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Uc-221 Accounting Treasury Industries Web Application, Ibrahima Diallo, Alexandra Baker, Tyler J Hood
Uc-221 Accounting Treasury Industries Web Application, Ibrahima Diallo, Alexandra Baker, Tyler J Hood
C-Day Computing Showcase
This accounting software project is designed to provide a comprehensive and efficient solution for financial management within an organization. By focusing on ease of usability, accuracy, and compliance, the software enables users to record, manage, and analyze accounts, journals, and financial transactions seamlessly. Core features include transaction journalization, chart of accounts setup, financial statement generation, and robust account management, all of which are supported by strong data validation and secure access controls. This system seeks to streamline accounting workflows, minimize human/manual errors, and enhance user experience. The ultimate aim is to deliver an intuitive yet powerful tool that supports effective …
Ur-213 Generative Ai & Cybersecurity, Seth G Canada, Shreya Katare
Ur-213 Generative Ai & Cybersecurity, Seth G Canada, Shreya Katare
C-Day Computing Showcase
This research project details the impact of Generative AI on Cybersecurity through both its potential enhancements and threats. Using advanced AI algorithms, this project explores how Generative AI can strengthen cybersecurity through systems like Anomaly Detection, Intrusion Detection Systems (IDS), and Malware Analysis. Also, this project addresses the growing challenges posed from Generative AI. In particular, issues surrounding Deepfake Phishing and Polymorphic Malware are discussed. Solutions to mitigate these issues are also provided to engage further understanding in the field. The goal of this research is to offer practical solutions for addressing the growing field of AI-driven cybersecurity.
Gmr-196 Integrated Sentiment And Behavioral Analysis Of Online Product Reviews, Kiran Yepuri, Naveen Mahankali
Gmr-196 Integrated Sentiment And Behavioral Analysis Of Online Product Reviews, Kiran Yepuri, Naveen Mahankali
C-Day Computing Showcase
The "Integrated Sentiment and Behavioral Analysis of Online Product Reviews" project helps businesses gain actionable insights from Product reviews by combining sentiment and behavioral analysis using NLP models like VADER and BERT. This dual approach categorizes reviews as positive, neutral, or negative and identifies themes such as preferences and complaints through Named Entity Recognition and topic modeling. By capturing both the emotional tone and specific product feedback, this method highlights consumer likes and pain points, assisting in targeted improvements for product design and customer service. The project addresses challenges in analyzing complex expressions like sarcasm, providing a robust framework for …
Gpr-185 A Multimodal Approach To Quiz Generation: Leveraging Rag Models For Educational Assessments, Mourya Teja Kunuku
Gpr-185 A Multimodal Approach To Quiz Generation: Leveraging Rag Models For Educational Assessments, Mourya Teja Kunuku
C-Day Computing Showcase
Crafting quiz questions that effectively assess students’ understanding of lectures and course materials, such as textbooks, poses significant challenges. Recent AI-based quiz generation efforts have predominantly concentrated on static resources, like textbooks and slides, often overlooking the dynamic and interactive elements of live lectures—contextual cues, discussions, and interactions—that contribute to the learning experience. In this work, we propose a Retrieval-Augmented Generation (RAG) model that processes multimodal inputs by combining text, audio, and video to produce quizzes that capture a fuller context. Our method incorporates Whisper for audio transcription and utilizes a Large Vision-Language Model (LVLM) to extract essential visual data …
Uc-184 Onaccount A Web-Based Accounting Software, Manuel A Jackson, Russell E Steele, Grzegorz Loj, Zachary B Powell
Uc-184 Onaccount A Web-Based Accounting Software, Manuel A Jackson, Russell E Steele, Grzegorz Loj, Zachary B Powell
C-Day Computing Showcase
This project streamline and improve the efficiency of the whole accounting process, by using current best practices for user interaction engineering and current design practices. Our software should be able to provide secure, user-friendly, and accessible financial management solutions anywhere and everywhere through various devices including desktop and mobile. Allowing users to manage their accounts whenever it seems necessary while still maintaining a high level of security. The project is inspired by the various complexity and problems regarding the accounting process in the real world such as financial reporting, miscalculations, and data security; by streamlining this process and making it …
