Abstract
The service efficiency of the university information services platform directly affects the efficiency of university management. However, the university has rich data resources and complex management, and how to construct a comprehensive, standardized, efficient, and shared university information services platform has become a research hotspot. In recent years, the progress of artificial intelligence (AI) in many aspects has created opportunities for its large-scale application in smart campuses. With the wide penetration of AI technology into all walks of life, especially in the fields of industry, commerce, finance, security, and so on, some technologies have experienced practical tests. Many scholars have discussed the significance and possibility of the application of AI in the field of education from a theoretical level. The university’s secure information services platform can penetrate into all details of university management. Therefore, this paper studies the construction of a university secure information services platform based on AI, taking the dormitory allocation of freshmen and the face recognition of each building as the entry points. First, the beetle antennae search algorithm was introduced to improve the clustering efficiency and accuracy of the K-means algorithm for intelligent dormitory allocation. Then, the improved variational auto encoder-generative adversarial networks (VAE-GAN) model and convolutional neural networks (CNN)-based face recognition algorithm are proposed to enhance the security of building entry in universities. Finally, the simulation results reveal that the proposed two algorithms improve the clustering efficiency in dormitory allocation and the security of the university in the basic construction of the university information services platform, respectively.
Highlights
Introduction e development ofChina’s education level has greatly improved in recent years. e impact of the development of information services in universities on education cannot be underestimated as it adapts to the development of technology and meets the knowledge needs of undergraduates and has achieved good results [1, 2]
Individual data of undergraduates are acquired in the form of a questionnaire survey, and multidimensional information such as undergraduates’ psychology, living habits, and family background is collected before enrollment. e questionnaire survey sets primary and secondary indicators
In order to improve the sensitivity of the classical K-means clustering algorithm to the randomly selected initial clustering center and the problem that K-means is easy to fall into the local optimal solution, this paper introduces the beetle antennae search (BAS) algorithm [32]
Summary
A K-means clusteringbased algorithm is studied to improve the efficiency and accuracy of dormitory allocation in the construction of a university information services platform. ere are many improved K-means algorithms for clustering and other problems. A K-means clusteringbased algorithm is studied to improve the efficiency and accuracy of dormitory allocation in the construction of a university information services platform. Zhu et al proposed an evidence distance-based improved K-means algorithm, which had better clustering effects and convergence [18]. Wang and Zhang proposed CNN with a multidimensional sequence occlusion face feature extraction module and used the deep learning method to improve the recognition rate [28]. The abovementioned related work has improved recognition rates, they are not designed for university management. They do not consider the security of information. 3. Improved K-Means Algorithm-Based Intelligent Dormitory Allocation in University. Based on the improved K-means clustering algorithm, this paper studies the intelligent dormitory allocation in universities
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