoencoder to derive neural molecular fingerprints. Building upon these advancements, we turn our attention to the transformer architecture, …

Temporal Machine Learning For Predicting Accidents And Violations In The Mining Industry, Nathan T. Kelley

Theses and Dissertations--Mining Engineering

This thesis examines the predictive capability of a temporal machine learning model for forecasting future accidents and violations at individual mines, based on historical data. Mine accidents were categorized by accident classification and violations were categorized by the Part Section. The primary datasets utilized were the mine safety and health administration’s (MSHA’s) Accident Injuries and Violations datasets. The available datasets were cleaned and organized by mine type and commodity, then divided into separate subsets for training, validating, and testing. Different models, cutoff metrics, learning rates, number of hidden layers, data processing methods, data processing divisions, number of points observed …