Personalized and Adaptive Curriculum Optimization Based on a Performance Correlation Analysis

In former work, the authors developed a modeling system for university learning processes, which aims at evaluating and refining university curricula to reach an optimum of learning success in terms of a best possible grade point average (GPA). This is performed by applying an Educational Data Mining (EDM) technology to former students curricula and their degree of success (GPA) and thus, uncovering golden didactic knowledge for successful education. We used learner profiles to personalize this technology. After a short introduction to this technology, we discuss the result of a practical application and draw conclusions. In particular, we could not obtain sufficient data to establish this kind of learner profiles. Therefore, we shifted our strategy from an “eager” one of holding an explicit model towards a “lazy” strategy of mining with data, which is really available without making “guesses” what they mean (profiles). In particular, we utilize the educational history of the students and vocational ambitions for student modeling.

This paper expounds the fundamental theory of data mining and customer segmentation, and studies on the specific implementation of data mining processes and technology. We put forward the customer segmentation application model based on data mining technology, which is applied in the retailing industry to carry out a empirical study.

As a confluence of data mining and World Wide Web technologies, it is now possible to perform data mining on Web log records collected from the Internet Web-page access history. The behaviour of Web page readers is imprinted in the Web server log files. Analyzing and exploring regularities in this behaviour can improve the system performance, enhance the quality and delivery of Internet information services to the end user, and identify populations of potential customers for electronic commerce. Thus, by observing people using collections of data, data mining can bring a considerable contribution to digital library designers. In a joint effort between the TeleLearning-NCE (Networks of Centres of Excellence) project on the Virtual University and the NCE-IRIS project on data mining, we have been developing a knowledge discovery tool, called WebLogMiner, for mining Web server log files. This paper presents the design of WebLogMiner, reports current progress and outlines future work in this direction.

Students currently mostly experience psychological issues like worry and fear, which are primarily brought on by the high demands placed on them. One psychotherapy technique is music therapy. The goal is to use music to enhance health, particularly as a tool to break down barriers both inside and outside the body. Based on data mining (DM) technologies, this paper examines the impact of music education on students' psychological health. The study demonstrates that the DM algorithm utilised in this work has the lowest error rate, with an average error rate of only 6.90%, followed by the ACA method with an average error rate of 17.48%, and finally the AI algorithm with an average error rate of 29.35%. As can be shown, this approach is more suited to research the effects of music instruction on students' psychological well-being. The functional module based on DM is developed through simulation experiments to confirm the application effectiveness of the DM algorithm. This is done by using the data source of DM and the structural model of the mining system to build this module on the foundation of the original psychological evaluation system.

With the rapid development of the logistics industry, the data and information of logistics are also growing geometrically. However, the current information system is usually unable to deeply analyze these huge data and information, so as to accurately understand the law of the development of the industry and the direction that should be guided . Therefore, it is necessary to establish a new information system. The traditional database management information system can not make good use of and analyze the large amount of data accumulated in the database. Data mining and data warehouse technology can solve this problem. Data mining is a technology dedicated to data analysis and understanding, revealing the knowledge contained in the data. It is one of the important goals of the application of information technology in the future. It can explore and analyze a large number of data, mining meaningful rules, in order to provide appropriate reference and suggestions for future decision-making. In recent years, data mining technology has been widely studied and applied, and has been successfully applied in the fields of Commerce, finance, medical treatment and so on.KeywordsData miningLogistics transportationMultimodal transportationGenetic algorithmDynamic programming

Today is the world of knowledge economy. The business unit which believes in creating new innovations and implementing this technology in their organization as it always provides an edge over traditional working. Patents are used for preserving this knowledge. A large number of patents are filed every day. Identifying the best patent as per business need and extracting relevant knowledge from patients will help organizations to enhance their business. There are many technologies of data mining that can be suitably used for this purpose. This paper compares the key features of different techniques used for mining hidden patterns from patents. Today is the world of knowledge economy. The business unit which believes in creating new innovations and implementing this technology in their organization as it always provides an edge over traditional working. Patents are used for preserving this knowledge. A large number of patents are filed every day. Identifying the best patent as per business need and extracting relevant knowledge from patients will help organizations to enhance their business. There are many technologies of data mining that can be suitably used for this purpose. This paper compares the key features of different techniques used for mining hidden patterns from patents. Today is the world of knowledge economy. The business unit which believes in creating new innovations and implementing this technology in their organization as it always provides an edge over traditional working. Patents are used for preserving this knowledge. A large number of patents are filed every day. Identifying the best patent as per business need and extracting relevant knowledge from patients will help organizations to enhance their business. There are many technologies of data mining that can be suitably used for this purpose. This paper compares the key features of different techniques used for mining hidden patterns from patents.

