Artificial Intelligence and Robotics Commons^™

Effective Transformer Networks For Undersampled Magnetic Resonance Image Reconstruction, Tahsin Rahman

Open Access Theses & Dissertations

The proliferation of data-driven tools for solving problems in every possible domain, coupled with rapid advances in computing technology, has led to an arms race of AI development and application research in industry and academia. One field of research that stands to gain immeasurably from this revolution is medical imaging. It is a critical part of modern diagnostics, and advancements in this area can directly benefit the average person by making healthcare more accessible, accurate, and affordable. Breakthroughs in mainstream image processing and computer vision have long fueled development in medical imaging, and it is now common to see cutting …

A Digital Engineering Framework For Ai-Driven Trade-Off Evaluation And Predictive Component Classification, Alejandro Silva Au

Open Access Theses & Dissertations

This thesis introduces a digital engineering tool designed to help engineers make smarter decisions when choosing actuators. At its core, the system brings together machine learning (specifically XGBoost) and a decision-making method called Multi-Utility Attribute Theory (MUAT). The goal is to support engineers in picking components based on what really matters for their designs, whether that's speed, cost, durability, or any other performance factor. What makes this tool stand out is its user-friendly interface that lets people interact with the system directly. It takes a set of actuator performance data, classifies each one into a relevant use category, and then …

Characterization Of Search Spaces And Effects On Machine Learning, Leo Ghelarducci

Doctoral Dissertations and Master's Theses

The present status of the field of Machine Learning (ML) focuses on optimization of popular models. Rarely are the effects of the problem characteristics upon the solution algorithm studied. There exists no standard for knowing when to apply ML algorithms to a given problem or how to estimate the effectiveness of results. Focusing on the search space of problems, a rigorous study was conducted to generate an in-depth understanding of the impact of search space characteristics to the performance of a ML algorithm, specifically a Genetic Algorithm (GA). The effects of specific problem characteristics, represented via solution space characteristics, on …

Modern Procedural Terrain Generation Techniques And Their Background, Hunter A. Barton

2025 Symposium

Procedural terrain generation has become a staple in many digital environments, enabling the automated creation of large-scale and realistic landscapes for applications such as video games and movies. This paper provides an in-depth look at smooth noise functions and their use for terrain generation, as well as an overview of some more modern methods of generation. A method utilizing machine learning stlye transfer was reproduced for this paper with some alterations to improve visualization and realism.

Machine Learning For Digital Biomarker-Based Detection Of Cognitive Decline, Seng Khoon The

Dissertations and Theses Collection (Open Access)

Dementia is a neurodegenerative disease with a prevalence rate expected to triple by 2050, posing a significant challenge for health services. To impede the increasing prevalence, medical professionals and scientists are actively investigating technology to detect cognitive decline at a reversible stage known as Mild Cognitive Impairment (MCI). Digital biomarker technology is an emerging pragmatic approach to permit objective, ecologically valid, and long-term continuous measurement of cognitive health status, rendering it as one of the promising technologies for early MCI detection. Despite its potential, it is nontrivial to encode, extract and combine predictive information from these digital biomarker technologies; advanced …

Towards Efficient Privacy-Preserving Deep Learning: He-Friendly Structures, Flexible Pruning, He-Efficient Architectures, And Secure Transformer Token Drop, Yifei Cai

Electrical & Computer Engineering Theses & Dissertations

Deep learning (DL) has become a powerful tool for solving complex problems, but developing DL models typically requires vast datasets, high computational resources, and expert knowledge—barriers that limit accessibility. Machine Learning as a Service (MLaaS) addresses this challenge by allowing resource-rich providers to deliver pre-trained DL models as services. However, privacy concerns arise: clients hesitate to share sensitive data, while providers protect their proprietary models. To address this, privacy-preserving MLaaS integrates cryptographic techniques into DL computations, as seen in frameworks like Cryptonets, SecureML, GAZELLE, CrypTFlow2, Cheetah, and BOLT. Among them, Homomorphic Encryption (HE) enables computation on encrypted data but remains …

A Comparative Study Of Neural Networks And Xgboost Models For Flight Time Prediction, Ioannis Paraschos, Taryn E. Trimble, Eshna Bhargava, Jake Klingler, Benjamin R. Nicolai

