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Articles 391 - 420 of 1161
Full-Text Articles in Computer Sciences
Uc-180 Intelligent Object Retrieval Using Mobile Manipulator, Zhiwen Zheng, Ellie Ireland
Uc-180 Intelligent Object Retrieval Using Mobile Manipulator, Zhiwen Zheng, Ellie Ireland
C-Day Computing Showcase
A mobile manipulator for intelligent object retrieval is presented. The system was integrated using state of the art R&D hardware and software, which implemented autonomous navigation, object recognition, and object pose estimation based optimal grasping. The retrieval of an object of interest is commanded that involves subsequent object detection and recognition while autonomously navigating using the known map and starting from an arbitrary position. From close proximity, object pose estimation based optimal grasp is selected to pick up the object. The object is retrieved back to the start position in this scenario. An 84% trial-phase precision in object retrieval is …
Uc-182 Designing A User-Centered Mobile Application For Anderson Power Services, Ryan Guzman, Cooper Goswick, Anthony Phan, Marie Fotso, Larnel Francois
Uc-182 Designing A User-Centered Mobile Application For Anderson Power Services, Ryan Guzman, Cooper Goswick, Anthony Phan, Marie Fotso, Larnel Francois
C-Day Computing Showcase
This paper presents the design and development process of a mobile application for Anderson Power Services, emphasizing both frontend and backend aspects as well as their design. The frontend focuses on creating a visually appealing and user-friendly interface by utilizing clear text, an accessible color scheme, appropriate logos, animations, and modern typography. On the development side, the app leverages tools like Expo for rapid front-end development and integrates the Java-based backend with the Google Sheets API for easy data management. The backend architecture incorporates OAuth 2.0 for secure authentication, Gradle to facilitate a connection between the JavaScript frontend to the …
Uc-186 Ksu Esport: Competitive Speedrun Plugin For Minecraft Java Edition, Rachel Amponsah, Adam Y Greene, Christopher Kirkwood, Weeldy Benjamin, Steven A Kelsey
Uc-186 Ksu Esport: Competitive Speedrun Plugin For Minecraft Java Edition, Rachel Amponsah, Adam Y Greene, Christopher Kirkwood, Weeldy Benjamin, Steven A Kelsey
C-Day Computing Showcase
The KSU Esports Minecraft Speedrun plugin transforms traditional, manually managed speedruns into an automated team-based competition event. Players are challenged to complete a set of objectives within a set time limit – promoting teamwork and strategic planning. Various modes are supported, such as weighted/unweighted speedruns, team-based speedruns, and player free-for-all. Designed for flexibility, the plugin allows for customizable settings and support for future versions of Minecraft.
Uc-189 Chessai, Joshua M Peeples, Ashton D Miller, Matthew D Corvacchioli, Dylan Luong, Allen L Smith
Uc-189 Chessai, Joshua M Peeples, Ashton D Miller, Matthew D Corvacchioli, Dylan Luong, Allen L Smith
C-Day Computing Showcase
Chess is a widely acclaimed two-player strategy game, where the primary objective is to checkmate the opponent's king, placing it in a position of imminent threat from which it cannot escape. Our aim was to innovate within this classic framework by developing a novel chess game that adheres to the traditional rules while enhancing accessibility for players of all skill levels. This game features a selection of AI models, each offering unique decision-making processes that create diverse gameplay experiences based on the chosen model. The AI operates by simulating every possible move on the board, meticulously evaluating each resulting position. …
Uc-197 It Capstone 4983: Honeybaked Ham Intranet Sharepoint Site Transformation Presentation, Martez D Andrews Ii, Kayla Pyram, Amir Abdolkarimi, Precious Flowers, Ardarius Ceasar
Uc-197 It Capstone 4983: Honeybaked Ham Intranet Sharepoint Site Transformation Presentation, Martez D Andrews Ii, Kayla Pyram, Amir Abdolkarimi, Precious Flowers, Ardarius Ceasar
C-Day Computing Showcase
The purpose of our team’s research is to explore and define ways in which we can advance aesthetics and functionalities of how website content and ideas are presented to HoneyBaked Ham end users. Our team has goals of identifying crucial focal point areas and various ways we can overall improve upon such. We will utilize practicality, ingenuity and creativity, in order to demonstrate and perform deliveries of proper new perspectives of the site. We will seek out such advancements we can add while remaining within necessary parameters, maintaining the respected, well renowned HoneyBaked Ham Brand. We would like it to …
Uc-202 Indy-5 Building Map Application, Harrison Varnadoe, Eduardo A Payan, Zach W Wilson, Lucas A Haas
Uc-202 Indy-5 Building Map Application, Harrison Varnadoe, Eduardo A Payan, Zach W Wilson, Lucas A Haas
C-Day Computing Showcase
This project’s goal is to develop a simple secure mobile application for all devices to provide a detailed interior map that can guide users to any location in the building. It will use QR codes for ease of access, and the app will provide guidance via room numbers and a visual route. Our scope includes designing the architecture of the app, creating a responsive and interactive map User interface in an app that is compatible across all devices for a nice user experience.
