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Human health big data evaluation based on FPGA processor and big data decision algorithm

机译:基于FPGA处理器和大数据决策算法的人体健康大数据评估

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At present, the development of health care industry is also very vigorous and prosperous, and has become one of the most widely developed industries in the world. Medical centers and service centers in various regions have begun to transform from medical model to health care model. This field programmable gate array has great advantages in this respect, and it is also one of the principles of patient-centered nursing. With the vigorous development of machine learning, its application scope is more and more extensive, and its application in medicine is also very common. People use machine learning to process big data in the medical field. In order to better manage patient data and realize patient-centered, we must analyze a large number of health data. The traditional management tools are not enough to support the analysis of modern data. Therefore, we should use advanced big data processing technology for relevant data processing, and use updated tools to meet the current medical needs. The signal processing based big data evaluation is to be done through FPGA. The proposed system contains three process these process are executed through the machine learning based. The first process preprocessing is used eliminate the noise of the image or irrelevant data avoided. The second process feature selection based decision tree technique used and then after the final process classification stage based machine learning technique is used to analysis of the big data accuracy level. FPGA based machine technique used to achieve the better result of the proposed system.
机译:目前,医疗保健行业的发展也非常蓬勃和兴旺,并已成为世界上最广泛发展的产业之一。各地区医疗中心和服务中心已经开始从医学模式,以医疗服务模式转变。此现场可编程门阵列在这方面具有很大的优势,并且还以病人为中心的护理的原则之一。随着机器学习的蓬勃发展,其应用范围也越来越广泛,其在医学领域的应用也很普遍。人们利用机器学习在医疗领域来处理大数据。为了更好地管理患者数据与实现以病人为中心,我们必须分析大量的健康数据。传统的管理工具不足以支持现代数据分析。因此,我们应该利用先进的大数据处理技术进行相关的数据处理,并且使用更新的工具,以满足当前的医疗需求。信号处理基于大数据的评价是通过FPGA来完成。所提出的系统包括三个过程,这些过程都是通过基于机器学习执行。第一工艺预处理用于消除避免了图像或不相关的数据的噪声。所使用的第二工艺特征为基础的选择决策树技术,然后最后工序分类阶段基于机器学习技术后用于大数据的精度水平的分析。基于FPGA的机器技术来实现所提出的系统的更好的结果。

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