C-Day Program

Undergraduate and graduate projects: scale 0- 10 with 0 representing "Poor" and 10 representing "Exceeds Expectations"

• Successfully completed stated project goals and reported deliverables (0-10)
• Methodology/Approach: All required elements are clearly visible, organized, and articulated (0-10)
• Effective verbal presentation (0-10)
• Evidence of Rigor (0-10)
• Merit and Broader impact (0-10)

Games: scale 0 - 10 with 0 representing "Poor" and 10 representing "Awesome"

• TECHNICAL: Technically sound with appropriate visual & audio fidelity(0-10)
• GAMEPLAY: Engaging & Fun, with an intuitive UI. Rules of play are clear. Includes a win/lose state(0-10)
• ORIGINALITY: Sound, Art, Design, or Code(0-10)
• Evidence of Rigor (0-10)
• Merit and Broader impact (0-10)

Alumni Judges will judge the Undergraduate Capstone projects to determine the 鈥渂est鈥� from those presented. Undergraduate Capstone Project titles start with the letters 鈥淯C 鈥撯�� on their poster. 

• Team Approach: 20 pts (did the team work together effectively to meet goals)
• Presentation: 20 pts (did the team sell the idea)
• Use of Technology: 40 pts (is technology being used an effective way)
• Feasibility/Impact for Business/Industry: 20 pts (doable/valuable/effective)

Academic courses undergraduate (e.g. capstones, games, innovative special topics projects, other course projects) 

• * UC-20 Playlist Synch (Undergraduate Capstone) by ; ; 
  Abstract: Our project is a web application that allows users to sign in and transfer music playlists from one music streaming service to another. Currently, it is only functional with Apple and Spotify music but there are plans to implement more in the future.
  Department: Computer Science
  Supervisor: Prof. Sharon Perry
  Topics: Mobile Application
   |  | 

• * UC-24 Wildling Rumble (Undergraduate Capstone) by ; 
  Abstract: When it comes to the combined field of digital board games, there needs to be a balance of what is necessary for the physical space and what is necessary for the digital space. The game must be justified as a combination of the two elements and not be able to shift completely to either side. In this study, we are exploring a new modality that uses Near Field Communication cards for transferring game data to the application. Our new method eases the requirement on players to keep track of the game state, as that is handled separately from the program.
  Department: Software Engineering or Game Design
  Supervisor: Dr. Henrik Warpefelt
  Topics: Games
   |  | 

• * UC-27 CRM Proposal (Undergraduate Capstone) by ; ; ; ; 
  Abstract: This project addresses concerns raised by a Sponsor regarding inefficiencies in managing CCSE capstone projects. Key pain points include organization, project management, and communication among stakeholders. The proposed solution involves implementing Customer Relationship Management (CRM) software. Upon gathering requirements from the Sponsor, including contact information capture, workflow management, document control, and customization capabilities, we evaluated several CRM platforms. A total of thirty-two CRMs were reviewed and Vtiger, OroCRM, and SuiteCRM were selected for testing. SuiteCRM was chosen for its comprehensive features and user-friendliness. The second step of the project involved testing SuiteCRM functionalities on a dedicated server, leading to its selection for deployment preparation. Final steps include documentation development, training material creation, and incorporating user feedback for optimal system configuration.
  Department: Information Technology
  Supervisor: Professor: Prof. Donald Privitera Project Sponsor & Coordinator: Alla Kemelmahker
  Topics: Enterprise Systems
   |  | 

• UC-33 Big Munchin' (Undergraduate Capstone) by ; ; ; ; 
  Abstract: Big Munchin' is a video game designed to help teach portioning skills and promote healthy eating habits. The overall metabolic health of individuals in America is comparatively low to other countries. Metabolic health is bolstered by several factors including exercise and a proper diet consisting of essential vitamins, proteins, and other important biomolecules.The increased cost of healthy nutrient-rich foods and a lack of proper nutritional education have hindered the overall metabolic health of modern Americans. Engaging individuals in learning more about nutritional health can be a difficult task made easier by an engaging experience that stays in the user鈥檚 minds. Big Munchin can be a vehicle for increasing Americans' overall awareness of nutritional health while also ensuring they better understand what foods can be beneficial for achieving those goals.
  Department: Software Engineering or Game Design
  Supervisor: Dr. Joy Li
  Topics: Games
   |  | 

• * UC-35 KSU CCSE CRM (Undergraduate Capstone) by ; ; 
  Abstract: Our task was to select, implement and customize a CRM solution for the College of Computing and Software Engineering to more effectively manage communication with industry partners and manage projects such as capstones, and C-Day. Our team selected SuiteCRM as our recommendation and have implemented an instance on a virtual machine provided by UITS. We have customized branding including using a KSU logo provided by the Office of Strategic Communications and Marketing, as well as customizations based on the official KSU color pallete. The process we used to select our CRM recommendation involved gathering requirements from our sponsor and comparing the features of open source CRMs, leading to our top selections. We created demo installations of these selections and followed example work flows to determine user friendliness and reliability. To verify our requirements we filled in a comparison chart and then during testing compared the process of completing tasks. We determined that SuiteCRM was the best choice for our project and moved forward with the installation.
  Department: Information Technology
  Supervisors: Prof. Donald Privitera, Alla Kemelmakher
  Topics: Enterprise Systems
   |  | 

