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Articles 20551 - 20580 of 291660

Full-Text Articles in Physical Sciences and Mathematics

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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Uc-242 Ac-10 Ai & Music Processing, Michael J Zboinski, Selam B Kelil, Sterling J Wilson, Michael Egwuatu Nov 2024

Uc-242 Ac-10 Ai & Music Processing, Michael J Zboinski, Selam B Kelil, Sterling J Wilson, Michael Egwuatu

C-Day Computing Showcase

There is a broad range of styles and philosophies, for teaching young children how to play music. Some are based on repetition and memorization of songs, and others build up a foundation of musical patterns and motifs. Arguably, the latter style, will better develop the skills needed for improvisation and composition of new music. Inspired by this observation, we aim to improve the ability of (recurrent) neural networks to synthesize music based on a more careful training.

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Uc-247 Using Dynamic Difficulty Adjustment (Dda) To Improve Health And Wellness Apps And Programs, Fernando Orfila Nov 2024

Uc-247 Using Dynamic Difficulty Adjustment (Dda) To Improve Health And Wellness Apps And Programs, Fernando Orfila

C-Day Computing Showcase

Physical inactivity, obesity and Type 2 Diabetes cost the United States’ economy more than $700 billion a year (CDC). Yet, individuals spend $137 billion dollars a year on gym memberships to get in shape and feel better, without attaining results and dropping out. “…63% of new members will abandon activities before the third month, and less than 4% will remain for more than 12 months of continuous activity.” (Sperandei et al). The personal training apps don’t fare better, with 71% of users disengaging within 90 days (Amagai et al). The higher chances of people dropping out are due to "a …

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Ur-239 Human-Ai Annotator Tool​, Anh Duong, Habeebah Muse, Chase Castro Nov 2024

Ur-239 Human-Ai Annotator Tool​, Anh Duong, Habeebah Muse, Chase Castro

C-Day Computing Showcase

The HK-01 Human-AI Annotator Tool is a web-based system developed to facilitate the annotation of Electronic Health Records (EHRs) for mental and behavioral health, using ICD-10 codes. The tool allows multiple expert annotators to tag critical information, making it easier to catalog patient data accurately. Future plans include integrating AI to streamline and scale the annotation process, improving both efficiency and accuracy.

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Gmc-146 Legal-Insight - Legal Text Summarizer, Ravi K Rogannagari, Sasi Pavan Khadyoth Gunturu, Lakshmi Narasimha Naidu Sripathi Nov 2024

Gmc-146 Legal-Insight - Legal Text Summarizer, Ravi K Rogannagari, Sasi Pavan Khadyoth Gunturu, Lakshmi Narasimha Naidu Sripathi

C-Day Computing Showcase

Many people struggle to fully understand complex legal documents, such as terms of service agreements, contracts, and privacy policies, due to dense jargon, small fonts, and lengthy paragraphs that make critical information difficult to grasp. This lack of clarity can lead individuals to inadvertently agree to terms they might not fully understand or miss important clauses. Recognizing these challenges, we developed LegalInsight to make legal information more accessible and comprehensible. LegalInsight simplifies lengthy legal documents by creating clear and concise summaries, allowing users to easily digest essential information. It also includes an interactive Q&A feature where users can ask specific …

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Gmc-158 Evaluating Tcp Protocol Performance In Cloud Environments, Nong Ming Nov 2024

Gmc-158 Evaluating Tcp Protocol Performance In Cloud Environments, Nong Ming

C-Day Computing Showcase

This research investigates the performance of four different TCP algorithms—BBR, Reno, Vegas, and Cubic in a high-latency and congested condition within a cloud-based environment using EC2 instances and Mininet for network simulation. The study aims to evaluate the throughput and congestion window (cwnd) behavior of each algorithm under various network conditions to identify their strengths and weaknesses. By analyzing the performance metrics across different TCP algorithms, we provide insights into their suitability for cloud infrastructure, contributing to optimized network protocol choices for cloud-based applications and services. The results offer valuable guidance for enhancing network performance in dynamic cloud environments.

