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Articles 8191 - 8220 of 63515
Full-Text Articles in Entire DC Network
Uc-43 Aletheianomous Ai: The Chat Bot Providing The Most Accurate Knowledge Information, David E Chavarro, Aimi Tran, Ethan B Byrd, Matthew J Fincher
Uc-43 Aletheianomous Ai: The Chat Bot Providing The Most Accurate Knowledge Information, David E Chavarro, Aimi Tran, Ethan B Byrd, Matthew J Fincher
C-Day Computing Showcase
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 …
Uc-48 Birding With Buddy, Lazare V Sawadogo, Ikhelowa E Adeji, Blake Graham, Zach Alpine, Troy C Sorrells
Uc-48 Birding With Buddy, Lazare V Sawadogo, Ikhelowa E Adeji, Blake Graham, Zach Alpine, Troy C Sorrells
C-Day Computing Showcase
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 …
Uc-56 Donation For Dummies, Jayvon L Triplett, Stephen A Mancini, Mitchell Thomason, Kendrell M Niles
Uc-56 Donation For Dummies, Jayvon L Triplett, Stephen A Mancini, Mitchell Thomason, Kendrell M Niles
C-Day Computing Showcase
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 …
Uc-64 Smart Evaluator Of Indirect Supplies Vendibility, Matthew S Periut, Cohen Miller, Carter Ray, Dawniqueca Steele, Justin Hughes
Uc-64 Smart Evaluator Of Indirect Supplies Vendibility, Matthew S Periut, Cohen Miller, Carter Ray, Dawniqueca Steele, Justin Hughes
C-Day Computing Showcase
The Smart Evaluator is a web-based software solution that analyzes industrial tools and their vending possibilities. It aims to streamline inventory research for sales teams, reducing manual data gathering and vendibility determination. To begin, users simply upload a basic item inventory spreadsheet, and start the program. From there, the program uses web scraping and ChatGPT to gather key data about the various tools including dimensions, weight, and fragility. Each item is then evaluated based on the collected data, and the optimum storage method is calculated. Once these tasks are performed, the results are stored in the system’s database for future …
Uc-69 Interactive Training Game Suite, Garrett J Perry, Joscelyn Cauley, Issabella Du, Sergiu Ursu, Rahaf Kokash
Uc-69 Interactive Training Game Suite, Garrett J Perry, Joscelyn Cauley, Issabella Du, Sergiu Ursu, Rahaf Kokash
C-Day Computing Showcase
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 …
Uc-71 An Environmentally Conscious Roguelike, Andrew Bland, Brendan Strawser, Adele Rousseau, Bewaji Adewunmi
Uc-71 An Environmentally Conscious Roguelike, Andrew Bland, Brendan Strawser, Adele Rousseau, Bewaji Adewunmi
C-Day Computing Showcase
This semester we have been creating an action-adventure video game based on the theme of "Saving the Environment".
Uc-81 Drinkinator App, Maximus A Smith, Toby Mose, Grayson Payne
Uc-81 Drinkinator App, Maximus A Smith, Toby Mose, Grayson Payne
C-Day Computing Showcase
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 …
Uc-82 Trip Logger, Reese D Gassner, Jason Zhou, Justin T Barker
Uc-82 Trip Logger, Reese D Gassner, Jason Zhou, Justin T Barker
C-Day Computing Showcase
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 …
Ur-49 Coronary Artery Segmentation Using Convolutional Neural Network, Connor Bell, Brenda Nyiam, Daron L Pracharn
Ur-49 Coronary Artery Segmentation Using Convolutional Neural Network, Connor Bell, Brenda Nyiam, Daron L Pracharn
C-Day Computing Showcase
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 …
Ur-62 Deep Learning Approach To Network Anomaly Detection, Rene P Lisasi, Jamia M Jackson
Ur-62 Deep Learning Approach To Network Anomaly Detection, Rene P Lisasi, Jamia M Jackson
C-Day Computing Showcase
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’s 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 …
Ur-78 Transforming Game Play: A Comparative Study Of Cnn And Transformer Based Q-Networks In Reinforcement Learning, William A Stigall
Ur-78 Transforming Game Play: A Comparative Study Of Cnn And Transformer Based Q-Networks In Reinforcement Learning, William A Stigall
C-Day Computing Showcase
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 …
Ur-87 Adversarial Patch Attack In Deep Learning Based Remote Sensing Object Detection Model, Kyle Bratcher
Ur-87 Adversarial Patch Attack In Deep Learning Based Remote Sensing Object Detection Model, Kyle Bratcher
C-Day Computing Showcase
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 …
Evaluating Introductory Computer Science Labs In The Presence Of Ai Tools, Nicholas Snow, Devin Chaimberlain, Abigail Pitcairn, Benjamin Sweeney
Evaluating Introductory Computer Science Labs In The Presence Of Ai Tools, Nicholas Snow, Devin Chaimberlain, Abigail Pitcairn, Benjamin Sweeney
Thinking Matters Symposium
This study explores the resistance of introductory computer science lab assignments to “shortcutting” by generative AI tools, such as ChatGPT. By analyzing the work of three distinct student personas on these assignments, we identified key characteristics of language and structure that influence an assignment's vulnerability to AI abuse. Based on these insights, we propose strategies for educators to adapt labs to both counteract AI shortcutting and encourage productive uses of AI.