• * UC-40 Asynchronous (Undergraduate Capstone) by ; ; ; ; 
  Abstract: Cultural assimilation is the topic on the mind of the protagonist of our game, Asynchronous. Maxwell, a diplomat from a real-time world, must adapt in a foreign land where the people live turn-based lives. We explore this topic through the lens of traditional JRPG gameplay where the player must decide when to adapt to this new culture and when to act on their own accord. By representing this idea ludically, we hope to better convey the mindset and emotional state of being an outsider to the player.
  Department: Software Engineering or Game Design
  Supervisor: Dr. Joy Li
  Topics: Games
   |  | 

• * UC-43 Aletheianomous AI: The Chat Bot Providing the Most Accurate Knowledge Information (Undergraduate Capstone) by ; ; ; 
  Abstract: For this project, our group aimed to create an intelligent chat bot that was accessible through the web client interface. Aletheianomous, our chat bot, was designed to provide accurate information ethically, aligned with human values. When applicable, the AI would offer the user citations to support its responses. For the back-end, a virtual machine (VM) server in AWS with access to the Graphics Processing Unit (GPU) would run three types of models: Sentence Separation Model, Search Query Extractor Model, and the Response Model. The front-end server using Microsoft Azure generates the web page for the user, exchanges chat data with the Microsoft SQL server, and communicates with the back-end server via REST APIs to request the chat bot to respond to user input. By using this architecture, the overall quality of our product exceeded our standards.
  Department: Computer Science
  Supervisor: Prof. Sharon Perry
  Topics: Artificial Intelligence
   |  | 

• * UC-48 Birding With Buddy (Undergraduate Capstone) by ; ; ; ; 
  Abstract: Birding with Buddy is an educational and entertaining immersive virtual 3D low-poly birdwatching to be experienced at the Carter Lake Nature Center to enable kids to embark on a quest to learn more about birds. Buddy the Beaver guides the user through different terrain types to identify diverse bird species with sounds. Integrate a bird identification system where players click on the binocular icon to switch to a binocular view. In this view, players can choose to Identify (multiple-choice) the correct bird, Hear the Call Again, or Consult a Field Guide. Featuring flippable pages with images and notable markings of different bird species. Allow players to consult the guide for additional information.
  Department: Information Technology
  Supervisor: Donald Privitera
  Topics: Games
   |  | 

• * UC-51 Difference Detection: Automated Defect Detection System for Modernized Display Units (Undergraduate Capstone) by ; ; ; ; 
  Abstract: In today's high-stakes military environments, the reliability and accuracy of software systems are paramount. Defects within these systems not only pose significant financial risks but can also endanger lives. To ensure the utmost safety and effectiveness, military systems undergo extensive testing and validation processes. However, the lifespan of these systems is far from indefinite. Environmental changes, advancements in adversarial capabilities, evolving mission requirements, and parts obsolescence necessitate continuous improvement efforts. One critical area of focus is the modernization of display units, which are vital for providing pilots with essential mission information and ensuring their safe return. The 402 Software Engineering Group (SWEG) at Robins AFB seeks an innovative solution to enhance defect detection in modernized display units using computer vision technology. By automating the evaluation process, our objective is to reduce manual intervention, allowing software developers to focus more on design and development tasks.
  Department: Software Engineering or Game Design
  Supervisosr: Dr. Yan Huang, Alla Kemelmakher, Robbins Air Force Base
  Topics: Other
   | 

• UC-56 Donation for Dummies (Undergraduate Capstone) by ; ; ; 
  Abstract: Donation For Dummies is a VR game designed to help people feel more relaxed and informed when donating blood. It consists of a theater room where a video plays explaining the process as well as what to do before and after donating. For people wanting a distraction, we have an arcade where players can enjoy minesweeper, matching, or solitaire. For those wishing to relax, we have an art gallery where players can virtually walk around and look at various pieces of art. The more relaxed player that do not wish to move around the game world can instead choose to enjoy the scenery from our meditation deck overlooking a valley. Anyone looking to become more informed about the process can make their way to the information center.
  Department: Software Engineering or Game Design
  Supervisor: Dr. Joy Li
  Topics: Games
   |  | 

• UC-60 MyFoodScan (Undergraduate Capstone) by , , , 
  Abstract: MyFoodScan is a mobile app that enables users to scan barcodes of various food and drink items to ensure they are in compliance with their specific dietary needs. Individuals utilize this application to make a customized profile based on diet, such as vegan, vegetarian, dairy-free, allergies, etc. MyFoodScan promotes compliance with dietary limitations. The application was developed with React Native, Expo Go, Google Firebase, using the React Native Camera for barcode scanning. The OpenFoodFacts API database is used for product information. The goal of this application is to enhance awareness and safety for various dietary needs and monitor dietary restrictions.
  Department: Computer Science
  Supervisor: Prof. Sharon Perry
  Topics: Mobile Application
   |  | 

• * UC-64 Smart Evaluator of Indirect Supplies Vendibility (Undergraduate Capstone) 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鈥檚 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
   |  | 