Beyond: Undergraduate Research Journal

Flight time prediction plays a crucial role in modern air travel, benefiting airlines and passengers alike. Accurate predictions enable airlines to optimize schedules, allocate resources effectively, and ensure passenger safety and satisfaction. In recent years, machine learning models, such as neural networks and XGBoost, have gained popularity for predicting flight times. This study aims to compare the performance of neural network and XGBoost models in predicting flight times, considering factors such as weather conditions, air traffic control, and aircraft performance. The results indicate that both models are effective, with XGBoost achieving slightly higher accuracy. However, neural networks offer advantages in …

Forecasting Influenza Rates Using Machine Learning: A Study Of Chatgpt's Predictive Accuracy, Sara Saleh

University Honors Theses

This study evaluates ChatGPT's ability to forecast influenza rates, such as the number of flu cases, hospitalizations, and death during peak season periods using CDC data, and comparing forecasts against actual results to calculate statistical accuracy and consistency. Influenza forecasting is essential for public health planning, but traditional methods may not always provide timely or accurate predictions. In this research study, ChatGPT was utilized to predict the influenza rates for the following week based on the previous week's data obtained from the FluView surveillance system. The predicted rates were compared to the actual influenza rates to assess the model's overall …

Property Testing Ai: An Efficient Frontier, Paul Sopher Lintilhac

Dartmouth College Ph.D Dissertations

In this dissertation, we take a step towards addressing the major problem of a lack of standardized and rigorous approaches to testing and evaluation of AI systems. Taking inspiration from both the fields of Property Testing and Property Based Testing (for programs), we develop a novel taxonomy of partially overlapping classes of properties of AI systems, including simple properties, compound properties, higher order properties, data relation properties, and architecture-utility properties. We argue that this taxonomy categorizes a diverse set of AI traits -- including accuracy, fairness, robustness, monotonicity, point-wise and global privacy properties, sensitivity, and more -- according to the …

Opening The Black Box With Regal: A Novel Explainable Ai Approach To Uncover Key Predictors In Search And Rescue Success, Brandon Hyunjun Kim

Master's Theses

The outcome of a search and rescue (SAR) operation is influenced by a complex, non-linear interplay among numerous factors, including geographic context, subject-specific characteristics, and environmental conditions. The high dimensionality and intricate dependencies among these variables pose significant challenges to traditional exploratory modeling approaches, limiting their ability to uncover meaningful patterns and relationships associated with mission success. This study introduces Rules Based Explanations for Generated neighborhoods Around Localized cases (REGAL), a novel adaptation of the Local Interpretable Model-agnostic Explanations (LIME) framework to explain deep multimodal neural networks and what key features it assesses to determine search and rescue success. REGAL …

Efficient Gan-Based Adversarial Example Generation Against Ml-Based Network Intrusion Detection Systems, Darren D. Hartono

Master's Theses

In the realm of network security, Network Intrusion Detection Systems (NIDS) are essential for identifying and mitigating malicious activities targeting networked devices. Traditionally, these systems have relied on signature-based and anomaly-based detection techniques. However, the increasing complexity and adapt- ability of cyber threats have driven the adoption of Machine Learning (ML) ap- proaches in modern NIDS, significantly improving their ability to detect a wider range of attack vectors. Despite these advancements, ML-based NIDS remain vulnerable to adversarial examples—deliberately crafted inputs designed to mislead models and trigger incorrect classifications. Originally identified in the field of computer vision, adversarial examples now pose …

Developments On Abbreviations Towards Machine Reading Comprehension, Sing Choi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Machine reading comprehension is a critical step in development of applications that require the semantic understanding of human speech-to-text driven work. Many devices such as smart home appliances like the Amazon Echo Dot, Google Home, or smart assistants like Apple Siri or Microsoft Cortana are examples of these applications. The comprehension task involves a deeper understanding and recognition of named entities such as person names, locations, medicals codes, quantities, abbreviations, and acronyms in speech or text data. In this dissertation, we explore and extend the different approaches and techniques in modern research that tackles the problem of recognition and definition …

Machine Learning Course: A 15-Week Interactive Curriculum With Code And Case Studies, Pegah Khosravi

Open Educational Resources

This open-access machine learning course is a comprehensive 15-week curriculum developed and published on GitHub with full Google Colab compatibility. It combines theoretical concepts with hands-on Python coding, real-world datasets, and structured projects covering regression, classification, clustering, deep learning, transformers, and multimodal AI. The course is designed for students, educators, and researchers interested in applied machine learning, including biomedical applications. It includes explainable AI components and ethical discussions to align with modern AI standards. The course is maintained by BioMind AI Lab at CUNY.

