Abstract With the rapid development of internet technology, distance education has become an important way of education. However, remote education management has many problems, such as controlling students’ learning progress, analyzing students’ interactivity, and evaluating teaching quality. To effectively address these issues, this article proposes constructing an intelligent model for remote education management based on data mining algorithms. This article first... With the rapid development of internet technology, distance education has become an important way of education. However, remote education management has many problems, such as controlling students’ learning progress, analyzing students’ interactivity, and evaluating teaching quality. To effectively address these issues, this article proposes constructing an intelligent model for remote education management based on data mining algorithms. This article first introduces the application of data mining algorithms in remote education management. Through data mining technology, valuable information can be extracted from a large amount of data, which helps teachers better understand students’ learning status and improve teaching quality. At the same time, data mining can also analyze students’ learning behavior and provide personalized learning suggestions and guidance. Read More Read Less

Abstract With the rapid development of the new energy industry, how to accurately evaluate and predict customer value to meet customer needs better and provide high-quality services has become an important issue the industry faces. To address this issue, we propose an optimization model based on genetic algorithms to improve the value and satisfaction of new energy customers. The model first... With the rapid development of the new energy industry, how to accurately evaluate and predict customer value to meet customer needs better and provide high-quality services has become an important issue the industry faces. To address this issue, we propose an optimization model based on genetic algorithms to improve the value and satisfaction of new energy customers. The model first collects relevant customer data, including historical electricity consumption, electricity consumption behaviour, complaints, etc. Then, we used genetic algorithms to process and analyze these data to find the optimal customer value evaluation model. A genetic algorithm is an optimization algorithm that simulates the evolution process of nature and can automatically search for and optimize solutions to problems. In model optimization, we used simulation experiments to evaluate the effectiveness and performance of different models. A simulation experiment is based on real data, which can simulate actual operations and predict future development trends. Through simulation experiments, we can compare the advantages and disadvantages of different models and select the optimal model for promotion and application. Read More Read Less

Abstract File management archives are important records formed by enterprises and government departments in business processing and decision-making, which are of great significance for business continuity, decision-making, and risk management. However, due to various factors such as human error, ageing and damage of archives, the accuracy and reliability of document management archives are often challenged. Therefore, how to effectively test and... File management archives are important records formed by enterprises and government departments in business processing and decision-making, which are of great significance for business continuity, decision-making, and risk management. However, due to various factors such as human error, ageing and damage of archives, the accuracy and reliability of document management archives are often challenged. Therefore, how to effectively test and adjust file management archives has become an important research issue. This article studies a method for testing and adjusting file management archives based on multiple neural feedback models. This method utilizes a multi-neural feedback model to test and regulate file management archives, aiming to improve the accuracy and reliability of file management archives and provide new ideas and methods for research and application in related fields. Read More Read Less