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Gmc-130 School Bus Monitoring Simulation W/ Apache Kafka, John Blake, Hudson Topping, Eun Sik Kim, Tiep Bui, Julio Lois Nov 2024

Gmc-130 School Bus Monitoring Simulation W/ Apache Kafka, John Blake, Hudson Topping, Eun Sik Kim, Tiep Bui, Julio Lois

C-Day Computing Showcase

Our project is a proof-of-concept of event-streaming bus telemetry data using Apache Kafka (Kafka), as requested by our client, Gwinnett County Public Schools (GCPS). The Kafka event stream is more efficient than GCPS’s current process of pulling bus telemetry data: calling APIs every 5 seconds. Moving to Kafka will provide GCPS near real-time insights into bus locations and speeds, giving visibility to whether buses drive safely and punctually. Our simulation produces synthetic bus data to be passed through Kafka and consumed. It features two UIs for system monitoring and data visualization.

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Gmc-168 Hybrid Approach Of Data Mining And Deep Learning For Network Intrusion Classification In Big Data, Md Shamsul Alam, Yash Patel, Yaswanth Srinivas Gurram Nov 2024

Gmc-168 Hybrid Approach Of Data Mining And Deep Learning For Network Intrusion Classification In Big Data, Md Shamsul Alam, Yash Patel, Yaswanth Srinivas Gurram

C-Day Computing Showcase

The growing complexity and volume of network traffic pose significant challenges to traditional intrusion detection systems (IDS), often leading to inefficiencies in detecting unauthorized access and malicious activities. To identify different types of network attacks, many intrusion detection systems (IDSs) have been proposed using artificial intelligence or machine learning, but the results are still not satisfactory for most of these systems. Recently in some research, deep learning models have shown promising performance in big data analysis. However, a combined data mining and deep learning approach in big data for the detection of intrusions has not been scrutinized. This research aims …

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Gmc-191 Optimizing K-Means Clustering For Customer Analytics: A Multi-Faceted Enhancement Approach, Carlos R Mota Jr, Bindu Neelam, Dedeepya Bonigala Nov 2024

Gmc-191 Optimizing K-Means Clustering For Customer Analytics: A Multi-Faceted Enhancement Approach, Carlos R Mota Jr, Bindu Neelam, Dedeepya Bonigala

C-Day Computing Showcase

This paper presents a detailed analysis of the al- gorithmic complexity of the K-means clustering algorithm, a foundational method in unsupervised machine learning. Although the problem of finding the optimal solution is NP-hard, K-means is widely used for efficiently partitioning data into clusters by minimizing within-cluster variance. We explore four main ideas for improvement:1) parallel points generation and processing for speeding up convergence, 2) penalty scoring for avoiding clusters with high variability within them, 3) utilization of other distance measurements such as Manhattan distance for providing better clustering in structures of objects of different nature and 4) probability addition in …

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Gmc-241 Gta Request / Hiring Management, Vineetha Madasu, Pavan Kalyan Mandapaneni, Tharun Derangula, Thushara Thotakura, Raghurama Reddy Nambuvaripalli Reddappa Nov 2024

Gmc-241 Gta Request / Hiring Management, Vineetha Madasu, Pavan Kalyan Mandapaneni, Tharun Derangula, Thushara Thotakura, Raghurama Reddy Nambuvaripalli Reddappa

C-Day Computing Showcase

The Graduate Teaching Assistant (GTA) Management System is designed to address inefficiencies in the GTA hiring and assignment process at the departmental level. This full-stack web application utilizes modern Python frameworks such as Flask for backend development, providing a robust and scalable foundation for data handling and business logic. The frontend is developed using HTML, CSS, and JavaScript, ensuring an intuitive and responsive user experience. Key features include user access, automated GTA request generation based on course catalog data, and real-time tracking of hiring progress. By integrating data import capabilities from sources like Owl Express via Excel, the system handles …

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Gmr-124 Emotion-Based Synthetic Feature Binary Classification Of Human Vs Llm Generated Text/Essay, Tong Xu, Rene P Lisasi, Patrick Wu Nov 2024

Gmr-124 Emotion-Based Synthetic Feature Binary Classification Of Human Vs Llm Generated Text/Essay, Tong Xu, Rene P Lisasi, Patrick Wu

