Computer Sciences Commons^™

Reducing Data Requirements In Polymer Science: Deep Neural Networks For Predicting Surface Tension Of Copolymer Compatibilizers, Md Mushfiqul Islam

USF Tampa Graduate Theses and Dissertations

In polymer chemistry, compatibilization involves adding a substance often a block or graft copolymerto stabilize polymer blends that would otherwise not mix well, leading to rough structures and weak me- chanical properties. Compatibilizers improve miscibility and reduce interfacial tension, which is critical for applications such as mixed-waste polymer recycling. Sequence-controlled polymers offer unique potential by combining the tunable chemistry of synthetic polymers with the precise, function-driven design of biological macromolecules, but unlike proteins, they lack large, evolution-shaped datasets to guide discovery. This research develops a deep learning framework to predict the surface tension of sequence-controlled copolymer compatibilizers across varying concentrations. …

Advancing Power System Reliability And Security With Efficient And Resilient Graph Neural Network Frameworks, Seyed Hamed Haghshenas

USF Tampa Graduate Theses and Dissertations

Enhancing the reliability and security of smart grids is critical for ensuring their seamless operation and resilience against disruptions. The increasing integration of distributed energy resources, advanced measurement devices, and cyber-physical elements introduces both opportunities and challenges for grid management. While these advancements provide enhanced visibility and operational control, they also expose the grid to vulnerabilities from cyber-physical stresses, such as cyber-attacks, equipment failures, and fluctuating power demands. Traditional methods for reliability assessment and threat detection often rely on model-based approaches that struggle to adapt to the complexity and dynamic nature of modern smart grids. These limitations necessitate novel data-driven …

Mosquito Classification And Explainability From Image Data Via Deep Learning Techniques, Farhat Binte Azam

USF Tampa Graduate Theses and Dissertations

According to the World Health Organization (WHO), mosquitoes are the deadliest animals on Earth, responsible for more human deaths annually than any other species. Mosquito-borne illnesses continue to pose severe risks to global health. In 2015 alone, there were an estimated 214 million malaria cases worldwide. Similarly, a 2016 report from the Centers for Disease Control and Prevention (CDC) revealed that Puerto Rico’s Department of Health received over 62,500 suspected cases of Zika, with 29,345 confirmed positive cases. In 2019, Southeast Asia experienced its worst dengue outbreak in recorded history. Of the approximately 4,500 mosquito species distributed across 34 genera, …

Myfoodrx: A Personalized Food-As-Medicine Mhealth Application For Food-Insecure Adults With Chronic Conditions, Jay Hiteshkumar Jariwala

USF Tampa Graduate Theses and Dissertations

Food insecurity (FI) remains a persistent public health challenge in the United States, disproportionately affecting underserved populations and contributing to higher rates of chronic conditions such as diabetes, hypertension, and obesity. Traditional Food-as-Medicine programs have emerged as promising interventions, but often follow a generalized, non-personalized approach that limits long-term effectiveness, especially among diverse, high-risk communities. This thesis presents MyFoodRx, a personalized mobile health (mHealth) application designed to address the intersection of food insecurity and chronic disease through tailored nutritional guidance, real-time pantry integration, and adaptive educational content.

Developed through a User-Centered Design (UCD) process, MyFoodRx leverages a modular client–server architecture, …

The Orange Glow In The Sunshine State: Three Visions Of Plato In Florida (1970-1990), Ryan Mcgahan

USF Tampa Graduate Theses and Dissertations

The PLATO network, a collection of mainframe computers, terminals, and educational software, has received an increasing amount of scholarly attention in the last decade as a social precursor to the modern internet. Existing histories have neglected to investigate the ways in which PLATO and its related business enterprise worked to accelerate the shift in university governance away from classical liberal ideas centering the public good and towards a more profit-centered neoliberal rationality. By tracing the rise of PLATO in Florida universities, this paper argues that PLATO aided and was aided by the shifting priorities of American universities in the 1970s …

Barriers To Machine Learning Adoption In Regulated Electric Utilities, Donald R. Shiflet Jr.

