Physical Sciences and Mathematics Commons^™

The Classification Of Internet Memes Through Supervised And Unsupervised Machine Learning Algorithms, William H. Little

Symposium of Student Scholars

Memes, those captivating internet phenomena, effortlessly deliver online entertainment. By leveraging time-series data from Google Trends, we can vividly illustrate and dissect the dynamic trends in meme popularity. Previous studies have discerned four distinct post-peak popularity patterns— "smoothly decaying," "spikey decaying," "leveling off," and "long-term growth"—and elegantly modeled these using ordinary differential equations.

This research introduces a programmatic approach that harnesses both supervised and unsupervised machine learning algorithms. The dataset, now expanded to over 2000 elements, becomes the canvas for exploration. The K-means algorithm identifies clusters, which then serve as labels for the supervised SVC algorithm. The overarching goal is …

Exploring Neural Networks For Breast Cancer Tissue Classification, Stephen Jacobs, Md Abdullah Al Hafiz Khan

Symposium of Student Scholars

Last year, more than 240 thousand women in the United States were diagnosed with breast cancer. These patients are benefitting from decades of data that have been collected by cancer research institutions around the world. Tissue samples are analyzed and cataloged by these institutions, and several facilities like the University of Wisconsin are sharing this historical data to promote the advancement of new cancer treatments. Deep learning and neural network models are being built for this data to help doctors diagnose faster and design treatment options for patients by comparing their tissue samples with these historical datasets. We will use …

Toxic Comment Classification Project, Brandon Solon

Symposium of Student Scholars

The digital landscape has blossomed thanks to the surge of online platforms, boosting the variety and volume of user-created content. But it's not without its shadows; cyberbullying and hate speech have also proliferated, making web spaces less safe. At our project centerstage, we work on creating a machine learning model skilled at spotting toxic comments with precision - this way contributing towards an internet society free from fear or discomfort. We put well-documented datasets to good use along with careful preprocessing maneuvers while trialing diverse machina-learning protocols as part of constructing solid classification architecture for usages beyond current limitations within …

Modeling Traffic At An Intersection, Kaleigh L. Mulkey, Saniita K. Fasenntao

Symposium of Student Scholars

The main purpose of this project is to build a mathematical model for traffic at a busy intersection. We use elements of Queueing Theory to build our model: the vehicles driving into the intersection are the “arrival process” and the stop light in the intersection is the “server.”

We collected traffic data on the number of vehicles arriving to the intersection, the duration of green and red lights, and the number of vehicles going through the intersection during a green light. We built a SAS macro code to simulate traffic based on parameters derived from the data.

In our program …

Underfunded And Understaffed: A Ground-Level View Of Ir Creation In Difficult Times, John Davison

Giving Undergraduate Research a Worldwide Voice: Institutional Repositories as Publishers

A discussion of the creation of OhioLINK, the institutional repository system for the Ohio University system.

More information at http://drc.ohiolink.edu/