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Uc-100 Indy 7 - Nutrition App, Silas E Hammond, Tho Mai, Christopher P Sarzen, Michael Ehme
Uc-100 Indy 7 - Nutrition App, Silas E Hammond, Tho Mai, Christopher P Sarzen, Michael Ehme
C-Day Computing Showcase
This project’s objective is to develop a fully functioning and polished mobile app for keeping track of caloric intake and monitoring other aspects of one’s 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 …
Uc-120 Virtual Companionship Chatbot, Cesar Tortolero, Rakshak Gurung, Noah Clark, Fabritzzio A Cabrejos
Uc-120 Virtual Companionship Chatbot, Cesar Tortolero, Rakshak Gurung, Noah Clark, Fabritzzio A Cabrejos
C-Day Computing Showcase
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.
Uc-60 Myfoodscan, Ibrahima Gueye, Brianna J Noel, Victoria O Kuswita, Je'dae Lisbon
Uc-60 Myfoodscan, Ibrahima Gueye, Brianna J Noel, Victoria O Kuswita, Je'dae Lisbon
C-Day Computing Showcase
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.
Uc-85 Helpr: Helping Extrapolate Labels For Police Reports Using Large Language Models, Hailey N Walker, William A Stigall
Uc-85 Helpr: Helping Extrapolate Labels For Police Reports Using Large Language Models, Hailey N Walker, William A Stigall
C-Day Computing Showcase
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: “Substance Abuse”, “Mental Health”, “Domestic/Social”, “Nondomestic/Social”, and “Other”. 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 …
Uc-92 Faculty Course Analysis Report Self Service Interface, Meet Patel, Trevor Harbin, Omit Deb, Edwin Mayhew
Uc-92 Faculty Course Analysis Report Self Service Interface, Meet Patel, Trevor Harbin, Omit Deb, Edwin Mayhew
C-Day Computing Showcase
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.
Uc-99 Interactive Training Games - Robins Air Force Base, Sean J Tenney, Vt Nguyen, Ian Ford, Mason Farmer, Aaron Hannah
Uc-99 Interactive Training Games - Robins Air Force Base, Sean J Tenney, Vt Nguyen, Ian Ford, Mason Farmer, Aaron Hannah
C-Day Computing Showcase
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.
Uc-51 Difference Detection: Automated Defect Detection System For Modernized Display Units, Umer Aziz, Harrison Rosser, Urooj Arshad, Teonta N Pegues, Katherine Vu
Uc-51 Difference Detection: Automated Defect Detection System For Modernized Display Units, Umer Aziz, Harrison Rosser, Urooj Arshad, Teonta N Pegues, Katherine Vu
C-Day Computing Showcase
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 …
Ur-116 Enhancing Engineering Education Through Llm-Driven Adaptive Quiz Generation, Devananda Sreekanth, Sreekanth Gopi
Ur-116 Enhancing Engineering Education Through Llm-Driven Adaptive Quiz Generation, Devananda Sreekanth, Sreekanth Gopi
C-Day Computing Showcase
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 …
Ur-12 Multiple Myeloma: Increase Longevity And Quality Of Life Through Early Detection, Desyne Martinez
Ur-12 Multiple Myeloma: Increase Longevity And Quality Of Life Through Early Detection, Desyne Martinez
C-Day Computing Showcase
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 …
Ur-15 Analyzing Breast Cancer Histopathology Images Using Deep Neural Network Models, Je'dae Lisbon, Michael Bolnik, Sepehr Eshaghian
Ur-15 Analyzing Breast Cancer Histopathology Images Using Deep Neural Network Models, Je'dae Lisbon, Michael Bolnik, Sepehr Eshaghian
C-Day Computing Showcase
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.
Ur-70 Faster Inequivalence Testing Using Robustness, Emily G Jackson
Ur-70 Faster Inequivalence Testing Using Robustness, Emily G Jackson
C-Day Computing Showcase
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 …
Ur-89 Performance Analysis Of Post-Quantum Computing Key Encapsulation Mechanism Algorithms, Dillon J Horton, Jose Gutierrez, Tyler G Standard
Ur-89 Performance Analysis Of Post-Quantum Computing Key Encapsulation Mechanism Algorithms, Dillon J Horton, Jose Gutierrez, Tyler G Standard
C-Day Computing Showcase
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) …
Ur-94 Emohydra: Multimodal Emotion Classification Using Heterogenous Modality Fusion, William A Stigall
Ur-94 Emohydra: Multimodal Emotion Classification Using Heterogenous Modality Fusion, William A Stigall
C-Day Computing Showcase
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.
