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Design Model for Information Classification in Big Data Environment

机译：大数据环境中信息分类的设计模型

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Deep learning is a standard machine learning approach which has accomplished a hug of advancement with completely traditional machine learning in the domain of areas. The current issue centers around how to extract and classify the greatest, huge scale real dataset. Consequently, the task manages proposing a framework that can efficiently contribute a to a extremely proficient and precise capable prediction model. In the human health services domain: A point to point analysis of the patient records and patient live condition. Change and improvement of the existing frameworks by appending or replacing manual work with the utilization of huge data and information based artificial intelligence forms. Applying deep learning to these domains has been one among the prevalent points of research. The model given by us has the capability of maintaining high a long term accuracy. In case of an NDLCM network, a stacked NDLCM layer makes possible learning a high level temporal feature without any need of fine tuning and preprocessing that may otherwise be important in case of another technique. In this proposed paper, we construct a deep learning computational model which uses the normal back propagation neural network training set that helps to build a precise prediction model.
机译：深度学习是一种标准的机器学习方法，在领域领域中完全采用传统的机器学习已取得了很大的进步。当前问题围绕如何提取和分类最大的，大规模的真实数据集。因此，任务管理提出了一个框架，该框架可以有效地为极其熟练和精确的能力预测模型做出贡献。在人类健康服务领域：对患者记录和患者生活状况的点对点分析。通过使用基于海量数据和信息的人工智能表格附加或替换手动工作来更改和改进现有框架。将深度学习应用于这些领域一直是研究的热点之一。我们提供的模型具有维持较高长期精度的能力。在NDLCM网络的情况下，堆叠的NDLCM层使学习高级时域特征成为可能，而无需进行微调和预处理，而在其他技术的情况下，微调和预处理可能很重要。在本文中，我们构建了一个深度学习计算模型，该模型使用正常的反向传播神经网络训练集来帮助建立精确的预测模型。

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