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Revisiting Bipolar Neutrosophic Graph And Interval-Valued Neutrosophic Graph, Takaaki Fujita Apr 2025

Revisiting Bipolar Neutrosophic Graph And Interval-Valued Neutrosophic Graph, Takaaki Fujita

Neutrosophic Systems with Applications

Graph theory explores networks composed of nodes (vertices) and their connections (edges). A graph class is a collection of graphs that share common structural properties, defined by specific rules or constraints. This paper examines various models of uncertain graphs, including Fuzzy, Intuitionistic Fuzzy , Neutrosophic, Turiyam Neutrosophic, and Plithogenic Graphs. In particular, this study focuses on Bipolar Graphs and Interval-valued Graphs, analyzing them within the frameworks of Fuzzy, Neutrosophic, Turiyam Neutrosophic, and Plithogenic Graphs.

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Assessing The Sustainable Blockchain-Metaverse-Iot Platform In The Healthcare Industry: An Intelligent Decision Support Model, Ibrahim M. Hezam, Ahmed M. Ali, Ibrahim A. Hameed, Karam Sallam, Mohamed Abdel-Basset Apr 2025

Assessing The Sustainable Blockchain-Metaverse-Iot Platform In The Healthcare Industry: An Intelligent Decision Support Model, Ibrahim M. Hezam, Ahmed M. Ali, Ibrahim A. Hameed, Karam Sallam, Mohamed Abdel-Basset

Neutrosophic Systems with Applications

Healthcare services must fulfill patients' desires for secure data sharing and high accessibility. Blockchain technology, through blockchain platforms (BPs), can overcome healthcare challenges. This study develops a decision-making methodology for selecting the best BP, by integrating blockchain with IoT and Metaverse, the proposed approach ensures data integrity, quality, privacy and security, secure data sharing, and interoperability. The decision-making methodology uses the multi-criteria decision-making (MCDM) methodology to handle conflicting criteria. Two MCDM methods are used in this study: CRiteria Importance Through Intercriteria Correlation (CRITIC) for weight computation, and Ranking of Alternatives with Weights of Criterion (RAWEC) for alternative ranking. To deal …

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Expanding The Horizons Of Nonagonal Neutrosophic Numbers As A Versatile Framework For Decision-Making And Scientific Applications In Covid-19, Muhammad Naveed Jafar, Kainat Muniba, Hamiden Abd El-Wahed Khalifa, Fahd Jarad Apr 2025

Expanding The Horizons Of Nonagonal Neutrosophic Numbers As A Versatile Framework For Decision-Making And Scientific Applications In Covid-19, Muhammad Naveed Jafar, Kainat Muniba, Hamiden Abd El-Wahed Khalifa, Fahd Jarad

Neutrosophic Systems with Applications

In this article, the concept of nonagonal neutrosophic numbers has been introducing in the disjunctive frame of reference. We also proposed the dependency and independency of membership function in regards to nonagonal neutrosophic number. We also introduce a new score function and its computation also formulated in a distinct rational viewpoint. We developed the concept of weighted arithmetic averaging operator and weighted geometric averaging operator for nonagonal neutrosophic numbers. It will open new doors for MCDM and develop the concept with new approaches. Additionally, we analyze the effect of COVID-19 for different ages.

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An Approach To Model Uncertainty In Fluid Behaviour With Navier-Stokes Equations In Neutrosophic Environment, Muhammad Saeed, Attia Hameed, Neha Andaleeb Khalid, Muhammad Salman Habib Apr 2025

An Approach To Model Uncertainty In Fluid Behaviour With Navier-Stokes Equations In Neutrosophic Environment, Muhammad Saeed, Attia Hameed, Neha Andaleeb Khalid, Muhammad Salman Habib

Neutrosophic Systems with Applications

The complex discipline of fluid dynamics examines the behavior of fluids and their interactions with adjacent objects. The Navier-Stokes equations are very important for explaining how fluids move, but they are not linear and often give answers that depend on the starting point and the boundaries. Modeling fluid behavior is challenging due to the inherently chaotic and unpredictable character of fluid dynamics. To deal with unknown or uncertain values in this research, we used neutrosophic logic to look at the Navier-Stokes equations in a new way. Neosophic logic permits the existence of values that are partially true and partially false; …

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A Novel Neutrosophic Decision-Making Approach For Optimizing Metaverse Headphone Design: Balancing Technical Performance And User Emotional Needs, Mai Mohamed, Amira Salam, Karam Sallam, Bilal Arain Apr 2025

