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A case study on the relationship between risk assessment of scientific research projects and related factors under the naive bayesian algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-16页

作者： Dong, Xuying Qiu, Wanlin Lingnan Univ Inst Policy Studies Hong Kong 999077 Peoples R China

This paper delves into the nuanced dynamics influencing the outcomes of risk assessment (RA) in scientific research projects (SRPs), employing the naive Bayes algorithm. The methodology involves the selection of diverse SRPs cases, gathering data encompassing project scale, budget investment, team experience, and other pertinent factors. The paper advances the application of the naive Bayes algorithm by introducing enhancements, specifically integrating the Tree-augmented naive Bayes (TANB) model. This augmentation serves to estimate risk probabilities for different research projects, shedding light on the intricate interplay and contributions of various factors to the RA process. The findings underscore the efficacy of the TANB algorithm, demonstrating commendable accuracy (average accuracy 89.2%) in RA for SRPs. Notably, budget investment (regression coefficient: 0.68, P < 0.05) and team experience (regression coefficient: 0.51, P < 0.05) emerge as significant determinants obviously influencing RA outcomes. Conversely, the impact of project size (regression coefficient: 0.31, P < 0.05) is relatively modest. This paper furnishes a concrete reference framework for project managers, facilitating informed decision-making in SRPs. By comprehensively analyzing the influence of various factors on RA, the paper not only contributes empirical insights to project decision-making but also elucidates the intricate relationships between different factors. The research advocates for heightened attention to budget investment and team experience when formulating risk management strategies. This strategic focus is posited to enhance the precision of RAs and the scientific foundation of decision-making processes.

关键词： naive bayesian algorithm Scientific research projects Risk assessment Factor analysis Probability estimation Decision support Data-driven decision-making

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Differential Classification Method in Different Teaching Models of Accounting Courses Based on naive bayesian Classification algorithm

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2019年第8期14卷 38-51页

作者： Ou, Xiuying Wenzhou Business Coll Sch Management Wenzhou Peoples R China

Accounting is an important management discipline with strong theoretical foundation and practical operation. Due to the differences between individuals in the process of learning, the mastery of the subject is different. This requires teachers to implement differential teaching from the differences in student personality in the process of teaching. However, when teachers use the concept of difference teaching to teach, the classification of students' differences is mostly calculated by manual quantification such as records, tests, surveys, etc. This kind of measurement and qualitative method not only wastes manpower, but also has personal subjectivity, blindly relies on individual subjective judgment to judge students' advantages and interests, and has accuracy and scientificity. This requires research on students' differential classification methods. Therefore, this paper proposes a student classification method based on naive bayesian algorithm. It constructs a classifier based on historical data, and then uses a well-structured and stable classifier to classify the actual pre-classification objects, and actually applies it to the teaching of accounting courses, realizing the difference in the teaching process. Provide data support for future differential teaching research. The results show that the naive bayesian classification algorithm can be used to analyze the difference in personality and learning of students. Presupposition and generative teaching objectives and students improve their self-awareness to better promote self-development.

关键词： Differential classification method naive bayesian algorithm student differential classification method Accounting Courses

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naive bayesian algorithm classification model with local attribute weighted based on KNN 2

Naive Bayesian algorithm classification model with local att...

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2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2017)

作者： Xin Mao Gang Zhao Ruoying Sun School of information management Beijing Information Science & Technology University

ISBN: (纸本)9781509064151;9781509064144

In order to better optimize and configure public transport resources, we will find the rules of the bus lines different people taking, and predict which bus lines different people choosing. To solve this problem, this paper proposed a new naive bayesian algorithm classification model with local attribute weighting based on K-nearest neighbor algorithm. In the process of algorithm calculated, we used the K-nearest neighbor algorithm to find the K neighbors to be classified, then calculated the probability of each attribute in K neighbors as the weight of the attribute. Later, we put the weight of attribute into naive bayesian classification process, which makes the classification model more realistic and predicted the label. We used K-nearest neighbor, decision tree and Gaussian naive bayesian algorithm as control group. Experiments were carried out on bus historical data in one city. The results show that the model has high accuracy in the bus line selection prediction.

