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- Member of: Theses and Dissertations
Description
Wave of Wellness is a mobile application meticulously designed to bridge the gap between technology and healthcare, focusing on enhancing the quality of life for the elderly and their caregivers. The app is embedded with the capability to monitor and track vital signs and biometric data, utilizing integrated sensors to provide real-time health insights. The primary objective of this project is to explore and answer the pivotal question: How can technology be utilized to uplift the living standards of the elderly and caregivers? This is achieved by promoting independence among the elderly, averting unnecessary hospitalizations, and offering valuable health data that can be crucial in medical interventions and lifestyle adjustments.
ContributorsMousa, Ibrahim (Author) / Osburn, Steven (Thesis director) / Turczan, Nathan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-12
Description
Phishing is one of most common and effective attack vectors in modern cybercrime. Rather than targeting a technical vulnerability in a computer system, phishing attacks target human behavioral or emotional tendencies through manipulative emails, text messages, or phone calls. Through PyAntiPhish, I attempt to create my own version of an anti-phishing solution, through a series of experiments testing different machine learning classifiers and URL features. With an end-goal implementation as a Chromium browser extension utilizing Python-based machine learning classifiers (those available via the scikit-learn library), my project uses a combination of Python, TypeScript, Node.js, as well as AWS Lambda and API Gateway to act as a solution capable of blocking phishing attacks from the web browser.
ContributorsYang, Branden (Author) / Osburn, Steven (Thesis director) / Malpe, Adwith (Committee member) / Ahn, Gail-Joon (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
For my thesis, I designed a program called Plexify that would take in truth tables as CSV files and be able to generate the minimized sum of products or product of sums expression for any of the output variables in the truth table. My program can run on any Windows device and the repository for the program can be found at https://github.com/RockPalmer/Plexify.
ContributorsPalmer, Rock (Author) / Osburn, Steven (Thesis director) / Platt, Dane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Machine learning algorithms have a wide variety of applications and use cases. They are robust in the sense that they can continue to learn and improve long after they have been deployed without much programmer supervision. One key area that machine learning has been used for is in the detection and classification of objects in images and videos. This so-called computer vision has typically been used by companies to extract user information from the images and videos that they post. Meta (formerly known as Facebook) had been using such algorithms to automatically tag users in pictures that were uploaded to the Facebook website up until November 2021 [1]. Although these algorithms have been used to exploit user’s privacy, they can also be used to help ensure this privacy.
For this creative project, I developed a machine learning model that could detect faces in a given picture and identify the area of the picture that these faces took up. Training a model from scratch can take millions of images of data and hundreds of hours on powerful GPUs. Since I didn’t have access to those resources, I began with a pre-trained model known as VGG16 by Karen Simonyan & Andrew Zisserman. From there, I took 90 pictures of myself and annotated where in the image my face was located. Since 90 pictures wouldn’t be enough data for this algorithm, I used an image augmentation algorithm to randomly crop, flip, change brightness, change gamma, and recolor the images to expand the dataset. In total, I used 5400 images to train the algorithm.
The machine learning model had a loss value that hovered around 0.1 thanks to the VGG16 model. It was able to accurately detect my face and also adapt whenever I moved my face horizontally and vertically across a camera. However, the model struggled to draw a bounding box whenever I moved my face forward or backward in the camera shot.
ContributorsGutierrez, Ariel (Author) / Osburn, Steven (Thesis director) / Panchoo, Anthony (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Many companies wish to create a personalized experience for customers using their websites. For many services that might mean changing the icon on a sign in page; however, as computers become more powerful and customers expect more from the services they use, companies are starting to investigate ways of running personalized code for their customers. Sadly, one big problem with this trend is that it is very new. This leads to many problems such as the lack of technologies fit for a certain scenario, the flooding of new technologies in only a specific field, and the overall general confusion in implementing a novel technology like this. This is why I believe that compounding a list of different technologies, each with a list of pros and cons, example implementations to give a feel of the technology, as well as benchmarks of each method to allow for individuals and companies to create better websites and services for their customers. I will also be going through a history of available technologies to give an idea on how this technology used to be used for, how it is used today, and how I believe it will be used in the future.
ContributorsLabourdette, Aidan (Author) / Osburn, Steven (Thesis director) / Chavez Echeagaray, Maria Elena (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
This project seeks to motivate runners by creating an application that selectively plays music based on smartwatch metrics. This is done by analyzing metrics collected through a person’s smartwatch such as heart rate or running power and then selecting the music that best fits their workout’s intensity. This way, as the workout becomes harder for the user, increasingly motivating music is played.
