Digital Commons Network^™

Enhancing Smartphone Authentication By Integrating Decision-Making Model With Touch Pressure, Finger Location Data, And Advanced Cybersecurity Techniques, Maytham M. Hamood, Moceheb Lazam Shuwandy, Rawan Adel Fawzi Alsharida

Iraqi Journal for Computer Science and Mathematics

Smartphone authentication methods face significant challenges in achieving high accuracy, robustness, and usability within cybersecurity applications. Traditional methods, such as passwords and biometric recognition, often lack adaptability and are prone to high false-positive rates, impacting security and user acceptance. This study presents a novel hybrid approach incorporating machine learning (ML) and the Analytic Hierarchy Process (AHP) in a framework to facilitate decision-making abilities and improve smartphone authentication. A novel dataset was constructed based on 3D touch sensor data (pressure levels and spatial dynamics) collected from 20 participants performing tasks per task over sessions, where AHP was used to rank/choose relevant …

Supplementary Files For: "Structure Identification For High-Dimensional Data In The Vicinity Of Bear Lake", Ben Shaw, Haley Burger, Brennan Bean, Kevin Moon

Browse all Datasets

This report focuses on seven water quality measurements taken at 43 different depths on the Bear Lake for the months of June - November in the years 2018 - 2023. These measurements create a high-dimensional dataset on which we apply state-of-the-art machine learning (ML) techniques to look for low-dimensional structure in the data. A similar effort was made for weather measurements taken near the lake. Our analysis revealed that water quality measurements tend to cluster (i.e., group together) by year, while weather measurements tend to cluster by time of the year. This suggests that the structure observed in the water …

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 …

Leveraging Intrinsic Properties For Classification Of Coal Seams Towards Spontaneous Combustion Proclivity And Predicting Susceptibility Using Machine Learning: Smart And Sustainable Mining Approach, Siddhartha Agarwal, Pradeep K. Gautam, Yuhao Zou, Rishabh Dwivedi, D.C. Panigrahi, C. Dagli, Atul Singh

Journal of Sustainable Mining

Mine fires and other hazards caused by spontaneous coal combustion are a pervasive and longstanding issue in Jharia coalfields, India. This study proposes a novel approach to classify coal seams based on their propensity to spontaneous combustion using the intrinsic properties of 30 coal samples from different seams. This method eliminates the need for expensive and time-consuming experimental determinations of susceptibility indices (SI) such as crossing point temperature (CPT), critical air blast (CAB), and differential thermal analysis (DTA). All clustering models, viz. hierarchical, k-means, and multidimensional scaling, aptly classify coal seams into three categories: highly risky, medium risky, and low …

Analysis Of Computational Approaches To Cognitive Diagnosis, Andrew Toussaint

Masters Theses & Specialist Projects

Access to good education is crucial to the well-being of individuals as well as communities. Recent technological advancements in the field of computer science show promise of generating precise descriptions of student cognitive states regarding specified knowledge concepts through a process called cognitive diagnosis. This can facilitate the creation of more targeted lesson plans and more personalized educational software. Experiments were conducted to evaluate the performance of four computerized cognitive diagnosis models. The models include three existing models: Item Response Theory, Neural Cognitive Diagnosis, Knowledge Association Neural Cognitive Diagnosis, and a proposed model, Concept Agnostic Knowledge Evaluation, which was used …

Machine Learning-Driven Music Genre Recognition, Redeate Kidanue

All Undergraduate Theses and Capstone Projects

Music is a tool that has been integrated into society for thousands of years; it has influenced social aspects of life and has also aided in communication. Today we have various uses for music that go past our traditional uses for entertainment and self-expression. For example, music therapy has been seen to show improvements in patients with Alzheimer’s disease, depression, and PTSD. Additionally, music has played a role in political movements, demonstrating its emotional power. Social media relies heavily on the music industry as many social media posts include music either in the background, or as the forefront of posts. …