A Novel Neutrosophic Decision-Making Approach For Optimizing Metaverse Headphone Design: Balancing Technical Performance And User Emotional Needs, Mai Mohamed, Amira Salam, Karam Sallam, Bilal Arain

Neutrosophic Systems with Applications

The concept of the metaverse, which combines various technologies to create a wide range of virtual experiences, has gained significant popularity in recent years. To fully engage in these metaverse environments, users rely on access devices such as virtual reality (VR) headsets and smartphones for augmented reality (AR). These devices must be lightweight, compact, and user-friendly to ensure comfort and enhance customer satisfaction. There is a growing focus on innovating and designing products that not only meet technical requirements but also address the emotional needs of users, ultimately improving the overall experience. Selecting the ideal design for Metaverse headphones is …

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Contact Graphs In Fuzzy And Neutrosophic Graphs, Takaaki Fujita Apr 2025

Contact Graphs In Fuzzy And Neutrosophic Graphs, Takaaki Fujita

Neutrosophic Systems with Applications

Graph Theory is a branch of mathematics dedicated to studying graphs, which depict relationships between objects through vertices and edges. A significant focus in this field is the study of contact graphs, where vertices correspond to sets, and edges represent intersections between those sets. To better model uncertainties in real-world situations, several graph types---such as fuzzy, neutrosophic, and Plithogenic graphs---have been developed. This paper investigates the concept of contact graphs within these frameworks, offering insights into their behavior under various uncertain conditions.

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Balancing Privacy And Security: A Comparative Analysis Of Ai-Driven Surveillance In The Uae And Usa, Belal Alghafri, Abdallah Tubaishat Apr 2025

Balancing Privacy And Security: A Comparative Analysis Of Ai-Driven Surveillance In The Uae And Usa, Belal Alghafri, Abdallah Tubaishat

All Works

AI-driven surveillance has emerged as a critical tool for enhancing public safety, enabling authorities to monitor and prevent crime and terrorism more effectively. In countries like the UAE and the USA, these systems are often implemented under the pretext of national security, offering advanced methods to track potential threats. However, the increasing reliance on AI for surveillance raises significant ethical concerns about privacy and individual freedoms. The boundary between protecting citizens and infringing on their privacy becomes increasingly blurred, potentially leading to abuses of power, diminished public trust, and a pervasive atmosphere of fear. This paper explores the complex relationship …

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Ontologies For Smart Agriculture: A Path Toward Explainable Ai-A Systematic Literature Review, Rima Grati, Najla Fattouch, Khouloud Boukadi Apr 2025

Ontologies For Smart Agriculture: A Path Toward Explainable Ai-A Systematic Literature Review, Rima Grati, Najla Fattouch, Khouloud Boukadi

All Works

Smart agriculture has grown significantly over the last few years, particularly with the integration of advanced technologies (e.g., the Internet of Things, robots, artificial intelligence, etc.), leading to the development of intelligent agricultural systems. However, these systems often lack data integration, interoperability, and semantic explainability. Various approaches have been proposed to address these challenges. This study provides a comprehensive literature review that addresses, on the one hand, the use of semantic resources (e.g., semantic web technologies and ontologies) to tackle data integration and interoperability in smart agriculture systems and, on the other hand, the integration of explainable artificial intelligence into …

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Predicting The Unpredictable: Predicting The March Madness Champion Using Statistical Modeling, Jack Sweeney Apr 2025

Predicting The Unpredictable: Predicting The March Madness Champion Using Statistical Modeling, Jack Sweeney

Honors Projects in Information Systems and Analytics

One of the more exciting and hardest parts of the men's college basketball postseason tournament, named March Madness, is to pick who wins each game and determine the overall champion of the tournament. The data analysis conducted will help determine the overall tournament winner. A machine learning model is implemented using college basketball metrics from previous years to determine the overall winner of this prestigious tournament in 2025. The model was able to pick up the winner in 2024. The result also finds that the most important features in determining the winner of the tournament are shooting guard height, small …

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Technology Anxiety In Virtual Reality Adoption: Examining The Impact Of Age, Past Experience, And Cybersickness, Eman Al Khalifah, Ramy Hammady, Mahmoud Abdelrahman, Ons Al-Shamaileh, Mostafa Marghany, Hatana El-Jarn, Alyaa Darwish, Yusuf Kurt Apr 2025

