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Articles 14521 - 14550 of 291666

Full-Text Articles in Physical Sciences and Mathematics

Gc-058 Personalized Wellness Recommendations, Varshini Yaganti Apr 2025

Gc-058 Personalized Wellness Recommendations, Varshini Yaganti

C-Day Computing Showcase

A health recommendation system using machine learning, built on Hadoop, Spark, and HDFS, represents a significant advancement in personalized healthcare. This project aims to leverage big data technologies to process and analyze vast amounts of medical data across a distributed computing environment, utilizing at least three virtual machines. The background of this project lies in the increasing prevalence of chronic diseases and the growing volume of health-related data collected by healthcare providers. The motivation for this project stems from several key factors. Firstly, traditional healthcare systems often struggle to provide personalized recommendations due to the sheer volume and complexity of …

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Gc-123 Deep Learning-Based Skin Cancer Detection, Andrew Polisetty, Akshay Krishna Varma Buddharaju, Siri Yellu Apr 2025

Gc-123 Deep Learning-Based Skin Cancer Detection, Andrew Polisetty, Akshay Krishna Varma Buddharaju, Siri Yellu

C-Day Computing Showcase

Skin cancer is increasingly becoming a severe health problem globally today, but early detection is essential to enhance survival rates. Nonetheless, conventional diagnosis relies largely on visual examinations by dermatologists, which can be subjective and time-consuming. This research examines the application of deep learning for the automation of skin cancer detection based on dermoscopic images from the HAM10000 dataset. The models VGG19, DenseNet121 and ResNet152 will be trained and evaluated, with class mbalance addressed using data augmentation strategies. The outputs will demonstrate the applicability of deep learning to improve skin cancer diagnosis. Classification optimization using ensemble modeling and its improved …

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Grm-011 Non-Invasive Convolution-Based Coronary Artery Blood Pressure Prediction, Rene Lisasi Apr 2025

Grm-011 Non-Invasive Convolution-Based Coronary Artery Blood Pressure Prediction, Rene Lisasi

C-Day Computing Showcase

The primary objective of this proposal is to develop an innovative technique for determining the functional significance of coronary artery lesions in patients with coronary artery disease (CAD) and evaluate its utility for clinical decision-making using coronary computed tomography angiography (CCTA).

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Grm-022 Exploring Coronavirus 2019 Datasets With Convolutional Neural Networks, Ryan Deem, Sandeep Kumar Vupputuri, Tyler Hood, Droan Patel, Stephen Jacobs Apr 2025

Grm-022 Exploring Coronavirus 2019 Datasets With Convolutional Neural Networks, Ryan Deem, Sandeep Kumar Vupputuri, Tyler Hood, Droan Patel, Stephen Jacobs

C-Day Computing Showcase

Over the past several decades, the healthcare sector has increased its data creation velocity at an astonishing rate. More doctors and patients have access to real-time imaging technology, which leads to earlier detection and diagnosis for a variety of diseases. In this project, we will explore several datasets that were gathered by various health organizations during the Coronavirus 2019 (COVID-19) pandemic. We will leverage big data analytics techniques and neural network modeling to gain deeper insights into the differentiated diagnosis of COVID-19.

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Grm-041 Ai-Driven Analysis Of Openalg Curriculum: Mapping Ai Competencies Across Georgia’S Higher Education Landscape, Michelle Ilechukwu, Iveren Leghemo, Timothy Ngeny Apr 2025

Grm-041 Ai-Driven Analysis Of Openalg Curriculum: Mapping Ai Competencies Across Georgia’S Higher Education Landscape, Michelle Ilechukwu, Iveren Leghemo, Timothy Ngeny

C-Day Computing Showcase

This project investigates the presence of artificial intelligence (AI) competencies across Georgia’s higher education curriculum using university course catalogs as the primary data source, supplemented by OpenALG materials. We applied large language models, including OpenAI’s ChatGPT and embedding APIs, to analyze over 34,000 courses summarizing content, classifying AI relevance, and mapping to global frameworks (AI4K12 and UNESCO). Techniques such as topic clustering, semantic similarity analysis, and geographic distribution mapping were used to uncover patterns in AI integration. Findings reveal that AI content is concentrated in computing disciplines and research universities, with limited coverage in community colleges, MSIs, and non-technical fields. …