Quantifying Multidimensional Effects Of Physicochemical Parameters On Pfas Adsorption Using A Hybrid Response Surface Methodology-Machine Learning Approach, Harsh V. Patel, Jazmin Green, John Park, Stephanie Luster-Teasley Pass, Renzun Zhao

Engineering Management & Systems Engineering Faculty Publications

Per- and polyfluoroalkyl substances (PFAS) contamination has posed a significant environmental and public health challenge due to their ubiquitous nature. Adsorption has emerged as a promising remediation technique, yet optimizing adsorption efficiency remains complex due to the diverse physicochemical properties of PFAS and the wide range of adsorbent materials. Traditional modeling approaches, such as response surface methodology (RSM), struggled to capture nonlinear interactions, while standalone machine learning (ML) models required extensive datasets. This study addressed these limitations by developing hybrid RSM-ML models to improve the prediction and optimization of PFAS adsorption. A comprehensive dataset was constructed using experimental adsorption data, …

Comparative Analysis Of Machine Learning Models For Glioblastoma Survival., Muna Awel

All Graduate Theses, Dissertations, and Other Capstone Projects

Glioblastoma multiforme (GBM) remains one of the most lethal brain tumors, necessitating improved survival prediction models that integrate clinical and molecular data. This study develops a comprehensive machine learning pipeline leveraging TCGA-derived multi-omics datasets to predict binary survival outcomes. The framework integrates four classifiers Logistic Regression, Random Forest, XGBoost, and Support Vector Machine (SVM) and includes rigorous preprocessing with MCAR testing, KNN imputation, feature scaling, and hyperparameter optimization via GridSearchCV. SMOTE was applied to mitigate class imbalance and enhance model robustness for minority survival classes. Comparative performance analyses revealed Random Forest and XGBoost as top performers, achieving the highest recall …

Development Of A Design Tool For Tow-Steered Composite Structures With Machine Learning-Assisted Modeling, Bangde Liu

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Fiber-reinforced composites (FRCs) are widely used in aerospace, automotive, and other engineering applications due to their lightweight characteristics and superior mechanical properties. Traditional FRCs employ a fixed fiber orientation in each layer, and while different layup sequences can tailor performance, the mechanical properties remain spatially uniform. Tow-steered composites, which enable fibers to follow curvilinear paths, offer the potential for spatially varying stiffness and strength, improving structural performance.

This dissertation addresses key challenges in modeling and designing tow-steered composite structures, including the lack of commercial design tools, the high computational cost of design optimization, and the need to efficiently evaluate manufacturing …

Exploiting Artificial Intelligence And Optimization For Smart Agriculture, Jackson K. Butcher

Theses and Dissertations--Computer Science

Dynamic integration of Cyber-Physical Systems (CPS) and Artificial Intelligence (AI) has become a vital component for unlocking the potential of smart agriculture. Currently, limitations such as limited computational resources, poor network connectivity, and rigid treatment strategies stifle optimal agricultural outcomes. This creates a challenge of leveraging the capabilities of modern artificial intelligence to combat the natural and artificial constraints of the smart agriculture environment. The primary contribution of this thesis is the development of frameworks to alleviate the overhead data and computational demand for AI within smart agriculture settings. The first framework, iCrop+, utilizes TinyML and LoRa to guarantee high-precision …

Detection Of Data Leakage And Disruption Of Covert Timing Channel In Secure Drone Communication Using Machine And Deep Learning, Jonathan Walatkiewicz

Master's Theses and Doctoral Dissertations

The utilization of recreational drones has experienced a substantial increase in both the United States and globally. However, it is noteworthy that most drones, classified as Internet of Things devices, are produced with a limited security lifecycle. This study's findings are of paramount importance, as traditional computing exploits can be applied to drones, designating them as high- value targets. This study examines the detectability and disruptability of covert timing channel traffic in secure drones. The investigation aims to ascertain the effects of multiple interarrival times, distances ranging from 1 to 330 feet, various detection algorithms, and stream sizes between 32-bit …

Sam Altman, Openai, And The Importance Of Corporate Governance, Lawrence J. Trautman, Larry D. Foster Ii

Journal of Law, Technology, & the Internet

The sudden firing of CEO Sam Altman by OpenAI’s three independent directors highlights concerns about whether a leading AI company, susceptible to internal conflict and unclear accusations, can be trusted to develop a technology with far-reaching consequences for billions of people.

