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Articles 6001 - 6030 of 63030

Full-Text Articles in Computer Sciences

Gmc-2162 Prompt Engineering And Its Effects On Ai And Human Relationships: A Contemporary Approach, Francis Madu, Naga Janaki Madhav Kadiyala, Nivesh Thallapally Nov 2024

Gmc-2162 Prompt Engineering And Its Effects On Ai And Human Relationships: A Contemporary Approach, Francis Madu, Naga Janaki Madhav Kadiyala, Nivesh Thallapally

C-Day Computing Showcase

A. Background: Prompt engineering refers to the process of designing and refining input prompts for AI models (especially language models like GPT) to improve their outputs. It has become a critical tool in maximizing the performance and utility of AI models in diverse applications, from customer service to content creation. Beyond technical aspects, the interaction between humans and AI is increasingly shaped by the effectiveness of these prompts. B. Motivation: As AI becomes more integrated into daily life, the way humans interact with AI models is profoundly influenced by prompt engineering. Misaligned prompts can lead to misunderstanding, confusion, or unintended …

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Gmr-229 Semantic Search Using Sentence Transformers, Roshni Satish, Arpana Challa Nov 2024

Gmr-229 Semantic Search Using Sentence Transformers, Roshni Satish, Arpana Challa

C-Day Computing Showcase

Traditional keyword-based search engines struggle to accurately capture the semantics of user queries in today's enormous digital resources. Our research study focuses on creating a semantic search engine that uses Sentence Transformers to improve information retrieval by understanding the context of queries and documents. Our method creates sentence embeddings for documents and user queries, allowing retrieval based on semantic similarity rather than keyword matching. The project involves data collection and preprocessing, feature extraction with Sentence Transformers, and implementation of a search engine that ranks documents based on cosine similarity to query embeddings. According to preliminary testing, this method greatly improves …

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Gpr-1194 Computer Vision-Enhanced Spectroscopy For Glucose Prediction: An In Vitro Validation Study, El Arbi Belfarsi Nov 2024

Gpr-1194 Computer Vision-Enhanced Spectroscopy For Glucose Prediction: An In Vitro Validation Study, El Arbi Belfarsi

C-Day Computing Showcase

This study introduces a novel computer vision-based spectral approach for non-invasive glucose detection using synthetic blood samples. We developed an experimental setup with glucose concentrations from 70 to 120 mg/dL, using two dye methods. Light sources tested included an 850 nm LED, 850 nm laser, 808 nm laser, and 650 nm laser, with image capture via a 1080p IR camera. Data augmentation, including Gaussian noise, contrast and brightness adjustments, rotations, and zooming, produced seven variants per image. Three machine learning models—CNN, AdaBoost, and ResNet—were evaluated, with the 850 nm light source yielding the best results: 87.5% of predictions fell within …

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Gpr-6126 Utilizing Ml Techniques For A Quantum Augmented Http Protocol, Nitin Jha Nov 2024

Gpr-6126 Utilizing Ml Techniques For A Quantum Augmented Http Protocol, Nitin Jha

C-Day Computing Showcase

Over the past decade, several small-scale quantum key distribution (QKD) networks have been implemented worldwide. However, achieving scalable, large-scale quantum networks relies on advancements in quantum repeaters, channels, memories, and network protocols. To enhance the security of current networks while utilizing available quantum technologies, integrating classical networks with quantum elements appears to be the next logical step. In this study, we propose modifications to the HTTP protocol's data packet structure, adjustments to end-to-end encryption methods, and optimized bandwidth distribution between quantum and classical channels for high-traffic network routes.

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Uc-181 Prison Minecraft Game Mode Plug-In, Ryan S Venable, Carson R Hunter, David Do Nov 2024

Uc-181 Prison Minecraft Game Mode Plug-In, Ryan S Venable, Carson R Hunter, David Do

C-Day Computing Showcase

A project designed for Kennesaw State University's owned Minecraft server. The project centers around creating a Minecraft plug-in, a software product that is easy to activate in any Minecraft server. This plug-in changes the standard rules of Minecraft to become a classic game mode called Prison where players are taken to a special map and tasked with collecting resources in specialized mines or by fighting each other for them to earn in game currency for the purpose of buying their way to more privileged positions in the prison, gaining access to new areas and features. Prison was designed to work …

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Ur-172 A Comparative Study Of Llm Effectiveness In Mental Health Assistance​, Kris Prasad Nov 2024

Ur-172 A Comparative Study Of Llm Effectiveness In Mental Health Assistance​, Kris Prasad

C-Day Computing Showcase

This study evaluates the effectiveness of LLMs in supporting mental health applications by analyzing their performance in understanding and categorizing user (mental health-related) inputs. We collected data from various mental health apps on the Google Play Store, including user reviews and app descriptions, and filtered content using a targeted mental health keyword bank. Sentiment analysis and keyword similarity scores were generated for reviews using RoBERTa-based models, this showed us how each review aligned with the mental health keywords advertised by the app and how users felt about the app. We prompted four modern LLMs: GPT-4o, Claude 3.5 Sonnet, Gemma 2, …

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Gmc-219 Athlete-Agent Connect Mobile App, Ayokunle Ijagbemi, Esther N Uzoka, Foluke Omoniyi Nov 2024

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 …

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Gmc-246 Enhancing Workforce Management Through Advanced Hr Analytics, Pradeep Rekapalli, Ruthvik Reddy Gurram Nov 2024

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.

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Gmc-218 Pet Rescue Ai-Based Support Application, Mariah Akintayo, Sibgha Ajmal, Kazi Nafis Ishtiaque, Krut Patel, Chelsi Alexander Nov 2024

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.

