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Recognition of Livestock Disease Using Adaptive Neuro-Fuzzy Inference System

机译:使用自适应神经模糊推理系统识别畜牧病

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The livestock health management system is based on the principal concept to investigate bird health status by collecting biological traits like their sound utterance. This theme is implemented on four different species of livestock to cure them of bronchitis disease. This paper includes the audio features of both healthy and unhealthy livestock. Particularly, the secure audio-wellbeing features are incorporated into the platform to spontaneously examine and conclude using livestock voice information to recognize diseased birds. One month of long-term recognition experimental studies has been conducted where the recognition accuracy of the set of diseased birds was about 99% using adaptive neuro-fuzzy inference system (ANFIS). This recognition accuracy of ANFIS in this regard is better than the performance of an artificial neural network. This is a reliable way for researchers to investigate and constitute evidence of disease curability or eradication of incurable ones.
机译:畜牧卫生管理系统基于主要概念来调查鸟类健康状况,通过收集生物特征,如声音话语。 这个主题是在四种不同种类的牲畜中实施,以治愈支气管炎疾病。 本文包括健康和不健康的牲畜的音频功能。 特别是,将安全的音频良好的功能结合到平台中,以自发地检查和结论使用牲畜语音信息来识别患病的鸟类。 已经进行了一个月的长期识别实验研究,其中使用自适应神经模糊推理系统(ANFIS)的患者患者的识别准确性约为99%。 在这方面的ANFI的这种识别准确性优于人工神经网络的性能。 这是研究人员对疾病可固化性或消除不可治区的证据的可靠方式。

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