Gr-397 Conceptualizing A Toc-Enhanced Chatbot: Pattern Recognition And Interaction, Sumaiya Tasneem, Sharon Elugoti, Chinni Cherrishma Reddy Aduri, Purna Pavan Kumar Kolli, Krishna Vamsi Anche
Gr-397 Conceptualizing A Toc-Enhanced Chatbot: Pattern Recognition And Interaction, Sumaiya Tasneem, Sharon Elugoti, Chinni Cherrishma Reddy Aduri, Purna Pavan Kumar Kolli, Krishna Vamsi Anche
C-Day Computing Showcase
A chatbot is a software which is capable of communicating with human by using natural language processing. In our project, we plan to develop a Python-based chatbot that integrates theory of computation (TOC) concepts, including finite automata and regular expressions. The chatbot will interact with users, recognizing patterns and keywords in their inputs. We’ll begin by defining initial regular expressions for basic user interactions including greetings and inquiries.Future developments may enhance regular expressions and broaden the chatbot’s TOC-related capabilities, creating a versatile educational tool with practical TOC applications.
Gr-405 Boosting Clickbait Detection Through Semantic Insights And Attention-Driven Neural Network, Lokesh Meesala
Gr-405 Boosting Clickbait Detection Through Semantic Insights And Attention-Driven Neural Network, Lokesh Meesala
C-Day Computing Showcase
The digital age has witnessed an explosion of online content, making it increasingly challenging for users to differentiate between reliable information and clickbait, which is often misleading or sensationalized. Clickbait contributes to the spread of misinformation, phishing attacks, and illegal marketing practices, and manipulates users’ decisions. Even from a business standpoint a clickbait might not lead to a conversion, A user might land on the page by following a clickbait and get frustrated and close the page. Additionally, with the increase in the usage of large language models for content writing it is even more challenging for the general user …
Gr-434 Phase Estimation’S Application In Qram, Ethan K Hunt
Gr-434 Phase Estimation’S Application In Qram, Ethan K Hunt
C-Day Computing Showcase
The paper proposes a new novel way of creating QRAM through quantum phase estimation. This is done by mapping a monotonically increasing sequence of natural numbers to a binary series and, ultimately, to a characteristic constant η which is then encoded as a phase in a quantum state. This process leverages quantum phase estimation, a fundamental quantum algorithm for finding the eigenvalues of a unitary operator which can be used as a form of QRAM in either Quantum or Hybrid models of computing
Gr-453 Medical Records Summarization Using Prompt-Based Nlp, Rawan Masadeh, Nicholas S Servies
Gr-453 Medical Records Summarization Using Prompt-Based Nlp, Rawan Masadeh, Nicholas S Servies
C-Day Computing Showcase
In this paper, we present an innovative Natural Language Processing (NLP) algorithm for summarizing medical records extracted from the MIMIC-IV dataset using state-of-the-art (SOTA) techniques in text summarization. The increasing volume of electronic health records (EHRs) demands efficient methods for extracting meaningful insights from these complex and extensive documents. Our algorithm leverages recent advancements in NLP, including transformer-based models, to automate summarizing medical records while preserving critical information. Our algorithm is trained and tested using the Medical Information Mart for Intensive Care (MIMIC)-IV database that provides critical care data for over 40,000 patients admitted to intensive care units at the …
Egr-490 Importance Of Food Recognition On Blood Glucose Monitoring, Afnan Crystal
Egr-490 Importance Of Food Recognition On Blood Glucose Monitoring, Afnan Crystal
C-Day Computing Showcase
Maintaining blood sugar under control requires eating a healthy and balanced diet, exercising, and adhering to medications. Dietary consumption must be under strict control for diabetic patients’ general health. Traditional techniques for monitoring dietary consumption include recollection and manual record-keeping, which can be tedious and prone to mistakes. However, automated technologies for maintaining records that make use of computer vision, such as food image recognition systems, can streamline chronic health management for diabetics. These solutions seek to efficiently track daily food intake and consequential calories to facilitate and encourage lifestyle improvements. With this goal in mind, we design a Machine …
Gr-496 Cardiac Arrest Prediction Model, Vineeth Amsham, Sai Reddy Balaiah, Tamilkumar Subbarayakgounder
Gr-496 Cardiac Arrest Prediction Model, Vineeth Amsham, Sai Reddy Balaiah, Tamilkumar Subbarayakgounder
C-Day Computing Showcase
The "Cardiac arrest prediction model" project melds machine learning with healthcare to tackle heart disease. It aims to surpass current diagnostic tools that fail to catch early signs of cardiac events, often leading to high mortality. By developing an ML model that identifies early predictors of cardiac arrest, the project seeks to enable early interventions. Using supervised learning for its pattern recognition strength, the goal is to predict heart attacks accurately and thus, revolutionize preventative care and outcomes. This effort marks a leap in medical diagnostics and moves towards personalized healthcare, potentially saving countless lives and pioneering a new direction …
Gc-444 It Course Profile Website, Manikanta Voruganti
Gc-444 It Course Profile Website, Manikanta Voruganti
C-Day Computing Showcase
Build a Dynamic course profile website for Bachelor of science in information technology courses, that display all the information regarding the course.