Technology Anxiety In Virtual Reality Adoption: Examining The Impact Of Age, Past Experience, And Cybersickness, Eman Al Khalifah, Ramy Hammady, Mahmoud Abdelrahman, Ons Al-Shamaileh, Mostafa Marghany, Hatana El-Jarn, Alyaa Darwish, Yusuf Kurt

All Works

This study examines the role of Technology Anxiety (TA), age, past use, and cybersickness in the adoption of Virtual Reality (VR) technology. Using an extended Technology Acceptance Model (TAM), the research integrates age and past use as antecedents of TA and evaluates their influence on perceived ease of use (PEoU), perceived enjoyment (PENJ), and user attitudes. Data from 206 participants were analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) following a VR pilgrimage experience. The findings challenge conventional assumptions, revealing that past VR use increased TA, contradicting prior studies that associate familiarity with reduced anxiety. Additionally, older users exhibited …

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Gender Bias Within Ai Imaging, Drew Quattrocchi Apr 2025

Gender Bias Within Ai Imaging, Drew Quattrocchi

Student Publications and Presentations

This study investigates AI-created gender bias in AI-created images through content analysis, contrasting the way gender is depicted in professions in leading AI image-creation tools such as Chat smith, Adobe Firefly, Midjourney, and Stable Diffusion. Employing a quantitative research method, this study contrasts AI-created images of gender-stereotypical careers for both male and female. Non-gendered careers will be used as well to identify patterns of stereotyping and bias. The area of emphasis lies in individual subjects within the images and scrutinizing visual elements such as clothing, accessories, background, face expressions, and gendered roles assigned to each. Particular emphasis is focused to …

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Fids: Accelerating Network Intrusion Detection Through Strategic Feature Selection, James Elmore Apr 2025

Fids: Accelerating Network Intrusion Detection Through Strategic Feature Selection, James Elmore

Cybersecurity Undergraduate Research Showcase

Network intrusion detection systems (IDS) typically analyze complete network flows to identify malicious traffic, requiring flows to conclude before classification. This approach creates detection delays for attacks like Slowloris that intentionally keep connections open for extended periods of time. This paper introduces a novel approach that classifies network traffic using only features available from the first few packets of a flow, enabling faster detection while maintaining high accuracy. We evaluate three random forest models on the CICIDS2017 dataset using expanding sets of features: the first-packet model trained on on features available from the first backward packet, the few-packet model which …

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The Evolution Of Russian And Chinese Disinformation Tactics And The Threat They Pose To The U.S. Cybersecurity, Jehovani Sese Apr 2025

The Evolution Of Russian And Chinese Disinformation Tactics And The Threat They Pose To The U.S. Cybersecurity, Jehovani Sese

Cybersecurity Undergraduate Research Showcase

This paper aims to discuss how disinformation has become the most powerful tool used against the United State by foreign operatives. Of these foreign operatives, Russia and China have shown the ability to use advanced tactics to truly affect the United States security. Often these tools came in the form of state-sponsored media, influence campaigns and fake online identities. This literature review explores the evolution of Russian and Chinese disinformation tactics, examining how these approaches have changed over time and become more sophisticated. This paper will highlight major campaigns which use tools such as bot networks, and social media manipulation. …

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Optimization Of Vehicle Routing For Cross-Infection Risk In The Epidemic, Xiaodong Shi, Yongcheng Guo, Mingqi Ma, Jiarui Pan Apr 2025

Optimization Of Vehicle Routing For Cross-Infection Risk In The Epidemic, Xiaodong Shi, Yongcheng Guo, Mingqi Ma, Jiarui Pan

Journal of System Simulation

Abstract: In view of the safety risks associated with logistics distribution route optimization during public health emergencies, this paper investigates the vehicle routing problem by incorporating the risk of cross-infection, integrates the cross-infection risk caused by logistics activities in the epidemic area into the logistics distribution model, and establishes a logistics vehicle distribution model with the goal of cross-infection risk and cost. An improved genetic algorithm is designed for model optimization and solution. Based on the integration of chaos initialization population and adaptive crossover and mutation operations, a neighbor exclusion operator is further proposed to enhance the global search ability …

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Seg-Swin: A Dual-Attention Transformer Model For Advanced Amd Classification And Lesion Detection Using Color Fundus Imaging, Niveen Nasr El-Den, Mohamed Elsharkawy, Ibrahim Saleh, Ali H. Mahmoud, Mohammed Ghazal, Ashraf Khalil, Ashraf Sewelam, Hani Mahdi, Ayman El-Baz Apr 2025

