Computer Sciences Commons^™

Ransomware In Healthcare: Threats, Impacts, And Mitigation Strategies, Mackenzie Dotson, Kasi Gorli, Alberto Coustasse

Management Faculty Research

Excerpt: The growing digitalization of healthcare has exposed hospitals to significant cybersecurity threats, particularly ransomware attacks. The Health Sector Cybersecurity Coordination Center (HC3) reported that as of mid-2024, there were 730 cyber-attacks worldwide against healthcare institutions, with 530 targeting the U.S. (AHA, 2024). Half of these incidents involved ransomware, a type of malware that restricts access to critical data until a ransom is paid (HHS, 2021). Hospitals are attractive targets for cybercriminals due to their essential role in patient care. Cybercriminals exploit vulnerabilities in hospital networks, often causing severe operational and financial damage. Factors such as understaffed IT teams, outdated …

Cyber Threats In Healthcare: The Ransomware Epidemic, Mackenzie Dotson, Kasi Gorli, Alberto Coustasse

Management Faculty Research

In this presentation, we will delve into the growing ransomware crisis in healthcare, examining how these cyber threats disrupt hospital operations, jeopardize patient safety, and impose significant financial burdens. From understanding how ransomware infiltrates hospital systems to exploring real-world case studies, we will uncover the devastating impact of these attacks. Our discussion will also focus on mitigation strategies, cybersecurity best practices, and policy recommendations to safeguard healthcare institutions from future threats.

Ai Models By Boodlebox: Purpose-Built Intelligence, Kyle Horn

SACAD: Scholarly Activities

Generative AI has transformed the way we interact with technology, enabling dynamic and intelligent conversations through AI-driven bots. This project explores my experience with BoodleBox, a platform that hosts AI chatbots, offering users access to leading AI models such as ChatGPT, Gemini, DALL·E, and DeepSeek. Through the FHSU Generative AI Initiative, I was granted access to experiment with these models and create my own custom AI bot tailored to specific needs. This poster highlights the process of developing a custom bot, including defining instructions, enforcing rules, and sharing the bot for others to use. Additionally, it discusses the background of …

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 …

Robust Spacecraft Autonomy For Deep Space Exploration In Special Euclidean Group Se(3), Matthew Wittal

Doctoral Dissertations and Master's Theses

Over the past half-century, humanity has gained extensive experience conducting manned spaceflight near Earth. Arguably, "near Earth" could even include the Moon — the most distant destination humans have reached. However, "near" in this work primarily refers low Earth orbit (LEO). One could argue that we have not truly left Earth since the Apollo, as spacecraft in some LEOs remain subject to atmospheric drag thus emphasizing their continued connection to Earth's immediate environment. Reflecting on this, it becomes clear that humanity has largely remained bound to Earth’s immediate vicinity since the Apollo missions reached the Moon. However, that is set …

Addressing Equity Issues In Elementary Computer Science Education: Knowns, Unknowns, And Implications For Future Work, Mike Karlin, Yin-Chan Janet Liao, Swati Mehta, Afreen Iqbal, Mahya Minaiy, Minhye Son, Jessica Pandya

Journal of Computer Science Integration

In 2016, a national coalition of stakeholders released the K-12 computer science (CS) framework. In the time since, there has been an increased push at the local, state, and national level to integrate CS knowledge and skills into K-12 education. Despite this push, significant equity issues exist within the field. While growing research has been done on CS equity issues at the high school level, we know these equity gaps often begin to emerge in elementary school where less is known. Therefore, we conducted a systematic literature review to better understand and explore the elementary CS equity research landscape from …

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 …

Evaluating A Large Language Model’S Accuracy In Chest X-Ray Interpretation For Acute Thoracic Conditions, Adam M. Ostrovsky

SKMC Student Presentations and Publications

BACKGROUND: The rapid advancement of artificial intelligence (AI) has great ability to impact healthcare. Chest X-rays are essential for diagnosing acute thoracic conditions in the emergency department (ED), but interpretation delays due to radiologist availability can impact clinical decision-making. AI models, including deep learning algorithms, have been explored for diagnostic support, but the potential of large language models (LLMs) in emergency radiology remains largely unexamined.