Gc-448 Project Title: Discover, Learn, And Protect: A Mobile App For Informal Stem Learning About Local Biodiversity And Environmental Issues., Elvin Mccray, David Y. Appah, Adedunmola Banu, Binh Tran, Zenya Tucker
Gc-448 Project Title: Discover, Learn, And Protect: A Mobile App For Informal Stem Learning About Local Biodiversity And Environmental Issues., Elvin Mccray, David Y. Appah, Adedunmola Banu, Binh Tran, Zenya Tucker
C-Day Computing Showcase
Our team assignment for this project was to create a mobile application that offers informal STEM learning about local biodiversity and environmental issues. Dr. Ying Xie, Professor in the College of Computing and Software Engineering (CCSE) is the owner of this project, who also laid out required features and provided necessary information, guidance, and advice for the project development. The core function of this application is to empower users to explore, identify and gain insights into the plant and animal species native to their region. Leveraging the capabilities of their smartphone’s camera, users can effortlessly scan, record, or locate local …
Gc-511 Predicting Stock Prices Using Different Machine Learning And Deep Learning Models, Afnan Crystal, Rohith Sundar Jonnalagadda, Hrithik Singh Chandel
Gc-511 Predicting Stock Prices Using Different Machine Learning And Deep Learning Models, Afnan Crystal, Rohith Sundar Jonnalagadda, Hrithik Singh Chandel
C-Day Computing Showcase
Our project focuses on the challenge of predicting the daily closing prices and stock movements of Amazon, one of the world's largest and most dynamic corporations. Amazon's stock prices are known for their unpredictability and are influenced by a multitude of intricate factors. Our project aims to provide accurate and reliable forecasts for Amazon's stock prices, going beyond mere predictions. The analysis employs a comprehensive approach, comparing the performance of three distinct machine learning and deep learning models: Linear Regression, Support Vector Machine (SVM), and Multi-Layered Perceptron (MLP) for financial time series data. The dataset we used spans from January …
Gr-469 A Simulation Model Of The Traffic Signal System Using Java, Lingtao Chen
Gr-469 A Simulation Model Of The Traffic Signal System Using Java, Lingtao Chen
C-Day Computing Showcase
A traffic signal controls the flow of traffic at the intersection of two or more roadways. The first system of traffic signals was installed in London, England, in 1868. In this project, I will develop a simulation model for the traffic signal system using Java. The model will simulate the traffic signal system at a single four-way intersection. Also, I will compare the system performance with different input parameters, such as the number of vehicles and the cycle length, using various performance metrics, such as average waiting time and average sojourn time.
Euc-472 Biomimetic Remote-Controlled Vehicle, Jonathan Ridley, Kian Brown
Euc-472 Biomimetic Remote-Controlled Vehicle, Jonathan Ridley, Kian Brown
C-Day Computing Showcase
The goal of this project is smoothly integrating instinctual concepts of control into devices beyond the body. It is essentially an attempt to extend the body without any complex prior training. To do this, we have developed a both a Bluetooth connection between hand movements and the motors of a multifaceted vehicle. Furthermore, the hand movements will be tracked using both an accelerometer and gyroscope found in the common hobbyist tool Arduino nano. Logging this data and processing it through the Bluetooth communication system, the intention is to provide real-time updates to the vehicle’s motors that ultimately sync the intentions …
Uc-491 Spectrum Analysis Cli Tool, Vitor B. Santos, Trey Redden, Sam Corella, Christopher Flores Santos, Jonathan Glennon
Uc-491 Spectrum Analysis Cli Tool, Vitor B. Santos, Trey Redden, Sam Corella, Christopher Flores Santos, Jonathan Glennon
C-Day Computing Showcase
The Spectrum Analysis CLI Tool takes in .mp4 recordings of a Spectrum Analyzer, converts them programmatically into values the application can understand and outputs this data into a .csv file. This file can be parsed/filtered by the user with commands during upload of the .mp4 recording, or anytime after the recording has been processed.