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Gmr-159 Llm Enabled Synthetic Dataset Generation For Human-Ai Teaming Algorithm, Sai Sanjay Potluri Nov 2024

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 …

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Gmc-4190 Cellnucleirag - Smart Search Tool For Cell Nuclei Research, Sai Chandana Koganti Nov 2024

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 …

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Gmr-208 Automatic Categorization Of Behavioral Health Issues In Police Reports, Mason V Pederson, Abm Adnan Azmee, Francis E Nweke Nov 2024

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 …

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Gmr-210 Cogni-Resource: Ai-Driven Reflective Feedback Analysis For Enhanced Learning Insights And Resource Discovery, Ashrith Kumar Devara Nov 2024

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 …

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Gmr-215 Efficient Sentiment Analysis Using Encoder-Only Transformer, Rohan Jonnalagadda, Srinidhi Kandimalla, Siri Yellu Nov 2024

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 …

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Gmr-7179 Improving Alzheimer’S Detection Via Synthetic Data Generation Using Gpt-4 And Multi-Level Embeddings, Venkata Sai Bhargav Mutala, Imaan Shahid Nov 2024

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 …

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Gmr-8193 Harnessing Ml-Powered Hpcc Systems For Advanced Cybersecurity Analytics, Zularbine Kamal Nov 2024

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.

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Gpr-132 Hyperparameter Optimization In Neural Network Using Binary Search Algorithm, Faysal Chowdhoury, Yinning Zhang, Sait Suer Nov 2024

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.

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Gpr-142 Optimization Of Fixed Time In Round Robin Scheduling Using Clustering Algorithms, Sait Suer Nov 2024

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 …

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Gpr-151 Compassionate Digital Assistant: Anchor, Christopher M Regan, Navneet Verma Nov 2024

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 …

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Gpr-233 Human-Assisted Ai For Detecting Mental Health Indicators In Social Media, Abm Adnan Azmee, Francis E Nweke, Mason V Pederson Nov 2024

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 …

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Gpr-6128 Joint Encryption And Error Correction For Quantum Communication, Nitin Jha Nov 2024

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 …

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Uc-133 Biomedical Deep Learning - A Staged Approach Using Trustworthy Deep Learning For Multi-Omics Data Classification, Yongbo An, Tianze Liu Nov 2024

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 …

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Uc-134 Volunteer Management System For Angels Among Us, Prince Duepa, Teodora Stoyanova, Radoslav Stoyanov, Madison W Jones, Rodrigo X Caballero Nov 2024

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

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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 Nov 2024

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.

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Uc-145 Eerie, Diana Kabar, Joshua Whorton, Alex J Gann, Skyler A Freeman Nov 2024

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.

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Uc-150 Azure Migration Assistant, Kunal Shenoi, Graham E Allen, Angel L Hernandez, Yvan Ngah Nov 2024

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.

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Uc-201 Halo: A Volunteer Management Application, Carson G Shattuck, Willie D Carder, Russell E Steele, Jeremiah Wisdom, Zeshan Khan Nov 2024

Uc-201 Halo: A Volunteer Management Application, Carson G Shattuck, Willie D Carder, Russell E Steele, Jeremiah Wisdom, Zeshan Khan

C-Day Computing Showcase

Halo is a comprehensive volunteer management application (VMA) developed to address specific needs identified by Angels Among Us Pet Rescue (AAU). AAU's current system is unable to effectively manage and store complex volunteer information. As the organization’s needs evolved, AAU required a more efficient and secure solution to handle volunteer information. Our team designed Halo using the React.js framework for the frontend, the python based FastAPI framework for the backend, and a PostgreSQL database schema, with Docker containerization to ensure consistent deployment across environments. This setup enables efficient management of volunteer and team data, along with support for exporting reports …

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Uc-207 Anderson Power Services Mobile Application, Ibrahima Diallo, Joel J Roche, Brandon D Portier, Ezra Boerman, Grzegorz Loj Nov 2024

Uc-207 Anderson Power Services Mobile Application, Ibrahima Diallo, Joel J Roche, Brandon D Portier, Ezra Boerman, Grzegorz Loj

C-Day Computing Showcase

The APS Customer Experience Mobile Application streamlines and enhances customer interactions for Anderson Power Services (APS), focusing on customers who have purchased generators. This mobile application provides real-time updates, milestone tracking, and installation insights, allowing APS customers to monitor their generator installation progress with ease. By integrating Google Sheets APIs, the app enables seamless data synchronization, providing accurate and timely updates on generator status. With its cross-platform support on iOS and Android, the application reduces the need for manual communication, improving customer satisfaction and operational efficiency by 20%.

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Uc-223 Cybersecurity Website Hardening Project, Robin N Tandongfor, Jack W Pursley, Elijah Vandorn, Kylah Wilson, Valentine P Wairimu Nov 2024

Uc-223 Cybersecurity Website Hardening Project, Robin N Tandongfor, Jack W Pursley, Elijah Vandorn, Kylah Wilson, Valentine P Wairimu

C-Day Computing Showcase

The project aims to secure the Akwaaba website using Apache, MariaDB, Red Hat OS, and PHP on a virtual machine. It includes assessing assets and vulnerabilities, implementing security policies, and conducting a red/blue team exercise for ethical hacking experience.

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Uc-225 Golf Course Pace Management Simulation, Ashton D Miller Nov 2024

Uc-225 Golf Course Pace Management Simulation, Ashton D Miller

C-Day Computing Showcase

The game of golf has been played for centuries so it has seen handfuls of evolutions throughout its time being played. Throughout the games evolution one factor of its existence has hardly ever changed, time. In current day golf standards, time and the management of time is a significant part of not only how well the game as a whole run but how the courses that own venues to play the game operate as well. In golf there is a standard for time known as the pace of play model, where groups that are sent off during the day are …

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