Seg-Swin: A Dual-Attention Transformer Model For Advanced Amd Classification And Lesion Detection Using Color Fundus Imaging, Niveen Nasr El-Den, Mohamed Elsharkawy, Ibrahim Saleh, Ali H. Mahmoud, Mohammed Ghazal, Ashraf Khalil, Ashraf Sewelam, Hani Mahdi, Ayman El-Baz

All Works

Age-related macular degeneration (AMD) is a prevalent retinal disorder in the elderly, often leading to significant vision impairment. The diagnosis of AMD is confirmed through various medical imaging modalities, with color fundus photography (CFP) being a primary tool. The detection and staging of AMD-severity depend on several factors, including the number and size of drusen, the presence of pigmentary changes, geographic atrophy, and neovascularization, all of which are identifiable through CFP. In this study, we introduce an innovative dual-vision transformer-based network designed to automatically detect AMD and classify its severity into either dry AMD or wet AMD using CFP. Early …

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Elder Fraud Metrics And Preventative Measures Of Chesapeake, Virginia, Joey J. Whitmore Jr. Apr 2025

Elder Fraud Metrics And Preventative Measures Of Chesapeake, Virginia, Joey J. Whitmore Jr.

Cybersecurity Undergraduate Research Showcase

Geriatric crime continues to escalate in the digital era, where older individuals are disproportionately being targeted because of their low digital literacy and high susceptibility to online frauds. In this paper, we examine the breadth of elder fraud in Chesapeake, Virginia using FBI Internet Crime Complaint Center (IC3) data and state-level cybersecurity initiatives and survey responses. Older adults aged 60 and up have reported losses of over $3.4 billion in 2023 alone, underscoring the importance of proactive measures. It assesses the public awareness from traditional and AI-based perspectives revealing significant gaps in digital safety literacy and fraud reporting mechanism among …

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Grm-011 Non-Invasive Convolution-Based Coronary Artery Blood Pressure Prediction, Rene Lisasi Apr 2025

Grm-011 Non-Invasive Convolution-Based Coronary Artery Blood Pressure Prediction, Rene Lisasi

C-Day Computing Showcase

The primary objective of this proposal is to develop an innovative technique for determining the functional significance of coronary artery lesions in patients with coronary artery disease (CAD) and evaluate its utility for clinical decision-making using coronary computed tomography angiography (CCTA).

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Grm-043 Performance Assessment Of Deepseek Versus Bard And Chatgpt In Detecting Alzheimer’S Dementia, Muhammad Awais Arshad Apr 2025

Grm-043 Performance Assessment Of Deepseek Versus Bard And Chatgpt In Detecting Alzheimer’S Dementia, Muhammad Awais Arshad

C-Day Computing Showcase

Alzheimer’s disease is a growing public health issue due to its progressive nature and increasing prevalence. Large language models (LLMs) offer promising avenues for non-invasive cognitive assessment through natural language understanding. In this study, we evaluate DeepSeek’s general-purpose model V3 and reasoning-enhanced R1 variant—for identifying Alzheimer’s dementia (AD) and Cognitively Normal (CN) individuals using transcripts derived from spontaneous speech. Two baseline prompting strategies (zero-shot, chain-of-thought ) were applied to both model types and an additional query (self-consistency prompting) was applied to assess better predictions. Accuracy was the primary performance metric. When positively identifying AD, the general-purpose DeepSeek V3 model produced …

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Grm-093 Advances In Non-Invasive Glucose Sensing: A Comprehensive In Vitro Analysis, El Arbi Belfarsi, Henry Flores Apr 2025

Grm-093 Advances In Non-Invasive Glucose Sensing: A Comprehensive In Vitro Analysis, El Arbi Belfarsi, Henry Flores

C-Day Computing Showcase

This study explores non-invasive glucose sensing using infrared (IR) imaging and electrical measurements in an in-vitro setup. Glucose samples (70–200 mg/dL) were prepared by diluting concentrated solutions (700–2000 mg/dL) 1:10 in synthetic blood concentrate, with 2 mg/dL increments. A custom 3D-printed black cuvette holder ensured consistent alignment of components, including either an IR camera or a 1550 nm photodiode, light sources (850 nm LED/laser, 808 nm, 650 nm, or 1600 nm), and a 3 mm skin-mimicking silicone layer. A Region Based Convolutional Neural Network (RCNN) trained on IR images achieved the lowest RMSE of 10.98 mg/dL at 850 nm LED. …

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Uc-019 Gwinnett County Public Schools - Data Masking Tool, Emmett Peters, Diego Frausto Ramirez, Abhay Tompkins Silas Talele, Jesus Trejo-Tamayo Apr 2025

