Deep learning (DL) has become a popular study topic in the field of artificial intelligence (AI) in recent years, due to its significant role in various application areas. It leverages supercomputing capacity in the era of big data to uncover the high-level abstract ideas in the original dataset and serves as decision support in the application sector by increasing the number of channels and the scale of parameters. This study designs and implements a heterogeneous medical education data analysis system based on DL technology. The proposed system adopts DL technology to model, analyzes the heterogeneous medical education data, uses the decision-level fusion strategy for the data model, and designs and implements the voting method and the weighting method. The decision value is statistically calculated to realize the improved DL algorithm for the medical college education data analysis method. In addition, this study also uses the Alzheimer’s disease public dataset with various structures and modalities of medical education data to compare and evaluate the systematic data preprocessing model performance and the effect of fusion methods. The experimental result validates the proposed model’s performance, demonstrating that the way of evaluating complete heterogeneous multimodal data is not only closer to the genuine diagnostic process but also aids clinicians in grasping the patient’s entire state and obtaining outcomes. Further, the essential ideas and implementation techniques of convolutional neural network (CNN) and stacked autoencoder as well as its application cases in medical college education data analysis are thoroughly explained.
The current medical universities culture can reflect their professional nature, showing a strong development trend. The current socialist economy of China is rapidly developing along with cloud computing for data centers. For cultural and educational activities, virtualization and software-defined data center (SDDC) technologies are being used. The most widely used open-source virtualization technologies are SDDC and virtualization. Users may utilize OpenStack to establish private cloud computing environments. The separate control and forwarding architecture of SDDC also make it naturally suitable for the data center’s network environment. Ryu controller has become one of the most widely used SDDC controllers because of its lightweight, high efficiency, and modularity features. Due to the large variety of network operations required by the SDDC, appropriate management and control function modules must be developed on Ryu when it is used as the controller. This study investigates the construction of campus culture at medical colleges and universities using SDDC. Our main objective is to improve the efficiency of data construction in medical colleges and universities and creating a positive cultural environment. On the one hand, the addition of functional modules makes the management of the controller itself easier. Ryu lacks an intuitive interactive platform. Although OpenStack provides an interactive interface, it cannot meet the integration requirements of Ryu. The unified control and school scheduling system have some research and application potential. The experimental results show that our proposed approach of designing an SDDC for medical colleges and universities has a significant impact and has vast potential for future studies.
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