This paper examines how OpenAI’s board of directors terminated CEO Sam Altman’s employment, a decision that risked hundreds of highly skilled employees walking out the door, disrupted billions of dollars in value, and jeopardized critical knowledge of this important technology. It is hoped that corporate directors—whether at for-profit and non-profit companies—along with venture capitalists, technologists, and government regulators can …

Deep Learning-Based Ensemble Two-Step Classification Of Medical Images Using Cnn Architectures And Ensemble Methods, Noreliz Alorico

Master's Theses or Doctor of Nursing Practice

Breast cancer remains one of the most common cancers amongst women globally. Early detection is crucial for improving survival rates. While mammography is widely used and an effective imaging technique, it can sometimes yield false positive or false negatives. Mammogram interpretation is highly operator-dependent, introducing variability and the potential for diagnostic errors. Additionally, mammographic images have limitations, such as low contrast in breast tissue and overlapping structures that can obscure lesions or mimic abnormalities. These limitations can lead to unnecessary biopsies or delayed diagnosis. These challenges highlight the needs for advanced and data driven diagnostic tools to support and enhance …

Aviation Weather Forecasting Utilizing An Artificial Neural Network, Joshua Mote

Theses

Weather forecasting is critical to minimize risk and maximize efficiency for flying operations and is challenging due to the uncertainty involved in atmospheric changes. Operational area weather is becoming more critical with the growing reliance on air domain for transportation. This study investigates whether the forecasting accuracy using a Hybrid Long-Short Term Memory (LSTM) Artificial Neural Network (ANN) outperforms the San Antonio International Airport (KSAT) Terminal Aerodrome Forecast (TAF) by integrating diverse data sources – regional Meteorological Aerodrome Report (METAR), TAF, Avian Advisory System (AHAS) Data and geomagnetic activity K Index Data. When tested on one year of historical data, …

Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu

Psychology Faculty Publications

Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated …

Tedvil: Leveraging Transformer-Based Embeddings For Vulnerability Detection In Lifted Code, Gary Mccully, John Hastings, Shengjie Xu

Research & Publications

Ransomware and other malware inflict devastating financial and operational damage on organizations worldwide by exploiting deeply embedded, hard-to-detect vulnerabilities in their systems. Detecting these vulnerabilities in compiled code before malicious actors exploit them remains a critical challenge in cybersecurity. This research introduces TEDVIL (Transformer-based Embeddings for Discovering Vulnerabilities in Lifted Code), a novel framework which uses transformer-based embeddings to train neural networks to detect vulnerabilities in lifted code. The framework was implemented using bidirectional (BERT and RoBERTa) and unidirectional (GPT-1 and GPT-2) transformer-based models to generate embeddings for training Long Short-Term Memory (LSTM) neural networks to detect stack-based buffer overflows …

Optimal Manipulation Motion Action Planner Enabled By Physics Informed Neural Networks, Jonathon E. Slightam, Logan E. Beaver

Mechanical & Aerospace Engineering Faculty Publications

Autonomous robotic manipulation in unstructured environments faces many challenges and is hindered by capabilities that bridge the gap between perception and acting on the world. Action plans that are centric to object motion rather than end-of-arm tooling behavior may aid this. This paper presents an autonomous action planner for a feedback linearizeable system comprised of three base motions that can be leveraged on their own or in combination to give custom motion plans. The optimization routine for the three different types of motion are presented, which are integrated into physics informed neural networks. A component of this is the autonomy …

Evaluating Aspect-Based Sentiment Analysis In Healthcare Drug Reviews Across Machine Learning, Deep Neural Networks, And Transformer Models, Eun Soo Park