Ur-510 Exploring The Impact Of Wavelength In Non-Invasive Blood Glucose Monitoring, John E. Oakley, Tahsin Kazi
Ur-510 Exploring The Impact Of Wavelength In Non-Invasive Blood Glucose Monitoring, John E. Oakley, Tahsin Kazi
C-Day Computing Showcase
Diabetes and metabolic diseases are some of the most crucial health issues of the 21st century. Monitoring blood glucose, the lead indicator of these diseases is a cumbersome process of constantly drawing blood or using subcutaneous needles. However, new technologies have emerged for non-invasive blood glucose monitoring that uses spectroscopy, which involves emitting light and capturing patient data with cameras. These new devices remove the cost of multiple tests, reduce the risk of skin conditions, and create more patient-friendly solutions. However, the hardware variables of these devices have not been tested thoroughly. One such avenue is via laser wavelength, which …
Gc-417 It Curriculum Success Portal, Alexander Thacker, Cedric Boakye-Danquah, Damola Adeyemo, Abdoulaye Ndiaye, Lydia Asante
Gc-417 It Curriculum Success Portal, Alexander Thacker, Cedric Boakye-Danquah, Damola Adeyemo, Abdoulaye Ndiaye, Lydia Asante
C-Day Computing Showcase
In this project, we built a curriculum and course web portal to have all curriculum and course information in one place with easy search and browse interfaces. Currently course data and information are scattered in various places. These include essential information like course description, learning outcomes, sample syllabus, offering schedule and history. It also includes curriculum development information for department use, such as coordinator, developer, revision schedule, open learning materials. We collected all sources of IT course information and built a database to integrate the data in one place. Through this, we can build a complete profile of a course …
Gr-515 Developing A Conversational Chatbot Using Seq2seq Model With Tensorflow, Drashtee Parmar, Ruthvik R. Anugu
Gr-515 Developing A Conversational Chatbot Using Seq2seq Model With Tensorflow, Drashtee Parmar, Ruthvik R. Anugu
C-Day Computing Showcase
Sequence-to-Sequence (Seq2Seq) modeling, when paired with Long-Short-Term Memory (LSTM) units, has demonstrated significant potential in developing conversational chatbot capable of participating in text-based conversation and providing human-like responses.The Cornell Movie-Dialogs Corpus will be used to extract dialogues, preprocess the data, and then use the output to train the Seq2Seq model. Our contributions include exploring the application of LSTM for Natural Language Generation (NLG) and creating a comprehensive chatbot system. According to the results of the experiment, our method works well for coming up with thoughtful answers during a conversation.
Ur-409 Enhancing Aircraft Electronic Warfare Testing With Automated Rf Spectrum Analysis, Anthony De Santiago, Matthew T. Morgan, Geonhyeong Kim, Jalon L. Bailey, Camille Reaves
Ur-409 Enhancing Aircraft Electronic Warfare Testing With Automated Rf Spectrum Analysis, Anthony De Santiago, Matthew T. Morgan, Geonhyeong Kim, Jalon L. Bailey, Camille Reaves
C-Day Computing Showcase
Military test ranges utilize a variety of Radio Frequency (RF) threat systems, to assess the effectiveness of Electronic Warfare (EW) systems during flight tests. A component of this process involves monitoring RF transmissions. Traditionally, system engineers at Robins Airforce Base have manually analyzed video from spectrum analyzers to confirm properties of specific threat systems. To streamline this analysis, our team's aim was to develop an automated solution for RF spectrum analysis. We employed a custom YOLO V8 model to isolate the analyzer screen and used a novel combination of frame differencing, summing, and agglomerative clustering techniques to extract relevant properties …
Eur-443 The Compression Connection: Ncd And Knn In Law Enforcement Text Analytics, Gabriel T. Gillott
Eur-443 The Compression Connection: Ncd And Knn In Law Enforcement Text Analytics, Gabriel T. Gillott
C-Day Computing Showcase
Facing a deluge of digital records, law enforcement needs advanced data sorting systems. This project uses a new NLP model, blending compression algorithms and KNN, to categorize Cobb County police reports by mental health, behavioral, and drug issues—vital for efficient resource allocation. The model employs Normalized Compression Distance (NCD) to discern text similarities, enhancing analysis of varying report styles. Early tests show promise in label categorization, but generalizing remains challenging, marking future research directions. This NLP advancement could revolutionize data handling in public safety, aiming to surpass current classification standards.