When processing the lost data of web learning resource information flow, the noise in the data signal cannot be eliminated, resulting in inaccurate detection of the lost data of web learning resource information flow ...
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When processing the lost data of web learning resource information flow, the noise in the data signal cannot be eliminated, resulting in inaccurate detection of the lost data of web learning resource information flow in the later stage. Therefore, a data mining algorithm is proposed based on weighted depth forest for web learning resource information flow loss. Based on building a brand-driven Web data acquisition model to collect data, this method uses clustering analysis technology to extract the lost data feature information of web learning resource information flow. It carries out wavelet threshold denoising on it. According to the characteristics of lost data, the lost datamining of web learning resource information flow is completed. Experimental results show that the proposed algorithm has a low error rate, high accuracy, high labour intensity, high efficiency and high performance.
In order to improve the low accuracy of traditional wireless network health information retrieval methods, a wireless network health information retrieval method is designed based on data mining algorithm. The invalid...
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In order to improve the low accuracy of traditional wireless network health information retrieval methods, a wireless network health information retrieval method is designed based on data mining algorithm. The invalid health information stored in wireless network is filtered by data mapping, and the health information is clustered by data mining algorithm. On this basis, the high-frequency words of health information are classified to realize wireless network health information retrieval. The experimental results show that exactitude of design way is significantly higher than that of the traditional method, which can solve the problem of low accuracy of the traditional wireless network health information retrieval method.
Demeanor evidence refers to the authoritative opinions of experts using analytical instruments to collect and interpret the formation of external physiological symptoms of the entire human body. With the development o...
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Demeanor evidence refers to the authoritative opinions of experts using analytical instruments to collect and interpret the formation of external physiological symptoms of the entire human body. With the development of modern technology, the observation of demeanor has entered a new stage of multimodal quantification. The quantifiable results of demeanor observation with the help of scientific and technological instruments play an important role in judicial work such as search, interrogation, and evidence review. The use of man-made reasoning in the field of legal settling has become increasingly broad. Specialists are giving increasingly more consideration to the securing of modular proof. This paper proposes the development of a component examination model for modular proof examination applications. The technique for this paper is to apply the guileless Bayes strategy, propose a superior information mining calculation, and lay out a model for proof observation and examination. The function of these methods is to systematically explore the basic theoretical issues of demeanor evidence based on the status quo of judicial application of demeanor evidence. Through the prediction of individual demeanor based on data mining algorithm, the evidence analysis model is designed, the neurobiological experiment is carried out, and the demeanor evidence animal stress model is constructed to verify the scientific basis of multidemeanor evidence observation. The experimental results showed that after repeated stimulation of SD rats, the maximum changes in the expression of HSP70 gene and SAA gene were 10.77 and 14.1 respectively, reflecting the high reliability of demeanor evidence biological experiments. The model can improve the accuracy of evidence use, and the correct use of demeanor evidence can realize the true litigation value of intelligent justice and the concept of human rights protection, and promote the construction of intelligent justice.
Population structure changes interact with economic development, moderate population and reasonable population structure are important guarantees for sustainable social and economic development. The research ignores t...
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Population structure changes interact with economic development, moderate population and reasonable population structure are important guarantees for sustainable social and economic development. The research ignores the specific impact of the change of population age structure on economic growth, and proposes and establishes a population economic function model based on data mining algorithm. Based on the changes of population structure in Liaoning Province in the past 20 years, Grey correlation analysis method is selected. The analysis shows that there is a close relationship between population structure and economic growth. Based on this research, the econometric method is used to construct a multiple linear regression model to further analyze the specific impact of population structure changes on economic growth. The analysis results show that the total population of urban areas, the total number of employed people in the primary industry, the number of middle school students per 10,000 people, and the total number of employed people in the tertiary industry are the four most significant demographic indicators for the per capita GDP of the study area. There is a significant positive correlation between the total number of employed people in the tertiary industry and per capita GDP and there is a significant negative correlation between the total number of employed people in the primary industry and the number of middle school students per capita and per capita GDP. The impact of other indicators on per capita GDP is not significant. According to the conclusion, countermeasures and suggestions to ease population structure change and promote the coordinated development of population and economy in the study area are put forward.
This article focuses on the evaluation model of intelligent manufacturing system based on data mining algorithm. Combining data mining algorithm with intelligent manufacturing system, the evaluation model of intellige...
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This article focuses on the evaluation model of intelligent manufacturing system based on data mining algorithm. Combining data mining algorithm with intelligent manufacturing system, the evaluation model of intelligent manufacturing system is established successfully. According to the characteristics of intelligent manufacturing system, the data is divided into training set, cross-validation set and test set, and then the training set is used to perform neural network training, adjustment and optimisation and verification set. The experiment found that the accuracy rate of the training group was higher than that of the test group, the highest accuracy rate of the training group was 69%, and the highest accuracy rate of the test group was 32.5%. The results show that using data mining algorithms for recognition can effectively cluster control chart patterns and improve recognition efficiency.
