摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑-每日新闻-关于机器学习的最新研究结果已经发表。根据新sRx编辑在俄亥俄州代顿的新闻报道，研究表明：“最近的现代计算和数字传输趋势为早期预测疾病提供了智能医疗系统。在医疗服务中，基于物联网(IoT)的模型在增强数据处理和检测方面发挥了重要作用。”我们的新闻记者从赖特州立大学的研究中得到一句话:“随着物联网的发展,处理数据需要更多的空间。传输Patie NT报告需要花费太多的时间和精力,造成了较高的延迟和能量。为了克服这个问题,本文利用物联网和边缘技术,提出了一种适用于医疗保健应用的有效资源分配预测模型,该模型由三个模块组成:利用过滤技术对数据进行预处理,利用深度Q网络进行资源分配和预测。和预测阶段，使用称为DBN-LSTM的优化DL模型和蛙跃优化。使用训练健康数据集训练DL模型，并预测目标场。已经使用来自物联网层的传感数据对其进行测试，期望患者健康状况采取适当的行动。通过使用边缘设备进行及时预测，医生和病人经常采取必要的行动。该系统的主要目标是通过提高Makespa N、ARU、LBL、TAT和准确率等(QoS)指标来保证低延迟。深度强化学习方法被广泛应用于资源分配。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting out of Dayton, Ohio, by New sRx editors, research stated, "Recent modern computing and trends in digital tra nsformation provide a smart healthcare system for predicting diseases at an earl y stage. In healthcare services, Internet of Things (IoT) based models play a vi tal role in enhancing data processing and detection." Our news journalists obtained a quote from the research from Wright State Univer sity, "As IoT grows, processing data requires more space. Transferring the patie nt reports takes too much time and energy, which causes high latency and energy. To overcome this, Edge computing is the solution. The data is analysed in the e dge layer to improve the utilization. This paper proposed effective prediction o f resource allocation and prediction models using IoT and Edge, which are suitab le for healthcare applications. The proposed system consists of three modules: d ata preprocessing using filtering approaches, Resource allocation using the Deep Q network, and prediction phase using an optimised DL model called DBN-LSTM wit h frog leap optimization. The DL model is trained using the training health data set, and the target field is predicted. It has been tested using the sensed data from the IoT layer, and the patient health status is expected to take appropria te actions. With timely prediction using edge devices, doctors and patients conv eniently take necessary actions. The primary objective of this system is to secu re low latency by improving the quality of service (QoS) metrics such as makespa n, ARU, LBL, TAT, and accuracy. The deep reinforcement learning approach is empl oyed due to its considerable acceptance for resource allocation."
NETL
NSTL