Computer Vision In Soccer: Yolov11 Analytics Engine For Quantifying Game Strategy, Connor S. Maurer

Data Science Undergraduate Honors Theses

Single-shot object detection capabilities significantly reduce computational overhead for real-time computer vision in sports analytics at 60 FPS. YOLO11’s lightweight CNN gives promising accuracy while meeting the low-latency demand of dynamic soccer matches. As data-driven approaches take over the sport of soccer, efficient player tracking systems become critical for informing coach’s strategies. I prototype the ETL (Extract, Transform, Load) process of data collected from a single- shot detection program and evaluate its viability for estimating player fatigue. YOLO11 detects players, the ball, and other characteristics, with the output transformed by homography to estimate the positions in the real world. These …

Machine Learning And Protein Engineering Approaches To Understanding Kinesin-5 Activity, Jason Eden Sanchez

Open Access Theses & Dissertations

Cancer is a term describing a collection of diseases that result in uncontrolled cell growth. Cancer has manifold etiologies and underlying cancers are rouge biochemical pathways involving many different proteins. In the current work, two approaches are used to enhance knowledge of kinesin-5, a potential cancer target involved in cell division. Kinesin-5 promotes cell division by cross-linking and separating microtubules in dividing cells. The first approach uses machine learning (ML) to identify small molecule inhibitors for kinesin-5. Though decades of research have uncovered classes of small-molecules which inhibit kinesin-5 in vitro and in vivo, no candidates have reached phase III …

Toward Robust Semantic Segmentation In Levee Infrastructure Monitoring: Enhancing Accuracy With High-Fidelity Synthetic Data And Ensemble Learning, Padam Jung Thapa

LSU New Orleans Theses and Dissertations

Abstract: Levees serve as critical flood protection structures, but failures due to inadequate maintenance and extreme water pressures have led to devastating events such as Hurricane Katrina. Manual inspections are slow, labor-intensive, and prone to human error, necessitating the development of automated solutions. This study proposes an AI-driven framework for levee inspection utilizing deep learning-based semantic segmentation to detect rutting and enhance the identification of sand boils. To address dataset limitations, high-fidelity synthetic images are generated using DreamBooth for fine-tuning, while ControlNet adds structural constraints to enhance realism and consistency. A semi-automatic convex hull annotation technique enhances labeling efficiency, and …

Privacy-Aware Ai-Based Agricultural Monitoring Using Internet Of Drones, Md Benozir Hossain

Honors Theses

Artificial Intelligence (AI) has become a vital tool for agricultural farming. AI-based image processing models utilizing different machine learning (ML) algorithms and deep learning (DL) offer advanced functionalities in disease detection, yield estimation, land use, etc. This thesis examines AI-driven techniques utilizing Convolutional Neural Networks (CNN) with the addition of Federated Learning (FL) to analyze satellite and drone images for agricultural insights, especially in detecting Cotton diseases. The AI models improve agricultural farming in many ways, such as using data to make critical decisions, reducing labor costs, pest infestations, etc. Moreover, these models allow farmers to minimize yield losses by …

Expressive And Interpretable User Engagement Prediction Using Multivariate Survival Processes, Akshay Aravamudan

Theses and Dissertations

The ability to characterize how information diffuses online is of paramount importance to stakeholders that are interested in tasks such as proposing solutions for mitigating and countering dis/misinformation, predicting user engagement of content in social media, planning marketing campaigns to roll-out products and planning dissemination of political campaign messaging among others. One such facet of learning the dynamics of information diffusion is the ability to predict user engagement or the popularity of a single piece of information as it spreads through an online medium. Existing works in this regard mainly either obfuscate user level information or utilize frameworks that are …

Using Natural Language Processing And Machine Learning To Detect Online Radicalisation In The Maldivian Language, Dhivehi, Hussain Ibrahim, Ahmed Ibrahim, Michael N. Johnstone