Uc-226 Real-Time Bus Monitoring Using Kafka, Samuel A Bostian, Michael Rizig, Charlie Mclarty, Brian A Pruitt, Allen Roman
Uc-226 Real-Time Bus Monitoring Using Kafka, Samuel A Bostian, Michael Rizig, Charlie Mclarty, Brian A Pruitt, Allen Roman
C-Day Computing Showcase
The GCPS Real-Time Bus Monitoring System aims to enhance bus operations for Gwinnett County Public Schools by transitioning from a polling-based system to a real-time Kafka event-streaming architecture. This project processes telemetry data from over 2,000 buses, simulating a scalable, near-instantaneous data flow into an SQL Server database. Key features include real-time data validation, efficient data storage, and containerized deployment for consistency across environments. Using an Agile approach, our team handled evolving requirements from the sponsor, who is new to senior project collaborations. This system enables GCPS to monitor bus locations with reduced latency, enhanced accuracy, and improved resource management, …
Uc-231 Symptom-Based Disease Prediction, Jarred M Barber, Kody Clark, Ryan Mwangi, Zhiwen Zheng
Uc-231 Symptom-Based Disease Prediction, Jarred M Barber, Kody Clark, Ryan Mwangi, Zhiwen Zheng
C-Day Computing Showcase
This project focuses on leveraging large-scale data sets and advanced analytical techniques to predict the onset of diseases. By integrating data from medical records, genetic information, and environmental factors, the project aims to identify patterns and risk factors associated with various diseases. Machine learning algorithms and statistical models are employed to enhance the accuracy of predictions, enabling early diagnosis and personalized healthcare interventions. This approach improves patient outcomes and contributes to the efficiency and effectiveness of healthcare systems.
Uc-248 Campus Ai Companion Mobile App, Dorian Taponzing Donfack, Aurelien Takou, Leopold Sokoudjou Gatsing, Yann Djoumessi, Manuella Koodjo
Uc-248 Campus Ai Companion Mobile App, Dorian Taponzing Donfack, Aurelien Takou, Leopold Sokoudjou Gatsing, Yann Djoumessi, Manuella Koodjo
C-Day Computing Showcase
The Campus AI Companion app is designed to enhance students' university experiences by providing personalized recommendations for courses, events, clubs, and career paths. Leveraging OpenAI’s language model and developed using React Native, this mobile application integrates academic and social guidance, tailored for individual users based on their interests and performance. This AI-driven companion aims to help students better navigate their university journey by providing seamless access to resources, activities, and support that align with their academic and personal goals.