USF Tampa Graduate Theses and Dissertations

Machine learning (ML) technologies have the potential to revolutionize regulated electric utilities by improving operational efficiency, enabling predictive maintenance, and optimizing energy management. Despite these advantages, the adoption of ML in this sector lags other industries due to technical, organizational, and regulatory barriers. This research, grounded in the Technology-Organization-Environment (TOE) framework, explores these barriers to uncover actionable solutions for integration. The study identifies key challenges, including explainability, cybersecurity, workforce resistance, and regulatory ambiguity to ML adoption in electric utilities. Utilizing an exploratory qualitative methodology, this approach integrates insights from the literature and industry interviews to rank barriers by frequency, severity, …

Beyond The Hype: The Fundamental Challenges Of Machine Learning-Based Android Malware Detection In Cybersecurity, Guojun Liu

USF Tampa Graduate Theses and Dissertations

Machine learning (ML) algorithms have achieved remarkable success across various domains, including cybersecurity. Inspired by these advancements, the academic security community has explored numerous ML-based approaches for Android malware detection. While ML holds significant promise in this domain, its practical deployment faces substantial challenges, including data collection, feature selection, app representation across different models, performance instability across datasets, and inherent limitations of learning-based malware detection. These challenges can lead to overly optimistic detection results and weaken the reliability of malware detection frameworks.

Android malware detection has been extensively studied using both traditional ML and deep learning (DL) approaches. Although many …

An Exploratory Analysis Of Automated Deception Detection For Mental Health Applications, Sayde Leya King

USF Tampa Graduate Theses and Dissertations

Deception in mental health settings can undermine therapeutic relationships, compromise treatment efficacy, and impact patient outcomes. Yet, research shows that mental health clinicians often perform no better than chance at detecting deceptive behavior in therapy. Automated deception detection, leveraging artificial intelligence (AI) and multimodal behavioral cues—such as eye gaze, body gestures, and facial expressions—offers a promising alternative. However, most existing research focuses on high-stakes legal contexts, limiting its applicability to mental health settings.

This dissertation addresses this gap by pursuing three key research objectives using a mixed-methods approach. First, we investigate mental health clinicians’ perspectives on AI-assisted deception detection through …

Efficient Methods And Algorithms For Analyzing Stochastic Systems, Mohammad Ahmadi

USF Tampa Graduate Theses and Dissertations

This dissertation addresses the challenges of stochastic analysis of safety-critical systems with biological components, where unexpected behavior can lead to catastrophic events. Two fundamental challenges hinder the analysis of such systems: their typically large or infinite state spaces, and the extreme rarity of error states of interest. While Monte Carlo simulation can analyze biochemical systems without storing the state space, accurately estimating rare event probabilities becomes computationally prohibitive. Conversely, probabilistic model checking excels at analyzing extremely low probability events but becomes impractical for systems with large or infinite state spaces due to memory constraints.This work proposes two main contributions to …

Enhancing State-Of-The-Art Motor Imagery Classification With Reinforcement Learning, Anton Shepelev

USF Tampa Graduate Theses and Dissertations

One of the key obstacles to the rapid adoption of non-invasive Brain-Computer Interfaces (BCIs) for Motor Imagery (MI) is the low signal-to-noise ratio, and the substantial data requirements which can be mentally taxing for users. EEGNet, a compact Convolutional Neural Network (CNN), has long been considered the state-of-the-art (SOTA) for MI classification, demonstrating strong performance even with limited data. However, recent studies advocate for integrating Deep Reinforcement Learning (RL) to further enhance classification accuracy by dynamically optimizing feature extraction and decision-making processes. Despite this potential, practical implementations remain scarce due to challenges in stabilizing RL training and adapting it to …

Integrative Multi-Omics And Clinical Data Analysis For Predicting Recurrence And Survival In Uterine Cancer, Varun Sai Raigir

USF Tampa Graduate Theses and Dissertations

The prediction of uterine cancer recurrence is very important for assisting women in reducing the cancer risks and also for the growing field of personalized medicine. The primary aim of this thesis is to investigate the integration of various omics data alongside clinical and therapeutic information to predict survival in uterine cancer. The combination is very important for understanding the risk factors, including clinical aspects, genetics, and the treatment schedule, in order to prescribe the appropriate way to reduce the risk of recurrence, make clinical interactions easier, and enhance personalized patient care. This study utilizes the publicly accessible TCGA dataset, …

Automated Data Analysis For Concussion Patient Records: A Flutter-Based Desktop Application, Fhaheem Tadamarry

USF Tampa Graduate Theses and Dissertations

Concussions are a prevalent and complex medical condition requiring careful clinical assessment and data-driven insights for effective management. This thesis presents the development of an automated data analysis system for concussion patient records, integrating Flutter-based desktop application development with SQL-driven data processing. The system provides a streamlined, interactive interface for clincians and researchers to upload, visualize, and analyze patient data efficiently.