关键词： naive bayesian algorithm KNN local attribute weighted

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naive bayesian Classification algorithm for Infrared Remote Sensing Image Based on Elastic Model

Naive Bayesian Classification Algorithm for Infrared Remote ...

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作者： Liu Shangzheng Liu Bin Nanyang Institute of Technology

The naive bayesian classification algorithm of infrared remote sensing image is affected by the similarity of uniform eigenvectors of different categories, which leads to the decline of classification accuracy. Therefore, the naive bayesian classification algorithm of infrared remote sensing image with elastic model is proposed. The spring elongation distance and the elastic coefficient decibel analog sample size and classification criteria are used to exclude the naive bayesian classification algorithm from being affected by the conditional independent hypothesis, avoiding noise interference, and thus achieving the goal of improving the accuracy of the classification algorithm. The experiments on infrared remote sensing images show that the naive bayesian classification algorithm based on the elastic model has strong operability and can improve the classification accuracy.

关键词： Infrared remote sensing image Elastic model naive bayesian algorithm

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Design and Implementation of the OLAP Cache Mechanism Based on Incremental Learning naive bayesian algorithm 1

Design and Implementation of the OLAP Cache Mechanism Based ...

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1st IEEE International Conference on Computer Communication and the Internet (ICCCI)

作者： Man, Yi Zhang, Jiongmin East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781467385152

In the era of the Big Data, cache is regarded as one of the most effective technique to improve the performance of accessing data. The majority of caches save each query result as a file, thus it is difficult to reuse the data from parts of some query results in the cache, and consequently some cached data were wasted. Through studying domestic and foreign related technologies, this paper designs an OLAP client-side cache mechanism using the Incremental Learning naive bayesian algorithm. The mechanism could decide whether to cache the current query results according to user's recent operations in order to increase the performance of cache. Ultimately, experiments illustrate that the mechanism is effective and efficient with the aspects of average query time and the cache hit rate.

关键词： OLAP OLAP cache naive bayesian algorithm Cache mechanism

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A Binary Approximate naive bayesian Classification algorithm Based on SOM Neural Network Clustering

A Binary Approximate Naive Bayesian Classification Algorithm...

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International Conference on Computer Systems, Electronics and Control (ICCSEC)

作者： Shen Honghong He Lili Zhejiang Univ Sch Informat Hangzhou 310000 Zhejiang Peoples R China

ISBN: (纸本)9781538635735

Although the classification performance of naive bayesian algorithm is relatively good, the time complexity and spatial complexity of the algorithm are linearly increasing with the increase of data volume. In order to reduce the complexity of naive bayesian algorithm, a two - point approach naive bayesian algorithm combining SOM neural network clustering is proposed. Firstly, the SOM neural network clustering algorithm is used to reduce the number of classes in the original data set, and the spatial complexity of the naive bayesian classification algorithm is reduced. Then, by using the dichotomy approach, Conditional probability approximation operation, the time complexity of the classification algorithm is reduced. The experimental results show that the proposed algorithm can reduce the time complexity and spatial complexity of the algorithm under the premise of ensuring the classification accuracy of the algorithm, and improve the classification performance of naive bayesian algorithm.

关键词： SOM neural network clustering dichotomous approximation naive bayesian algorithm

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Design and Implementation of the OLAP Cache Mechanism Based on Incremental Learning naive bayesian algorithm

Design and Implementation of the OLAP Cache Mechanism Based ...

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2016 First IEEE International Conference on Computer Communication and the Internet

作者： Yi Man Jiongmin Zhang School of Computer Science and Technology East China Normal University

ISBN: (纸本)9781467385169

In the era of the Big Data,cache is regarded as one of the most effective technique to improve the performance of accessing *** majority of caches save each query result as a file,thus it is difficult to reuse the data from parts of some query results in the cache,and consequently some cached data were *** studying domestic and foreign related technologies,this paper designs an OLAP client-side cache mechanism using the Incremental Learning naive bayesian *** mechanism could decide whether to cache the current query results according to user’s recent operations in order to increase the performance of ***,experiments illustrate that the mechanism is effective and efficient with the aspects of average query time and the cache hit rate.