METHODS: This study assessed ChatGPT's feasibility in interpreting chest X-rays for acute thoracic conditions commonly encountered in the ED. A subset of 1400 images from the NIH Chest X-ray dataset was analyzed, representing seven pathology …

The Present And Future Of Ai: Ethical Issues And Research Opportunities, Ankita Srivastava, Marco Marabelli, Danielle Blanch-Hartigan, Jeffrey Moriarty, Evan Carey

Computer Information Systems Faculty Publications

Healthcare is currently a fast-changing industry with AI and generative AI (GenAI) playing a prominent role in the transformation of clinical as well as managerial practices. Clinical practices involve AI to diagnose diseases and develop new drugs and compounds, while managerial practices concern AI-supporting processes such as billing patients and insurance companies, handling electronic medical records, and supporting remote connections with patients, increasingly using virtual and augmented reality. Yet, all these opportunities offered by AI come with challenges involving potential ethical issues, such as discrimination, bias, lack of accessibility, and privacy issues. In March 2024, we organized a panel with …

Info-Cels: Informative Saliency Map-Guided Counterfactual Explanation For Time Series Classifications, Peiyu Li, Omar Bahri, Pouya Hosseinzadeh, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi

Computer Science Student Research

As the demand for interpretable machine learning approaches continues to grow, there is an increasing necessity for human involvement in providing informative explanations for model decisions. This is necessary for building trust and transparency in AI-based systems, leading to the emergence of the Explainable Artificial Intelligence (XAI) field. Recently, a novel counterfactual explanation model, CELS, has been introduced. CELS learns a saliency map for the interests of an instance and generates a counterfactual explanation guided by the learned saliency map. While CELS represents the first attempt to exploit learned saliency maps not only to provide intuitive explanations for the reason …

The Impact Of Artificial Intelligence On Quality Of Higher Education, Pragati K. Rouniyar

Honors Thesis

Artificial Intelligence (AI) is redefining higher education, captivating scholars with its promise to personalize learning and streamline institutions. However, underneath this assurance exists a network of ethical challenges, disparities in equity, and inquiries regarding academic integrity that require our focus. In pursuit of this goal, this research employs a mixed-methods strategy—through the implementation of surveys and semi-structured interviews—to investigate the transformative effects of AI on higher education, concentrating on its repercussions for teaching techniques, learning results, and institutional processes. This study’s findings indicate that AI can personalize educational experiences to meet individual needs, ease course administrative workload, and assist with …

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 …

Enhanced Detection Of Apt Vector Lateral Movement In Organizational Networks Using Lightweight Machine Learning, Mathew Nicho, Oluwasegun Adelaiye, Christopher D. Mcdermott, Shini Girija

All Works

The successful penetration of government, corporate, and organizational IT systems by state and nonstate actors deploying APT vectors continues at an alarming pace. Advanced Persistent Threat (APT) attacks continue to pose significant challenges for organizations despite technological advancements in artificial intelligence (AI)-based defense mechanisms. While AI has enhanced organizational capabilities for deterrence, detection, and mitigation of APTs, the global escalation in reported incidents, particularly those successfully penetrating critical government infrastructure has heightened concerns among information technology (IT) security administrators and decisionmakers. Literature review has identified the stealthy lateral movement (LM) of malware within the initially infected local area network (LAN) …

Advanced Techniques In Symmetric Key Cryptanalysis, Debasmita Chakraborty

Doctoral Theses

Symmetric key cryptographic primitives are essential tools used extensively in daily digital interactions. These primitives are mainly designed to provide three key services: ensuring data confidentiality, maintaining data integrity, and verifying the authenticity of data sources. The primary types of symmetric key primitives that deliver these services include block ciphers, stream ciphers, hash functions, message authentication codes, and authenticated encryption with associated data. This thesis mainly explores the security analysis of hash functions, several block ciphers, and stream ciphers using some advanced cryptanalytic techniques. We begin by examining the collision security of a hash function, specifically under the assumption that …

High Flyer, Christina Clements

SPARK Symposium Presentations

I created a game for my game development class. It is a plane flying game where you have to move up and down to avoid missiles that shoot at you randomly.