• UC-69 Interactive Training Game Suite (Undergraduate Capstone) by ; ; ; ; 
  Abstract: It is now common knowledge that simple lectures are not the most effective way for the average person to learn and retain knowledge. The Core of Engineers at the Warner Robins Air Logistic Center have tasked us to transform their PowerPoint presentations into interactive training games to improve comprehension, interaction and retention while saving time and logistical resources compared to giving a traditional lecture. We been tasked with creating game modules covering STINFO or what is or is not considered classified information, Records Management, and the No FEAR Act detailing whistleblower rights and protocols. Our team has developed a log in and sign up portal, robust menus and UI/UX, a system to administer and save results of a quiz, and a 3D environment in which the user can explore and complete tasks which will assist in the learning of these three applications. Our project uses the Unity Game engine, Unity WebGL for hosting in browser, and the Playfab database, as well as assets from the Unity Asset store to improve and streamline our development.
  Department: Software Engineering or Game Design
  Supervisor: Alla Kemelmakher
  Topics: Games
   | 

• * UC-71 An Environmentally Conscious Roguelike (Undergraduate Capstone) by , , , 
  Abstract: This semester we have been creating an action-adventure video game based on the theme of "Saving the Environment".
  Department: Software Engineering or Game Design
  Supervisor: Dr. Joy Li
  Topics: Games
   |  | 

• * UC-73 Spacewalkers: Tilt 5 (Undergraduate Capstone) by ; ; ; 
  Abstract: Our project (and presentation as a whole) is to create an immersive and fun casual game experience using the Tilt 5 system. We made a tower defense game that uses the player鈥檚 input with the Tilt 5 in order to defend against enemies. By delivering a variety of enemies, levels, and towers in our game, we further enhanced the experience.
  Department: Software Engineering or Game Design
  Supervisor: Dr. Sungchul Jung
  Topics: Games
   | 

• UC-75 MineTicket Discord Ticket Management Bot (Undergraduate Capstone) by ; ; ; ; 
  Abstract: We made use of the discord.py python library to manage the framework of the bot, since discord relies on a RESTful API for all of its bots. This layer of abstraction allowed us to focus on the important things, such as sanitizing inputs for SQL queries and building out a large, fully customizable feature set so future adjustments are made simple and easy as changing entries within a configuration file. Our goal was to make the bot as easy to update as possible, with the hope that, should requirements change in the future, the sponsor can quickly modify existing code to adapt it to new situations and unforeseen requirements.
  Department: Information Technology
  Supervisor: Prof. Donald Privitera
  Topics: Games
   |  | 

• UC-81 Drinkinator App (Undergraduate Capstone) by ; ; 
  Abstract: The "Drinkinator" app represents an innovative solution aimed at revolutionizing the beverage industry by providing users with a personalized drink recommendation system. Rooted in a motivation to diversify people's beverage selections and enhance their drinking experiences, the app facilitates exploration of a wide range of mixed drinks, wines, and beers tailored to individual preferences. Leveraging data analysis and user profiling techniques, the app offers tailored suggestions, showcasing a deep understanding of consumer behavior and taste preferences. Utilizing React Native for front-end development and JavaScript for back-end functionality, the app integrates various libraries to ensure seamless user experiences and efficient data retrieval. With its unique approach, the "Drinkinator" app not only enriches user experiences but also holds significant business potential. Its personalized recommendation system can drive user engagement and retention, paving the way for targeted advertising and promotional opportunities within the beverage industry. Moreover, the app's potential to establish valuable partnerships further underscores its intellectual and business merit, positioning it as a leading platform for beverage enthusiasts seeking novel and diverse drinking experiences.
  Department: Software Engineering or Game Design
  Supervisor: Prof. Sharon Perry
  Topics: Mobile Application
   | 

• * UC-82 Trip Logger (Undergraduate Capstone) by ; ; 
  Abstract: We developed an Android mobile app using the Software Development Life Cycle (SDLC) approach to enable users to track their travel distance and time via GPS, fostering greater emissions awareness through their driving habits of distance and time taken. Built with the Flutter framework and Dart language, the app features a user-friendly interface created with Flutter widgets that manage both appearance and user interactions. Our streamlined architecture comprises three layers: the presentation layer for UI elements, the application layer containing the core logic, and the data layer, which locally stores trip data in CSV format to ensure quick access and reliability. We integrated the Geolocator package within Flutter for GPS functionality to obtain user coordinates and calculate distances between locations. This streamlined architecture and choice of technologies optimized the app鈥檚 efficiency and user-friendliness, allowing us to promote environmentally conscious driving.
  Department: Computer Science
  Supervisor: Prof. Sharon Perry
  Topics: Mobile Application
   |  | 