Artificial Intelligence And Film: A Journey In Public Perception From 1960 To The Present Day, Kayla Anderson, Andrew Roggeman, Joseph Fuller
Artificial Intelligence And Film: A Journey In Public Perception From 1960 To The Present Day, Kayla Anderson, Andrew Roggeman, Joseph Fuller
Celebrating Scholarship and Creativity Day (2018-)
An analysis of accomplishments in film from the 1960s-2020s that feature Artificial Intelligence to give a full picture of how public perception has changed towards these technologies over time, supplemented by historical and technological context.
Semantic Segmentation Of Point Cloud Sequences Using Point Transformer V3, Marion Sisk
Semantic Segmentation Of Point Cloud Sequences Using Point Transformer V3, Marion Sisk
Master's Theses
Semantic segmentation of point clouds is a basic step for many autonomous systems including automobiles. In autonomous driving systems, LiDAR sensors are frequently used to produce point cloud sequences that allow the system to perceive the environment and navigate safely. Modern machine learning techniques for segmentation have predominately focused on single-scan segmentation, however sequence segmentation has often proven to perform better on common segmentation metrics. Using the popular Semantic KITTI dataset, we show that by providing point cloud sequences to a segmentation pipeline based on Point Transformer v3, we increase the segmentation performance between seven and fifteen percent when compared …
A System Of Communication Between Two Computers Using Novel Frequency Shift Keying Techniques, Jared Reyes
A System Of Communication Between Two Computers Using Novel Frequency Shift Keying Techniques, Jared Reyes
Honors Thesis
Frequency shift keying (FSK) is an old but powerful form of modulation that powered much of the early modems of the 1960’s, and the author felt inspired to make his own version of audio binary FSK modulation. He researched the general history and legacy of the Bell 103, a modem using FSK that defined telecommunication for the next few decades. Using research of the most common English characters of recent emails to determine which English characters should have the shortest bit length, a novel character encoding standard was created using variable bit rate. In addition, he has created a modulation …
Machine Learning-Based Gps Jamming And Spoofing Detection, Alberto Squatrito
Machine Learning-Based Gps Jamming And Spoofing Detection, Alberto Squatrito
Doctoral Dissertations and Master's Theses
The increasing reliance on Global Positioning System (GPS) technology across various sectors has exposed vulnerabilities to malicious attacks, particularly GPS jamming and spoofing. This thesis presents an analysis into detection and mitigation strategies for enhancing the resilience of GPS receivers against jamming and spoofing attacks. The research entails the development of a simulated GPS signal and a receiver model to accurately decode and extract information from simulated GPS signals. The study implements the generation of jammed and spoofed signals to emulate potential threats faced by GPS receivers in practical settings. The core innovation lies in the integration of machine learning …
Designing For Deployable, Secure, And Generic Machine Learning Systems, Li-Yun Wang
Designing For Deployable, Secure, And Generic Machine Learning Systems, Li-Yun Wang
Dissertations and Theses
Machine learning systems have catalyzed numerous image-centric applications owing to the significant achievements of machine learning algorithms and models. While these systems have showcased the efficacy of machine learning models, certain challenges persist, such as machine learning system design and security vulnerabilities inherent in deep neural networks. Moreover, the deployment of deep neural network models remains a significant hurdle. This dissertation introduces a multimedia prototyping framework tailored for visual analytical applications, improving the reusability of video analysis software tools with minimal performance overhead. Furthermore, we present novel image-processing techniques designed to bolster the robustness of deep neural networks and propose …
Lmcrot: An Enhanced Protein Crotonylation Site Predictor By Leveraging An Interpretable Window-Level Embedding From A Transformer-Based Protein Language Model, Pawel Pratyush, Soufia Bahmani, Suresh Pokharel, Hamid D. Ismail, Dukka Bahadur
Lmcrot: An Enhanced Protein Crotonylation Site Predictor By Leveraging An Interpretable Window-Level Embedding From A Transformer-Based Protein Language Model, Pawel Pratyush, Soufia Bahmani, Suresh Pokharel, Hamid D. Ismail, Dukka Bahadur
Michigan Tech Publications