The economic interaction between the countries of the world is gradually strengthening. Among them, the US stock market is a "barometer" of the global economy, which has a huge impact on the global economy. ...
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The economic interaction between the countries of the world is gradually strengthening. Among them, the US stock market is a "barometer" of the global economy, which has a huge impact on the global economy. Therefore, it is of great significance to study the data in the US stock market, especially the data mining algorithm of abnormal data. At present, although datamining technology has achieved many research results in the financial field, it has not formed a good research system for time series data in stock market anomalies. According to the actual performance and data characteristics of the stock market anomaly, this paper uses datamining techniques to find the abnormal data in the stock market data, and uses the isolated point detection method based on density and distance to analyze the obtained abnormal data to obtain its implicit useful information. However, due to the defects of traditional data mining algorithms in dealing with stock market anomalies containing uncertain factors, that is, the errors caused by other human factors, this paper introduces the roughening entropy of the uncertainty data and applies its theory to the field of datamining, a data mining algorithm based on rough entropy in the US stock market anomaly is designed. Finally, the empirical analysis of the algorithm is carried out. The experimental results show that the data mining algorithm based on rough entropy proposed in this paper can effectively detect the abnormal fluctuation of time series in the stock market.
Intrusion detection technology plays an important role in ensuring information security. This paper briefly describes the intrusion detection technology and its development history. Based on the analysis of power info...
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Intrusion detection technology plays an important role in ensuring information security. This paper briefly describes the intrusion detection technology and its development history. Based on the analysis of power information network structure and its security partition, this paper proposes a power information network intrusion detection framework for the intrusion attack problem of power information network and elaborates the implementation of each module. The association rule analysis algorithm and the association relationship between network data stream features can effectively detect the intrusion behaviour in the power information network. Experiments show that the intrusion detection system can effectively detect the intrusion attacks in the power information network and effectively protect the power information.
With the continuous development of Internet technology and electronic information technology, big data technology and cloud computing technology also rise and develop, and have a positive impact on people's lives....
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With the continuous development of Internet technology and electronic information technology, big data technology and cloud computing technology also rise and develop, and have a positive impact on people's lives. datamining system can deeply mine the value information contained in big data, so as to assist users to solve practical problems and help users to make correct decisions and judgments. This paper presents an energy analysis of data mining algorithm based on cloud platform for Internet of things (IoT). First of all, an improved Apriori algorithm is proposed, which is based on Boolean matrix and sorting index rules. Then Boolean matrix is obtained after scanning the data set and the Boolean matrix is preprocessed to delete the useless transactions and the item set, which are combined with sorting index to produce other item sets, effectively improving the efficiency of frequent item mining, which effectively reduce the memory usage. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm needs human intervention in the global parameter selection, and the process of regional query is complex and the query is easy to lose objects. An improved parameter adaptive and regional query density clustering algorithm is proposed, which can effectively delete the redundant data in the high-level complex data space on the premise of retaining the internal nonlinear structure of the IoT data. The efficiency of clustering is also improved accordingly Finally, the simulation based on cloud platform verifies the effectiveness and superiority of the algorithm.
Blended teaching is a kind of teaching that combines online teaching with traditional teaching, which is defined as "online and offline". Through the organic combination of these two teaching forms, students...
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Blended teaching is a kind of teaching that combines online teaching with traditional teaching, which is defined as "online and offline". Through the organic combination of these two teaching forms, students' learning can be from shallow to deep. Therefore, based on the data mining algorithm, this paper designs the method of College English online and offline blended teaching effect. First, it collects the College English blended teaching resources, then builds the College English online and offline teaching support, debugs the College English teaching environment, and finally designs the College English blended teaching model based on the data mining algorithm, so as to realize the College English online and offline blended teaching, The experiment shows that the method designed in this paper can effectively improve the reading ability of College English, and has certain application value.
With the introduction of the information age, enterprise financial management has been challenged as never before, and the application of Internet of Things (IoT) technology can effectively improve the efficiency of f...
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With the introduction of the information age, enterprise financial management has been challenged as never before, and the application of Internet of Things (IoT) technology can effectively improve the efficiency of financial accounting management and realize the informationization of financial management. In order to solve the problem of enterprise financial accounting data processing, a data mining algorithm is constructed, which uses datamining technology to obtain massive information data and cluster analysis processing to realize the fusion of multiple uncertainty information processing models. Firstly, the financial information cloud platform is designed by using the IoT technology. The financial risk index coefficient of the enterprise is judged by the association rules. Finally, the research sample is divided into the risk group and the normal group according to the ST classification standard, and the 296 financial indicators of the two groups are correlated. The research results show that if the enterprise with a score below 40 points has financial risk, the accuracy rate is 70.9%, which is slightly lower than the financial risk warning model of the decision tree. Through the research of this paper, it has enlightenment to the financial accounting management of IoT enterprises. The datamining technology is applied in the processing of massive data information of accounting, which is more efficient.
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