Uc-019 Gwinnett County Public Schools - Data Masking Tool, Emmett Peters, Diego Frausto Ramirez, Abhay Tompkins Silas Talele, Jesus Trejo-Tamayo

C-Day Computing Showcase

In today’s data-driven world, organizations handle vast amounts of sensitive information, including personally identifiable information (PII), health records, and financial data. For institutions like schools, this data often includes sensitive details about students, parents, and staff, making data protection not just important, but critical. With increasing privacy regulations such as GDPR and HIPAA, organizations must implement robust measures to protect this information while still enabling its use for legitimate purposes like testing, analytics, and development. Our web-based data masking tool addresses this need by allowing organizations to protect sensitive data without compromising its usability. By applying dynamic masking rules to …

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Uc-125 Database Masking Tool - Project 04 - Team 1, Lleyton Callison, Josh Tettey-Enyo, Reda Salimi, Stephen Sigmon, Alec Quillen Apr 2025

Uc-125 Database Masking Tool - Project 04 - Team 1, Lleyton Callison, Josh Tettey-Enyo, Reda Salimi, Stephen Sigmon, Alec Quillen

C-Day Computing Showcase

The Database Masking Tool for Gwinnett County Public Schools secures sensitive data while preserving its analytical utility. Developed alongside an in-depth research paper, this web-based solution enables real-time masking of information in SQL Server and MySQL databases. Utilizing automated field recognition, it applies three masking techniques—Faker-based masking, hash masking, and pseudonymization through generalized masking—to protect personally identifiable information. Key features include an intuitive interface for configuring masking rules, real-time data previews, and an export function for generating masked datasets in multiple formats. Built with a React-Flask stack and containerized for consistency, the system supports compliance with GDPR, HIPAA, and FERPA. …

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Ur-115 Mobinav: Accessible Campus Navigation, Eric Legostaev, Damien Castro, Dom Evans Apr 2025

Ur-115 Mobinav: Accessible Campus Navigation, Eric Legostaev, Damien Castro, Dom Evans

C-Day Computing Showcase

MobiNav addresses the gap in campus navigation by providing personalized route planning for individuals with diverse mobility requirements. The system uses dual-layer routing (Google Maps API and custom OSRM routing), real-time obstacle reporting, and detailed accessibility feature mapping. It creates custom routes considering wheelchair access, elevation changes, building entrances, and temporary obstacles. Initially scoped for Kennesaw State University's Marietta campus, it is designed for scalability to other locations.

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Grm-081 Evaluation Of Hand-Crafted Features With Mask Images Obtained From Pannuke Dataset Using Bayesian Optimization And Machine Learning Models, Siri Yellu Apr 2025

Grm-081 Evaluation Of Hand-Crafted Features With Mask Images Obtained From Pannuke Dataset Using Bayesian Optimization And Machine Learning Models, Siri Yellu

C-Day Computing Showcase

Semantic image segmentation enables computing systems to understand the semantic patterns of image pixels by using deep learning models to classify the pixels into specific labels. The deep-learning models’ performance in image classification has been evaluated by comparing the predicted images using deep-learned features with human-labeled images or mask images. However, there remains a substantial need to investigate the performance of machine learning models that do not use deep learned features but use hand-crafted features. In this project, we perform a comprehensive evaluation of the performance of the eight machine learning models using 46 hand-crafted features extracted from the PanNuke …

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Grp-087 Empowering Alzheimer’S Caregivers: Designing Explainable And Personalized Ai For Mental Health Support​, Syeda Umme Salma, Chandra Rekha Renduchintala, Isa Siddique Apr 2025

Grp-087 Empowering Alzheimer’S Caregivers: Designing Explainable And Personalized Ai For Mental Health Support​, Syeda Umme Salma, Chandra Rekha Renduchintala, Isa Siddique

C-Day Computing Showcase

This study presents the design of an AI-powered caregiver support app aimed at personalized mental health and burden management for individuals caring for Alzheimer’s patients. The design is grounded in insights drawn from a comprehensive analysis of 28 recent studies on AI-driven mental health interventions. These findings informed the implementation of key features, including machine learning models such as Random Forest, clustering, and supervised learning to create adaptive care plans tailored to patient and caregiver profiles. The system dynamically adjusts task schedules based on engagement data and provides interpretable recommendations through SHAP. With built-in emotional check-ins, mood tracking, and caregiver-centric …