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Grm-043 Performance Assessment Of Deepseek Versus Bard And Chatgpt In Detecting Alzheimer’S Dementia, Muhammad Awais Arshad Apr 2025

Grm-043 Performance Assessment Of Deepseek Versus Bard And Chatgpt In Detecting Alzheimer’S Dementia, Muhammad Awais Arshad

C-Day Computing Showcase

Alzheimer’s disease is a growing public health issue due to its progressive nature and increasing prevalence. Large language models (LLMs) offer promising avenues for non-invasive cognitive assessment through natural language understanding. In this study, we evaluate DeepSeek’s general-purpose model V3 and reasoning-enhanced R1 variant—for identifying Alzheimer’s dementia (AD) and Cognitively Normal (CN) individuals using transcripts derived from spontaneous speech. Two baseline prompting strategies (zero-shot, chain-of-thought ) were applied to both model types and an additional query (self-consistency prompting) was applied to assess better predictions. Accuracy was the primary performance metric. When positively identifying AD, the general-purpose DeepSeek V3 model produced …

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Grm-050 Context-Aware Misinformation Detection Using Fine-Tuned Bert And Bilstm With Attention, Rakshak Gurung, Nino Tkabladze Apr 2025

Grm-050 Context-Aware Misinformation Detection Using Fine-Tuned Bert And Bilstm With Attention, Rakshak Gurung, Nino Tkabladze

C-Day Computing Showcase

Misinformation spreads fast, and 60% of consumers now question media reliability (Redline Digital, 2023). Manual verification is slow, and most systems still rely on binary real/fake classification, which overlooks nuanced types of misinformation. We propose a multi-class deep learning approach using a fine-tuned BERT model and a custom BiLSTM with attention to better detect categories like satire, conspiracy, and bias. Our models were trained on a balanced subset of the Fake News Corpus using nine distinct misinformation classes. By addressing both class imbalance and linguistic ambiguity, this system enhances contextual understanding and improves detection across varied news content. Our approach …

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Grm-060 Abstractive Summarization Of Informal Text: Fine-Tuning Transformers On Reddit Discussions, Nong Ming, Arpana Challa, Sharon Edward John Apr 2025

Grm-060 Abstractive Summarization Of Informal Text: Fine-Tuning Transformers On Reddit Discussions, Nong Ming, Arpana Challa, Sharon Edward John

C-Day Computing Showcase

In recent years, the rapid growth of social media platforms has led to an information overload, as a result, the ability to compress long and complex texts into short and precise summaries is essential, especially in online discussions and comment sections. Summarizing such content is difficult due to inconsistencies in sentence structure, slang, abbreviations, and the lack of formal grammar. State-of-the-art models such as BART and PEGASUS have shown promising results, but their performance on informal datasets remains lower compared to structured text benchmark. To address these challenges, we fine-tune BART and PEGASUS on the Reddit TIFU dataset, leveraging their …

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Grm-080 Leveraging Data Science For Resilience: Improving Trauma-Informed Care Practice For Adverse Childhood Experience With Ai & Data Science Application, Mohmmad Arif Shaik Apr 2025

Grm-080 Leveraging Data Science For Resilience: Improving Trauma-Informed Care Practice For Adverse Childhood Experience With Ai & Data Science Application, Mohmmad Arif Shaik

C-Day Computing Showcase

Adverse Childhood Experiences (ACEs) have long-lasting effects on physical health, mental well-being, education, and socioeconomic outcomes. Resilient Georgia (RG), a statewide initiative, seeks to address ACEs through trauma-informed care and data-driven strategies. However, challenges in data collection, analysis, and tracking hinder the effectiveness of these efforts. This study explores the role of data science and interactive visualization tools in improving outcomes for individuals and communities affected by ACEs. A key focus of this research is the development of a data science management application designed to enhance data collection and facilitate real-time decision-making. The application features interactive dashboards that allow stakeholders …

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Grm-083 Leveraging Graph Attention Networks And Bert For Robotic Surgery Report Generation, Akshay Krishna Varma Buddharaju Apr 2025

Grm-083 Leveraging Graph Attention Networks And Bert For Robotic Surgery Report Generation, Akshay Krishna Varma Buddharaju