With the gradual popularization of smart devices and the prosperity of Internet technology, the domestic automobile industry is also undergoing a shift from traditional power to new energy. Although the existing new energy technology has been widely used in the automotive field, there is still a certain gap between our country's overall new energy technology innovation capability and developed countries. Finding a business model suitable for the development of new energy vehicles has become a key factor in promoting the development of the new energy vehicle industry. Therefore, this article mainly discusses the path research of data mining analysis technology in the business model innovation of new energy vehicles. First, list the current status of the existing traditional business models of 100 new energy automobile companies, and then analyze the car sales data of the A company in the 100 companies in the past five years. The total annual car sales of A company have gradually increased, from 5512 in 2017 to 9070 in 2020. In just four years, the sales of cars have increased by nearly 3500. To a certain extent, data mining technology provides decision-making support for the continuous innovation of business models of new energy automobile enterprises and brings more automobile sales to enterprises.KeywordsData miningNew energy vehiclesBusiness modelsCluster analysis

With the explosion of information and ever-increasing information resources, humanity has entered a brand-new cloud computing era. We are confronted with a brand-new problem: how to quickly and accurately extract the required information from vast information resources. It is even possible to argue that university educational administration is inextricably linked to university teaching accomplishments. The teaching data mining (DM) technology was created in order to extract the required information from vast information resources. People’s ability to find data using cloud computing technology (IT) has improved. This paper analyzes the division and optimal allocation of talent types in universities, in combination with the development strategy and HR characteristics of universities, and provides strategies for the rational allocation of talents in universities, as well as support for the information relationship of HR in universities, using DM technology research. The emergence of DM technology alleviates the problem of data accumulation-induced information explosion. In today’s universities, there are an increasing number of talents. In terms of talent allocation, the information-based talent allocation scheme offers new approaches to talent acquisition and university education development in the modern era.

The purpose is to enhance the timeliness of news, provide real-time news for the audience, and attract more traffic for the news media, thereby helping news media grow financially and strengthening the network security communication efficiency and accuracy. First, an intelligent real-time image acquisition system is introduced, and its principle and advantages are analyzed. Second, an intelligent real-time image acquisition model based on DL (Deep Learning) algorithm is established by combining DM (Data Mining) technology. Finally, the application of an intelligent image acquisition system in network communication is adjusted. The research results show that the combination of DL intelligent real-time image acquisition system with DM technology can effectively deal with various network communication scenarios, enhancing the accuracy and timeliness of network communication. The AlexNet model performs excellently in all aspects, with or without data enhancement or equalization. The recognition rate reaches 99.6% after data equalization and enhancement, and the AlexNet model can accurately classify news and non-news sceneries. This shows that the application of an intelligent real-time image acquisition system based on DM technology can effectively enhance the real-time performance of network communication. The results set an example for the application of relevant intelligent image acquisition technology in the journalism industry.

With the development of the times, data mining(DM) technology has been widely used. Data processing has become an important task in industrial design. Traditional design methods and data processing methods can not be well integrated. This requires the use of advanced technology in the design process to solve problems encountered in the design. Industrial design can not only bring better economic benefits to enterprises, but also improve the brand image of enterprises to a certain extent. Nowadays, the development and progress are constantly accelerated, and play a vital role in the production development of enterprises. Enterprises should actively apply DM technology to product innovation and improvement development, so as to meet consumer needs and promote the development of enterprises.

In recent years, data mining technology has attracted widespread attention. The process of data mining is to quickly discover knowledge that is not obvious but still valuable in the data. This process is an advanced process. The use of DMT (data mining technology) for law extraction, DM (data mining), sorting and analysis can provide an effective decision-making reference. The rapid advancement of technology enables organizations to accumulate large amounts of data. However, extracting useful information has become a huge challenge. The imbalance of RED (regional economic development) has always attracted people's attention. With the start of the western development and the shift of the national regional policy focus, our country's RD (regional development) has set off a climax. RED research has also entered a new stage. RED strategy and regional planning have become the focus of research. The main content of this paper is the application research of DMT in regional economic analysis. This article mainly uses a combination of quantitative and qualitative methods for experiments. First, the analysis of the current situation of the domestic regional economy is carried out. The results show that the east is still the strongest economic region in our country. The GDP of the east accounts for 49.7% of the country. The overall economic strength of the west is weaker than that of the east and central, but its economic development speed is at the leading level in the country, and the GDP growth rate has reached 20.5% over the previous year. Secondly, the main factors affecting the regional economy are labor force and natural resources. Finally, the application effect of DMT (data mining technology) in regional economic analysis is analyzed. The results showed that 74 people believed that data mining technology could optimize the development structure of the regional economy, 52 people suggested that data mining technology can affect the factors of regional economic development, and the remaining 43 and 31 people respectively believed that the use of data mining technology could weaken distance cost and Reduce information asymmetry.KeywordsData mining technologyCluster analysisRegional economyMacro analysis