MOTIVATION: Recent advancements in natural language processing have highlighted the effectiveness of global contextualized representations from Protein Language Models (pLMs) in numerous downstream tasks. Nonetheless, strategies to encode the site-of-interest leveraging pLMs for per-residue prediction tasks, such as crotonylation (Kcr) prediction, remain largely uncharted. RESULTS: Herein, we adopt a range of approaches for utilizing pLMs by experimenting with different input sequence types (full-length protein sequence versus window sequence), assessing the implications of utilizing per-residue embedding of the site-of-interest as well as embeddings of window residues centered around it. Building upon these insights, we developed a novel residual ConvBiLSTM network designed …
Skeletal 2-Groups And Category Theory, Nicholas Small '25, Alexander Stepanov '26, Haley Waiksnis '25
Skeletal 2-Groups And Category Theory, Nicholas Small '25, Alexander Stepanov '26, Haley Waiksnis '25
Mathematics & Computer Science Student Scholarship
Nicholas Small ’25, Mathematics and Computer Science major
Alexander Stepanov ’26, Mathematics and Studio Art major
Haley Waiksnis ’25, Mathematics major
Faculty Mentor: Dr. Laura Murray, Mathematics and Computer Science
Autonomous Remote Erosion Monitoring Using Computer Vision, Emily Gelchie '24, Gabriel Benz '25
Autonomous Remote Erosion Monitoring Using Computer Vision, Emily Gelchie '24, Gabriel Benz '25
Mathematics & Computer Science Student Scholarship
Emily Gelchie ’24, Computer Science major
Gabriel Benz ’25, Computer Science major
Faculty Mentor: Dr. Martin Helwig, Mathematics and Computer Science
Hello, World., Elliot Cetinski, Evan Chartock, Olivia Cross, Kiran Drew, Kaya Eller, Ben Little, Joey Nolan, Spencer Toth, Sophie Wahl-Taylor, Sadie Walker, Destiny Young, Annie Zulick
Hello, World., Elliot Cetinski, Evan Chartock, Olivia Cross, Kiran Drew, Kaya Eller, Ben Little, Joey Nolan, Spencer Toth, Sophie Wahl-Taylor, Sadie Walker, Destiny Young, Annie Zulick
Theater and Dance Presentations
This project works to theatrically represent the current state of Artificial Intelligence (AI), as well as its benefits and drawbacks, in the style of the Living Newspaper. Originating from a Great Depression-era job program, the Living Newspaper sought to take headlines and present them onstage for a poignant and contemporary social critique. This work does the same, melding different angles of the AI debate into a single production that emphasizes the rapidly progressing state of modern AI technology and the need for humans to consider the impacts such technologies will have. Furthermore, it asks the audience to question their position …
Avatars Runnin' On Dunkin, Keelin Robbins '26
Avatars Runnin' On Dunkin, Keelin Robbins '26
Mathematics & Computer Science Student Scholarship
Keelin Robbins ’26, Computer Science major, Finance minor
Faculty Mentor: Dr. Martin Helwig, Mathematics and Computer Science
Combining Empirical And Physics-Based Models For Solar Wind Prediction, Rob Johnson, Soukaina Filali Boubrahimi, Omar Bahri, Shah Muhammad Hamdi
Combining Empirical And Physics-Based Models For Solar Wind Prediction, Rob Johnson, Soukaina Filali Boubrahimi, Omar Bahri, Shah Muhammad Hamdi
Computer Science Faculty and Staff Publications
Solar wind modeling is classified into two main types: empirical models and physics-based models, each designed to forecast solar wind properties in various regions of the heliosphere. Empirical models, which are cost-effective, have demonstrated significant accuracy in predicting solar wind at the L1 Lagrange point. On the other hand, physics-based models rely on magnetohydrodynamics (MHD) principles and demand more computational resources. In this research paper, we build upon our recent novel approach that merges empirical and physics-based models. Our recent proposal involves the creation of a new physics-informed neural network that leverages time series data from solar wind predictors to …
Predicting Ffar4 Agonists Using Structure-Based Machine Learning Approach Based On Molecular Fingerprints, Zaid Anis Sherwani, Syeda Sumayya Tariq, Mamona Mushtaq, Ali Raza Siddiqui, Mohammad Nur-E-Alam, Aftab Ahmed, Zaheer Ul-Haq
Predicting Ffar4 Agonists Using Structure-Based Machine Learning Approach Based On Molecular Fingerprints, Zaid Anis Sherwani, Syeda Sumayya Tariq, Mamona Mushtaq, Ali Raza Siddiqui, Mohammad Nur-E-Alam, Aftab Ahmed, Zaheer Ul-Haq
Pharmacy Faculty Articles and Research
Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigating the atherogenic, obesogenic, pro-carcinogenic, and pro-diabetogenic effects, normally associated with the free fatty acids bound to FFAR4. In this research, molecular structure-based machine-learning techniques were employed to evaluate compounds as potential agonists for FFAR4. Molecular structures were encoded into bit arrays, serving as molecular fingerprints, which were subsequently analyzed using the Bayesian network algorithm to identify patterns for screening the data. The shortlisted hits obtained via machine learning protocols …
