Artificial Intelligence and Robotics Commons^™

Bayesian Network: An Explainable Artificial Intelligence (Xai) Approach To Human Performance Modelling For Control Room Operations, Houda Briwa

Theses

Alarm systems in process industry control rooms routinely exceed the performance targets set by standards such as EEMUA 191, placing operators under conditions where reliable performance is most difficult to achieve. Predicting how operators respond under such conditions is central to risk management, yet current Human Reliability Assessment (HRA) methods depend on expert judgement that is rarely tested against operational evidence, assume independence among factors known to interact, and do not explicitly represent the cognitive processes through which performance emerges. In Resilience Engineering terms, these methods encode Work-as-Imagined with limited means to assess how far expectations hold when work is …

Adaptive Multimodal Smart Home Control On A Raspberry Pi 5 Using Hand Gestures, Voice Cues, And User Feedback, Vaibhav Bora

Theses

A real time multimodal smart home control system deployed on a Raspberry Pi 5 is presented. The system combines hand gestures, short voice cues, and proximity aware interaction to execute household commands such as light brightness control, fan speed adjustment, and stop or kill switch actions. Lightweight gesture and keyword spotting voice classifiers were trained offline and exported to TensorFlow Lite for efficient on device inference. For more natural spoken phrases, the system additionally integrates a locally deployed pretrained offline ASR component rather than a speech recognizer trained from scratch. Using a USB camera and microphone, the system operates fully …

Edge Co-Occurrence Regularization For Node Classification, Kadir Altunel

Theses

We propose a simple yet effective regularization technique for node classification on graphs that leverages edge-based label co-occurrence patterns. We first train an MLP on node features to produce class probability distributions, then compute a fixed penalty matrix from edge-based co-occurrence statistics of these predictions. This penalty matrix, which captures unlikely class combinations on connected nodes, is then used to regularize GNN training without further updates. We evaluate this approach across multiple homophilic datasets (Cora, CiteSeer, PubMed, ogbn-arxiv) and heterophilic benchmarks (Chameleon, Squirrel, Actor, Roman-Empire) using three GNN architectures: GCN, GraphSAGE, and H2GCN. Results show consistent improvements on homophilic graphs, …

A Web Application For Generating Argument Maps For Essays Using Llms, Alexis R. Chalmers

Theses

Argumentative writing is a critical skill that strengthens students’ reasoning, communication, and analytical abilities. However, maintaining a clear and organized argument structure while writing can be challenging. Argument maps — visual diagrams which explicitly show an argument’s structure — have been shown to improve students’ writing, but are rarely used outside of the planning stage of an essay due to the time and effort required to create them. Automatically generating argument maps from student essays helps students to evaluate the structure of their argument as they write and makes identifying unsupported claims visible. To evaluate whether large language models (LLMs) …

Limitations Of Using Large Language Models For Automated Essay Scoring, Thomas A. Fink

Theses

Background: Automated essay scoring (AES) is a challenging deep learning problem. The two most widely used methods for predicting essay quality scores, supervised learning-based and LLM-based, have their own limitations. Although supervised learning-based methods are more accurate, they only predict a score and do not offer descriptive feedback to students. On the other hand, LLM-based methods can offer rubric-guided feedback but are known to be less accurate.

Methods: This work focuses on improving the accuracy of state-of-the-art LLM-based AES methods. We began by thoroughly investigating why these methods were performing poorly for certain datasets and certain examples. This led us …

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 …

Customizing Ai Strategies Across Multiple Generations, Matthew Harrer

Theses

This project investigates how artificial intelligence can help brands and marketers connect more effectively with Generation X, Millennials, and Generation Z. The literature review lays the groundwork that focuses on consumer behaviors and the integration of AI into digital marketing practices for each generation. The second part of the project involves a secondary data analysis of 21 recent marketing surveys and reports that explores topics related to trust, personalization, and social media. By integrating the findings into an insightful guidebook, marketers will be able to maximize these insights into clear actionable strategies.

Gamified Gait Rehabilitation Via Real-Time Biofeedback And Adaptive Hip-Exoskeleton Control, Mariya Huzaifa Tohfafarosh

Theses

Gait impairments arise from systemic diseases, age-related degeneration, musculoskeletal dysfunctions, or neurological conditions. While traditional rehabilitation can be effective, they often face challenges such as high costs, inaccessibility, and low patient engagement. To address these challenges, my work introduces a virtual reality-based rehabilitation (VRBR) system, integrating real-time motion and electromyographic (EMG) muscle activation feedback with a gamified virtual environment for enhanced adaptability and engagement. The system includes a custom-designed hip-exoskeleton that provides adaptive spring-like assistance or resistance, supporting both mobility-impaired users and strength training. Assistance levels can be tuned to match the user's progress. Additionally, a custom pressure insole was …