C-Day Computing Showcase

Sentimental analysis is a popular method to classify text into various emotional tones and intentions. In the meanwhile, the emergence of large language models (LLMs) has become ever more capable, their potential to cause harm through information fabrication, misleading propagation, or mere lack of capability has also increased. Therefore, our project is designed to discover any patterns that could potentially uncover texts origins of human and LLM during sentimental analysis. Our dataset covers over 57,000 lengthier essay samples (70% human vs 30% LLM), we use the state-of-art pre-trained DistilRoBERTa-base, a powerful pre-trained language model that is a more condensed and …

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Gmr-165 An Empirical Study Of Prompt-Based Non-Functional Requirements Classification, Allen Kim Nov 2024

Gmr-165 An Empirical Study Of Prompt-Based Non-Functional Requirements Classification, Allen Kim

C-Day Computing Showcase

In modern software development, Non-Functional Requirements (NFR) are essential to satisfy users’ needs. Distinguishing different categories of NFR is tedious, error-prone, and time consuming due to the complexity of software systems. In our project, we conducted a comprehensive study to evaluate the performance of prompt-based NFR classification by designing various handcraft templates and soft templates on the pre-trained language model (i.e., BERT). Our experimental results show that handcraft templates can achieve best effectiveness (e.g., 83.52% in terms of F1 score) but with unstable performance for different templates.

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Gmr-216 Ai/Ml-Based Water Quality Monitoring Mobile App For Predicting E.Coli In Surface Waters, Keerthi Priya Karumanchi Nov 2024

Gmr-216 Ai/Ml-Based Water Quality Monitoring Mobile App For Predicting E.Coli In Surface Waters, Keerthi Priya Karumanchi

C-Day Computing Showcase

E.coli contamination in surface waters has proven to be a significant public health concern, requiring innovative monitoring solutions. This paper presents the design of an AI-driven mobile application to predict whether E.coli bacteria are present at levels exceeding acceptable thresholds in surface waters. The methodology employs sensor devices to collect water quality data parameters, such as water temperature, pH, dissolved oxygen, and turbidity. A dataset is generated based on these parameters, and machine learning (ML) algorithms are applied to evaluate accuracy, precision, recall, and processing time. Additionally, our ML algorithms establish a correlation matrix among water quality parameters to identify …

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Gmr-7175 Enhancing Alzheimer’S Diagnosis Through Spontaneous Speech Recognition: A Deep Learning Approach With Data Augmentation, Venkata Sai Bhargav Mutala Nov 2024

Gmr-7175 Enhancing Alzheimer’S Diagnosis Through Spontaneous Speech Recognition: A Deep Learning Approach With Data Augmentation, Venkata Sai Bhargav Mutala

C-Day Computing Showcase

Alzheimer’s disease (AD) is a growing public health issue due to its progressive nature and rising prevalence. This study explores a neural network model trained on speech data from the ADReSS2020 Challenge dataset to distinguish AD patients from healthy individuals, using log-Mel spectrogram features. To improve accuracy, five data augmentation methods, including pitch and time shifting, were used. The results highlight deep learning, combined with data augumentation, as a promising, scalable, and noninvasive approach for early AD diagnosis

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Gmr-8195 Machine Learning-Enhanced Hpcc Systems For Alzheimer's Disease Detection: A Scalable Solution For Early Diagnosis, Zularbine Kamal Nov 2024

Gmr-8195 Machine Learning-Enhanced Hpcc Systems For Alzheimer's Disease Detection: A Scalable Solution For Early Diagnosis, Zularbine Kamal

C-Day Computing Showcase

Alzheimer's disease is an incurable brain disorder that gradually deteriorates memory and cognitive abilities, leading to symptoms such as memory loss, confusion, difficulty in thinking, and changes in language, behavior, and personality. Early diagnosis that is effective, innovative, and cost-efficient can help mitigate damage to nerve cells. Detecting these symptoms through voice responses and analyzing the corresponding transcripts offers a promising approach. This poster demonstrates how an open-source platform can be utilized for text classification to identify Alzheimer's disease, employing fast, inexpensive, and non-invasive methods to complement other diagnostic techniques

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