ContributorsDoyle, Niklas (Author) / Osburn, Steven (Thesis director) / Miller, Phillip (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Music, Dance and Theatre (Contributor)
Created2023-05
Description
The purpose of the thesis project is to address the rising issue of fake real estate listings and scams prevalent in listing platforms by developing an advanced program that employs various data verification methods to identify potential fraudulent listings. With the rise of online real estate transactions, the need for establishing trust and credibility between buyer and seller has never been more important. This research will create a system that will protect potential buyers from falling victim to fake listings and shield sellers from purchasing on scam-related platforms.
Through analysis, the program will identify any inconsistency and warning signs that may indicate a fake listing. This thesis project aims to enhance the overall integrity and dependability of real estate listing platforms, fostering a secure environment for buyers and sellers to participate in online property transactions.
ContributorsAguilar, Javier (Author) / Osburn, Steven (Thesis director) / Malpe, Adwith (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
This study presents a comparative analysis of machine learning models on their ability to determine match outcomes in the English Premier League (EPL), focusing on optimizing prediction accuracy. The research leverages a variety of models, including logistic regression, decision trees, random forests, gradient boosting machines, support vector machines, k-nearest neighbors, and extreme gradient boosting, to predict the outcomes of soccer matches in the EPL. Utilizing a comprehensive dataset from Kaggle, the study uses the Sport Result Prediction CRISP-DM framework for data preparation and model evaluation, comparing the accuracy, precision, recall, F1-score, ROC-AUC score, and confusion matrices of each model used in the study. The findings reveal that ensemble methods, notably Random Forest and Extreme Gradient Boosting, outperform other models in accuracy, highlighting their potential in sports analytics. This research contributes to the field of sports analytics by demonstrating the effectiveness of machine learning in sports outcome prediction, while also identifying the challenges and complexities inherent in predicting the outcomes of EPL matches. This research not only highlights the significance of ensemble learning techniques in handling sports data complexities but also opens avenues for future exploration into advanced machine learning and deep learning approaches for enhancing predictive accuracy in sports analytics.
ContributorsTashildar, Ninad (Author) / Osburn, Steven (Thesis director) / Simari, Gerardo (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
In this thesis, I propose a framework for automatically generating custom orthotic insoles for Intelligent Mobility and ANDBOUNDS. Towards the end, the entire framework works together to ensure users receive the highest quality insoles through human quality checks. Three machine learning models were assembled: the Quality Model, the Meta-point Model, and the Multimodal Model. The Quality Model ensures that user uploaded foot scans are high quality. The Meta-point Model ensures that the meta-point coordinates assigned to the foot scans are below the required tolerance to align an insole mesh onto a foot scan. The Multimodal Model uses customer foot pain descriptors and the foot scan to customize an insole to the customers’ ailments. The results demonstrate that this is a viable option for insole creation and has the potential to aid or replace human insole designers.
ContributorsNucuta, Raymond (Author) / Osburn, Steven (Thesis director) / Joseph, Jeshua (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Navigation for the visually impaired and blind remains to be a major barrier to
independence. Existing assistive tools like guide dogs or white mobility canes provide
limited, immediate information within a range of about 5 feet. Alternatively, assistive
applications for navigation only provide static, generalizable information about a
broader area that could be a few hundred feet radius to miles. Currently, no solution
effectively covers the 5 to 20 feet range, leaving users without crucial information
about their surroundings in this mid-distance area. This project explores the potential
of state-of-the-art vision-language models (VLMs) to provide new navigation solutions
for the visually impaired and blind that bridge the aforementioned gap in information
about the environment. VLMs prove capable of identifying key objects and reasoning
from corresponding text and images in real time, making them the ideal candidate for
assistive technology. Leveraging these capabilities, these models may be integrated
into wearable or extendable devices that allow users to receive continuous support in
unfamiliar environments, improving their independence and maintaining safety. This
project investigates the practical application of VLMs in real-world scenarios, with
an emphasis on ease of use and reliability. This work has the potential to expand the
role of assistive technology in daily life and complement existing solutions for more
intuitive and responsive understanding.
ContributorsRaines, Kelly (Author) / Senanayake, Ransalu (Thesis director) / Osburn, Steven (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor)
Created2024-12