From The Bleachers To The Browser: Redefining Fan Experience With Ar And Ai In Smaller Teams, Jennifer Lee Wunder

Theses

This project documents the creation and deployment of HootyHoo, an interactive augmented reality (AR) mascot experience designed for the O’Fallon Hoots, a small-scale collegiate summer baseball team. Built using accessible, open-source tools such as WebXR, Mixamo, Meshy, Botpress, Claude and ChatGPT, this prototype merges AI-driven conversation with animated 3D avatar interaction—redefining how fans engage with sports organizations digitally. Unlike enterprise-level applications used by professional franchises, HootyHoo is entirely browser-based, eliminating the need for app downloads and ensuring maximum accessibility for families and new fans with smartphones. The experience centers on Hooty, the team mascot, who answers questions about baseball and …

Surveying The Role Of Visual Analytics In Human-Machine Teaming, Naga Datha Saikiran Battula

Theses

Humans and machines both possess their unique capabilities and have their strengths and weaknesses, which can be complementary to one another and allow them to achieve a common goal. Teaming in the modern era involves text prompts, voice commands, gesture recognition, touch interfaces, and the latest visualization techniques that allow parties/agents to interact. Communication through visualization plays a vital role in allowing robust insights to be gained through a glance. Using visualization as a medium between humans and machines can increase the communication bandwidth. Human-machine teaming has witnessed much progress, with many theories and practical examples emerging. In the report, …

Automated Segmentation Of The Ulnar Nerve In Mri Using Deep Learning Techniques, Akhil Nagulapalli

Theses

Cubital Tunnel Syndrome (CuTS), a condition caused by compression of the ulnar nerve, results in numbness, tingling, pain, and even muscle atrophy, affecting fine motor skills and diminishing patient quality of life. Accurate diagnosis of CuTS is challenging, as current diagnostic methods—including clinical exams, nerve conduction studies, and unaided MRI—often lack the precision to reliably identify the nerve and detect compression in its early stages. Deep learning-based segmentation offers a promising solution, enabling precise and automated identification of nerve structures in MRI images, which could significantly improve diagnostic accuracy and support timely intervention.

A novel deep learning model for segmenting …

Applications Of Neural Networks In Parkinson’S Disease Diagnosis, Saladin Minhaaj

Theses

Parkinson's disease (PD) is a complex and debilitating neurodegenerative disorder that affects millions of people worldwide. Early and accurate diagnosis is crucial for effective treatment and management of PD. This thesis explores the application of neural networks in PD diagnosis, leveraging their ability to learn patterns from large datasets and make accurate predictions.

Thesis provides an overview of PD, including its symptoms, diagnosis, and current challenges in diagnosis. We then delve into the fundamentals of neural networks, including supervised learning, mathematical interpretations, and parametric models. This research focuses on the development of neural network models that can accurately diagnose PD …

The Impact Of Ai On Ux: Challenges And Opportunities, Susan Stephanie Wells

Theses

Integrating artificial intelligence (AI) in user experience (UX) design is reshaping the field of UX, offering new opportunities and challenges for designers. This thesis project explores the multifaceted relationship between AI and UX design, focusing on the challenges, opportunities, and skills demanded of UX designers in the age of AI. Through a review of academic research and real-world experiences, this project studies the impact of AI on web design processes, UX testing, and data analysis. Key findings highlight the transformative potential of AI in enhancing user experiences, from suggesting website structures to facilitating UX testing and data analysis.

Comparative analysis …

Integrating Artificial Intelligence For Automated Storytelling In Turn-Based Strategy Games, Timothy Ripper

Theses

This project is inspired by turn-based strategy games, Final Fantasy Tactics, X-Com 2, and modern turn-based strategy games. This project is structured around the use of artificial intelligence for storytelling within strategy games. The focus of this project utilizes artificial intelligence in creating a quest generation system for storytelling. The resulting quest system creates new quests dynamically after communicating with an artificial intelligence allowing players to potentially experience an ever-expanding story from quests

Future-Proofing The Past: Artificial Intelligence In The Restoration Of Andalusian Architectural Heritage: A Case Study Of The Alhambra Palace, Granada, Spain, Kholoud Bader Hasan Ghaith

Theses

This thesis explains the contribution of artificial intelligence in heritage restoration as an icon of Andalusian architecture by using the Alhambra as an example. The task of sustaining heritage is increasing dramatically due to the accumulation of heritage assets and the need for modern and innovative operations to cope with preservation tasks. Therefore, this thesis reviews the role of artificial intelligence in improving the restoration operation to improve accuracy and efficiency. I applied the case study as a scientific methodology to explain this work to overcome scientific and subjective obstacles, such as scarce data and software integration while explaining the …

