Application of Data Mining in the Evaluation of Enterprise Lean Management Effect

In order to improve the effect of enterprise lean management, this study proposes a lean data mining algorithm based on the characteristics of lean data in enterprise management. This study connects data mining and lean production to study the data of enterprise management operation, proposes an intelligent data processing model suitable for modern enterprise management, and constructs the model function module in combination with the enterprise operation management process. Moreover, this study constructs an evaluation system for the effect of enterprise lean management based on data mining. The system provides a human-computer interaction interface, and operators can use various functions and services provided by the system through a visual interface. Through experimental research, it can be known that the enterprise lean effect evaluation system based on data mining proposed in this study can play an important role in enterprise lean management.

FRACTAL ANALYSIS AS A TOOL FOR AUTOMATIC DETECTION OF ARTIFACTS IN ELECTROENCEPHALOGRAM RECORDS

В статье обсуждается применение методов фрактального анализа для решения задачи автоматической фильтрации сигнала ЭЭГ от артефактов различной природы. Изучается возможность использования показателя Херста в качестве информативного признака для алгоритмов интеллектуальной обработки данных. The article discusses the possibility of using fractal analysis to solve the problem of automatic filtering of the EEG signal from artifacts of various nature. The possibility of using the Hurst exponent as an informative feature for intelligent data processing algorithms is investigated

Analytical activities of Russian customs authorities: Status, problems, prospects

Subject. The article discusses the organization of analytical activities in the customs authorities of the Russian Federation. Objectives. The aim is to assess the existing organization of analytical activities in the customs authorities of the Russian Federation and formulate promising directions for its development based on foreign experience in the introduction of intelligent data processing technologies. Methods. The study rests on methods of expert assessment of the analytical activities of customs authorities. Results. We disclose the content and role of analytical activities of the customs authorities of the Russian Federation, consider the organization of their analytical units from the perspective of modern theory and practice of management. Using the expert analysis methods, we evaluate the status of analytical activities of the said authorities and describe the mechanism of their development on the basis of foreign experience in the introduction of intelligent data processing technologies. The developed analysis technology can be used at all structural levels of the Russian Federal Customs Service, in assessing and analyzing the databases of customs authorities. Conclusions. The proposed technology can increase the speed of analytical work of customs officials through automating this process. This will accelerate the customs controls over goods and vehicles, traveling across the EAEU customs border, improve the performance of the risk profiles being developed, and reduce the burden on customs officials.

Risk assessment model of compromising personal data on mobile devices

Development of the information space to an avalanche-like increase in the volume of mobile data on the Internet. The generated digital portraits of users are becoming one of the main products for sale. The high quality of user digital portraits and their number is achieved through the use of intelligent data processing methods and the presence of large data sets. The volume of data processed by mobile devices and the number of modern services that collect various types of information make the issue of ensuring the confidentiality of user information the most important. Existing security mechanisms for mobile operating systems, as a rule, are aimed at neutralizing harmful effects and do not ensure the safety of personal data from legitimate services. The article proposes a model for assessing the risks of compromising personal data on mobile devices based on the correlation analysis of public information about service developers in order to detect the possibility of aggregating data from various sources.