The proposed solution automates data cleaning, preprocessing, and statistical analysis, ensuring robust and reliable insights into demographic, clinical, and recovery-related factors. Key analyses include sex-based differences injury mechanisms, prior head injury impact, mood disorder correlations, and time-to-treatment variations. The …

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 …

The Asset Management Optimization Engine: An Ai And Machine Learning Model Approach To Pavement Asset Management, Matt Versdahl

USF Tampa Graduate Theses and Dissertations

While state Departments of Transportation (DOT) face major funding challenges, the need to find optimal ways to preserve and maintain pavement assets remains. Asset management employs a lowest cost lifecycle method to analyze asset costs and determine the best investment strategies to preserve it throughout its lifecycle. As new technology emerges, so do opportunities to leverage it. DOTs collect a significant amount of performance data on pavement and use it to decide how to keep it in a state of good repair. The literature in this area focuses on engineering techniques applied to treatment strategies. This dissertation research focuses on …

Context And Interpretability In Affective Computing Applications, Saandeep Aathreya Sidhapur Lakshminarayan

USF Tampa Graduate Theses and Dissertations

Affective Computing (AC) is a subdomain of AI that primarily deals with recognizing and interpreting human emotions. This field inherently intersects with psychological studies, as the comprehension of human emotions and behaviors necessitates an understanding of their underlying cognitive processes. One such concepts that lends itself from psychology is context. Roughly speaking, context in AC is defined as any meta information (e.g., environment) that can be utilized to describe the interaction between a user and a model to solve a particular application (e.g., emotion recognition). This doctoral dissertation comprises of two distinct yet interconnected components (Part I and II), the …

Applications Of Linear Discriminant Analysis In The Biomechanics Of Anterior Cruciate Ligament Injury, Taofeek Braimoh

USF Tampa Graduate Theses and Dissertations

Anterior cruciate ligament (ACL) injury is a prevalent and significant concern in sports medicine, often resulting in long-term consequences that affect quality of life. Despite advancements in medical technology, current methods for addressing the problem of ACL injuries remain inefficient, subjective, and limited in their predictive power. This study explores the potential of Linear Discriminant Analysis (LDA), a supervised machine learning (ML) technique, to improve the diagnosis and risk profiling of ACL injuries. This research aims to create an objective, effective, and precise technique for determining the risk of ACL injuries by examining key biomechanical, physical, and demographical features. The …

“Smart Trap”: A Portable Device For Real-Time Mosquito Capturing And Classification Using Image-Based Analysis, Fahim Rahman

USF Tampa Graduate Theses and Dissertations

Capturing mosquitoes in real-time and taking high-quality images for classification with state-of-the-art methods is not only time-consuming but also expensive. Sometimes even carefully controlled environments and experimental setups fail to capture living mosquitoes. Catching live mosquitoes is necessary to be able to study aspects of their physiology and behavior that cannot be investigated by collections of resting mosquitoes and dead specimens, and to help estimate the local population numbers. My thesis introduces a “Smart Trap”, a small portable device that can attract mosquitoes in real-time, capture them, take high-quality images with dual cameras, and store those images in the cloud. …

Llms In Network Intrusion Detection – A Comprehensive Analysis, Sudharshan Balaji

USF Tampa Graduate Theses and Dissertations

Network Intrusion Detection Systems (NIDS) play a critical role in identifying and mitigating malicious activities within computer networks. With the rapid evolution of natural language processing (NLP), Large Language Models (LLMs) have emerged as transformative tools across various domains. LLMs, such as OpenAI’s GPT series and Meta’s LLaMA models,have demonstrated remarkable performance in tasks like language generation, reasoning, and classification. Their ability to understand and process vast amounts of data has enabled groundbreaking advancements in areas like healthcare, finance, and cybersecurity. Recent trends highlight their potential to handle unstructured data, perform complex reasoning, and adapt to a wide range of …