All Graduate Theses, Dissertations, and Other Capstone Projects

Sentiment analysis has become a critical area of research in Natural Language Processing (NLP), enabling insights from unstructured text. Within this field, Aspect-Based Sentiment Analysis (ABSA) plays a practical role in domains such as healthcare, where patients drug reviews often contain diverse opinions across multiple aspects, including overall comments, perceived benefits, and side effects. However, aspect-level classification remains challenging due to class imbalance, subtle sentiment expression, and the limitations of traditional models. This research investigates the performance of three modeling paradigms: traditional machine learning (SVM, SVC, and XGBoost), deep learning (CNN-BiLSTM), and transformer-based approaches (DistilBERT sentence-pair classification). Using the UCI …

Predicting Lung Cancer Severity Using Machine Learning Algorithms: Enhanced By Statistical Analysis, Esin Bilgin

Theses, Dissertations and Culminating Projects

Cancer is a serious and severe cause seen in every region of the world and severely affects the quality of life and life span. Among the various types of cancer, lung cancer is one of the most critical, having a fatal impact on life. While medical imaging techniques, laboratory results, and biomarkers play a significant role in diagnosis and prognosis, clinical studies are also crucial in monitoring the progression of cancer and identifying diagnostic and prognostic factors. The findings demonstrate satisfactory accuracy, and the analysis incorporates statistical data with machine learning techniques. These findings play a pivotal role in supporting …

Predicting Early Customer Inactivity In The Banking Sector Using Machine Learning: A Churn Prevention, Ivana Mc Fadden

ICT

Customer churn, when customers stop using a company’s services, is a challenge for the banking sector (Singh et al., 2023). High churn rates often signal poor customer experiences, resulting in revenue losses and increased costs to obtain new clients. Goyal and Srivastava (2015) stress that fostering loyalty through exceptional service and understanding customer needs is important for long-term retention.

This project aims to predict early customer inactivity, an indicator of churn, by using machine learning. Early identification of at-risk customers will allow banks to apply targeted interventions, reduce acquisition costs, and improve customer satisfaction (Singh et al., 2023). By analysing …

Using Machine Learning To Predict Credit Card Fraud, Pedro Henrique Das Chagas Morais

ICT

Credit card fraud poses a significant challenge to financial institutions, leading to substantial financial losses and declining customer trust. This project develops and evaluates machine learning models to detect fraudulent credit card transactions using a large, realistic synthetic dataset. Following data preprocessing, exploratory analysis, and class-balancing using SMOTE, four supervised models—Logistic Regression, Decision Tree, Random Forest, and XGBoost—were trained and compared. Performance was assessed using metrics suited to imbalanced classification, including AUC, Recall, Precision, F1-score, and Average Precision. Results show that XGBoost, particularly after hyperparameter optimisation, delivered the strongest performance (AUC 0.99, Recall 0.83, AP 0.70), outperforming other models and …

Fake News Detection Using Machine Learning Models., Anne Higgins

ICT

This project investigates the use of machine learning to detect fake news, addressing the societal and political challenges posed by the widespread dissemination of false and misleading information. Using automated classification techniques, the project analyses news content to predict the likelihood of intentional deception. The methodology follows the CRISP-DM framework, encompassing data preparation, model development, and evaluation. By leveraging machine learning, the study aims to support organisations, governments, and digital platforms in mitigating misinformation, while also considering ethical, interpretability, and strategic implications. The findings provide actionable insights for enhancing content moderation and reducing the influence of disinformation in digital media …

Data Analysis For Maintenance Reliability., Romulo Menezes Santos

ICT

This project explores the application of predictive maintenance (PdM) in modern manufacturing, highlighting its strategic role in reducing downtime, optimising resources, and improving operational efficiency. Traditional reactive and preventive maintenance approaches are insufficient for Industry 4.0 environments, where machine failures can cause significant financial and operational losses. By leveraging sensor data, historical performance records, and machine learning techniques, PdM enables early detection of potential equipment failures, allowing maintenance teams to act proactively. The approach not only enhances operational reliability and safety but also supports strategic decision-making, cost control, and competitive advantage in globalised manufacturing contexts.

