Due to recent growing interest, the importance of preventive and efficient healthcare using big data scattered throughout various IoT devices is being emphasized in healthcare, as well in the IT field. The analysis of information in healthcare is mainly prediction using a user’s basic information and static data from a knowledge base. In this study, a knowledge-based dynamic cluster model using a convolutional neural network (CNN) is suggested for healthcare recommendations. The suggested method carries out a process to extend static data and a previous knowledge base from an ontology-based ambient-context knowledge base beyond knowledge-based healthcare management, which was the focus of previous study. It is possible to acquire and expand a large amount of high-quality information by reproducing inferred knowledge using a CNN, which is a deep-learning algorithm. A dynamic cluster model is developed, and the accuracy of the predictions is improved in order to enable recommendations on healthcare according to a user environment that changes over time and based on environmental factors as dynamic elements, rather than static elements. Also, the accuracy of the predictions is verified through a performance evaluation between the suggested method and the previous method to validate effectiveness, and an approximate 13% performance improvement was confirmed. Currently, the acquisition of knowledge from unstructured data is in its early stages. It is expected that symbolic knowledge-acquisition technology from unstructured information that is produced and that changes in real time, and the dynamic cluster model method suggested in this study, will become the core technologies that promote the development of healthcare management technology.

中文翻译：

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基于知识的卷积神经网络动态聚类管理模型

由于近来人们的兴趣日益浓厚，因此在医疗保健以及IT领域都强调了使用散布在各种IoT设备中的大数据进行预防和有效医疗保健的重要性。医疗保健中的信息分析主要是使用用户的基本信息和来自知识库的静态数据进行的预测。在这项研究中，提出了使用卷积神经网络（CNN）的基于知识的动态聚类模型，以用于医疗保健建议。所提出的方法执行了从基于本体的环境上下文知识库扩展静态数据和先前知识库的过程，而不是基于知识的医疗保健管理，这是先前研究的重点。通过使用CNN再现推断的知识，可以获取并扩展大量的高质量信息，这是一种深度学习算法。开发了动态聚类模型，并提高了预测的准确性，以便根据随时间变化的用户环境并基于作为动态元素而非静态元素的环境因素，提出有关医疗保健的建议。另外，通过在建议的方法和先前的方法之间进行性能评估来验证预测的准确性，以验证有效性，并确认了大约13％的性能改进。当前，从非结构化数据中获取知识尚处于早期阶段。期望从本研究中提出的非结构化信息产生的符号知识获取技术和实时变化的信息，以及动态聚类模型方法，