• * UC-85 HELPR: Helping Extrapolate Labels for Police Reports using Large Language Models (Undergraduate Capstone) by ; 
  Abstract: Police officers spend many hours a week documenting their findings when reporting to a 911 call. There is so much detail in these reports that they remain an untapped resource for future data analytics by the police department. The reports are currently being analyzed by human experts and categorized into the following categories: 鈥淪ubstance Abuse鈥�, 鈥淢ental Health鈥�, 鈥淒omestic/Social鈥�, 鈥淣ondomestic/Social鈥�, and 鈥淥ther鈥�. To assist the experts and reduce the amount of time that is spent on reading and analyzing, we are proposing the use of large language models (LLMs) to tag police reports based on their content. Two models, Mistral-7B and TinyLlama, have been trained and fine-tuned to reduce the time needed to complete police report documentation. Both models output both the tag and the reason for the chosen tag, so one of the potential uses is for it to be used to train human analyzers in the future. For the finetuned Mistral-7B model we observed a 84% and 88% agreement with both human annotators and a 96% and 92% agreement with at least one human annotator on sample tagging and sample reasoning respectively.
  Department: Computer Science
  Supervisors: Prof. Sharon Perry, Dr. Hafiz Khan
  Topics: Artificial Intelligence
   |  | 

• UC-92 Faculty Course Analysis Report Self Service Interface (Undergraduate Capstone) by ; ; ; 
  Abstract: This project aims to enhance the efficiency of generating and submitting Faculty Course Analysis Reports (FCARs) for the faculty of CCSE in accordance with ABET accreditation requirements. The scope involves the design and implementation of a locally hosted web-based application. This application will streamline the process, allowing FCARs to be requested and generated in real time. Development will be conducted on a Linux-based platform, and security measures will be integrated based on the NetID system.
  Department: Information Technology
  Sponsor: Prof. William Forsyth
  Course Instructor: Prof. Donald Privitera
  Topics: Other
   |  | 

• UC-99 Interactive Training Games - Robins Air Force Base (Undergraduate Capstone) by ; ; ; ; 
  Abstract: Our project involves converting three PowerPoint training presentations on STINFO, No Fears Act, and Records Management into engaging web-based games. Commissioned by Robins Air Force Base, our team utilizes Unity WebGL for game development and React/Firebase for website hosting. The goal is to provide Air Force personnel with interactive training modules accessible from their desks, enhancing learning retention and engagement. By gamifying the content, we aim to make learning enjoyable while ensuring critical information retention. This interdisciplinary project merges game development and web technologies to modernize training methods and improve educational outcomes for military personnel.
  Department: Software Engineering or Game Design
  Supervisors: Dr. Yan Huang, Alla Kemelmakher, Brian Woods
  Topics: Games
   | 

• * UC-100 Indy 7 - Nutrition App (Undergraduate Capstone) by ; ; ; 
  Abstract: This project鈥檚 objective is to develop a fully functioning and polished mobile app for keeping track of caloric intake and monitoring other aspects of one鈥檚 health. To accomplish this, we will be using React Native to develop a front end which allows users to select their goals and get active assistance in moderating what they eat. This will be done through the scanning of barcodes of any food purchased. These barcodes will then be used to query the Food Data Central API to provide the user with as much information as needed. This will include possible allergies, calorie totals, protein totals, and more relevant information.
  Department: Computer Science
  Supervisor: Prof. Sharon Perry
  Topics: Mobile Application
   | 

• * UC-105 Model UN Crisis Software (Undergraduate Capstone) 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: Computer Science
  Supervisor: Prof. Sharon Perry
  Topics: Other
   |  | 

• * UC-120 Virtual Companionship Chatbot (Undergraduate Capstone) 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
   |  | 

Academic courses graduate (e.g. capstones, games, innovative special topics projects, other course projects)

• * GC-8 Informal Learning Artificial Intelligence Large Language Model Fine-Tuning on the Select Topic of Entrepreneurship (Master's Capstone) by ; ; 
  Abstract: This project explored the usage and development of open-source Large Language Model (LLM) Artificial Intelligence (AI) with a chat feature, specifically to fine-tune on the topic of entrepreneurship. This project sought to showcase the adaptability of open-source LLMs and highlight challenges and solutions faced in leveraging those LLMs. The primary goal was to show proof of concept that training a LLM on the selected subject can create a specialized AI chat for informal learning.
  Department: Information Technology
  Supervisor: Dr. Ying Xie
  Topics: Artificial Intelligence
   |  | 

• GC-17 CRM for CCSE Department of KSU (Master's Capstone) 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: Sponsors: Alla Kemelmakher
  Instructor: Dr. Ying Xie
  Topics: Enterprise Systems
   | 

• GC-22 Fabric moderation ticketing mod for Minecraft (Master's Capstone) by ; ; ; 
  Abstract: This project targets enhancing the KSU Esports program鈥檚 Minecraft server by implementing an in-game ticketing system. The system will enable players to report any instances of in-game incidents/issues seamlessly within the game environment. The integration with the KSU Minecraft Discord server will facilitate efficient communication between players and administrators. With a user-friendly interface and optimized resource usage, the system aims to streamline moderation processes.
  Department: Information Technology
  Supervisor: Dr. Ying Xie
  Topics: Games
   |  | 

• GC-67 Automatic Daily Financing Headlines Collection, Storage, Analysis and Presentation (Master's Capstone) 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
   |  | 

• * GC-84 Owl Cyber Defense Systems (Master's Capstone) 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
   |  | 

• GC-101 Learning Resource Finder: A web Scraping tool for educational materials (Master's Capstone) 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
   | 

• GC-104 EdAI 鈥� AI Enabled Teaching Robot for Informal Learning (Master's Capstone) by ; ; 
  Abstract: We began our project by researching various popular, open-source AI tools that are available today. After we chose to focus on ChatGPT as our AI tool, we decided on cybersecurity as our subject matter. Next, we researched traditional cybersecurity training methods used by companies to train their employees on cybersecurity issues. Our project focused on determining whether or not open-source AI tools such as ChatGPT could replace traditional cybersecurity training tools and methods for companies.
  Department: Information Technology
  Supervisor: Dr. Ying Xie
  Topics: Artificial Intelligence
   |  | 

Research projects completed by undergraduate students.

