Undergraduate Capstone Winners

First Place

UC-120 Virtual Companionship Chatbot by ; ; ;
Abstract: Loneliness affects about 77% of college students at some point, highlighted by a Gitnuss report. Our project aims to mitigate this by introducing a personalized chatbot that serves as an emotional outlet for students. The application is built on a React Native frontend, employs a DistilGPT-2 language model using the QUAC dataset, and is backed by a Python server. We plan to deploy it on an Azure NC6s_v3 Cloud server, integrating Firebase Real-Time Database for Android and iOS compatibility.
Department: Computer Science
Supervisor: Prof. Sharon Perry
Topics: Mobile Application
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Undergraduate Capstone First Place Spring 24

Second Place

UC-105 Model UN Crisis Software by ; ;
Abstract: Utilizing an Agile approach, this project develops a web-based solution, the Model UN Crisis Software, to streamline the management of crisis committees in Model UN conferences. The software is developed using Microsoft Visual Studio and Microsoft SQL Server Management Studio, adhering to the .NET framework and related conventions. It leverages Microsoft Azure SQL Database for back-end data storage and follows the ASP.NET core MVC framework, utilizing powerful .NET tools such as C# and Razor. The developed software provides comprehensive features to reduce the strain of hosting a crisis committee, such as directive and news management, user management, and a messaging system.
Department: Information Technology
Supervisor: Prof. Sharon Perry
Topics: Other
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Undergraduate Capstone Second and Third Place Spring 24

Third Place

UC-64 Smart Evaluator of Indirect Supplies Vendibility by ; ; ; ;
Abstract: The Smart Evaluator is a web-based software solution that analyzes industrial tools and their vending possibilities. It aims to streamline inventory research for sales teams, reducing manual data gathering and vendibility determination. To begin, users simply upload a basic item inventory spreadsheet, and start the program. From there, the program uses web scraping and ChatGPT to gather key data about the various tools including dimensions, weight, and fragility. Each item is then evaluated based on the collected data, and the optimum storage method is calculated. Once these tasks are performed, the results are stored in the system’s database for future reference to reduce computation time should a particular item already exist. The overall output of the system is a completed list of items with its necessary data and vendibility options.
Department: Software Engineering or Game Design
Supervisor: Dr. Yan Huang
Project Owners: Vladimir Rusanov and Amir Reza Kashani (Stanley Black and Decker)
Topics: Artificial Intelligence
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Undergraduate Capstone Second and Third Place Spring 24

Graduate Capstone Winners

First Place

GC-84 Owl Cyber Defense Systems by ; ; ; ;
Abstract: Owl Cyber Defense Systems is a fictitious (for now?) start-up offering comprehensive cybersecurity solutions for small and medium businesses. Our premier flagship product is an AI Chatbot that answers security related questions and provides vetted code and security settings to secure and harden a variety of systems. Starting with the initial concept, we methodically progressed through the business planning process, carefully considering technology usage and design. This comprehensive approach ultimately enabled us to develop a robust set of client offerings. We used a hybrid approach combining Agile Scrum and traditional Waterfall methodologies to complete the project. We utilized Jira Project Management to track the Epics and tasks required to plan, design, develop, test, and release into our production systems. Each milestone was represented by a custom Sprint in Jira designed to produce weekly scrum updates and automated person-hour tracking reports culminating in the final project presentation.
Department: Information Technology
Supervisor: Dr. Ying Xie
Topics: Security
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Graduate Capstone First Place Spring 24

Second Place

GC-101 Learning Resource Finder: A Web Scraping Tool for Educational Materials by
Abstract: The Learning Resource Finder is a pioneering tool designed to alleviate the challenges associated with navigating the vast landscape of online educational content. Leveraging sophisticated web scraping techniques and API integrations, this tool empowers users to efficiently discover relevant learning materials tailored to their specific needs. Through a user-friendly Flask-based web interface, users initiate search queries, which are then encoded and utilized to fetch pertinent URLs from leading educational platforms such as JavaTPoint, W3Schools, Coursera, Udemy, and GeeksforGeeks, as well as Google search results. The core of the web scraping process lies in the meticulous extraction of URLs from HTML content using Scrapy, ensuring precision by eliminating irrelevant or redundant links while excluding duplicate URLs and Wikipedia sources to maintain resource integrity. The Learning Resource Finder represents a crucial advancement in the realm of digital learning, providing users with a curated compilation of educational materials to enhance their learning journey.
Department: Computer Science
Supervisor: Dr. Nasrin Dehbozorghi
Topics: Artificial Intelligence
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Graduate Capstone Second and Third Place Spring 24