Research outputs 2022 to 2026

Early detection of online radical content is important for intelligence services to combat radicalisation and terrorism. The motivation for this research was the lack of language tools in the detection of radicalisation in the Maldivian language, Dhivehi. This research applied Machine Learning and Natural Language Processing (NLP) to detect online radicalisation content in Dhivehi, with the incorporation of domain-specific knowledge. The research used Machine Learning to evaluate the most effective technique for detection of radicalisation text in Dhivehi and used interviews with Subject Matter Experts and self-deradicalised individuals to validate the results, add contextual information and improve recognition accuracy. The …

Impact Of Data Snooping On Deep Learning Models For Locating Vulnerabilities In Lifted Code, Gary Mccully, John Hastings, Shengjie Xu

Research & Publications

This study examines the impact of data snooping on neural networks used to detect vulnerabilities in lifted code, and builds on previous research that used word2vec and unidirectional and bidirectional transformer-based embeddings. The research specifically focuses on how model performance is affected when embedding models are trained with datasets, which include samples used for neural network training and validation. The results show that introducing data snooping did not significantly alter model performance, suggesting that data snooping had a minimal impact or that samples randomly dropped as part of the methodology contained hidden features critical to achieving optimal performance. In addition, …

Applying Software Engineering Black-Box Methods For Testing Machine Learning Models, Timothy Elvira

Doctoral Dissertations and Master's Theses

This dissertation proposes researching an approach to incorporate and align Software black-box testing methods into Machine Learning (ML) applications, specifically in the context of computer vision models. Typically, testing methods within Software Engineering (SE) encompass a range of test types that assess levels of a software system, such as Unit, Integration, Functional, and System testing [1]. The testing spectrum offers two perspectives on the system: black-box, where the system’s code is hidden, and white-box, where the system's code is exposed for testing. Software Quality pairs testing with requirements, in a many-to-one relationship, to ensure proper validation of the software system. …

Multimodal Ai-Driven Biomarker For Early Detection Of Cancer Cachexia, Sabeen Ahmed

USF Tampa Graduate Theses and Dissertations

Cancer cachexia is a metabolic syndrome characterized by substantial skeletal muscle loss, impacting cancer patients' survival and quality of life. Despite its clinical significance, early detection remains a challenge due to the lack of standardized diagnostic criteria and the reliance on indirect markers. This work presents an AI-driven approach to enhance cachexia detection and monitoring by integrating multiple deep learning methodologies. We explore transformer architectures for time-series analysis to model sequential medical data, enabling disease prediction and progression modeling. To ensure robust and reliable decision-making in clinical settings, we explore Bayesian deep neural networks for uncertainty estimation. Additionally, we introduce …

Investigating Key Structures In Protective Scenes For Llms, Eben M. Weisman

University Honors Theses

This research delves into the realm of "protective scenes" within Large Language Models (LLMs), exploring their impact on bias mitigation, deception, and context preservation. The study investigates the use of roleplay prompting human-like behavior and reasoning in LLMs, focusing on the Character-LLM framework's concept of protective scenes with graduated levels of protection. By combining insights from psychology, cognitive science, and computational analysis, this research aims to develop a framework for understanding how protective scenes influence roleplay performance in LLMs, ultimately contributing to the development of more reliable and ethical AI systems.

Language Processing: The Precedence Of Neural Networks On The Account Of Hidden Markov Models, Dia Eddin Abuzeina

An-Najah University Journal for Research - B (Humanities)

Background: since its discovery at the beginning of the last century, Markov models gain a great popularity, and have been widely used in different domains. However, the most prominent use was in computational linguistics, or what is known as natural language processing (NLP). Abstractly, Markov models are nothing but a statistical representation of a particular system. The mathematical statistical representation of a given system is the heart of Markov theory. Markov models characterized by solid mathematical representation, which significantly promotes using it. No doubt, Markov models are mainly used in prediction and classification, to serve computational linguistics as well as …

Multiparametric Mri Along With Machine Learning Predicts Prognosis And Treatment Response In Pediatric Low-Grade Glioma, Anahita Fathi Kazerooni, Adam Kraya, Komal Rathi, Meen Chul Kim, Arastoo Vossough, Nastaran Khalili, Ariana Familiar, Deep Gandhi, Neda Khalili, Varun Kesherwani, Debanjan Haldar, Hannah Anderson, Run Jin, Aria Mahtabfar, Sina Bagheri, Yiran Guo, Qi Li, Xiaoyan Huang, Yuankun Zhu, Alex Sickler, Matthew R Lueder, Saksham Phul, Mateusz Koptyra, Phillip Storm, Jeffrey Ware, Yuanquan Song, Christos Davatzikos, Jessica Foster, Sabine Mueller, Michael J Fisher, Adam Resnick, Ali Nabavizadeh