• * UR-12 Multiple Myeloma: Increase Longevity and Quality of Life through Early Detection (Undergraduate Research) by 
  Abstract: Multiple Myeloma is a rare form of bone marrow cancer where plasma cells accumulate in the blood stream attacking the skeletal system, nervous system, and kidneys of predominantly African Americans. The disease results in high mortality rates within 5 years of initial diagnosis. Multiple Myeloma has subtle symptoms of bone pain; doctors often send people to physical therapy missing the diagnosis. Current research on the International Myeloma Foundation website includes summaries of blood tests of Multiple Myeloma patients. This study seeks to identify the best blood test predictors of Stage 3, the most aggressive stage of Multiple Myeloma. The cost savings are also considered of using these blood tests as an initial screening as compared to a bone marrow biopsy. Using 21 blood test results and demographic information on 203 Multiple Myeloma patients from 2008-2019 in Algeria, Logistic Regression was conducted to identify the best predictor of Stage 3 Multiple Myeloma versus Stage 1 and 2. Wald Confidence Intervals were used to estimate the odds ratios. Cost savings were calculated by determining the cost differential of less invasive blood tests versus the more invasive bone marrow biopsy. The Logistic Model was able to distinguish 65.43% of the time between whether patients have Stage 3 versus Stages 1 and 2. The odds of having Stage 3 Multiple Myeloma increase 1.04 to 1.56 times for each one g/dL decrease in MCHC. Lower patient MCHC levels are more indicative of the patient having Stage 3. Using an MCHC blood test has an estimated cost savings of $1734 per patient as compared to a bone marrow biopsy. Testing a patient with bone pain for MCHC can facilitate earlier Multiple Myeloma diagnosis, allowing physicians to administer earlier treatments, thereby improving patients鈥� longevity and quality of life while coping with the disease.
  Department: Data Science and Analytics
  Supervisors: Prof. Susan Mathews Hardy, Dr. Gene Ray
  Topics: Data/Data Analytics
   | 

• UR-15 Analyzing Breast Cancer Histopathology Images Using Deep Neural Network Models (Undergraduate Research) by ; ; 
  Abstract: Our project aims to explore human tissue cells digitized by whole slide scanners for a better understanding of complex tumor microenvironments in breast cancer histopathology images, using various deep neural network models. First, we experimented with 70% percentages of tumor cells on image classification using ResNet50, VGG16, and Inception-ResNet. Second, we performed instance image segmentation using Mask-RCNN. Third, we applied two well-known explainable artificial intelligence (AI) techniques including Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP) to determine the effectiveness of the models.
  Department: Computer Science
  Supervisor: Dr. Sanghoon Lee
  Topics: Artificial Intelligence
   |  | 

• UR-49 Coronary Artery Segmentation Using Convolutional Neural Network (Undergraduate Research) by ; ; 
  Abstract: The project contributes to the advancement of medical imaging technology by overcoming the challenges associated with segmenting coronary arteries from ICA images. By leveraging deep learning algorithms, the system can effectively extract coronary arteries with high accuracy, providing valuable information for CAD diagnosis and treatment planning. Accurate and efficient coronary artery segmentation can improve the workflow of cardiologists and enhance the quality of patient care. A robust automated segmentation model could potentially reduce the time and resources required for manual annotation by experienced cardiologists, leading to cost savings and increased efficiency in clinical settings. Additionally, the developed model could be integrated into the education system with an interactive GUI for cardiologists to draw and learn the anatomical structure of the coronary artery system.
  Department: Computer Science
  Supervisors: Dr. Chen Zhao, Prof. Sharon Perry
  Topics: Artificial Intelligence
   |  | 

• UR-62 Deep Learning Approach to Network Anomaly Detection (Undergraduate Research) by ; 
  Abstract: A model of network anomaly detection capable of detecting a multitude of network attacks. This model is based on the hypothesis that by studying a system鈥檚 network records for irregular patterns during system usage, network anomalies can be identified. This model contains information about the type of attacks and metrics. This model is to be used in any type of distributed environment. The general purpose of this model is to detect when an attack is or has happened using deep learning techniques to optimize the training speed, accuracy and robustness of attack detection. This is done to stop the epidemic of attacks that hit companies like GitHub, Nobel Foundation, Vodafone, and some Russian banks [13], with Google being the only company to block the 46 million DDOS attacks per second [13].
  Department: Computer Science
  Supervisor: Dr. Chen Zhao
  Topics: Artificial Intelligence
   |  | 