Third Place

GC-17 CRM for CCSE Department of KSU by ; ;
Abstract: This project outlines the development of a bespoke Customer Relationship Management (CRM) system specifically designed for the College of Computer and Software Engineering (CCSE). The initiative aims to centralize customer information into a unified repository, thereby enhancing the confidentiality, management, and optimization of data and communication processes within the college. The CRM system will integrate features for detailed profiles, communication optimization, complex workflow management, document repository, and data migration to ensure efficiency and data integrity. It will also facilitate seamless interaction with Microsoft 365 and Outlook, supporting the college's operational needs and maintaining its commitment to excellence in education and collaboration. The project is set to be executed in three major milestones, involving product selection, system implementation, user testing, and final deployment, with the end goal of improving overall customer relationship management. The team, led by Venkatesh Alla, alongside team members Keerthi Nannapaneni and Vinay Kumar Rapolu, under the guidance of project owners Alla Kemelmakher and Nasiya Sharif, and advisor Ying Xie, plans to use a dedicated project management tool, Jira, for collaboration and task tracking. This CRM initiative not only aims to meet the specific needs of the CCSE but also to set a benchmark for operational efficiency and data management in educational institutions.
Department: Information Technology
Supervisor: Alla Kemelmakher
Instructor: Dr. Ying Xie
Topics: Enterprise Systems
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Graduate Capstone Second and Third Place Spring 24

Undergraduate Research

First Place

UR-87 Adversarial Patch Attack in Deep Learning Based Remote Sensing Object Detection Model by
Abstract: Advancements in the field of machine learning have led to object detection systems that can approach or even improve upon human performance. Based on deep learning, these systems play a crucial role in many aspects, and continue to be improved on and see expanded adoption. However, these systems are vulnerable to adversarial attacks that rely on targeted noise to spoof detection. Researchers have applied this concept to increase real world adversarial performance by restricting this noise to a patch that can be placed on new images to disrupt object detection. Previous research has focused on patches applied to person recognition (Thys et al. 2019). We focus on the vulnerabilities inherent in remote sensing systems. We develop an adversarial patch to defeat a modern object detection system, YOLOv8. This work demonstrates that remote sensing can still be defeated by an adversarial patch attack and will inform future efforts to develop model robustness against these attacks.
Department: Computer Science
Supervisors: Dr. Kazi Aminul Islam, Sumaiya Tasneem (Supervising Graduate Assistant)
Topics: Artificial Intelligence
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Undergraduate Research First Place Spring 24

Second Place

UR-89 Performance Analysis of Post-Quantum Computing Key Encapsulation Mechanism Algorithms by ; ;
Abstract: Within the next twenty years or so experts predict that we will have quantum computers which will make certain kinds of encryption that we rely on ineffective and vulnerable to malicious entities. Post quantum computing (PQC) algorithms fill in that security gap that classical encryption algorithms can not. A particular category of PQC algorithms are key exchange mechanism (KEM) algorithm. The goal of these algorithms is to securely generate a shared symmetric key which can be used for encrypting future communication between the hosts. An important use case for these algorithms is in securing the Transport Layer Security protocol (TLS) against quantum adversaries. Due to the widespread use of TLS, it is critical that any new standard use PQC algorithms which are both efficient and secure. To this end we test each of the PQC KEM algorithms provided by oqs-provider library to compare their performance impact on the TLS handshake.
Department: Computer Science
Supervisor: Prof. Sharon Perry
Faculty Mentor: Dr. Manohar Raavi
Topics: Security
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Undergraduate Research Second and Third Place Spring 24

Third Place

UR-94 EmoHydra: Multimodal Emotion Classification using Heterogenous Modality Fusion by
Abstract: Affective computing is a field of growing importance, as human society becomes more integrated with machines. Human feelings are both complex and multi-modal, expressed through various methods and nuances in behavior. In this work we introduce EmoHydra, a multi-modal model created through the fusion of three top-level models fine-tuned on text, vision, and speech respectively. Despite heterogenous heads performing well on the unseen data, as well as generalizing well to other benchmarks, logit concatenation proves to be ineffective at predicting Multimodal data, therefore we implement Multi-Head Attention as our fusion mechanism.
Department: Computer Science
Supervisor: Dr. Chen Zhao
Topics: Artificial Intelligence
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Undergraduate Research Second and Third Place Spring 24