React Native Photo & Video Streaming/Processing Api Integration, Anamaria Oharciuc, William Carr, Sushanth Ambati, Ryan Blaisdell, Kyle Reed, Jack F. Myers
React Native Photo & Video Streaming/Processing Api Integration, Anamaria Oharciuc, William Carr, Sushanth Ambati, Ryan Blaisdell, Kyle Reed, Jack F. Myers
STEM Student Research Symposium Posters
RunSignup is a software company specializing in event management technology. Event organizers, known as Race Directors, utilize their platform to create and manage events, organize media albums, stream their events to the public, and more. In order to allow Race Directors to complete these actions in real-time at their events, RunSignup asked our team to develop a mobile app. With the app, Race Directors would be able to upload and stream photos to the event’s photo albums as soon as they are taken, and they could livestream the event to YouTube directly from their mobile device. Even if the device …
Blueberry Drone Ai: Smart Farming Of Blueberries Using Artificial Intelligence And Autonomous Drones, Robert Czarnota, Anthony Segrest, Anthony Thompson, Harper Zappone, Hieu Nguyen, Nguyen Thanh, Ik Jae Lee, Lori Green, Tuan Le
Blueberry Drone Ai: Smart Farming Of Blueberries Using Artificial Intelligence And Autonomous Drones, Robert Czarnota, Anthony Segrest, Anthony Thompson, Harper Zappone, Hieu Nguyen, Nguyen Thanh, Ik Jae Lee, Lori Green, Tuan Le
STEM Student Research Symposium Posters
This project seeks to assist blueberry growers in New Jersey with preventing blueberry scorch disease. Plants can’t be cured of scorch, so they have to be removed to prevent the disease from spreading to other bushes. This project aims to use object detection and classifier machine learning models in order to detect scorch disease with photos from intelligent drones. Images are first tiled, then processed through and convolutional neural network that detects scorch symptoms. Lastly, a fully connected neural network is implemented to make a final prediction.
Using Data Mining To Analyze Job Reviews, Nicholas Bornkamp, Tony Breitzman
Using Data Mining To Analyze Job Reviews, Nicholas Bornkamp, Tony Breitzman
STEM Student Research Symposium Posters
Job review websites like Glassdoor are not always clear on how well the company operates, especially as viewed from differing levels of employment. For instance, a middle or upper manager from Amazon may have an overall positive review of the company with minor issues about it, but someone who works in the warehouse may have a mixed experience. To solve this issue and determine any correlation between employee level and their review, data mining techniques were utilized such as website scraping and neural network training to develop a model that analyzes employee reviews.
Missionlog R - Air Force Mission History Report Management System With Encryption & Database Integration, Matthew Bachrach, Sam Jeffery, Michael Lim, Joe Johnston, Jack Healy, Andrew Siciliano, Jack F. Myers
Missionlog R - Air Force Mission History Report Management System With Encryption & Database Integration, Matthew Bachrach, Sam Jeffery, Michael Lim, Joe Johnston, Jack Healy, Andrew Siciliano, Jack F. Myers
STEM Student Research Symposium Posters
MissionLog R is a mobile and web application designed to allow for the filing, confirmation, and finalization of a mission history report for the US Air Force. The mobile application is used by filing members to fill out their report on their issued iPad. The form has been fitted with data validation and ghost data so the user knows what type of input that specific field is looking for. The web application can be used by the filing member to import their report from their iPad if no WiFi was available when they filled out the report. Then, the confirming …
Self-Sovereign Digital Identities, Maryam M. Ahmed, Bijayata Shrestha, Nick Ivanov
Self-Sovereign Digital Identities, Maryam M. Ahmed, Bijayata Shrestha, Nick Ivanov
STEM Student Research Symposium Posters
In today's digital landscape, the dominance of internet giants over our digital identities raises concerns regarding user control and privacy. These companies aggregate vast amounts of data from diverse sources, ranging from online interactions to personal information shared on their platforms. This centralized control impedes individual autonomy and privacy. To address these challenges, we propose Self-Sovereign Digital Identities (SSDIs) as a solution. SSDIs empower individuals with control over their online identity information, encompassing ownership, security, privacy, and portability. By decentralizing identity management, SSDIs offer users autonomy and enhance privacy protection. Moreover, we introduce Sans-Chain Smart Contracts, a novel approach to …