Abstract
Data analysis depends heavily on the gathering, reasoning, and modeling of sensor-generated data. Applications for the Internet of Things (IoT) face difficulties in studying and decoding real-time data delivered through various wireless links. A data stream tracking technique called Event Process Healthcare (EPH) is used to extract relevant information from network results for use in immediate decision-making. For the data analysis of dependable healthcare applications, an event-driven IoT architecture with an event, context, and service layer is presented in this paper. In the proposed EPH method, a new algorithm known as Cloud-based Deep Learning (CDN) is introduced, which supports both patients and the healthcare industry utilizing a combination of machine learning techniques, an intelligent cloud system, and the deep learning norms serve as the foundation. Simulation is used to obtain empirical results, and it dramatically improves healthcare parameters furthermore, the EPH technique boosted precision, cut expenses, and improved health outcomes.
Published Version
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