• * UR-70 Faster Inequivalence Testing Using Robustness (Undergraduate Research) by 
  Abstract: We propose a new method for quickly testing the inequivalence of two Boolean functions, when one function is represented as an ordered binary decision diagram (OBDD), and the other is represented in conjunctive normal form (CNF). Our approach is based on a notion of classifier robustness from the fields of explainable AI (XAI) and adversarial machine learning. In particular, we show that two Boolean functions that are very similar in terms of their truth values, can be very different in terms of their robustness, which in turn, provides a witness to their inequivalence. A more efficient approach to inequivalence testing has an impact on the development of more efficient model counters and knowledge compilers. In turn, such developments facilitate advances in explainable AI and adversarial ML.
  Department: Computer Science
  Supervisor: Dr. Arthur Choi
  Topics: Artificial Intelligence
   | 

• * UR-78 Transforming Game Play: A Comparative Study of CNN and Transformer based Q-Networks in Reinforcement Learning (Undergraduate Research) by 
  Abstract: In this study we investigate the performance of Deep Q-Networks utilizing Convolutional Neural Networks (CNNs) and Transformer architectures across 3 different Atari Games. The advent of DQNs have significantly advanced Reinforcement Learning, enabling agents to directly learn optimal policy from high dimensional sensory inputs from pixel or RAM data. While CNN based DQNs have been extensively studied and deployed in various domains Transformer based DQNs are relatively unexplored. Our research aims to fill this gap by benchmarking the performance of both DCQNs and DTQNs across the Atari games' Asteroids, Space Invaders and Centipede. Our research finds that our Transformer Agent learned slower than the CNN-based agent, and was slower to learn game-extending policies.
  Department: Computer Science
  Supervisor: Course Instructor: Jitendra Sai Kota
  Topics: Artificial Intelligence
   |  | 

• UR-87 Adversarial Patch Attack in Deep Learning Based Remote Sensing Object Detection Model (Undergraduate Research) 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
  Supervisor: Dr. Kazi Aminul Islam
  Supervising Graduate Assistant: Sumaiya Tasneem
  Topics: Artificial Intelligence
   | 

• UR-89 Performance Analysis of Post-Quantum Computing Key Encapsulation Mechanism Algorithms (Undergraduate Research) 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
   | 

• * UR-94 EmoHydra: Multimodal Emotion Classification using Heterogenous Modality Fusion (Undergraduate Research) 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
   |  | 

• * UR-116 Enhancing Engineering Education Through LLM-Driven Adaptive Quiz Generation (Undergraduate Research) by ; 
  Abstract: This study aims to develop an Artificial Intelligence (AI) quiz generation system for engineering students to enhance personalized learning. In the rapidly evolving field of educational education, the emergence of AI and, more specifically, Large Language Models (LLMs) such as GPT-4, Llama, Claude, and Gemini, has marked a significant advancement. Our literature review method employs a systematic approach, analyzing peer-reviewed articles, conference papers, and authoritative reports to uncover the trends and challenges in AI-driven quiz generation. The notable gap identified in our literature review is the lack of LLM-based quiz generation methods specifically for engineering education, which incorporate interactive and adaptive learning features to enhance student engagement and comprehension. This study examines the application of OpenAI LLM with a Retrieval-Augmented Generation (RAG) system in creating personalized quiz questions for engineering education, focusing on a novel methodology to enhance learning experiences through dynamic, adaptive quizzes and tutorials, particularly targeting the development of math reasoning skills in visual contexts. The proposed methodology leverages the MathVista dataset, comprising 6,141 examples, to enhance the capabilities of the OpenAI LLM. The RAG system populated with this dataset serves as a reference context for generating more relevant and accurate quiz questions. Prompt engineering techniques guide the OpenAI LLM in creating detailed multiple-choice questions (MCQs) focused on visual-mathematical reasoning challenges. The quizzes are designed to adapt to varying levels of student performance, incorporating feedback loops to customize future quizzes based on student responses. The evaluation of our AI pipeline's effectiveness employed metrics such as accuracy, relevance, and adaptability. The results indicated a significant performance in the generation of accurate questions with the least hallucinations.
  Department: Computer Science
  Supervisor: Dr. Nasrin Dehbozorgi
  Topics: Artificial Intelligence
   | 

Research projects completed by master's students.

• GMR-7 A Novel Identity Verification Framework using a Hybrid Biometric System (Master's Research) by ; ; ;
  Abstract: In recent years, the field of big data analytics has gained immense attention due to the increasing volume and complexity of data being generated from various sources. One of the key applications of big data analytics is in the field of identity verification, where it is used to process and analyze large amounts of biometric data to authenticate individuals. A hybrid biometric system that combines multiple biometric modalities has been shown to be more effective in identity verification than a single modality system. In this project, we propose an Identity Verification Framework using a Hybrid Biometric System that leverages big data analytics techniques to improve the accuracy and efficiency of the verification process. The framework uses a combination of iris recognition and face recognition modalities to authenticate individuals. The proposed framework involves several stages, including data collection, preprocessing, feature extraction, and classification. The dataset used in the project includes 460 images in total, including 5 photographs of each left and right iris from 46 individuals. Iris segmentation is used to extract the iris region, and Gabor wavelets are used to extract texture features from the iris image. Face detection and recognition are performed using OpenCV's Haar Cascade classifier and deep learning-based face recognition models, respectively.
  Department: Computer Science
  Supervisor: Dr. Dan Lo
  Topics: Data/Data Analytics
   | 