Master's Research

First Place

GMR-23 Jamming Signal Detection Using Extreme Gradient Boosting (XGBoost) Algorithm by ;
Abstract: Radar jamming involves sending intentionally disruptive radio waves toward the target radar, which might over-saturate its receiver so it can’t receive anything or deceive it into interpreting false information. Machine learning (ML) techniques increased the capability to automatically learn the experience without being explicitly programmed. Machine learning models usually require a large, labeled sample to perform. Building a robust jamming detection model will be challenging due to the wide variability of jamming signals and less available labeled samples. In this project, we developed an eXtreme Gradient Boosting(XGBoost) algorithms for radar jamming signal classification and achieved superior performance compared with Random forest and Support Vector Machine(SVM) considering the unique environment and challenges in RADAR/SDR signals.
Department: Computer Science
Supervisors: Dr. Kazi Aminul Islam, Dr. Sumit Chakravarty
Topics: Data/Data Analytics
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Graduate Master's Research First Place Spring 24

Second Place

GMR-90 DigiMindReady: Enhancing Military Readiness with Edge AI-Driven Wellness, Education, and Digital Discipline through mHealth Innovation by ; Anjum, Nafisa
Abstract: Military personnel often need to operate in high-stakes situations. Combating such volatile missions primarily includes control over cognitive overload, reckless mindset, and maintaining concentration amid distractions to sustain operational effectiveness. Military training significantly focuses on human performance, which benefits military readiness. However, the 21st century has introduced unanticipated challenges, such as adverse effects of excessive screen time, external distractions, and over-reliance on technology to the US military, on top of existing issues like anxiety and emotional stability, adversely impacting military readiness and decreasing quality of life. A strategic investigation into these issues and the advancement of effective tools to address these challenges in the military context are called for to improve digital mind readiness. This research introduces DigiMindReady, a mobile health (mHealth) application pioneered to counteract these effects through edge AI-driven personalized features, education, and digital discipline innovations. DigiMindReady proposes a tailored solution that operates entirely offline to ensure security, featuring a collection of functionalities to promote digital wellness, enhance learning, and maintain operational readiness without compromising security. The app's main features include personalized wellness recommendations, a digital education hub, screen time management, and a digital rewards system to encourage healthy digital habits.
Department: Information Technology
Supervisor: Dr. Nazmus Sakib
Topics: Mobile Application
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Graduate Master's Research Second and Third Place Spring 24

Third Place

GMR-118 Stress Detection by Wearable Devices: Integrating Multimodal Physiological Signals and Machine Learning Techniques by
Abstract: In the medical field, the most common complaint of patients is “stress”. Stress can cause severe effects on the human body. For example, prolonged mental stress can cause serious health issues in long term such as hypertension, cardiovascular diseases, increased susceptibility to infections, and depression. These health issues can be prevented by early detection of stress and by taking preventive measures. The most common detecting stress was determined from the questionnaires or from the interactive sessions conducted to assess the people's affective state. However, this method is not highly reliable and can be biased depending on the person who is conducting the session. To address this issue, detecting stress from physiological values such as electrocardiogram, blood volume pulse and body temperature can be an effective solution. When a person is under stress, the sympathetic nervous system (SNS) of the person triggers a physiological response that leads to a change in the heart rate, and muscle tension. Hence, understanding physiological values can be an effective way to detect the state of mind. To understand the effect of physiological values on the state of the art, we implemented a stress detection method by using the publicly available “Wearable Stress and Affect Detection” (WESAD) dataset, which has physiological data collected from 15 subjects. This data is collected from the wrist-worn and chest-worn sensors. These physiological values include blood volume pulse, electrocardiogram, electrodermal activity, electromyogram, respiration, body temperature and three-axis acceleration. In this research, we used wrist-worn sensor data to identify the state of mind. To achieve this task, we implemented classification machine learning models with the help of Logistic Regression, Decision Tree and Random Forest machine learning algorithms. Further, we implemented Stacking Ensemble Learning (SEL).
Department: Information Technology
Supervisor: Dr. Seyedamin Pouriyeh
Topics: Data/Data Analytics
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Graduate Master's Research Second and Third Place Spring 24

