University of International Business and Economics, Beijing 100050, China.
Comput Intell Neurosci. 2022 Aug 9;2022:5732379. doi: 10.1155/2022/5732379. eCollection 2022.
In order to alleviate the "difficulty in seeing a doctor" for the masses, continuously optimize the service process, and explore new financial service processes for admission and discharge, this study proposes a cloud-fog hybrid model UCNN-BN based on an improved convolutional neural network and applies it to financial services in smart medical care. Decision-making applications: this research improves and designs the UCNN network based on AlexNet and introduces small convolution layers to form convolution groups, making the network more adjustable. The network structure is simpler and more flexible, and it is easy to adjust the algorithm. The number of parameters is small, and it can be directly superimposed without having to add new network hidden layers. The experimental results show that the recognition rate of the UCNN network on the FER2013 and CK+ datasets is higher than that of other recognition methods, and the recognition rates on the FER2013 and CK+ datasets are 98% and 68.01%, due to other methods. This shows that the improved convolutional neural network used in this study for financial services in smart medical care has certain applicability, and small convolution kernels help to extract more subtle features, so as to identify more accurately.
为缓解群众“看病难”问题，不断优化服务流程，探索出入院新的金融服务流程，本研究提出了一种基于改进卷积神经网络的云雾混合模型 UCNN-BN，并将其应用于智慧医疗中的金融服务决策中。应用：本研究在 AlexNet 的基础上对 UCNN 网络进行改进和设计，引入小卷积层形成卷积组，使网络更具可调性。网络结构更简单、更灵活，易于调整算法。参数数量少，无需添加新的网络隐藏层即可直接叠加。实验结果表明，UCNN 网络在 FER2013 和 CK+数据集上的识别率高于其他识别方法，在 FER2013 和 CK+数据集上的识别率分别为 98%和 68.01%，这说明本研究用于智慧医疗金融服务的改进卷积神经网络具有一定的适用性，小卷积核有助于提取更细微的特征，从而更准确地识别。