• * GMR-23 Jamming Signal Detection Using Extreme Gradient Boosting (XGBoost) Algorithm (Mixed teams (graduate and undergraduate Research)) by ;
  Abstract: Radar jamming involves sending intentionally disruptive radio waves toward the target radar, which might over-saturate its receiver so it can鈥檛 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
   | 

• GMR-29 Identification of AI-Generated Images (Master's Research) by ; ; ;
  Abstract: With the quick rise of Artificial Intelligence (AI), generative AI models have greatly increased the volume and velocity of data creation. Among that data, AI-generated images have become a highly discussed topic, especially when discussing the potential dangers of these AI models. Due to these dangers, being able to distinguish AI-generated art from human-made art is becoming a necessity. Additionally, as these AI-models improve, it is becoming increasingly difficult for humans to determine whether art is AI-generated or human-made. This paper proposes the further exploration of the effectiveness of a current state of the art AI-image identification model.
  Department: Computer Science
  Supervisor: Dr. Dan Lo
  Topics: Data/Data Analytics
   | 

• GMR-45 Cloud Based Bus Tracking and Ticketing System (Master's Research) by ;
  Abstract: In many urban areas, public transportation systems frequently fail to fulfill passenger demand, causing aggravation owing to a lack of real-time information about bus locations, timetables and delays. Outdated ticketing processes, which are labor-intensive and prone to mistakes, compound the annoyance by failing to match current travelers' expectations. Furthermore, transportation operators encounter difficulties with fleet management, route optimisation, and issue response due to a lack of comprehensive data analytics. Reliable monitoring systems are required to prioritize passenger and bus safety, and achieving sustainability targets necessitates efficient operations to reduce fuel consumption and emissions. A scalable and customizable cloud-based bus tracking and ticketing system is critical for addressing these difficulties and improving the entire public transit experience.
  Department: Computer Science
  Supervisor: Dr. Manohar Raavi
  Topics: IoT/Cloud/Networking
   | 

• GMR-47 A Two-Stage Prediction Model For House Prices (Master's Research) by ; ; ;
  Abstract: Predicting house prices is a challenging task that researchers from various fields (economics, statistics, politics, etc.) have attempted to answer. An accurate house prediction is useful not only to policymakers to improve their policies, but also to help sellers and buyers in the real estate market make well- informed decisions. Commonly, prediction models are trained on the whole dataset. However, as Azimlu et al [1] suggested, such models might not perform very well on dispersed data. They propose a new approach which first divides the whole dataset into smaller clusters, and then each cluster would be trained with an appropriate machine learning algorithm. It is approved to provide a more accurate prediction.
  Department: Computer Science
  Supervisor: Dr. Dan Lo
  Topics: Data/Data Analytics
   | 

• * GMR-59 Customer Segmentation for Marketing Campaigns Using K-means Clustering (Master's Research) by ; ; ; ;
  Abstract: The objective of this project is to use K-means clustering an unsupervised machine learning algorithm to categorize customers based on characteristics such as demograp hics, purchasing history and interaction behavior. The purpose is to discover different client segments that can be targeted with specialized marketing techniques that improve marketing campaign efficiency and increase consumer satisfaction and engagement.
  Department: Computer Science
  Supervisor: Dr. Dan Lo
  Topics: Data/Data Analytics
   | 

• GMR-72 AI-based Discourse Analysis System (ADAS) for Improved STEM Education (Master's Research) by 
  Abstract: In the rapidly evolving fields of Artificial Intelligence and Natural Language Processing, significant opportunities have emerged to transform educational practices. Discourse analysis, particularly in science education, plays a critical role in fostering scientific thinking among students. However, the manual application of tools like the Classroom Discourse Analysis Tool is rource-intensive and impractical on a large scale. This abstract proposes the development of an AI-based Discourse Analysis System tailored for educational settings, designed to automate and enrich the analysis of classroom discourse. Leveraging the latest in Artificial Intelligence and Natural Language Processing, this web-based application will provide teachers nationwide with the ability to upload audio transcripts and receive comprehensive, objective feedback on classroom interactions. This system not only promises to streamline the analysis process but also to offer insights that were previously unattainable due to the manual nature of discourse analysis. By providing interactive tutorials, sophisticated feedback systems, and automated assessments, our solution aims to significantly enhance the efficiency and effectiveness of teaching and learning in science education. Ultimately, this initiative seeks to empower educators with advanced tools to cultivate a scientific mindset among students, thereby shaping the future of education.
  Department: Computer Science
  Supervisor: Dr. Nasrin Dehbozorgi
  Project Owner: Dr. Soon Lee
  Topics: Artificial Intelligence
   | 

• GMR-90 DigiMindReady: Enhancing Military Readiness with Edge AI-Driven Wellness, Education, and Digital Discipline through mHealth Innovation. (Master's Research) by , 
  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
   | 

