Construction and implementation of a college talent cultivation system under deep learning and data mining algorithms

Ma, Haizhou; Ding, Aili

doi:10.1007/s11227-021-04036-4

Cited by 19 publications

(18 citation statements)

References 22 publications

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“…It can push dynamic hot and concerned content to each user in real time, especially for items seeking promotion, which has a good practical effect. Its performance is relatively stable in popular movies, focus news, and other application fields, but the accuracy of recommendation results is not satisfactory [16].…”

Section: Classification and Comparison Of Recommendationmentioning

confidence: 99%

Research on Data Mining Algorithm of Associated User Network Based on Multi-Information Fusion

Kang

Hua

2022

Journal of Sensors

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In order to explore how to realize network mining for associated users, an algorithm of associated user mining based on recommendation system is proposed. This method recommends key technical problems and solutions based on multi-information fusion to explore the research of user network data mining. The data mining algorithm of associated user network based on multi-information fusion is about 35% higher than the previous method. This article is combined with common scoring data to improve the accuracy of algorithm results. The experiment was carried out on the real data set FilmTrust to evaluate the proposed new algorithm, measure the prediction accuracy of the score prediction results, and compare the offline test results to verify the effectiveness of the new algorithm.

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Section: Classification and Comparison Of Recommendationmentioning

confidence: 99%

Research on Data Mining Algorithm of Associated User Network Based on Multi-Information Fusion

Kang

Hua

2022

Journal of Sensors

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“…Ma and Ding emphasized the importance of data mining classification algorithms in predicting vehicle collision patterns occurring in training accident data sets and also explored feature selection algorithms including CFS, FCBF, Feature Ranking, MIFS, and MODTree to improve the performance of the classifier's accuracy. e results show that the Feature Ranking method significantly improves the accuracy of the classifier [12]. Rodrigues et al used a data mining algorithm to process data type conversion and converted the original data type of the data source to be exported to a common data type.…”

Section: Introductionmentioning

confidence: 99%

Construction of German Machine Intelligent Translation System Based on Data Mining Algorithm

Liu

2022

International Transactions on Electrical Energy Systems

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The continuous expansion of the international market makes more people pay attention to learning different languages. As one of the most popular languages, German is favored by scholars. The use of intelligent translation systems to learn German has become the main way of learning. The increasing development of science and technology is accompanied by the emergence of a large number of new German words. The traditional German intelligent translation system has a slow update speed of words and cannot recognize new words, which seriously affects the accuracy of the translation. According to these questions, in this paper, the data mining algorithm is introduced into the German machine intelligent translation system. By evaluating the German machine intelligent translation system using a data mining algorithm in four aspects: translation speed, accuracy rate, recall rate, and user satisfaction, the evaluation results found that the translation speed of the system increased by 4.68%, the accuracy rate increased by 4.2, the recall rate has increased by 3.8%, and the user satisfaction score has increased by 0.825 points. The data mining algorithm can improve the German machine intelligent translation system and users’ interest in learning and promote German education.

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“…Based on the indicators shown in Table 1, the level of enterprise tax risk is divided by deep learning, which is used as the division index to continuously mine the level of enterprise tax risk [36]. The risk quantity analysis of enterprise tax risk according to high-risk points is shown in Figure 4.…”

Section: Construction Of Enterprise Tax Risk Intelligentmentioning

confidence: 99%

Intelligent Assessment Method of Enterprise Tax Risk Based on Deep Learning

Guo¹

2022

Wireless Communications and Mobile Computing

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Enterprise tax risk involves many aspects. In order to accurately realize enterprise tax risk assessment, an intelligent assessment method of enterprise tax risk based on deep learning is proposed. Collect enterprise tax risk information; reasonably distinguish the same type of risk information according to category, type, and appearance; extract the correlation characteristics of enterprise tax risk assessment; use the correlation characteristics to build a deep learning enterprise tax risk assessment model; search the best parameters of the automatic encoder; build a stacked automatic encoder based on the automatic encoder; combine it with the classifier; and complete the design of enterprise tax risk intelligent evaluation model. The experimental results show that the deviation ratio of Receiver Operating Characteristic (ROC), the risk assessment index of the proposed method, is less than 0.38 as a whole. Therefore, the enterprise tax risk intelligent assessment method based on deep learning is better.

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Construction and implementation of a college talent cultivation system under deep learning and data mining algorithms

Cited by 19 publications

References 22 publications

Research on Data Mining Algorithm of Associated User Network Based on Multi-Information Fusion

Research on Data Mining Algorithm of Associated User Network Based on Multi-Information Fusion

Construction of German Machine Intelligent Translation System Based on Data Mining Algorithm

Intelligent Assessment Method of Enterprise Tax Risk Based on Deep Learning

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