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Can Tactile Internet be a Solution for Low Latency Heart Disorientation Measure: An Analysis

机译:触觉互联网能否成为低时延性心脏病的一种解决方案:分析

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To reduce the delay for accessing real-time data access from various applications (healthcare, transportations, virtual reality etc.), there is an exponential increase in the usage of Tactile Internet (TI) technology in recent era. Motivated from this, in this paper, we propose a TI-based random forest (RF) learning algorithm for heart disease predictions. The aim of this paper is to monitor and analyse the human activities for real-time data collection. The proposed approach is an analysis of heart ailments and can be used regularly for the health measure. For this purpose, the RF model is trained to map the collected sensor data features to output normal and abnormal states of the patient suffering from heart disorientation. Moreover, it removes excessive dependence on input values and cover possible alternate paths. Simulated results demonstrate that the proposed approach reduces the average delay and provides less training time in comparison to the pre existing conventional techniques.
机译:为了减少从各种应用程序(保健,运输,虚拟现实等)访问实时数据访问的延迟,近来触觉Internet(TI)技术的使用呈指数增长。因此,在本文中,我们提出了一种用于心脏病预测的基于TI的随机森林(RF)学习算法。本文的目的是监视和分析人类活动以进行实时数据收集。所提出的方法是对心脏疾病的分析,可以定期用于健康测量。为此,训练射频模型以映射收集的传感器数据特征,以输出患有心脏迷失方向的患者的正常和异常状态。此外,它消除了对输入值的过度依赖,并涵盖了可能的替代路径。仿真结果表明,与现有的常规技术相比,该方法减少了平均延迟,并提供了更少的训练时间。

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