Role Of Eye-Tracking Technology And Software Algorithms In Enhancing Adhd Detection And Diagnosis: A Systematic Literature Review, Lauren E. Perkins

Honors College Theses

This systematic literature review explores the role of eye-tracking technology and software algorithms in enhancing the detection and diagnosis of ADHD. ADHD, a neurodevelopmental disorder affecting both children and adults, is traditionally diagnosed through behavioral assessments, which may lack objectivity. Recent studies suggest that eye-tracking, specifically focusing on saccades, fixations, and blink rates, offers the potential for more accurate and objective measures of ADHD. The review examines clinical trials, observational studies, and machine learning research to assess the correlation between ADHD and eye movement patterns. Results indicate that individuals with ADHD exhibit distinct eye movement patterns, which can be quantified …

Alterity And Kinship: Co-Writing Posthumanist Speculative Nonfiction With Ai, Jeffrey Bardzell, Maliheh Ghajargar

Engineering Faculty Articles and Research

As a response to the climate crisis, scholarly literature has introduced new theoretical perspectives, such as posthumanism, which seek to reimagine the relationships between humans and nonhuman others, including environments, animals, and plants. Reimagining these relationships depends in large part on our ability to engage nonhumans in their otherness, or alterity, but doing so is challenging. Responding to calls throughout posthuman literature for experimental new modes of imaginative encounter with nonhumans, and inspired by speculative traditions from literature to design, we devise a methodology involving “creative experiments” aimed at disrupting, decentering, and disorienting the human-centered thinking that interferes with humans’ …

Cyber Threat Intelligence For Smart Grids Using Knowledge Graphs, Digital Twins, And Hybrid Machine Learning In Scada Networks, Nabeel Al-Qirim, Munir Majdalawieh, Anoud Bani-Hani, Hussam Al Hamadi

All Works

In the SCADA (Supervisory Control and Data Acquisition) network of a smart grid, the network switch is connected to multiple Intelligent Electronic Devices (IEDs) that are based on protective relays. False-Data Injection Attacks (FDIA), Remote-Tripping Command Injection (RTCI), and System Reconfiguration Attacks (SRA) are three types of cyber-attacks on SCADA networks, resulting in single-line-to-ground (SLG) fault, IED-relay failure, and circuit-breaker open issues occur. The existing cyber threat intelligence (CTI) approaches of grids are unable to provide visualization of cyber-attacking grid effects. To understand the full effect of the attacks, there is a need for a knowledge-graph method-based digital-twin cyber-attack visualization …

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 …

Adapting To Ai: The Evolving Role Of Faculty In Higher Education, Ronald R. Danault

Faculty Publications

Artificial intelligence (AI) is changing the face of higher education, and there are important issues regarding the future of the faculty (Stoerger, 2024). Although there are concerns about the impact of AI on the conventional faculty roles in teaching, assessment, and administration, these tools are now being adopted in learning processes. Rather than dismissing AI as a threat, it acts as a catalyst for reshaping the way faculty members teach with the help of AI and, hence, become facilitators of the learning process (Haoyang & Towne, 2025).

This paper aims to discuss the integration of AI in the higher education …

Leveraging Artificial Intelligence To Strengthen Human Resilience Against Phishing Attacks, Muhammad Mavins

Cybersecurity Undergraduate Research Showcase

Phishing attacks are a major cybersecurity threat, tricking people with fake emails, scam websites, and social engineering tactics. As these attacks become more advanced, traditional security measures are no longer enough to stop them. This paper looks at how Artificial Intelligence (AI) can help detect and prevent phishing while also making people more aware of these threats. Using machine learning (ML), natural language processing (NLP), and behavioral analysis, AI can examine email content, sender behavior, and metadata to spot phishing attempts. AI-powered cybersecurity training can also teach people to recognize and respond to phishing by using personalized phishing tests and …

On Large Language Models In National Security Applications, William N. Caballero, Phillip R. Jenkins

Faculty Publications

The overwhelming success of GPT-4 in early 2023 highlighted the transformative potential of large language models (LLMs) across various sectors, including national security. This article explores the implications of LLM integration within national security contexts, analyzing their potential to revolutionize information processing, decision-making, and operational efficiency. Whereas LLMs offer substantial benefits, such as automating tasks and enhancing data analysis, they also pose significant risks, including hallucinations, data privacy concerns, and vulnerability to adversarial attacks. Through their coupling with decision-theoretic principles and Bayesian reasoning, LLMs can significantly improve decision-making processes within national security organizations. Namely, LLMs can facilitate the transition from …

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.