Gmr-4234 Evaluating Instance Segmentation Models On Histopathology Datasets, Sai Chandana Koganti
Gmr-4234 Evaluating Instance Segmentation Models On Histopathology Datasets, Sai Chandana Koganti
C-Day Computing Showcase
Instance segmentation is transforming digital pathology by enhancing the speed and accuracy of tissue sample analysis through advanced image processing techniques. Whole Slide Imaging (WSI) converts traditional microscope slides into high-resolution digital formats, enabling detailed examinations. This paper presents a brief experimental survey of instance segmentation models on two prominent histopathology datasets: PanNuke and NuCLS. Unlike previous surveys that merely describe deep learning models for general pathology images, we conduct experiments using state-of-the-art models including Mask R-CNN, Detectron2, YOLOv8, YOLOv9, and HoverNet on both datasets. Our study evaluates these models for both binary and multiclass instance segmentation tasks. The NuCLS …
Gpr-187 Deep Learning Models For Protein-Protein Binding Affinity Prediction, Lingtao Chen
Gpr-187 Deep Learning Models For Protein-Protein Binding Affinity Prediction, Lingtao Chen
C-Day Computing Showcase
Binding affinity (BA) prediction is important for drug discovery and protein engineering. It seeks to understand the interaction strength between proteins and their ligands (or proteins). This information assists in the design of proteins with enhanced or novel functions, as well as understanding the molecular mechanisms of drug action. This paper presents the development and comparative analysis of two deep learning models, a convolutional neural network (CNN) and a transformer model. Many variants of models in this research were developed using TensorFlow. One model that utilizes ProteinBERT was developed using PyTorch. The CNN model captures local sequence features effectively, while …
Gmc-137 Iot Security Vulnerabilities And How To Improve Them, Austin D Klein, Ryeon N Naderi, Tyler J Hood, Keren Bassourou
Gmc-137 Iot Security Vulnerabilities And How To Improve Them, Austin D Klein, Ryeon N Naderi, Tyler J Hood, Keren Bassourou
C-Day Computing Showcase
With the increased usage of IoT devices in homes as well as different industries, vulnerabilities have also increased significantly. The IoT devices are small in size, and it is hard to incorporate security in the software because security has high demand for computation. We have been conducting this research in order to find more suitable security methods that are lightweight as well as efficient. We have decided to move away from key hiding algorithms, which have increased time and space consumption, in favor of smaller and quicker block cipher algorithms.
Gmc-157 Text-To-Digital Person Video Generator: Digitalavatargen, Akansha Kesharwani, Nisha Bagdwal, Md E Hossain, Drashti Patel, Nikhil Adigoppula
Gmc-157 Text-To-Digital Person Video Generator: Digitalavatargen, Akansha Kesharwani, Nisha Bagdwal, Md E Hossain, Drashti Patel, Nikhil Adigoppula
C-Day Computing Showcase
The Text-to-digital person video generator: DigitalAvatarGen project uses AI to create lifelike videos of 2D digital avatars from user text input. Users enter text, select a voice and select or upload an avatar, and generate a video using DigitalAvatarGen web application which uses Google TTS and SadTalker, to synchronize voice, expressions, and lip movements. Key contributions include a customizable user interface, personalized voice and avatar options, and an optimized backend for efficient video generation. This tool provides an engaging, realistic solution for applications in education, media, and customer interaction.
Gmc-2162 Prompt Engineering And Its Effects On Ai And Human Relationships: A Contemporary Approach, Francis Madu, Naga Janaki Madhav Kadiyala, Nivesh Thallapally
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 …
Gmr-229 Semantic Search Using Sentence Transformers, Roshni Satish, Arpana Challa
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 …
Gpr-1194 Computer Vision-Enhanced Spectroscopy For Glucose Prediction: An In Vitro Validation Study, El Arbi Belfarsi
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 …
Gpr-6126 Utilizing Ml Techniques For A Quantum Augmented Http Protocol, Nitin Jha
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.
Uc-181 Prison Minecraft Game Mode Plug-In, Ryan S Venable, Carson R Hunter, David Do
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 …
Ur-172 A Comparative Study Of Llm Effectiveness In Mental Health Assistance, Kris Prasad
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, …
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 …