Tour Demand Forecasting In Ireland: Development And Evaluation Of Classical, Deep Learning, And Hybrid Models, Ruben Elias Charleston Montfort

ICT

Tourism plays a significant role in global economies by supporting employment, infrastructure, and national development. As international travel continues to grow, accurate tourism demand forecasting has become increasingly important for effective planning and decision-making. In Ireland, tourism is a key economic sector attracting millions of visitors annually. For tour operators such as Irish Day Tours, reliable demand forecasting is essential for optimizing logistics, resource allocation, marketing strategies, and customer satisfaction. Advances in machine learning and deep learning techniques offer new opportunities to improve forecasting accuracy and support data-driven decision-making within the tourism industry.

Generative Ai's Copyright Enigma: A Comparative Study Of Fair Use And Fair Dealing, Taysir Awad

IP Theory

At the dawn of this decade, generative Artificial Intelligence (AI) models were at the apogee of modern science and technology. Their emergence introduced the world to a new paradigm of creativity and innovation, where machines can synthesize art, literature, and design with unprecedented sophistication, blurring the boundaries between human ingenuity and algorithmic computation. These models have the capacity to regenerate Oscar Wilde with the depiction of Ansel Adams, rewrite Harry Potter with William Shakespear’s proverbial tongue, and redesign St. Peter’s Basilica with Gothic arches, Seljuk carved stones, and an Antoni Gaudi roof architecture, relocated in the heart of New York …

Estimating Pedestrian Crossing Times At Scramble Crossings Via Machine Learning And Agent-Based Modeling, Sho Takami

CURE Proceedings

Scramble crosswalks differ from conventional crosswalks in their ability for pedestrians to cross diagonally. This research compares the average crossing times and investigates the walking behaviors that pedestrians adopt to produce the speediest times in the two crosswalk configurations. Identification of the most efficient set of walking behaviors is done through an agent-based model, whereas producing polynomials relating crossing times to the most prominent walking behaviors is done through regression algorithms in machine learning. With the combination of these two approaches, it is revealed that pedestrians must adopt a relaxed walking style to make each crosswalk configuration efficient. Additionally, between …

Training Set Augmentation And Harmonization Enables Radiomic Models To Detect Early Onset Of Lung Cancer, Claire Huchthausen, Menglin Shi, Gabriel L.A. Sousa, James Larner, Einsley Janowski, Jonathan Colen, Krishni Wijesooriya

Data Science Faculty Publications

Radiomics-based machine learning models have the potential to detect lung cancer at inception from CT scans and transform patient outcomes. Low malignancy rates in early-development pulmonary nodules (PNs) and variable image acquisition hinder development of clinically applicable radiomics-based early detection models. To address these challenges, we augmented training using later-development PNs and harmonized for acquisition effects. We first trained machine learning models to predict PN malignancy using radiomic features from scans of early-development benign and malignant PNs (n = 187) harmonized using ComBat. Observing near-chance performance, we augmented training with later-development benign and malignant PNs (n = 225). We evaluated …

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, …

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 …

Ecology-Driven Machine Learning For Predicting Risk Of Spread For Aquatic Invasive Species., Leif Eric Howard

Graduate Student Theses, Dissertations, & Professional Papers

Effective management of aquatic invasive species (AIS) requires predictive tools that leverage environmental and ecological data to forecast spread into uninvaded areas. However, species distribution models (SDMs) often struggle to capture key invasion processes such as dispersal, propagule pressure, and biotic interactions. This thesis integrates ecological, dispersal, and environmental predictors within a machine learning framework to enhance AIS risk prediction and model transferability across space.

In Chapter 2, 61 novel predictors were developed for 2,203 Minnesota lakes to model zebra mussel (Dreissena polymorpha) occurrence, incorporating variables related to propagule pressure (e.g., boat visitation), community composition (e.g., fish species …