Department of Neurosurgery Faculty Papers

Pediatric low-grade gliomas (pLGGs) exhibit heterogeneous prognoses and variable responses to treatment, leading to tumor progression and adverse outcomes in cases where complete resection is unachievable. Early prediction of treatment responsiveness and suitability for immunotherapy has the potential to improve clinical management and outcomes. Here, we present a radiogenomic analysis of pLGGs, integrating MRI and RNA sequencing data. We identify three immunologically distinct clusters, with one group characterized by increased immune activity and poorer prognosis, indicating potential benefit from immunotherapies. We develop a radiomic signature that predicts these immune profiles with over 80% accuracy. Furthermore, our clinicoradiomic model predicts progression-free …

Comparing Unidirectional, Bidirectional, And Word2vec Models For Discovering Vulnerabilities In Compiled Lifted Code, Gary Mccully, John Hastings, Shengjie Xu, Adam Fortier

Research & Publications

Ransomware and other forms of malware cause significant financial and operational damage to organizations by exploiting long-standing and often difficult-to-detect software vulnerabilities. To detect vulnerabilities such as buffer overflows in compiled code, this research investigates the application of unidirectional transformer-based embeddings, specifically GPT-2. Using a dataset of LLVM functions, we trained a GPT-2 model to generate embeddings, which were subsequently used to build LSTM neural networks to differentiate between vulnerable and non-vulnerable code. Our study reveals that embeddings from the GPT-2 model significantly outperform those from bidirectional models of BERT and RoBERTa, achieving an accuracy of 92.5\% and an F1-score …

Credit Card Fraud Detection Via Model Retraining And Fine-Tuning, Anamol Khadka

Computer Science and Engineering Student Research - Archive

Credit card fraud detection is a critical task in financial systems, especially given the rarity and evolving nature of the fraudulent behavior. The highly imbalanced class levels of the fraudulent and non-fraudulent transactions make it a challenging classification problem to solve. This study investigates the effectiveness of machine learning models: Logistic Regression, XGBoost, and Multi-Layer Perceptron (Neural Network), evaluated under temporal retraining and fine-tuning scenarios using a publicly available, highly imbalanced dataset of European credit card transactions. The dataset includes 284,807 transactions, of which only 492 (0.172%) are labeled as fraudulent, making it a well-known example of an imbalanced classification …

Intergenerational Classification Of Reddit Comments Based On Slang And Emoji Usage, James T. Dracup

West Chester University Master’s Theses

The rapid evolution of language, driven by technological advancements, has created notable cultural gaps between generations, particularly in how they communicate. This gap is most apparent in the growing use of slang and emojis among younger generations. This study aims to explore whether Reddit comments can be classified by generation based on the usage of slang and emojis, the frequency of their use across generations, and how such features (slang and emojis) might influence the meaning of traditional language. Using Reddit’s API, we collected comments from four generational subreddits and applied various machine learning models, Naïve Bayes, Neural Networks, and …

Enhanced Network Anomaly Detection Using Machine Learning Models, Ousmane Barry

CCAC Theses and Dissertations

This dissertation investigates enhanced network anomaly detection using Machine Learning (ML) models. The study addresses two distinct classification problems: binary classification and multiclass classification. In the binary classification task, network traffic data is categorized as either "normal" or "abnormal," where abnormal includes all non-normal traffic. Leveraging the balanced nature of the dataset, this study develops optimized models that achieve consistently high classification performance. Key metrics, including precision, recall, and F1 scores, are used to ensure robust evaluation and reliable detection across all classes.

For multiclass classification, only classes present in both training and test datasets are included to ensure meaningful …

Csc36000 - Modern Distributed Computing Assignment 2, Saptarashmi Bandyopadhyay

Open Educational Resources

This assignment is designed to help the student identify and mitigate common errors in Distributed Computing such as race conditions and reaching consensus, as well as reflecting on how Distributed Computing concepts apply to their class project.