Conversational Open-Domain Question Answering For Resource-Constrained Languages, Emrah Budur, Tunga Güngör

Turkish Journal of Electrical Engineering and Computer Sciences

The growing interest in Conversational AI has led to the development of Conversational OpenQA systems as a crucial step for meeting users' information needs in real world scenarios. Conversational OpenQA systems enhance standard OpenQA performance by leveraging conversation history of the users. However, building effective Conversational OpenQA systems requires large-scale Conversational OpenQA datasets, often limited to the English language, hindering progress in low-resource languages. We present a robust Conversational OpenQA system enhanced by conversational context, designed for languages with limited resources and exemplified in our case study for Turkish. To address data limitations in a cost-effective way, we repurpose existing …

A Case Study Of Gray-Box Fuzzing With Byte- And Tree-Level Mutation Strategies In Xml-Based Applications For Exposing Security Vulnerabilities, Şerafetti̇n Şentürk, Vahi̇d Garousi, Nejat Yumuşak

Turkish Journal of Electrical Engineering and Computer Sciences

Fuzzing is an automated process for detecting crashes and vulnerabilities in software system and it is classified as grammar- or mutation-based in terms of input generation. While the grammar-based fuzzing generates inputs from a specification and takes highly-structured inputs, mutation-based fuzzing generates inputs by modifying input files and abstract syntax trees randomly. There are not many case studies comparing the crash detection capabilities in the scope of mutation-based fuzzing. To add to the body of empirical evidence in this area, this case study compares fuzzing with different mutation strategies to evaluate their effectiveness in three aspects: fault detection effectiveness, fault …

Optimizing Parameters For Efficient Computation With Fully Homomorphic Encryption Schemes, Cavi̇dan Yakupoğlu Karaağaç, Kurt Rohloff

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, we aim to provide a parameter selection approach for the BFVrns scheme, one of the prominent fully homomorphic encryption (FHE) schemes. Selecting parameters for lattice-based FHE schemes poses a practical challenge for both experts and nonexperts. To solve this problem, we introduce a hybrid approach that combines theoretical approach with experimental analysis. First, we employ regression analysis to examine the impact of parameters on both performance and security. The varying behavior of FHE parameters in terms of performance, security, and ciphertext expansion factor (CEF) makes parameter selection more challenging. To address this issue, we employ a multi-objective …

Decomposition Lstm With Dual Multi-Head Self-Attention For Wind Turbine Drivetrain State Forecasting, Haikun Jia, Huini Sun, Shuang Bai

Turkish Journal of Electrical Engineering and Computer Sciences

Due to the clean and renewable nature of wind energy, accurate prediction of rotor loads and operating states for wind turbine units has become of paramount importance. Currently, traditional methods relying on expert analysis combined with instrument testing for qualitative reasoning are both time-consuming and labor-intensive, and their accuracy guarantees are limited. In response to wind farm data entailing the interweaving of data from multiple sources and the diverse interrelations across various features and time steps, this study introduces a method for predicting rotor loads and operating states. Initially, we employ an iterative multi-scale seasonal-trend decomposition block to capture latent …

Fuzzy-Virtual Inertia Control To Improve The Frequency Response Of Multi-Area Power Systems, Nourelhouda Djaraf, Yacine Daili, Abderrahim Zemmit, Abdelghani Harrag

Turkish Journal of Electrical Engineering and Computer Sciences

Virtual inertia control (VIC) is essential for power systems dominated by electronic devices to compensate for the lack of inertia and ensure frequency regulation. However, most existing VICs often focus solely on optimizing the virtual inertia parameter to adapt to the high penetration of renewable energy sources (RESs) without considering the damping factor. This oversight can lead to significant fluctuations and power mismatches, especially in interconnected systems where the coordination between MGs is sensitive and essential, and there is a risk of propagation of deviations between MGs, which makes the control more complex. To address these issues, this paper presents …