首页|Study Data from Wright State University Provide New Insights into Machine Learni ng (An Effective Prediction of Resource Using Machine Learning In Edge Environme nts for the Smart Healthcare Industry)
Study Data from Wright State University Provide New Insights into Machine Learni ng (An Effective Prediction of Resource Using Machine Learning In Edge Environme nts for the Smart Healthcare Industry)
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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."
DaytonOhioUnited StatesNorth and C entral AmericaCyborgsEmerging TechnologiesMachine LearningWright State U niversity
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