Life Sciences Commons^™

Poultry Pose Estimation With Deeplabcut, Chiyou Vang

Computer Science and Computer Engineering Undergraduate Honors Theses

This honors thesis dives into the realm of deep learning-based pose estimation research and investigates the potential of DeepLabCut (Lauer, et al., 2021) in accurately and efficiently estimating the pose of poultry. With accurate pose estimation being a crucial aspect in understanding the behavior and movement of animals, this thesis aims to contribute to the development of more effective methods for pose estimation, especially for poultry.

To comprehensively evaluate the performance of DeepLabCut, two different types of chickens were tested in this thesis: a model toy chicken and actual live chickens. Videos were recorded for both types, and key points …

Smart-Insect Monitoring System Integration And Interaction Via Ai Cloud Deployment And Gpt, Ahmed Moustafa

Computer Science and Computer Engineering Undergraduate Honors Theses

The Insect Detection Server was developed to explore the deployment and integration of an Artificial Intelligence model for Computer Vision in the context of insect detection. The model was developed to accurately identify insects from images taken by camera systems installed on farms. The goal is to integrate the model into an easily accessible, cloud-based application that allows farmers to analyze automatically uploaded images containing groups of insects found on their farms. The application returns the bounding boxes and the detected classes of insects whenever an image is captured on-site, enabling farmers to take appropriate actions to address the issue …

Contrastive Learning For Unsupervised Auditory Texture Models, Christina Trexler

Computer Science and Computer Engineering Undergraduate Honors Theses

Sounds with a high level of stationarity, also known as sound textures, have perceptually relevant features which can be captured by stimulus-computable models. This makes texture-like sounds, such as those made by rain, wind, and fire, an appealing test case for understanding the underlying mechanisms of auditory recognition. Previous auditory texture models typically measured statistics from auditory filter bank representations, and the statistics they used were somewhat ad-hoc, hand-engineered through a process of trial and error. Here, we investigate whether a better auditory texture representation can be obtained via contrastive learning, taking advantage of the stationarity of auditory textures to …

Using Deep Learning For Children Brain Image Analysis, Rafael Toche Pizano

Computer Science and Computer Engineering Undergraduate Honors Theses

Analyzing the correlation between brain volumetric/morphometry features and cognition/behavior in children is important in the field of pediatrics as identifying such relationships can help identify children who may be at risk for illnesses. Understanding these relationships can not only help identify children who may be at risk of illnesses, but it can also help evaluate strategies that promote brain development in children. Currently, one way to do this is to use traditional statistical methods such as a correlation analysis, but such an approach does not make it easy to generalize and predict how brain volumetric/morphometry will impact cognition/behavior. One of …

Trunctrimmer: A First Step Towards Automating Standard Bioinformatic Analysis, Z. Gunner Lawless, Dana Dittoe, Dale R. Thompson, Steven C. Ricke

Computer Science and Computer Engineering Undergraduate Honors Theses

Bioinformatic analysis is a time-consuming process for labs performing research on various microbiomes. Researchers use tools like Qiime2 to help standardize the bioinformatic analysis methods, but even large, extensible platforms like Qiime2 have drawbacks due to the attention required by researchers. In this project, we propose to automate additional standard lab bioinformatic procedures by eliminating the existing manual process of determining the trim and truncate locations for paired end 2 sequences. We introduce a new Qiime2 plugin called TruncTrimmer to automate the process that usually requires the researcher to make a decision on where to trim and truncate manually after …

Simulating Foodborne Pathogens In Poultry Production And Processing To Defend Against Intentional Contamination, Silas B. Lankford

Computer Science and Computer Engineering Undergraduate Honors Theses

There is a lack of data in recent history of food terrorism attacks, and as such, it is difficult to predict its impact. The food supply industry is one of the most vulnerable industries for terrorist threats while the poultry industry is one of the largest food industries in the United States. A small food terrorism attack against just a single poultry processing center has the potential to affect a much larger population than its immediate consumers. In this work, the spread of foodborne pathogens is simulated in a poultry production and processing system to defend against intentional contamination. An …

A Support Vector Machine Base Model For Predicting Heparin-Binding Proteins Using Biological Metrics And Xb Patterns As Features, Joseph W. Sirrianni

Computer Science and Computer Engineering Undergraduate Honors Theses

Heparin is a highly sulphated and negatively charged polysaccharides belonging to the glycosamino- glycans(GAGs) family. It is widely used in medical treatments as an injectable anticoagulant. Although many heparin-binding proteins have been identified through experimental studies, there are still many proteins needing to be classified as heparin-binding or not. Many studies have been aimed at prediction of heparin binding patterns or motifs in the primary structure of proteins. For example XBBXBX and XBBBXXBX are two well-known patterns or motifs. In spite of intensive studies, still no good model has emerged which reasonably predicts proteins in the protein database as heparin-binding …