C-Day Computing Showcase

This project focuses on generating surgical reports from robotic surgery videos by leveraging graph-based representations of instrument-tissue interactions. We utilize Graph Attention Networks (GAT) to model these interactions, which are then integrated into a BERT-based language model for caption generation. Our approach enhances the accuracy of automated surgical reporting by capturing spatial and relational dependencies within surgical scenes. The model is evaluated on the Robotic Instrument Segmentation dataset from the 2018 MICCAI Endoscopic Vision Challenge(Endovis-18) and TORS surgery dataset, achieving high performance across multiple metrics, including BLEU-n, Cider, and ROUGE scores. By automating report generation, this study aims to assist …

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Grm-093 Advances In Non-Invasive Glucose Sensing: A Comprehensive In Vitro Analysis, El Arbi Belfarsi, Henry Flores Apr 2025

Grm-093 Advances In Non-Invasive Glucose Sensing: A Comprehensive In Vitro Analysis, El Arbi Belfarsi, Henry Flores

C-Day Computing Showcase

This study explores non-invasive glucose sensing using infrared (IR) imaging and electrical measurements in an in-vitro setup. Glucose samples (70–200 mg/dL) were prepared by diluting concentrated solutions (700–2000 mg/dL) 1:10 in synthetic blood concentrate, with 2 mg/dL increments. A custom 3D-printed black cuvette holder ensured consistent alignment of components, including either an IR camera or a 1550 nm photodiode, light sources (850 nm LED/laser, 808 nm, 650 nm, or 1600 nm), and a 3 mm skin-mimicking silicone layer. A Region Based Convolutional Neural Network (RCNN) trained on IR images achieved the lowest RMSE of 10.98 mg/dL at 850 nm LED. …

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Grm-102 Analysis Of Climate Change Effects On Bird Migration​​ Patterns Using Long-Term Data​​ ​, Aadil Syed, Mary Beyioku, Osi Aizebeokhai, Felix Dogbe Apr 2025

Grm-102 Analysis Of Climate Change Effects On Bird Migration​​ Patterns Using Long-Term Data​​ ​, Aadil Syed, Mary Beyioku, Osi Aizebeokhai, Felix Dogbe

C-Day Computing Showcase

To investigate how climate change has affected bird migration patterns over the past decades, focusing on changes in migration timing, routes, and population trends. This project will aim to identify correlations between climate variables and observed changes in bird behavior, contributing to conservation efforts and climate change research.

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Grm-118 Analysis Of Climate Change Effects On Bird Migration Patterns Using Long-Term Data, Saikiran Banneni, Naga Sreeja Arabu, Bhavana Modepu, Abilash Kanduri, Leelakarthik Devisetty Apr 2025

Grm-118 Analysis Of Climate Change Effects On Bird Migration Patterns Using Long-Term Data, Saikiran Banneni, Naga Sreeja Arabu, Bhavana Modepu, Abilash Kanduri, Leelakarthik Devisetty

C-Day Computing Showcase

This project examines the impact of climate change on bird migration patterns by integrating bird observation data from eBird with climate data from the NOAA Global Historical Climate Network (GHCN). Focusing on species such as the Arctic Tern, the study analyzes changes in migration timing, routes, and population trends over recent decades. Migration paths were visualized using QGIS, while MODIS land cover data helped assess habitat changes along these routes. Temporal analysis revealed noticeable shifts in migration timing, with earlier arrivals in some regions correlating with rising temperatures and changing precipitation patterns. For future predictions, CHELSA climate data was combined …

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Grm-120 Coding Neurodivergent, Veronica Bramlett Apr 2025

Grm-120 Coding Neurodivergent, Veronica Bramlett

C-Day Computing Showcase

This literary research provides a look at neurodivergent individuals learning coding, specifically Python, and the struggles and benefits that come from the way their brains are wired. This literature research was conducted to look at the benefits and struggles of learning to code as a neurodivergent individual. One area that has not been studied extensively is the learning of Python, or coding in general, by neurodivergent populations. This includes the benefits a neurodivergent learner may glean from learning Python as well as the challenges they may encounter - anything which makes their experience different from that of a neurotypical Python …

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Grm-131 Xr Agent (A Mllm Powered Xr System), Yukang Shen Apr 2025

