摘要
由机器人与机器学习每日新闻的新闻记者兼新闻编辑-研究人员详细介绍了人工智能的新数据。根据NewsRx记者来自密西西比州斯塔克维尔的新闻，研究表明，“动物佩戴的加速计产生了不同的行为特征，可以使用随机森林决策树等机器学习方法准确分类。”这项研究的资助者包括像山一样思考，以改善动物生产系统生态、能源预算和机械模型；美国农业部国家食品和农业研究所。新闻记者从密西西比州立大学的研究中获得了一句话：“这项研究的目的是识别加速计信号在节俭行为中的传递。我们通过(1)描述离散行为中加速计信号的功能差异，(2)确定信号预处理的最佳窗口大小，”和(3)演示达到理想的模型精度水平所需的观察数量，.杂交的阉牛（Bos Taurus indicus；n=10）配备了包含摄像机和三轴加速计的GPS Coll Ars（读取率=40 Hz）。加速计信号的不同行为，特别是放牧行为，由于头朝下的姿势，是AP母体。"
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news originating from Starkville, Mississippi, by NewsRx correspondents, research stated, “Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machin e learning methods such as random forest decision trees.” Funders for this research include Thinking Like A Mountain To Improve Animal Pro duction Systems Ecology, Energy Budgets, And Mechanistic Models; Usda National I nstitute of Food And Agriculture. The news reporters obtained a quote from the research from Mississippi State Uni versity: “The objective of this study was to identify accelerometer signal separ ation among parsimonious behaviors. We achieved this objective by (1) describing functional differences in accelerometer signals among discrete behaviors, (2) i dentifying the optimal window size for signal pre-processing, and (3) demonstrat ing the number of observations required to achieve the desired level of model ac curacy,. Crossbred steers (Bos taurus indicus; n = 10) were fitted with GPS coll ars containing a video camera and tri-axial accelerometers (read-rate = 40 Hz). Distinct behaviors from accelerometer signals, particularly for grazing, were ap parent because of the head-down posture.”
NETL
NSTL