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Optimization of architectural art teaching model based on Naive bayesian classification algorithm and fuzzy model

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第2期39卷 1965-1976页

作者： Liu, Ying Inner Mongolia Univ Technol Sch Architecture Hohhot Inner Mongolia Peoples R China Inner Mongolia Engn Technol & Res Ctr Green Bldg Hohhot Inner Mongolia Peoples R China

At present, the teaching of architectural art in China is still relatively traditional, and there are still some problems in the actual teaching. Based on this, this study combines the Naive bayesian classification algorithm with the fuzzy model to construct a new architectural art teaching model. In teaching, the Naive bayesian classification algorithm generates only a small number of features for each item in the training set, and it only uses the probability calculated in the mathematical operation to train and classify the item. Moreover, by combining the fuzzy model, the materials needed for architectural art teaching can be quickly generated, and the teaching principles and implementation strategies of architectural art are summarized. In addition, this paper proposes an attribute weighted classification algorithm combining differential evolution algorithm with Naive Bayes. The algorithm assigns weights to each attribute based on the Naive bayesian classification algorithm and uses differential evolution algorithm to optimize the weights. The research shows that the method proposed in this paper has certain effect on the optimization of architectural art teaching mode.

关键词： bayesian classification algorithm fuzzy model architectural art differential evolution algorithm

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Computer network security and information security technology optimization by using bayesian classification algorithm

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2023年 1-10页

作者： Fu, Rui Guangdong Ind Polytech Informat Construct Ctr Guangzhou 510000 Guangdong Peoples R China

The naive bayesian classification algorithm is known for its perfect theoretical system, clear and simple star structure, good adaptability and high classification accuracy, but its premises and assumptions are often untenable in practical applications. In order to solve this problem and improve the performance of the algorithm by checking the naive Bayes classification algorithm and various improved algorithms, and tau-y coefficient is used to determine the weight. And through experiments, it is proved that the improved classification efficiency algorithm can effectively improve the classification efficiency, especially on the data set with a small total number of samples, it has a good classification effect. The development of Internet technology has brought serious challenges to network security, in the present, a lot of classified information will be stored in a dedicated web server, the web server is easy to attack, so that some leak classified information, whether people's personal information or confidential research information, once the leak, will bring more losses. Therefore, to promote the development of Internet security and ensure the security of confidential information is the focus of research. In this paper, through the bayesian classification method, the construction of computer network classified information security application system, so as to promote the development of network security.

关键词： bayesian classification algorithm Confidential information security Application system classification efficiency

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Secret information security system in computer network based on bayesian classification and nonlinear algorithm

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NONLINEAR ENGINEERING - MODELING AND APPLICATION 2022年第1期11卷 620-628页

作者： Wu, Hao ShiJiaZhuang Univ Appl Technol Dept Informat Engn Shijiazhuang 050081 Hebei Peoples R China

To solve the problem that most computer network security assessment systems cannot comprehensively analyze data, this article proposes a research method for computer network confidentiality information security system based on the bayesian classification algorithm. This research takes the computer network security secret-related information security as the core, cleverly uses the EBCA back-propagation model, and comprehensively uses nonlinear functions to propose a computer network secret-related information security application system considering EBCA, which effectively solves the complex cross-linking relationship. The effectiveness of the method is verified by simulation experiments. Simulation experiments show that when the network performance is in the optimal state, the slope is 1, the intercept is 0, and the fitting degree is 1. The method proposed in this article is especially suitable for the nonlinear characteristics of computer network security and can accurately, comprehensively, and systematically reflect the security operation status of network security and confidential information. Then, through the simulation test, it is found that the method in this study can effectively meet the error requirements of equipment integrity and equipment normal rate.

关键词： bayesian classification algorithm security index computer network security secret information security

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Application of Computer algorithm in New Media Content Recommendation System 3

Application of Computer Algorithm in New Media Content Recom...

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3rd International Conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Wu, Qiangyin Changchun Humanities and Sciences College Changchun130117 China

ISBN: (纸本)9798331519032

Nowadays, the information acquisition channels of media have gradually shifted to the Internet, and the decline of traditional media has become a foregone conclusion in the face of new media backed by big data, and the strong rise of new media has also changed the mode of information push. This personalized push mechanism has always had the problem of poor alignment with the customer's interest points. Therefore, this paper proposes to optimize the content recommendation system by using the computer algorithm represented by the bayesian classification algorithm, which not only improves its performance, but also tries to alleviate the information cocoon phenomenon brought by the current new media. In the final comparison experiment on the average absolute error between the user's predicted rating and the actual rating, the average absolute error of the experimental group based on the proposed method was 0.2974, while the average absolute error of the control group using the conventional system was 0.3481. In other words, this method greatly improves the control of the content recommendation system over the user's preferences, so as to push the content that they are more interested in. © 2024 IEEE.

关键词： bayesian classification algorithm computer algorithm content recommendation system new media

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