Data mining technology refers to the use of mathematics, statistics, computer science and other methods to process a large amount of information to obtain useful conclusions and provide valuable decisions for people. With the rapid development and popularization of the Internet era and the more and more extensive application of computers in various fields, data mining technology has become a hot research field in today’s society. Based on the data center, this paper studies the data mining technology. Firstly, this paper expounds the definition of data mining, and studies the process of data mining and the steps of processing data. Then, this paper also designs and studies the framework of data mining, and tests the performance of the algorithm. Finally, the test results show that data mining technology can well meet the target requirements.

In the monitoring and analysis of physiological and biochemical indicators of athletes, traditional data mining (DM) technology cannot extract compelling features and laws when processing high-dimensional and complex multivariate data, and the accuracy of the analysis results is low. The lack of real-time monitoring of the dynamically changing physiological state makes it impossible to detect athletes’ overtraining or fatigue in time, which affects the training effect and the health of athletes. This paper constructs an improved XGBoost (eXtreme Gradient Boosting) model to clean and normalize the collected physiological and biochemical data, remove outliers and fill in missing values, and construct a variable set representing the characteristics of different training periods to provide high-quality input data for subsequent model analysis. This paper combines the SHAP (SHapley Additive exPlanations) method to quantify the importance of each feature, selects the variables that contribute most to the recognition of the training state to optimize the model input, reduce the model complexity, and improve the computational efficiency. Based on the original XGBoost model, the loss function can be adjusted and the adaptive learning rate mechanism can be added to enable the model better to capture the dynamic changes of physiological and biochemical indicators and improve the prediction accuracy. Combined with the prediction results of the improved model, a real-time monitoring system was designed to track the changes in the physiological state of athletes during different training periods, and to issue an alarm when abnormal trends were detected to assist coaches in adjusting training plans. The experimental results show that in the feature evaluation, three key physiological indicators, namely blood oxygen saturation, blood lactate concentration, and heart rate, are extracted, which reduces the computational complexity of the subsequent model. In the four training stages of the basic period, load period, high-intensity period and recovery period, the loss values of the XGBoost model were approximately 0.5, 0.42, 0.4 and 0.35 respectively. In the monitoring data of 4 batches of football players, with 100 players in each batch, the accuracy rate remained above 0.83 and the response time was below 2 s. The experiment proved the effectiveness of the research model in the monitoring and analysis of physiological and biochemical indicators.

Over the last decade data warehousing and data mining tools have evolved from research into a unique and popular applications, ranging from data warehousing and data mining for decision support to business intelligence and other kind of applications. The chapter presents and discusses data warehousing methodologies along with the main components of data mining tools and technologies and how they all could be integrated together for knowledge management in a broader sense. Knowledge management refers to the set of processes developed in an organization to create, extract, transfer, store and apply knowledge. The chapter also focuses on how data mining tools and technologies could be used in extracting knowledge from large databases or data warehouses. Knowledge management increases the ability of an organization to learn from its environment and to incorporate knowledge into the business processes by adapting to new tools and technologies. Knowledge management is also about the reusability of the knowledge that is being extracted and stored in the knowledge base. One way to improve the reusability is to use this knowledge base as front-ends to case-based reasoning (CBR) applications. The chapter further focuses on the reusability issues of knowledge management and presents an integrated framework for knowledge management by combining data mining (DM) tools and technologies with CBR methodologies. The purpose of the integrated framework is to discover, validate, retain, reuse and share knowledge in an organization with its internal users as well as its external users. The framework is independent of application domain and would be suitable for uses in areas, such as data mining and knowledge management in e-government.

In recent years, research on Educational Data Mining (EDM) has developed rapidly. However, most researches focus on data source issues, and ignore the importance of data preprocessing and data mining algorithms. This paper has studied EDM, with a special focus on educational big data mining algorithms. Firstly, it analyzed the relevant elements of EDM and introduces big data technology based on the requirements of educational data application. Then it introduced the common educational big data mining algorithms and their applications, and finally discussed the development trend of educational big data mining algorithms.