关键词： OLAP OLAP cache naive bayesian algorithm Cache mechanism

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Chinese microblog sentiment classification considering users' reviews based on naive bayesian algorithm

Chinese microblog sentiment classification considering users...

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The International Conference on Future Communication,Information and Computer Science(ICFCICS 2014)

作者： B.Yan B.Zhang H.Y.Su H.Zheng Key Lab of Intelligent Information Technology Beijing Institute of technology

Nowadays,with the rapid development of social networks,community-oriented Web sentiment analysis technology has gradually become a hot topic in the field of data *** concise and flexible,Chinese microblog poses new challenges for sentiment *** paper proposes an approach to classify Chinese microblog sentiments into positive and negative by the plain naive bayesian *** on data preprocessing,sentiment lexicon construction,combining element of users' reviews,this research posits this Sentiment Classification which is a novel method of attaching microblog users' reviews to the target microblog in order to improve the accuracy of sentiment classification.

关键词： Chinese microblog sentiment classification naive bayesian algorithm users' reviews

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Application of improved distributed naive bayesian algorithms in text classification

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JOURNAL OF SUPERCOMPUTING 2019年第9期75卷 5831-5847页

作者： Gao, Hongyi Zeng, Xi Yao, Chunhua China Elect Technol Grp Corp Chengdu 610041 Sichuan Peoples R China

The naive Bayes classifier is a widely used text classification method that applies statistical theory to text classification. Due to the particularity of the text, related feature items may generate new semantic information, which may be lost when the traditional vector space model represents text. This paper mainly studies the construction and improvement of distributed naive Bayes automatic classification system. The application of Hadoop cloud computing in web page classification is one of the focuses of this article. Firstly, the text classification system and bayesian classification model are analyzed and discussed, including the representation and extraction of text information, text classification methods and bayesian text classification methods. Then, in view of the shortcomings of the above-mentioned naive bayesian text classification method, when training text, we use the mutual information method to check the correlation between the feature sets generated after feature selection, and then combine the features with higher correlation degree appropriately. Through a series of tests, the experimental data show that the improved text classification system can achieve better classification results.

关键词： Distributed naive bayesian algorithm Text classification Feature selection

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Research on Data Pollution Prevention in Network Intrusion Detection Systems Based on naive Bayes 24

Research on Data Pollution Prevention in Network Intrusion D...

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3rd International Conference on Cryptography, Network Security and Communication Technology (CNSCT)

作者： Lu, Yinglun Xie, Tian Shen, Jiyue Xu, Sheng Sun, Pan Zhejiang Zheneng Elect Power Co LTD Hangzhou 310026 Zhejiang Peoples R China Zhejiang Zheneng Shaoxing Binhai Thermal Power Co Shaoxing 312000 Zhejiang Peoples R China Zhejiang Energy Grp Finance Co LTD Hangzhou 310026 Zhejiang Peoples R China Zhejiang Zheneng Energy Serv Hangzhou 310026 Zhejiang Peoples R China

ISBN: (纸本)9798400716959

Data pollution often occurs in a covert manner, making it difficult for traditional network intrusion detection systems to detect. Intruders can use various technical means and strategies to confuse data, and can manipulate it at multiple levels, thereby increasing the difficulty of system detection. Therefore, this study proposes a data pollution prevention method for network intrusion detection systems based on naive Bayes. Design the overall architecture of a network intrusion detection system, based on which the naive bayesian algorithm is introduced to complete the data pollution defense from two perspectives: classification of contaminated data and hierarchical encryption. The experimental results show that the data encryption time of the research method is shorter, the defense rate is higher, and the false detection rate is lower.

关键词： naive bayesian algorithm Network intrusion detection system Data pollution prevention Layered encryption classification method

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