Ur-147 An 8-Bit Digital Computer Design & Implementation (Team Coa-Wm1), Adrian L Sherard, Jesus Flores, Biswash Lamsal, Blake Hammontree, William Pitts
Ur-147 An 8-Bit Digital Computer Design & Implementation (Team Coa-Wm1), Adrian L Sherard, Jesus Flores, Biswash Lamsal, Blake Hammontree, William Pitts
C-Day Computing Showcase
8 bit computer design using NI multisim
Gmc-219 Athlete-Agent Connect Mobile App, Ayokunle Ijagbemi, Esther N Uzoka, Foluke Omoniyi
Gmc-219 Athlete-Agent Connect Mobile App, Ayokunle Ijagbemi, Esther N Uzoka, Foluke Omoniyi
C-Day Computing Showcase
The Athlete-Agent Connect app aims to bridge the gap between athletes and agents, simplifying the process of professional engagement. By providing a digital space for talent acquisition and event coordination, the app fosters networking, recruitment, and collaboration within the sports industry. The platform’s features are tailored to meet the needs of athletes looking for representation and agents seeking clients, with tools for direct communication, event planning, and a calendar of relevant sports gatherings. This mobile app serves as a dedicated platform for athletes and sports agents to connect, collaborate, and enhance professional opportunities. The app enables athletes to hire agents …
Gmc-246 Enhancing Workforce Management Through Advanced Hr Analytics, Pradeep Rekapalli, Ruthvik Reddy Gurram
Gmc-246 Enhancing Workforce Management Through Advanced Hr Analytics, Pradeep Rekapalli, Ruthvik Reddy Gurram
C-Day Computing Showcase
The business analytics of employee data is a concern that human resource departments worldwide deal with. Some big organizations have entire teams working on analyzing these metrics. To obtain insights about employee turnover rates, performance trends, and compensation patterns from data, the Data warehousing techniques—OLAP and ETL—can be used to handle data. This paper aims to develop an OLAP model for multi-dimensional analysis using data warehousing techniques that help extract valuable insights from the data. Popular datasets will be used, and the model will be evaluated according to standards.
Gmc-218 Pet Rescue Ai-Based Support Application, Mariah Akintayo, Sibgha Ajmal, Kazi Nafis Ishtiaque, Krut Patel, Chelsi Alexander
Gmc-218 Pet Rescue Ai-Based Support Application, Mariah Akintayo, Sibgha Ajmal, Kazi Nafis Ishtiaque, Krut Patel, Chelsi Alexander
C-Day Computing Showcase
This project focuses on the development of an AI-driven support application for foster caregivers at Angels Among Us Pet Rescue. The application provides foster caregivers with real time assistance through an interactive chatbot, task reminders and resource management capabilities, streamlining the caregiving process. By leveraging automation and AI, the application enhances both the foster experience and operational efficiency aligning with the organizations mission of improving animal care.
Gmr-159 Llm Enabled Synthetic Dataset Generation For Human-Ai Teaming Algorithm, Sai Sanjay Potluri
Gmr-159 Llm Enabled Synthetic Dataset Generation For Human-Ai Teaming Algorithm, Sai Sanjay Potluri
C-Day Computing Showcase
This research explores using Large Language Models (LLMs) to generate synthetic datasets for Human-AI teaming algorithms, focusing on mental health assessments. We create a diverse dataset simulating human-AI collaboration scenarios in diagnostic processes. The synthetic data is labeled through an innovative approach involving two human annotators and three LLMs, using majority voting for consensus-based annotations. This dataset serves as a resource for training and evaluating Human- AI teaming algorithms, enabling exploration of collaboration dynamics between human expertise and AI in complex decision-making. Our approach addresses the scarcity of real-world data in Human-AI teaming scenarios and provides a controlled environment for …
Gmc-4190 Cellnucleirag - Smart Search Tool For Cell Nuclei Research, Sai Chandana Koganti
Gmc-4190 Cellnucleirag - Smart Search Tool For Cell Nuclei Research, Sai Chandana Koganti
C-Day Computing Showcase
CellNucleiRAG is a specialized tool developed to address a significant challenge in medical research: the rapid retrieval and synthesis of detailed information on cell nuclei. Understanding cell nuclei characteristics is crucial in fields like pathology, oncology, and diagnostics, where detailed cell analysis can guide disease identification and treatment planning. However, accessing relevant, organized information on specific cell nuclei types, datasets, models, and methods is often time-consuming, requiring manual searches through multiple, disparate sources. CellNucleiRAG solves this problem by acting as a smart search engine, designed specifically for cell nuclei research, combining traditional retrieval methods with advanced AI capabilities. Built with …
