标题： 基于智能物联网的可穿戴设备，使用一种新型机器学习技术检测和监测常见牛病 显示英文标题

标题： Smart IoT-Based Wearable Device for Detection and Monitoring of Common Cow Diseases Using a Novel Machine Learning Technique

摘要： 人工观察和监测个体奶牛以检测疾病在大规模农场运营中面临重大挑战，因为该过程劳动密集、耗时且容易导致准确性下降。 依赖人类观察往往会导致症状识别的延迟，因为动物数量众多可能会阻碍对每头奶牛的及时关注。 因此，疾病检测的准确性和精确性会受到严重影响，可能影响动物健康和整体农场生产力。 此外，组织和管理人力进行奶牛健康的人工观察和监测是一项复杂且经济负担重的任务。 这需要专业人员的参与，从而导致农场维护成本上升和运营效率低下。 因此，开发一种自动化、低成本且可靠的智能系统是有效应对这些挑战的关键。 尽管在这一领域已经进行了若干研究，但很少有研究同时考虑多种常见疾病的高预测精度检测。 然而，物联网（IoT）、机器学习（ML）和信息物理系统的发展使奶牛健康监测的自动化成为可能，提高了准确性并降低了运营成本。 本研究提出了一种基于物联网的信息物理系统框架，旨在监测奶牛的日常活动和健康状况。 提出了一种新的机器学习算法，用于利用收集到的生理和行为数据诊断常见的奶牛疾病。 该算法旨在通过分析一系列记录的生理和行为特征来预测多种疾病，从而实现准确高效的健康评估。 显示英文摘要

摘要： Manual observation and monitoring of individual cows for disease detection present significant challenges in large-scale farming operations, as the process is labor-intensive, time-consuming, and prone to reduced accuracy. The reliance on human observation often leads to delays in identifying symptoms, as the sheer number of animals can hinder timely attention to each cow. Consequently, the accuracy and precision of disease detection are significantly compromised, potentially affecting animal health and overall farm productivity. Furthermore, organizing and managing human resources for the manual observation and monitoring of cow health is a complex and economically demanding task. It necessitates the involvement of skilled personnel, thereby contributing to elevated farm maintenance costs and operational inefficiencies. Therefore, the development of an automated, low-cost, and reliable smart system is essential to address these challenges effectively. Although several studies have been conducted in this domain, very few have simultaneously considered the detection of multiple common diseases with high prediction accuracy. However, advancements in Internet of Things (IoT), Machine Learning (ML), and Cyber-Physical Systems have enabled the automation of cow health monitoring with enhanced accuracy and reduced operational costs. This study proposes an IoT-enabled Cyber-Physical System framework designed to monitor the daily activities and health status of cow. A novel ML algorithm is proposed for the diagnosis of common cow diseases using collected physiological and behavioral data. The algorithm is designed to predict multiple diseases by analyzing a comprehensive set of recorded physiological and behavioral features, enabling accurate and efficient health assessment.