Grm-131 Xr Agent (A Mllm Powered Xr System), Yukang Shen

C-Day Computing Showcase

This project proposes “XR Agent”, a uncoupled and efficient framework for developing AI-powered extended reality (XR) applications on head-mounted displays (HMDs). Leveraging multimodal artificial intelligence—including MediaPipe(Google open-source CV Model) for computer vision (object segmentation, recognition, pose estimation), multimodal large language models (MLLMs) like Gemini, and Unity’s cross-platform XR development ecosystem—the framework aims to create an extensible base system that enables rapid prototyping and deployment of intelligent XR applications. Currently, it was deployed on the Meta Quest 3 platform, XR Agent explores novel HCI(Human Computer Interaction) paradigms, combining real-time sensor data processing, immersive visualization, and adaptive AI-driven logic. This work addresses …

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Grp-010 Autonomous Agents In The Loop: Strengthening Educational Recommenders With Computer Use Agents, Mourya Teja Kunuku Apr 2025

Grp-010 Autonomous Agents In The Loop: Strengthening Educational Recommenders With Computer Use Agents, Mourya Teja Kunuku

C-Day Computing Showcase

Pedagogical Design Patterns (PDPs) serve as reusable, research-informed strategies that support effective teaching, yet their discoverability remains a major hurdle for educators. In this work, we extend the PDPR (Personalized Dynamic Practice and Reflection) system with a Retrieval-Augmented Generation (RAG) framework powered by a fine-tuned large language model (LLM) to deliver context-aware PDP recommendations. A key innovation in our proposed system is the integration of a Computer-Using Agent (CUA), which acts as a fallback mechanism when the internal knowledge base lacks sufficient coverage or yields low-confidence responses. This agent autonomously interacts with a live desktop environment—using browser automation, mouse control, …

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Grp-053 Expand: Explainable Ai Integrated Deep Learning-Based Reconstruction Of The Lost Packets​, Nasim Ahmed, A E M Ridwan Apr 2025

Grp-053 Expand: Explainable Ai Integrated Deep Learning-Based Reconstruction Of The Lost Packets​, Nasim Ahmed, A E M Ridwan

C-Day Computing Showcase

Advanced networking technology faces challenges with diverse usage, especially packet loss. Researchers tried deep learning to predict losses, but these black-box methods cannot explain the correlation between packet loss and parameters or mitigate losses. We propose a deep learning model to reconstruct lost packets in a complex networking scenario while integrating an explainable AI approach to explain the correlation between the networking parameters and the packet loss.. Integrating an elementary networking simulation designed in the ns2 platform, we collected data about networking packets and their associated parameters, based on which we trained and tested our deep learning model. Our approach …

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Grp-071 Next-Generation Dapps Development With Self-Service Ai Agents, Viraaji Mothukuri Apr 2025

Grp-071 Next-Generation Dapps Development With Self-Service Ai Agents, Viraaji Mothukuri

C-Day Computing Showcase

Our research introduces a decentralized agent mesh architecture that transforms blockchain application development from fragmented human-driven processes to autonomous, systematized workflows through human-AI collaboration. We’ve reimagined blockchain application development from the ground up by creating a decentralized agent ecosystem where humans and AI collaborate as peers rather than tools. Our innovation lies in the autonomous yet interconnected nature of specialized LLM powered agents handling contract creation, backend logic, frontend interfaces, and security auditing. Our proposed architecture distributes expertise across AI agents that operate in a peer-to-peer network. Furthermore, to address the emerging threat of systemic vulnerabilities from AI-generated code patterns, …

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Grp-090 A Novel Superpixel–Rag–Transformer Approach For Three-Class Melanoma Segmentation, Pablo Ordonez Apr 2025

Grp-090 A Novel Superpixel–Rag–Transformer Approach For Three-Class Melanoma Segmentation, Pablo Ordonez

C-Day Computing Showcase

Melanoma is one of the deadliest skin cancers, with early detection relying on accurately identifying both the lesion core and its often ambiguous border. Traditional CNN and U-Net models struggle with fuzzy transitions and irregular boundaries. We propose a three-class segmentation framework that labels regions as background, border, or lesion core. Our method over-segments images into superpixels, builds a Region Adjacency Graph (RAG) to capture spatial context, and generates embeddings using transformer-based autoencoders. This approach combines local image statistics with global semantic structure. Experiments on the HAM10000 dataset show improved precision and recall, especially in challenging border regions, outperforming CNN/U-Net …

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Grp-105 Prediction Of Greenhouse Gas Emissions From Electric Vehicle Charging And Road Traffic In The United States, Faysal Chowdhoury, Sai Nikhila Kanigiri Apr 2025