Relationship-Influenced Cyber Hygiene (Rich) In Community Banks, Monte L. Ward

USF Tampa Graduate Theses and Dissertations

This dissertation is a research study that introduces the theoretical model of Relationship-Influenced Cyber Hygiene (RICH) through its investigation of the phenomenon the researcher experienced: how the interpersonal relationship between top management and cybersecurity personnel within smaller community banks influences the cyber hygiene of top management. Due to the value proposition of community banks to their clients and the limited budget of smaller institutions for investment in cybersecurity controls and initiates, community banks need additional strategies to achieve stronger cyber hygiene, especially as it relates the greatest weakness in most cybersecurity infrastructures—the human element. This study identifies and addresses a …

Improving College Students’ Attention Retention With A Brain-Controlled Drone Simulation, Ji Won Bae

USF Tampa Graduate Theses and Dissertations

In contemporary society, individuals are continuously exposed to a plethora of stimuli, which can precipitate distractions and impede cognitive performance in tasks such as professional work and academic studies. This investigation proposes an innovative approach aimed at enhancing attentional focus through the utilization of Brain-Computer Interfaces (BCI). BCIs represent advanced methodologies for deciphering neural activity patterns. Specifically,electroencephalography (EEG), a technique for monitoring the brain's electrical signals, serves as the foundation for BCI applications. EEG analyses reveal distinctive wave patterns indicative of states of concentration, vigilance, and cognitive engagement. Consequently, BCIs hold promise for the real-time assessment of users' attentional states. …

Robotic Multi-Object Grasping From A Pile: Techniques And Algorithms For Enhanced Dexterity, Tianze Chen

USF Tampa Graduate Theses and Dissertations

As robots become increasingly integrated into real-world applications such as warehousing, fulfillment centers, and manufacturing, the need for efficient and adaptable robotic systems grows. One of the key challenges is enabling robots to grasp multiple objects simultaneously, as this significantly boosts the efficiency of tasks like batch picking, sorting, and object transferring, reducing both time and energy consumption. This dissertation presents a comprehensive multi-object grasping (MOG) pipeline that includes pre-grasp selection, end-pose selection, grasping synergy calculation, and a data-driven model for estimating the number of objects being grasped. Central to this work is the development of the Experience Forest structure, …

Developing Robotic Task Planning Methods For Diverse Real-World Challenges, Md Sadman Sakib

USF Tampa Graduate Theses and Dissertations

The deployment of robotic systems across various domains has expanded significantly, with applications ranging from domestic tasks, such as cleaning and cooking, to industrial operations requiring precision and automation. These advancements highlight the critical importance of effective task planning in ensuring that robots can perform tasks safely, efficiently, and autonomously. However, task planning in robotics faces challenges related to generalization, executability, flexibility, and limitations in existing knowledge bases.

This dissertation addresses these challenges through innovative strategies aimed at enhancing robotic task planning and execution. We first focus on adapting to unknown scenarios by utilizing the Functional Object-Oriented Network (FOON) to …

Exploring Factors That Influence Artificial Intelligence Adoption In Banks And Credit Unions, Vijaya S. Tumma

USF Tampa Graduate Theses and Dissertations

The importance of Artificial Intelligence (AI) is exploding in the banking sector, fueled by enhanced productivity, improved efficiencies, and personalized services to the consumers. For credit unions, the adoption of AI technologies presents opportunities and challenges. This research explores the factors influencing AI adoption in the banking sector through the lens of Unified Theory of Acceptance and Use of Technology (UTAUT) framework. This study aims to explore the influence of key aspects of UTAUT model, Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) on the intention of AI adoption among credit unions, banks, and their …

Coding Connections: Exploring Relationships Between Computer Science Learning And Mathematics Achievement In Secondary Education, Bradley Hayes

USF Tampa Graduate Theses and Dissertations

This dissertation in practice explores the intersection of computer science education, specifically computational thinking and programming, with mathematics achievement among 15-year-old students in selected English-speaking countries. The research addresses a gap in understanding whether skills developed through computer science can positively influence mathematics performance by assessing the extent to which learning in computer science transfers to mathematics.