Discourses Across Periods Of Time, Amanda Raquel Moreno

Theses

This literature review explores the revolutionary effect of generative artificial intelligence (AI) and virtual reality (VR) on digital art history, specifically concentrating on their capacity to enable dialogical exchanges with historical figures and deepen the understanding of artworks. This study considers the current state of research, detecting key methodologies, areas of improvement, and possible challenges and ethical concerns. The example historical figure used in this analysis is the iconic Mexican artist Frida Kahlo. Kahlo’s refusal to correspond to a specific artistic style makes her an ideal subject for generative AI and VR-based investigation, offering fresh insights into her work. The …

A Smart Chatbot System For Digitizing Service Management To Improve Business Continuity, Asraa Mohammed Albeshr

Theses

Chatbots, also called digital systems that require a natural language-based interface for user interaction, are increasingly being integrated into our daily lives. These chatbots respond intelligently to voice and text and function as sophisticated entities. Its functioning includes the recognition of multiple human languages through the application of Natural Language Processing (NLP) techniques. These chatbots find applications in various areas such as e-commerce services, medical assistance, recommendation systems, and educational purposes. This reflects the versatility and widespread adoption of this technology. AI chatbots play a crucial role in improving IT support in IT Service Management (ITSM) for better business continuity. …

Un-Fair Trojan: Targeted Backdoor Attacks Against Model Fairness, Nicholas Furth

Theses

Machine learning models have been shown to be vulnerable against various backdoor and data poisoning attacks that adversely affect model behavior. Additionally, these attacks have been shown to make unfair predictions with respect to certain protected features. In federated learning, multiple local models contribute to a single global model communicating only using local gradients, the issue of attacks become more prevalent and complex. Previously published works revolve around solving these issues both individually and jointly. However, there has been little study on the effects of attacks against model fairness. Demonstrated in this work, a flexible attack, which we call Un-Fair …

Land Cover Image Segmentation Based On Individual Class Binary Segmentation, Sathyanarayanan Somasunder

Theses

Remote sensing techniques have been developed over the past decades to acquire data without being in contact of the target object or data source. Their application on land-cover image segmentation has attracted significant attention in recent years. With the help of satellites, scientists and researchers can collect and store high resolution image data that can be further processed, segmented, and classified. However, these research results have not yet been synthesized to provide coherent guidance on the effect of variant land-cover segmentation processes. In this paper, we present a novel model that augments segmentation using smaller networks to segment individual classes. …

Rm-Net: Rasterizing Markov Signals To Images For Deep Learning, Kajal Gupta

Theses

Statistical machine learning approaches are quite famous for processing Markov signal data. They can model unobserved states and learn certain characteristics particular to a signal with good accuracy. However, with the advent of Deep learning the novice ways of solving a problem has shifted towards this more sophisticated algorithm, which is much better, powerful and more accurate. Specifically, Convolutional Neural Nets (CNN) have shown many promising results on images and videos. Here we illustrate how CNN can be applied to a 1D numeric signal using signal rasterization technique. We start by rasterizing a 1D numeric Markov signal into an image …

New Methods For Deep Learning Based Real-Valued Inter-Residue Distance Prediction, Jacob Barger

Theses

Background: Much of the recent success in protein structure prediction has been a result of accurate protein contact prediction--a binary classification problem. Dozens of methods, built from various types of machine learning and deep learning algorithms, have been published over the last two decades for predicting contacts. Recently, many groups, including Google DeepMind, have demonstrated that reformulating the problem as a multi-class classification problem is a more promising direction to pursue. As an alternative approach, we recently proposed real-valued distance predictions, formulating the problem as a regression problem. The nuances of protein 3D structures make this formulation appropriate, allowing predictions …

Translating Counting Problems Into Computable Language Expressions, Zach Prescott

Theses

The realm of automated problem solving is a relatively new field, even in the context of natural language processing. One area where this is often demonstrated is that of creating a program that can solve word problems. The program must understand the problem, perform some processing, and then convey this information to a user in a way that is accessible and understandable. There has been quite a lot of progress in this area with simpler problems. However, when it comes to understanding problems that involve a level of NLP, the results are not conclusive. In this paper, we would like …

Model-Based Deep Siamese Autoencoder For Clustering Single Cell Rna-Seq Data, Zixia Meng

