Date of Award

2023

Degree Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Statistics

First Advisor

Abdeltawab Hendawi

Abstract

Avian reoviruses continue to cause disease in turkeys with varied pathogenicity and tissue tropism. Turkey enteric reovirus (TERV) has been identified as a causative agent of enteritis or inapparent infections in turkeys. The new emerging variants of turkey reovirus tentatively named as turkey arthritis reovirus (TARV) and turkey hepatitis reovirus (THRV) are linked to tenosynovitis/arthritis and hepatitis, respectively. Turkey arthritis and hepatitis reoviruses are causing significant economic losses to the turkey industry. These infections can lead to poor weight gain, uneven growth, poor feed conversion, increased morbidity and mortality, and reduced marketability of commercial turkeys. To combat these issues, it is essential to detect and classify types of reoviruses in turkey populations.

This research aims to employ clustering methods, specifically K-means and Hierarchical clustering, to differentiate three types of turkey reoviruses as well as to identify novel emerging variants of turkey reoviruses. Additionally, it focuses on classifying variants of turkey reoviruses by leveraging various machine learning algorithms such as Support Vector Machines, Naive Bayes, Random Forest, Decision Tree, and deep learning algorithms including Convolutional Neural Networks. The experiments are conducted using real turkey reovirus sequence data, allowing for robust analysis and evaluation of the proposed methods. The results indicate that machine learning methods achieve average accuracy of 92%, f1-Macro of 93%, and F1-Weighted of 92% scores in classifying reovirus types. In contrast, the CNN model demonstrates average accuracy 85%, f1-Macro of 71%, and F1-Weighted of 84% scores in the same classification task. The superior performance of the machine learning classifiers provides valuable insights into reovirus evolution and mutation, ultimately aiding in the detection of emerging variants of pathogenic TARVs and THRVs.

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