To achieve this, a quantitative methodology was employed, incorporating pilot study data from a single school and large-scale survey data from the Programme for International Student Assessment (PISA) 2022. The analysis assessed the correlation between regular participation in programming activities and mathematics attainment, …

Balancing Context And Clarity Through Visualizations For Better Decision-Making, Bhavana Doppalapudi

USF Tampa Graduate Theses and Dissertations

In this era of a data driven world, the effective communication of data through visualizations is pivotal for converting complex information into accurate insights. Effective visualizations not only enhance users' comprehension of complex data but also induce trust, leading to better decision-making. This dissertation explores methods to improve trust in visualizations by providing additional context and enhancing their clarity, ensuring appropriate data interpretations and better decisions from users.

The work in the dissertation begins by examining the impact of scatterplots combined with statistical metrics such as accuracy on users' trust and decision-making focused on recommender systems. In addition, we show …

Analyzing And Extending Machine Learning Frameworks On High Risk Domains, Chengbin Hu

USF Tampa Graduate Theses and Dissertations

Machine learning (ML) has become a transformative force in high-risk domains such as genomics and cybersecurity, where accurate predictions and robust defenses are essential. This dissertation advances ML frameworks in these areas by developing methods to enhance predictive power in health applications and assess vulnerabilities in machine learning systems.

In the genomics field, the work addresses challenges in Non-Invasive Prenatal Testing (NIPT) of monogenic disorders by proposing a deep learning model that reconstructs the fetal genome using maternal plasma cell-free DNA (cfDNA) and parental whole-genome sequencing (WGS) data. This model achieves high accuracy in single nucleotide variation (SNV) prediction, surpassing …

Telu Activation Function For Fast And Stable Deep Learning, Alfredo Fernandez

USF Tampa Graduate Theses and Dissertations

We propose the Hyperbolic Tangent Exponential Linear Unit (TeLU), a neural network hidden activation function defined as $TeLU(x)=x \cdot tanh(e^x)$. TeLU’s design is grounded in the core principles of key activation functions, achieving strong convergence by closely approximating the identity function in its active region while effectively mitigating the vanishing gradient problem in its saturating region. Its simple formulation enhances computational efficiency, leading to improvements in scalability and convergence speed. Unlike many modern activation functions, TeLU seamlessly combines the simplicity and effectiveness of ReLU with the smoothness and analytic properties essential for learning stability in deep neural networks. TeLU’s ability …

Exploring Llm Integration And Its Influence On Agent Behavior And Productivity In Insurance Call Centers, Gerardo L. Wibmer Gonzalez

USF Tampa Graduate Theses and Dissertations

This study investigates the theoretical impacts of supportive AI tools, specifically Large Language Models (LLMs), on agent behavior and communication dynamics in call centers. While technological advancements have streamlined operations, limited research addresses the indirect ways these tools influence agent behavior. Using frameworks like context switching—the cognitive shift required when external stimuli prompt attention shifts—and the Hawthorne effect, where perceived observation modifies behavior, we examine how LLMs shape communication patterns. In call centers, this context switching occurs indirectly as agents adapt to AI note-taking features, whereas in industries like Architecture, Engineering, and Construction (AEC), automation tools prompt more immediate changes …

On The Role Of Prediction In Streaming Hierarchical Learning, Ramy Mounir

USF Tampa Graduate Theses and Dissertations

In today's world, AI systems need to make sense of large amounts of data as it unfolds in real-time, whether it's a video from surveillance and monitoring cameras, streams of egocentric footage, or sequences in other domains such as text or audio. The ability to break these continuous data streams into meaningful events, discover nested structures, and predict what might happen next at different levels of abstraction is crucial for applications ranging from passive surveillance systems to sensory-motor autonomous learning. However, most existing models rely heavily on large, annotated datasets with fixed data distributions and offline epoch-based training, which makes …

Enhancing Post Silicon Visibility Using Language Modelling Techniques, Nathaniel Joseph Fender

USF Tampa Graduate Theses and Dissertations

The debugging phase is a critical time in the development of a new system on chip product. Specifically, the post-silicon validation phase is one of the most important, as it allows engineers to test the behavior of a device in a real world setting. However, the issue of noisy or incomplete data is a frequent issue when attempting to debug an SoC design during this step. This thesis examines the utility of utilizing machine learning models for the purpose of repairing missing data in a system trace. We trained various models using the transformer architecture to identify missing data in …