Grp-105 Prediction Of Greenhouse Gas Emissions From Electric Vehicle Charging And Road Traffic In The United States, Faysal Chowdhoury, Sai Nikhila Kanigiri

C-Day Computing Showcase

Electric vehicles (EVs) are widely considered a cleaner alternative to internal combustion engine vehicles. But their growing use creates indirect emissions via two main channels: more traffic congestion from more vehicle activity and more demand on power plants providing electricity for EV charging, usually depending on fossil fuels. This work offers a comprehensive, data-driven framework to forecast greenhouse gas (GHG) emissions connected to road traffic as well as EV-related power generation. Based on vehicle and speed characteristics, we forecast vehicle-level energy consumption and emission rates using a multi-model architecture that includes a Feed Forward Neural Network (FNN). While the Meta …

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Grp-134 Characterizing And Understanding The Performance Of Small Language Models On Edge Devices, Md Romyull Islam Apr 2025

Grp-134 Characterizing And Understanding The Performance Of Small Language Models On Edge Devices, Md Romyull Islam

C-Day Computing Showcase

In recent years, significant advancements in computing power, data richness, algorithmic development, and the growing demand for applications have catalyzed the rapid emergence and proliferation of large language models (LLMs) across various scenarios. Concurrently, factors such as computing resource limitations, cost considerations, real-time application requirements, task-specific customization, and privacy concerns have also driven the development and deployment of small language models (SLMs). Unlike extensively researched and widely deployed LLMs in the cloud, the performance of SLM workloads and their resource impact on edge environments remain poorly understood. More detailed studies will have to be carried out to understand the advantages, …

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Uc-016 Multifamily Loan Performance, Jonathan Bell Apr 2025

Uc-016 Multifamily Loan Performance, Jonathan Bell

C-Day Computing Showcase

This study explores the performance of multifamily loans using a logistic regression model to predict loan outcomes as either “closed” or “current”. Utilizing a dataset of over one million observations and 54,771 unique loan observations, we classify loan status based on Freddie Mac’s mortgage performance codes, with closed loans including modification with a loss, foreclosures, real estate owned, and fully closed loans. Through explanatory analysis, it reveals a nearly balanced distribution between the binary variables. This dataset supports the use of a logistic regression to model the probability of loan default or completion. The findings have implications for risk mitigation …

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Uc-019 Gwinnett County Public Schools - Data Masking Tool, Emmett Peters, Diego Frausto Ramirez, Abhay Tompkins Silas Talele, Jesus Trejo-Tamayo Apr 2025

Uc-019 Gwinnett County Public Schools - Data Masking Tool, Emmett Peters, Diego Frausto Ramirez, Abhay Tompkins Silas Talele, Jesus Trejo-Tamayo

C-Day Computing Showcase

In today’s data-driven world, organizations handle vast amounts of sensitive information, including personally identifiable information (PII), health records, and financial data. For institutions like schools, this data often includes sensitive details about students, parents, and staff, making data protection not just important, but critical. With increasing privacy regulations such as GDPR and HIPAA, organizations must implement robust measures to protect this information while still enabling its use for legitimate purposes like testing, analytics, and development. Our web-based data masking tool addresses this need by allowing organizations to protect sensitive data without compromising its usability. By applying dynamic masking rules to …

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Uc-020 Indy Micro - Virtual 8-Bit Computer, Matthew Watson, Elizabeth King, Junhyeok Lee, Gavin Frey Apr 2025

Uc-020 Indy Micro - Virtual 8-Bit Computer, Matthew Watson, Elizabeth King, Junhyeok Lee, Gavin Frey

C-Day Computing Showcase

The Indy Micro is a desktop application which simulates the functionality of an eight-bit personal computer. Its aim is to mimic the feel of owning one such computer in that era, as well as provide an engaging way to learn about low-level computing concepts. The Micro consists of two components: the virtual machine, which is based on the Von Neumann architecture, and the code editor, which allows users to write assembly code and execute it on the virtual machine. The aim is for the Indy Micro to serve as an educational jumping-off point, a step between the casual programmer and …

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Uc-027 Ksublocks Tower Defense, Matthew Elledge, Annagrace Gwee, Bryan Nguyen, Logan Slicker, Ashley Ahn Apr 2025

Uc-027 Ksublocks Tower Defense, Matthew Elledge, Annagrace Gwee, Bryan Nguyen, Logan Slicker, Ashley Ahn

C-Day Computing Showcase

Our project, KSUBlocks Tower Defense, is a Minecraft Plugin designed to create a game mode in the Tower Defense genre. We aim to create a unique and fun game for the students in the KSU Minecraft server. Our project is entirely configurable allowing for easy maintenance and room for future expansions, while ensuring the server performance remains steady alongside KSU's other game modes. It is developed in Java, utilizing IntelliJ and Paper API. We plan to deploy it on the KSU Minecraft Server upon finalization.

