摘要
由一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-关于人工智能的最新研究结果已经发表。根据NewsRx编辑来自罗德岛Y大学的消息，这项研究表明：“禽呼肠孤病毒通常会引起火鸡的疾病，具有不同的致病性和组织嗜性。Turkey肠道呼肠孤病毒已被鉴定为火鸡肠炎或隐性感染的病原体。”我们的新闻编辑从罗德岛大学的研究中获得了一句话：“新出现的土耳其呼肠孤病毒变种，暂命名为土耳其art hritis呼肠孤病毒(TARV)和土耳其肝炎呼肠孤病毒(THRV)，分别与tenos ynovitis/arthritis和肝炎有关。土耳其关节炎和丙型肝炎病毒正在给土耳其工业造成重大的经济损失。这些感染可能导致体重增长不佳，增长不平衡。”为了解决这些问题，对火鸡呼肠病毒进行检测和分类是至关重要的。本研究旨在采用聚类方法，特别是K-均值聚类和层次聚类，区分火鸡呼肠病毒的三种类型，并识别新出现的变种。该实验利用支持向量机、朴素贝叶斯、随机森林、决策树等多种机器学习算法和包括卷积神经网络RKS(CNNs)在内的深度学习算法对火鸡呼肠孤病毒变种进行分类。实验使用真实的火鸡呼肠孤病毒序列数据，允许对所提出的方法进行稳健的分析和评估。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on artificial in telligence have been published. According to news originating from the Universit y of Rhode Island by NewsRx editors, the research stated, “Avian reoviruses cont inue to cause disease in turkeys with varied pathogenicity and tissue tropism. T urkey enteric reovirus has been identified as a causative agent of enteritis or inapparent infections in turkeys.” Our news editors obtained a quote from the research from University of Rhode Isl and: “The new emerging variants of turkey reovirus, tentatively named turkey art hritis reovirus (TARV) and turkey hepatitis reovirus (THRV), are linked to tenos ynovitis/arthritis and hepatitis, respectively. Turkey arthritis and hepatitis r eoviruses are causing significant economic losses to the turkey industry. These infections can lead to poor weight gain, uneven growth, poor feed conversion, in creased morbidity and mortality and reduced marketability of commercial turkeys. To combat these issues, detecting and classifying the types of reoviruses in tu rkey populations is essential. This research aims to employ clustering methods, specifically K-means and Hierarchical clustering, to differentiate three types o f turkey reoviruses and identify novel emerging variants. Additionally, it focus es on classifying variants of turkey reoviruses by leveraging various machine le arning algorithms such as Support Vector Machines, Naive Bayes, Random Forest, D ecision Tree, and deep learning algorithms, including convolutional neural netwo rks (CNNs). The experiments use real turkey reovirus sequence data, allowing for robust analysis and evaluation of the proposed methods.”
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