Gmr-208 Automatic Categorization Of Behavioral Health Issues In Police Reports, Mason V Pederson, Abm Adnan Azmee, Francis E Nweke
Gmr-208 Automatic Categorization Of Behavioral Health Issues In Police Reports, Mason V Pederson, Abm Adnan Azmee, Francis E Nweke
C-Day Computing Showcase
911 is often the first place contacted for dealing with behavioral health related (BHR) issues. Its estimated at least a fifth of all calls are related to behavioral health, and with BHR affected convicts having a recidivism rate of around 30%, its not hard to see how straining these issues can become on systems already stretched thin, where chronic understaffing is often a reality. A great solution would be if we could intervene as soon as possible to get people the treatment they need, police reports would be excellent for identifying and treating these individuals, but annotation is a long …
Gmr-210 Cogni-Resource: Ai-Driven Reflective Feedback Analysis For Enhanced Learning Insights And Resource Discovery, Ashrith Kumar Devara
Gmr-210 Cogni-Resource: Ai-Driven Reflective Feedback Analysis For Enhanced Learning Insights And Resource Discovery, Ashrith Kumar Devara
C-Day Computing Showcase
Cogni-Resource is a unified platform enhanced by AI that merges the introspective analysis of Cogni-Reflect with the precise resource exploration functions of the Learning Resource Finder, providing a holistic tool to improve educational environments. The Cogni-Reflect component uses advanced Large Language Models (LLMs) to examine student reflections, giving educators instant insights into learning results, difficulties, and areas where students may require extra assistance. Cogni-Reflect allows instructors to adjust their teaching by analyzing key themes and topics in reflective narratives, leading to a more adaptive and successful learning atmosphere. Using both web scraping and OpenAI API integration, the Learning Resource Finder …
Gmr-215 Efficient Sentiment Analysis Using Encoder-Only Transformer, Rohan Jonnalagadda, Srinidhi Kandimalla, Siri Yellu
Gmr-215 Efficient Sentiment Analysis Using Encoder-Only Transformer, Rohan Jonnalagadda, Srinidhi Kandimalla, Siri Yellu
C-Day Computing Showcase
In the era of social media, sentiment analysis has emerged as a vital instrument for comprehending public opinion, especially on sites like LinkedIn and Twitter. Because user-generated content is informal and noisy, traditional sentiment classification techniques like Naive Bayes and Support Vector Machines sometimes find it difficult to capture context, sarcasm, and long-term interdependence. In order to improve sentiment analysis accuracy for social media datasets with a specific focus on sentiments related to corporate layoffs, this study suggests an encoder-only transformer model. Our method successfully captures intricate phrase patterns and contextual subtleties in textual data by leveraging the self-attention mechanism …
Gmr-7179 Improving Alzheimer’S Detection Via Synthetic Data Generation Using Gpt-4 And Multi-Level Embeddings, Venkata Sai Bhargav Mutala, Imaan Shahid
Gmr-7179 Improving Alzheimer’S Detection Via Synthetic Data Generation Using Gpt-4 And Multi-Level Embeddings, Venkata Sai Bhargav Mutala, Imaan Shahid
C-Day Computing Showcase
This study leverages large language models (LLMs), particularly GPT-4, to overcome the data limitations often encountered in Alzheimer’s detection. We utilize GPT-4 for data augmentation, generating synthetic speech transcripts to enhance machine learning model training. Our approach combines fine-tuned BERT embeddings with CLAN-derived linguistic features, as well as sentence-level embeddings, to improve classification performance on the ADReSS2020 dataset. BERT and CLAN features capture detailed linguistic variants, while sentence embeddings offer robust semantic representations, collectively enhancing the accuracy and generalization of the models. Among the classifiers tested, the Random Forest model shows the best performance, achieving an accuracy of 88% with …
Gmr-8193 Harnessing Ml-Powered Hpcc Systems For Advanced Cybersecurity Analytics, Zularbine Kamal
Gmr-8193 Harnessing Ml-Powered Hpcc Systems For Advanced Cybersecurity Analytics, Zularbine Kamal
C-Day Computing Showcase
Information security in the era of AI and automation is the biggest challenge for cybersecurity professionals. Traditional information security protection has limitations in detecting zero-day attacks, which can be overcome with machine learning-based information security. An ML-powered intrusion detection system uses statistical analysis to spot deviations from normal behavior and helps to detect new and unknown threats. This poster will demonstrate how an open-source platform can be used for cybersecurity by leveraging various machine-learning algorithms.