PhD Research

First Place

GPR-103 Personalized Pedagogy through a LLM-based Recommender System by ;
Abstract: The educational domain is undergoing transformation due to the incorporation of Artificial Intelligence (AI), Large Language Models (LLMs), and generative AI technologies, raising the need for educators to integrate cutting-edge technological advancements and methodologies into their teaching approaches. Pedagogical Design Patterns (PDPs) have become prominent for their role in sharing effective educational practices and narrowing the divide between academic research and actual teaching methods. Despite their potential, the lack of widely accessible resources and the scattered nature of publishing outlets pose significant barriers to the broad application of PDPS. To address this issue, we propose the application of large language models to recommend educational strategies based on existing PDPs. Our approach employs a locally sourced knowledge database and the Retrieval Augmented Generation (RAG) framework to formulate context for LLM queries. Preliminary results are encouraging, demonstrating an accuracy rate of 0.83 and a strong relevance of the recommended pedagogical practices to the posed queries. This paper details the initial outcomes of our project, which paves the way for further refinement of the model. The goal is to equip new educators with seasoned insights, to enhance their instructional methods.
Department: Computer Science
Supervisor: Dr. Nasrin Dehbozorgi
Topics: Artificial Intelligence
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Graduate PhD Research First Place Spring 24

Second Place

GPR-13 Effect Of Noise And Topologies On Multi-Photon Quantum Protocols by
Abstract: Quantum-augmented networks aim to use quantum phenomena to improve detection and protection against malicious actors in a classical communication network. This may include multiplexing quantum signals into classical fiber optical channels and incorporating purely quantum links alongside classical links in the network. In such hybrid networks, quantum protocols based on single photons become a bottleneck for transmission distances and data speeds, thereby reducing entire network performance. Furthermore, many of the security assumptions of the single-photon protocols do not hold up in practice because of the impossibility of manufacturing single-photon emitters. Multi-photon quantum protocols, on the other hand, are designed to operate under practical assumptions and do not require single photon emitters. As a result, they provide higher levels of security guarantees and longer transmission distances. However, the effect of channel and device noise on multiphoton protocols in terms of security, transmission distances, and bit rates has not been investigated. In this paper, we focus on channel noise and present our observations on the effect of various types of noise on multi-photon protocols. We also investigate the effect of topologies such as ring, star, and torus on the noise characteristics of the multi-photon protocols. Our results show the possible advantages of switching to multi-photon protocols and give insights into the repeater placement and topology choice for quantum-augmented networks.
Department: Computer Science
Supervisors: Dr. Abhishek Parakh, Dr. Mahadevan Subramaniam
Topics: IoT/Cloud/Networking
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Graduate PhD Research Second and Third Place Spring 24

Third Place

GPR-16 Attention Driven Framework for Detecting Mental Illness Causes from Social Media by ; ;
Abstract: Mental health is a critical aspect of our overall well-being. Mental illness refers to conditions that impact an individual's psychological state, resulting in considerable distress, and limitations in functioning day-to-day tasks. Due to the progress of technology, social media has merged as the platform, for individuals to share their thoughts and emotions. The psychological state of individuals can be accessed with the help of data from these platforms. However, it is challenging for conventional machine learning models to analyze the diverse linguistic contexts of social media data. In this work, we propose a novel attention-driven deep framework to overcome these challenges. Our proposed framework utilizes multi-level (word, sentence, and document) data to identify the causes behind mental illness. The efficacy and effectiveness of our proposed model are shown by extensive evaluation on Reddit data. The insights from this research deepen our understanding of different factors behind mental illness and would aid mental health professionals in formulating effective interventions.
Department: Computer Science
Supervisors: Dr. Md Abdullah Al Hafiz Khan, Dr. Yong Pei
Topics: Artificial Intelligence
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Graduate PhD Research Second and Third Place Spring 24

Audience Favorite Presenter

GC-67 Automatic Daily Financing Headlines Collection, Storage, Analysis and Presentation by ; ; ; ;
Abstract: The "Automatic Daily Financing Headlines Collection, Storage, Analysis, and Presentation" project aims to streamline the process of gathering financial headlines from financial news sources, storing them systematically, performing analysis, and presenting the insights in a user-friendly format. This automation project is designed to provide timely and relevant financial information to users interested in staying informed about market trends, economic news, and financial events. By automating the entire workflow, users can stay informed, make data-driven decisions, and navigate the dynamic landscape of financial news with ease. The project embodies the fusion of automation, data analytics, and user-centric design to create a powerful tool for staying ahead in the world of finance.
Department: Information Technology
Supervisor: Dr. Ying Xie
Topics: Data/Data Analytics
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Audience Favorite Presenter Spring 24