Rajeswari Kathiah, Praveena Daya A, Sathish Selvakumar, Kunnumbrath Arathi, Meenakshisundaram K
Deciphering the Nexus: Exploring Learning Styles and Academic Success Among Medical Students Through a Comprehensive Study
Cureus
avian reoviruses continue cause disease turkeys varied pathogenicity tissue tropism turkey enteric reovirus identified causative agent enteritis inapparent infections turkeys new emerging variants turkey reovirus tentatively named turkey arthritis reovirus tarv turkey hepatitis reovirus thrv linked tenosynovitisarthritis hepatitis respectively turkey arthritis hepatitis reoviruses causing significant economic losses turkey industry infections lead poor weight gain uneven growth poor feed conversion increased morbidity mortality reduced marketability commercial turkeys combat issues detecting classifying types reoviruses turkey populations essential research aims employ clustering methods specifically kmeans hierarchical clustering differentiate three types turkey reoviruses identify novel emerging variants additionally focuses classifying variants turkey reoviruses leveraging various machine learning algorithms support vector machines naive bayes random forest decision tree deep learning algorithms including convolutional neural networks cnns experiments use real turkey reovirus sequence data allowing robust analysis evaluation proposed methods results indicate machine learning methods achieve average accuracy 92 f1macro 93 f1weighted 92 scores classifying reovirus types contrast cnn model demonstrates average accuracy 85 f1macro 71 f1weighted 84 scores classification task superior performance machine learning classifiers provides valuable insights reovirus evolution mutation aiding detecting emerging variants pathogenic tarvs thrvs