• * GMR-107 An Integrated Architecture For Maintaining Security In Cloud Computing Using Blockchain (Master's Research) by ; ;
  Abstract: Cloud services are vulnerable to assaults because of their widespread availability. Since cloud computing is still a relatively new service, there is a real risk that sensitive information might be altered while in transit. Because of this, bad actors may gain an edge by manipulating data. Clients using the cloud for a wide range of use cases want to know that their data is reliable and secure. Blockchain, on the other hand, is an immutable digital ledger that may be used with cloud computing to provide an immutable cloudbased data storage and processing system. In this work, we present a method that integrates blockchain technology with cloud computing to guarantee the security of data encrypted using any homomorphic encryption method. The suggested technique uses a distributed network of processing CSPs determined by client needs to circumvent the CSP's ultimate jurisdiction over the data. All CSPs work together to calculate a single, unified hash value for use in their shared database.Bitcoin and Ethereumblockchain networks keep track of master hash values to guarantee the production of immutable data.
  Department: Computer Science
  Supervisor: Dr. Manohar Raavi
  Topics: IoT/Cloud/Networking
   | 

• GMR-118 Stress Detection by Wearable Devices: Integrating Multimodal Physiological Signals and Machine Learning Techniques (Master's Research) by 
  Abstract: In the medical field, the most common complaint of patients is 鈥渟tress鈥�. 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 鈥淲earable Stress and Affect Detection鈥� (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
   | 

Research projects completed by PhD students.

• * GPR-13 Effect Of Noise And Topologies On Multi-Photon Quantum Protocols (PhD Research) 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
   | 

• GPR-16 Attention Driven Framework for Detecting Mental Illness Causes from Social Media (PhD Research) 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
   | 

• * GPR-18 Case Exploration: Automatic Keyword Matching Framework for Behavioral Health (PhD Research) by ; 
  Abstract: In this demonstration, we propose a framework for exploring, identifying, and matching repeated behavioral health keywords in first-responder reports to the current set of Behavioral Health Index Terms provided by subject matter experts (SMEs). The tool incorporates behavioral health-related keywords and has a Graphical User Interface (GUI) that allows non-technical users to explore and analyze 911 first-responder reports. We utilized an inverted index, best-matching (BM25), and plain-text searching algorithms to match keywords in first-responder reports. This tool provides a comprehensive approach to report analysis by identifying indicators of mental health disorders and taking into account the assessments of humanities and social science professionals
  Department: Computer Science
  Supervisor: Dr. Md Abdullah Al Hafiz Khan
  Topics: Software Engineering
   | 

• * GPR-63 Adaptive Attention Aware Fusion for Human-in-Loop Behavioral Health Detection (PhD Research) by ; 
  Abstract: Identifying behavioral health is paramount for law enforcement officers to provide appropriate follow-up community care. In the current practice, law enforcement offices manually identify these behavioral health cases to allow the designation of the relevant follow-up resources. In this work, we develop a tool to automatically detect behavioral health cases from police public narrative reports by identifying behavioral health indicator signals. We propose a novel adaptive attention-aware fusion model for detecting behavioral health signals in sensitive police reports. Our model leverages contextual and semantic information from the reports and relevant behavioral health cues as keywords from a pre-trained attention-weighted keyword-based model. Our model also employs label self-attention mechanisms to correlate label embeddings with the report and keyword representations. Furthermore, we propose a novel clustering-based uncertainty-enabled informative sampling query strategy to integrate humans-in-the-loop in the active learning framework to reduce required annotation from experts. This querying strategy selects the most informative and diverse samples for expert annotation. Our experimental results showed that the proposed model outperforms state-of-the-art classifiers on a dataset of 300 manually annotated ground truth police reports, achieving an accuracy of 87.58% and an F1-score of 85.67%. Applying our querying strategy to our proposed model increased the detection of behavioral health, achieving an accuracy of 92% and an F1- score of 91.1%. Also, our proposed model achieves an accuracy score of 93.75% and an F1-score of 93.61% on unseen samples. Lastly, our proposed model demonstrates its interpretability by extracting the keywords associated with each behavioral health category.
  Department: Data Science and Analytics
  Supervisors: Dr. Md Abdullah Al Hafiz Khan, Dr. Dominic Thomas.
  Topics: Artificial Intelligence
   | 

• GPR-103 Personalized Pedagogy through a LLM-based Recommender System (PhD Research) 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
   | 

• GPR-108 Revolutionizing Reflective Learning in Higher Education: An LLM-Based Analytical Approach (PhD Research) by , 
  Abstract: This project introduces a novel LLM-based system to automate The analysis of student reflections, enhancing reflective learning in higher education. Leveraging advanced ML and NLP technologies, the system provides personalized, in-depth feedback by identifying learning outcomes and challenges. Employing the OpenAI API and LangChain framework, it offers a nuanced understanding of student learning trajectories. The methodology involves collecting data via the Minute Paper technique, enabling targeted instructional adjustments. Preliminary results indicate a significant improvement in analyzing and addressing students' educational needs.
  Department: Computer Science
  Supervisor: Dr. Nasrin Dehbozorgi
  Topics: Artificial Intelligence
   |