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Uc-029 Graphbat: Subterranean Data Visualizer, Caroline Roberson, Abdalla Ugas, Nathan Karg, Hayden Harper Apr 2025

Uc-029 Graphbat: Subterranean Data Visualizer, Caroline Roberson, Abdalla Ugas, Nathan Karg, Hayden Harper

C-Day Computing Showcase

GraphBat is a desktop data visualization application designed for speleology and similar fields that bundles common graph types with a unique heatmap tool which few comparable apps provide. It was developed in Python and is intended as an open-source tool available for use and extension by the scientific community. The heatmaps offer two data interpolation methods—inverse distance weighting and linear interpolation—to visualize the spread of data across a space using a real-world map and sensor data relative to the space. GraphBat aims to expediate scientific analysis and facilitate the presentation of results across many fields of subterranean study.

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Uc-061 From Frustration To Function: Enhancing Usability In Public Transportation, Marcus Harrison, Hong Nguyen, Mohamed Khatib, Austin Krusemark Apr 2025

Uc-061 From Frustration To Function: Enhancing Usability In Public Transportation, Marcus Harrison, Hong Nguyen, Mohamed Khatib, Austin Krusemark

C-Day Computing Showcase

Public transportation apps have recently become an essential tool for helping individuals navigate complex transit systems, however, many users still face issues with usability, accessibility, and reliability. Taking this into consideration, this project aims to evaluate the user experience of these apps and how one in particular can be improved. In doing so, our group hopes to create a more user-friendly experience that can make public transportation easier and more reliable for everyone.

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Uc-100 Agentic Ai Quiz Generation: Personalized Tutoring Through Intelligent Retrieval And Adaptive Learning, Devananda Sreekanth Apr 2025

Uc-100 Agentic Ai Quiz Generation: Personalized Tutoring Through Intelligent Retrieval And Adaptive Learning, Devananda Sreekanth

C-Day Computing Showcase

This research presents a personalized, agentic AI-powered system for multiple-choice question (MCQ) generation tailored to college-level tutoring in machine learning and software engineering domains. The primary objective is to enhance adaptive learning through reliable, context-aware quiz generation using long-context large language models (LLMs) and modular agent workflows. Our methodology is based on an eight-stage agentic architecture that separates tasks into two main phases: vector indexing and personalized quiz generation. In the indexing phase, academic PDFs are parsed, chunked with LangChain’s RecursiveCharacterTextSplitter, embedded via Google's text-embedding-005, and indexed using FAISS. A verification agent ensures topic alignment and integrity of the vector …

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Ai Assistance In Legal Analysis: An Empirical Study, Johnathan H. Choi, Daniel Schwarcz Apr 2025

Ai Assistance In Legal Analysis: An Empirical Study, Johnathan H. Choi, Daniel Schwarcz

Journal of Legal Education

No abstract provided.

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Leveraging Attention Mechanism To Unlock Gene And Protein Attributes, Ala Jararweh Apr 2025

Leveraging Attention Mechanism To Unlock Gene And Protein Attributes, Ala Jararweh

Computer Science ETDs

Advancing personalized medicine depends on effectively integrating and interpreting the vast, heterogeneous landscape of biological data, from genomic sequences and transcriptomics to the insights embedded in scientific literature. Current machine learning models often focus on single data modalities, limiting their capacity to capture the multifaceted nature of biological systems. We address this gap by developing three attention-based machine-learning models integrating diverse data modalities. Firstly, DeepVul is a multi-task model that leverages cancer transcriptome data to predict genes critical for cancer survival and their corresponding drugs. Subsequently, LitGene refines gene representations by integrating textual information from the scientific literature. Finally, Protein2Text …

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Leveraging Benford’S Law And Machine Learning For Financial Fraud Detection, Benjamin R. Fu Apr 2025

Leveraging Benford’S Law And Machine Learning For Financial Fraud Detection, Benjamin R. Fu

Cybersecurity Undergraduate Research Showcase

Financial fraud, particularly credit card fraud, continues to pose substantial challenges to financial institutions due to its increasing frequency and impact on consumer trust. While traditional rule-based methods have provided foundational defenses, their limitations in scalability and adaptability have accelerated the adoption of machine learning (ML) techniques. Concurrently, Benford’s Law—a statistical principle often used in forensic accounting—has demonstrated efficacy in detecting anomalies within naturally occurring numerical datasets. This study explores a hybrid fraud detection approach that integrates Benford’s Law with supervised machine learning algorithms, including Logistic Regression, Random Forest, and k-Nearest Neighbors. Using the publicly available European credit card fraud …

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