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Uc-030 Heartspeak​ Ai, Nabeel Faridi, Shammah Charles, Isha Minocha, Ethan Barnes, Aravind Iyer Apr 2025

Uc-030 Heartspeak​ Ai, Nabeel Faridi, Shammah Charles, Isha Minocha, Ethan Barnes, Aravind Iyer

C-Day Computing Showcase

This project is Sentiment Analysis AI for comprehensive text review analysis and more. The system leverages a fine-tuned BERT-based models to classify overall sentiment, detect emotions, identify sarcasm, and extract aspect-level opinions. Evaluations show robust performance across tasks, with sentiment accuracy around 69%, aspect analysis. Emotion and sarcasm. The pipeline provides actionable insights, empowering businesses to refine products and improve customer satisfaction with OpenAI Integration.

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Uc-037 Dynamic Requirements For A Software Training Environment​, Cassidie Grogan, Jesus Valdez, Dawson White Apr 2025

Uc-037 Dynamic Requirements For A Software Training Environment​, Cassidie Grogan, Jesus Valdez, Dawson White

C-Day Computing Showcase

STEDR outlines the development of a software training environment for Warner Robins Air Base (Robins) to enhance employee coding skills to foster innovative solutions for Air Force projects. STEDR is a proof of concept serving as a dynamic requirements document represented by a user interface, to be delivered to a development team. STEDR involved two phases: (1) requirements gathering via interviews; and (2) interactive user interface development for feedback. The resulting proof of concept, includes an interactive UI and refined requirements, and serves as the foundation for a collaborative project with KSU, enabling the computing colleges to contribute to military …

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Uc-048 Dinengo - Ai Genie, John Sheffield, Juwon Atunnise, Jaz Ankrah, Alex Colas, Kayla Grant Apr 2025

Uc-048 Dinengo - Ai Genie, John Sheffield, Juwon Atunnise, Jaz Ankrah, Alex Colas, Kayla Grant

C-Day Computing Showcase

This projects aims to enhance the flagship product from Driven Software Solutions called DineNGo by implementing a new chatbot to help users with technical troubleshooting. This will allow for instant technical support for common issues and reduces the number of support tickets being created. It provides informed and brief response in a quick manner to walk users through whatever technical issues they are currently having with the DineNGo software. It was built with an Angular frontend and a Node.JS backend as well as a MongoDB database for querying information.

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Uc-101 Sight-Singing Feedback, Terah Dann, Sandy Giroux, Nathanael Johnson, Amber Scarbro Apr 2025

Uc-101 Sight-Singing Feedback, Terah Dann, Sandy Giroux, Nathanael Johnson, Amber Scarbro

C-Day Computing Showcase

This project creates an engaging and interactive music-learning experience. Users start by selecting a tempo and melody number. The app then displays sheet music to guide them through the exercise. While singing, performers receive real-time visual feedback on pitch accuracy and tempo progression, allowing for dynamic adjustments and improved performance precision. The system continuously updates the music staff based on user performance, ensuring seamless interaction. This approach integrates technology with musical education, enhancing skill development through intuitive, data-driven feedback. By combining user-driven selections, interactive visualization, and real-time analysis, the application provides a structured, engaging platform for improving musical skills.

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Uc-111 Accessible Interactive Map​, Justin Connick, Megan Ingram, Derrick Novak, Spencer Williams, Emily Zhu Apr 2025

Uc-111 Accessible Interactive Map​, Justin Connick, Megan Ingram, Derrick Novak, Spencer Williams, Emily Zhu

C-Day Computing Showcase

Finding that walking campus gets you out of breath? We did too! Using React and Flask, we are building a web application that directs KSU students to the path with the lowest elevation and shows the shifts in between. It also displays accessible doors. The purpose of this app is to develop a more inclusive application so people with asthma, cardiovascular issues, and wheelchairs at KSU can safely traverse campus.

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