With the development of the Internet of Things, the requirement of a wide range of human-centered services may now make use of as many computing resources for media technologies and holographic images. The IoT system can monitor the status of equipment in real-time with a robust infrared image recognition algorithm. However, few researchers discuss data mining on images with valuable information. In this study, we present a generic approach that is based on the mining decision tree and holographic image improvement data analysis. We employed advanced data mining techniques to achieve image stability and use light to form a three-dimensional image with real space. The suggested model improves digital image signal transmission and noise through the grey neural network technique and, furthermore, utilization decision tree induction to create attributes-to-target label relations from image pixels. The experimental results show that the suggested approach may be highly efficient and effective for interactive image systems and image mining. Our approach may also be widely utilized and includes extremely efficient convergence systems for essential framework elements.

中文翻译：

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使用支持数据挖掘的图像增强和物联网进行公共信息传播

随着物联网的发展，广泛的以人为中心的服务的需求现在可能会利用尽可能多的计算资源用于媒体技术和全息图像。物联网系统可以通过强大的红外图像识别算法实时监控设备状态。然而，很少有研究人员讨论对具有有价值信息的图像进行数据挖掘。在这项研究中，我们提出了一种基于挖掘决策树和全息图像改进数据分析的通用方法。我们采用先进的数据挖掘技术来实现图像稳定性，并利用光线形成具有真实空间的三维图像。建议的模型通过灰色神经网络技术改善数字图像信号传输和噪声，此外，利用决策树归纳从图像像素创建属性到目标标签关系。实验结果表明，所提出的方法对于交互式图像系统和图像挖掘可能是高效和有效的。我们的方法也可能被广泛使用，并且包括用于基本框架元素的极其有效的收敛系统。