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 diver...
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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.
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 differe...
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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 naivebayesian 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.
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...
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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 naivebayesian 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.
The naivebayesian 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. Theref...
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The naivebayesian 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 naivebayesian 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 naivebayesian 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 naivebayesian classification algorithm based on the elastic model has strong operability and can improve the classification accuracy.
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...
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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.
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...
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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 naivebayesian 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.
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 dat...
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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 naivebayesian *** 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.
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 ch...
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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 naivebayesian *** 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.
E-learning is an interactive online learning mode that can enhance students' interest in learning in an entertainment environment. This article aims to develop an English vocabulary learning recommendation system ...
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E-learning is an interactive online learning mode that can enhance students' interest in learning in an entertainment environment. This article aims to develop an English vocabulary learning recommendation system based on decision tree algorithm and naive bayesian algorithm to provide personalized learning suggestions and help learners learn and remember words more effectively. Firstly, a large amount of English vocabulary data was collected, preprocessed, and feature extracted. Decision tree algorithms were selected as the foundation, and through analysis of existing data, a decision tree was generated to classify new data. Using learners' characteristics (such as age, learning objectives, etc.) and learning history (such as learned words, learning time, etc.) as inputs, a personalized recommendation model was constructed, This model can recommend suitable learning content for learners based on their personalized needs and learning situation. In order to better understand learners' learning progress, a naivebayesian model was trained using learners' learning history and progress information to analyze their current learning progress and predict their future learning situation. Through testing and evaluation of actual learners, the recommendation system performs well in providing personalized learning suggestions, and has a significant improvement effect on learners' vocabulary learning.
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 info...
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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 naivebayesian 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.
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