Theses

In the biological field, the smallest unit of organisms in most biological systems is the single cell, and the classification of cells is an everlasting problem. A central task for analysis of single-cell RNA-seq data is to identify and characterize novel cell types. Currently, there are several classical methods, such as K-means algorithm, spectral clustering, and Gaussian Mixture Models (GMMs), which are widely used to cluster the cells. Furthermore, typical dimensional reduction methods such as PCA, t-SNE, and ZIDA have been introduced to overcome “the curse of dimensionality”. A more recent method scDeepCluster has demonstrated improved and promising performances in …

Analysis Of Gameplay Strategies In Hearthstone: A Data Science Approach, Connor W. Watson

Theses

In recent years, games have been a popular test bed for AI research, and the presence of Collectible Card Games (CCGs) in that space is still increasing. One such CCG for both competitive/casual play and AI research is Hearthstone, a two-player adversarial game where players seeks to implement one of several gameplay strategies to defeat their opponent and decrease all of their Health points to zero. Although some open source simulators exist, some of their methodologies for simulated agents create opponents with a relatively low skill level. Using evolutionary algorithms, this thesis seeks to evolve agents with a higher skill …

Deepcon-Pre: Improved Protein Contact Map Prediction Using Inverse Covariance And Deep Residual Networks, Nachammai Palaniappan

Theses

As with most domains where machine learning methods are applied, correct feature engineering is critical when developing deep learning algorithms for solving the protein folding problem. Unlike the domains such as computer vision and natural language processing, feature engineering is not rigorously studied towards solving the protein folding problem. A recent research has highlighted that input features known as precision matrix are most informative for predicting inter-residue contact map, the key for building three-dimensional models. In this work, we study the significance of the precision matrix feature when very deep residual networks are trained. Using a standard dataset of 3456 …

Protein Inter-Residue Distance Prediction Using Residual And Capsule Networks, Andrew Dillon

Theses

The protein folding problem, also known as protein structure prediction, is the task of building three-dimensional protein models given their one-dimensional amino acid sequence. New methods that have been successfully used in the most recent CASP challenge have demonstrated that predicting a protein's inter-residue distances is key to solving this problem. Various deep learning algorithms including fully convolutional neural networks and residual networks have been developed to solve the distance prediction problem. In this work, we develop a hybrid method based on residual networks and capsule networks. We demonstrate that our method can predict distances more accurately than the algorithms …

Deep Morphological Neural Networks, Yucong Shen

Theses

Mathematical morphology is a theory and technique applied to collect features like geometric and topological structures in digital images. Determining suitable morphological operations and structuring elements for a give purpose is a cumbersome and time-consuming task. In this paper, morphological neural networks are proposed to address this problem. Serving as a non-linear feature extracting layers in deep learning frameworks, the efficiency of the proposed morphological layer is confirmed analytically and empirically. With a known target, a single-filter morphological layer learns the structuring element correctly, and an adaptive layer can automatically select appropriate morphological operations. For high level applications, the proposed …

Efficient Reduced Bias Genetic Algorithm For Generic Community Detection Objectives, Aditya Karnam Gururaj Rao

Theses

The problem of community structure identification has been an extensively investigated area for biology, physics, social sciences, and computer science in recent years for studying the properties of networks representing complex relationships. Most traditional methods, such as K-means and hierarchical clustering, are based on the assumption that communities have spherical configurations. Lately, Genetic Algorithms (GA) are being utilized for efficient community detection without imposing sphericity. GAs are machine learning methods which mimic natural selection and scale with the complexity of the network. However, traditional GA approaches employ a representation method that dramatically increases the solution space to be searched by …

The Use Of Machine Learning To Detect Suckling In Pre-Weaned Calves, Sukumar Katamreddy

Theses

The weaning of cattle is a process which is known to be labour intensive and to have stressful effects on both cow and calf Common methods used in the weaning process include the temporary removal of a mother from the calf and manual observation and intervention. Early and speedy weaning is known to have a number of benefits, including health benefits for both cow and calf, additional weight gains for the calves as well as reduced labour and feed requirements. The process known as Two-Stage Weaning is recognised to be an effective low-stress approach to weaning in which the calf …

A Parallelized Method For Solving Large Scale Integer Linear Optimization Problems Using Cut-And-Solve With Applications To Cgwas, John Brandenburg

Theses

The commercial solver CPLEX has been one of the top solvers of mixed-integer and purely integer linear problems for some time. Its method of solving, Branch-and-Cut, has been shown to be highly effective, but has its limits in terms of input sizes which are tractable, and cannot be effectively parallelized beyond a small number. Here we present a different method of solution, Cut-and-Solve, which utilizes the power of CPLEX to effectively parallelize any mixed-integer or integer linear problem. We have utilized Cut-and-Solve in a novel way to offer optimal solution guarantees more quickly. We will show comparisons of Cut-and-Solve to …