Gpr-132 Hyperparameter Optimization In Neural Network Using Binary Search Algorithm, Faysal Chowdhoury, Yinning Zhang, Sait Suer
Gpr-132 Hyperparameter Optimization In Neural Network Using Binary Search Algorithm, Faysal Chowdhoury, Yinning Zhang, Sait Suer
C-Day Computing Showcase
Hyperparameter searching is a crucial process for every neural network training. However, this process is notably time-consuming due to the vast number of possible combinations and the influence these hyperparameters have on each other. The common approach is using grid search to exhaust all the options, which is computationally very expensive. In this research, we propose a new algorithm for this problem that is inspired by binary search and returns a significant improvement in time efficiency.
Gpr-142 Optimization Of Fixed Time In Round Robin Scheduling Using Clustering Algorithms, Sait Suer
Gpr-142 Optimization Of Fixed Time In Round Robin Scheduling Using Clustering Algorithms, Sait Suer
C-Day Computing Showcase
This project introduces a method to optimize the fixed time in Round Robin scheduling using unsupervised clustering, specifically DBSCAN. Traditionally, fixed time is chosen arbitrarily, often leading to inefficiencies like increased waiting times and frequent context switches. Our approach leverages DBSCAN to identify clusters of processes based on arrival and burst times, as well as to detect outliers that may need unique fixed times. This adaptive, data-driven adjustment has demonstrated improved performance over traditional methods, reducing waiting time, minimizing context switches, and enhancing system throughput. Simulations confirmed the effectiveness of this approach, especially in datasets with outlier processes, where DBSCAN …
Gpr-151 Compassionate Digital Assistant: Anchor, Christopher M Regan, Navneet Verma
Gpr-151 Compassionate Digital Assistant: Anchor, Christopher M Regan, Navneet Verma
C-Day Computing Showcase
Mental health support is crucial, but access to professional care can be limited by cost, availability, and social stigma. Digital solutions, particularly chatbots, offer an accessible and scalable approach to providing mental health support. However, current chatbot solutions may not always reflect the diversity of users' emotional experiences, and they may lack the specialized domain knowledge and adaptability required for effective mental health counseling. This research project aims to address these challenges by developing a compassionate AI digital assistant that can use specialized natural language processing (NLP) models to provide empathetic and targeted responses based on the nature of the …
Gpr-233 Human-Assisted Ai For Detecting Mental Health Indicators In Social Media, Abm Adnan Azmee, Francis E Nweke, Mason V Pederson
Gpr-233 Human-Assisted Ai For Detecting Mental Health Indicators In Social Media, Abm Adnan Azmee, Francis E Nweke, Mason V Pederson
C-Day Computing Showcase
Mental health is essential to overall well-being, and mental illness includes conditions that affect a person’s psychological health, causing significant distress and limiting daily functioning. With advancements in technology, social media has become a platform where individuals openly share their emotions and thoughts, offering a unique window into their psychological states. However, traditional machine learning models struggle to interpret social media data's wide range of linguistic nuances. To analyze this data effectively, collaboration with human experts is crucial. This study proposes an innovative human-AI teaming framework that integrates human expertise with artificial intelligence (AI) to address these challenges. Our framework …
Gpr-6128 Joint Encryption And Error Correction For Quantum Communication, Nitin Jha
Gpr-6128 Joint Encryption And Error Correction For Quantum Communication, Nitin Jha
C-Day Computing Showcase
Secure quantum networks are foundational for developing a quantum internet in the future. However, current age quantum channels are prone to noise, which can introduce errors in transmitted data. Traditionally, error correction is applied to the message separately, after encryption, resulting in additional overhead that should be minimized. In response, we propose a unified approach that combines encryption and error correction into a single process. This work represents an initial effort to integrate these functions for secure quantum communication by integrating the Calderbank-Shor-Steane (CSS) Quantum Error Correction (QECC) code with the three-stage secure quantum communication protocol. Additionally, the protocol supports …
Uc-133 Biomedical Deep Learning - A Staged Approach Using Trustworthy Deep Learning For Multi-Omics Data Classification, Yongbo An, Tianze Liu
Uc-133 Biomedical Deep Learning - A Staged Approach Using Trustworthy Deep Learning For Multi-Omics Data Classification, Yongbo An, Tianze Liu
C-Day Computing Showcase
Genetic data such as mRNA, miRNA, and DNA methylation offer precious insights into the underlying causes variant diseases. These types of data provide various layers of information, simultaneously enhancing our understanding of the disease and improving diagnostic accuracy. Combining mRNA, miRNA, and DNA methylation data allows for a multi-dimensional approach to identifying biomarkers, potentially leading to earlier and more accurate diagnosis. However, integrating all modalities is not practical. The clinical cost increases significantly with every modality incorporated. In contrast to previous methods, our model uses partial modalities when possible. We will use subjective logic and trustworthy deep learning under the …
Uc-134 Volunteer Management System For Angels Among Us, Prince Duepa, Teodora Stoyanova, Radoslav Stoyanov, Madison W Jones, Rodrigo X Caballero
Uc-134 Volunteer Management System For Angels Among Us, Prince Duepa, Teodora Stoyanova, Radoslav Stoyanov, Madison W Jones, Rodrigo X Caballero
C-Day Computing Showcase
This project focused on creating a Volunteer Management System (VMS) for Angels Among Us (AAU) - a non-profit organization dedicated to rescuing and rehabilitating stray and abandoned animals. This application was developed to: * Handle comprehensive volunteer information * Streamline operations and better manage volunteer data * Support AAU specific use cases * Include a data enrichment capability through a newly developed GUI * Allow authorized users to add more comprehensive information to each volunteer record * implement reporting throughout the data migration process
Uc-140 Streamlining School Bus Monitoring: Gcps's Transition To Real-Time Kafka Event Processing, Sarah Fashinasi, Tyler J Hood, Jeffrey Sanderson, Amali B Mchie, Alexandra Baker
Uc-140 Streamlining School Bus Monitoring: Gcps's Transition To Real-Time Kafka Event Processing, Sarah Fashinasi, Tyler J Hood, Jeffrey Sanderson, Amali B Mchie, Alexandra Baker
C-Day Computing Showcase
This project develops a prototype real-time bus monitoring system for Gwinnett County Public Schools using simulated Kafka event streaming to replace current API polling methods. The system processes simulated Asset Location and Speed events, mimicking Samsara's Kafka Connector, performing data validation before storing in SQL Server. The containerized solution demonstrates the potential for near real-time visibility into school bus operations.
Uc-145 Eerie, Diana Kabar, Joshua Whorton, Alex J Gann, Skyler A Freeman
Uc-145 Eerie, Diana Kabar, Joshua Whorton, Alex J Gann, Skyler A Freeman
C-Day Computing Showcase
Eerie is a psychological horror/thriller game that plunges players into the harrowing journey of Alice, a young girl trapped in her home. As she navigates the dimly lit corridors of her once-familiar environment, Alice grapples with haunting hallucinations and a distorted reality that intertwines the tangible and surreal. The gameplay revolves around her desperate quest to recover cherished belongings, each revealing deeper layers of her fractured story. Players must confront both real enemies and manifestations of Alice’s psyche, creating a tense dynamic that challenges them to strategize against both physical threats and the shadows of her fears.
Uc-150 Azure Migration Assistant, Kunal Shenoi, Graham E Allen, Angel L Hernandez, Yvan Ngah
Uc-150 Azure Migration Assistant, Kunal Shenoi, Graham E Allen, Angel L Hernandez, Yvan Ngah
C-Day Computing Showcase
Migrating to the Azure cloud platform poses unique cost-assessment and planning challenges. Our project introduces a user-friendly, AI-driven tool to simplify this process by providing real-time cost predictions and personalized migration strategies. Built with a React frontend and a Flask-based Python backend, this tool integrates Azure Pricing APIs to ensure accurate data. Future improvements include adding alerts, custom fine-tuned model, CI/CD, multi-cloud support, and a discovery agent for enhanced functionality.