Modelirovanie i analiz dannyh最新文献

The article addresses data analysis methods for solving the problem of choosing the best solution in order to optimize resources using the example of Skillbox company. The problems of choosing information resources are considered and solved, taking into account market offers. The author considers various methods of data analysis with an emphasis on minimizing the resources required for their processing. In the article, the author describes the practical application of the "Digital Assistant" program, which was developed by the Skillbox company to measure product metrics and analyze the behavior of students. Using the big data analysis methods described by the author, ways to optimize educational business processes that lead to an increase in the profitability of educational courses are considered. Heat maps are considered as one of the main ways to analyze big data, and their practical application is described. The author describes the relationship between business metrics and changes occurring with the educational product and suggests methods and frequency of surveys and feedback processing about the educational path of students.

This article discusses an approach to solving the problem of speech recognition based on probabilistic neural networks. The problem is formulated as a problem of command recognition. Commands have equal lengths (in words). Each word position has its own set of candidates. The recognition algorithm for solving this problem was developed. The core of the algorithm is a probabilistic network that recognizes modified estimates of spectral densities. The algorithm allows for high precision of recognition, which is sufficient for the creation of a voice user interface.

The article presents the practical results of the work of a model sample of special educational conditions for students with visual impairments in terms of studying the disciplines of mathematical and computer cycles, studying undergraduate programs in mathematics and applied computer science.

Representations of iterated Stratonovich and Ito stochastic integrals are obtained on the basis of the spectral form of mathematical description, they have both theoretical and practical significance. The latter is due to the possibility of constructing quite simple algorithms for the approximate modeling iterated stochastic integrals, which are necessary for the implementation of numerical methods for solving stochastic differential equations. The use of spectral representations of iterated stochastic integrals in numerical methods forms the numerical-spectral methods. Algorithms for them are presented in the form of programs for the computer algebra system Mathcad.

The article presents the results of a sociological study aimed at assessing the effectiveness of the use of learning management systems (LMS) in a mixed form of conducting educational activities in mathematical disciplines with face-to-face seminars and lectures and organizing the independent work of students of technical specialties of the university using the LMS. The research is based on the results of a pedagogical experiment conducted in the spring semester of 2022/2023 on the introduction of an electronic textbook into the educational process at the Moscow Aviation Institute on the course "Theory of the function of a complex variable" using the distance learning system CLASS.NET. The studies are carried out by interviewing students and teachers participating in the pedagogical experiment. The most popular functions of an electronic textbook by teachers and students have been identified.

The paper deals with the methodology of estimating the cockpit information and control field using indicators of oculomotor activity and a probabilistic neural network, which is used to calculate the final estimate of the considered display version. This methodology was tested on the universal prototyping bench basis with the participation of pilots. The using of this methodology contributes to the rapid and qualitative ergonomic assessment of the cockpit information and control field, which in turn increases the efficiency of human-machine interaction and, as a consequence, the safety of air transportation.

The work is aimed at analyzing the potential advantages of using quantum approaches in applied problems of artificial intelligence. In this paper, the task of classifying medical images extracted from histopathological images of sections of lymph nodes is set. The theoretical basis used for the construction of quantum and hybrid-quantum computing elements used in the article will be given. Quantum analogues of classical machine learning algorithms and neural networks will be considered. The paper will give a step-by-step description of the data transformation, the construction of models and their training, followed by an analysis of the results obtained and the performance of the simulation of quantum computing.

This study aims to address the limited understanding of organizational climate and performance by conducting a comprehensive bibliometric analysis of scholarly publications. The methodology involves analyzing publications using bibliometric techniques and VOSviewer. The results indicate that organizational performance, employee engagement, job satisfaction, leadership, and leadership culture are prominent topics within the field. The top five countries in terms of published documents and citations are the USA, India, the UK, Australia, and Malaysia. Recent publications have prioritized topics such as quality of work life, innovation, productivity, well-being, organizational commitment, work engagement, and corporate social responsibility. This study provides valuable insights for researchers, practitioners, and organizations to improve employee performance and productivity. The significance of this work lies in its ability to inform future research directions and guide collaboration efforts. Ultimately, this study advances the understanding of organizational climate and performance with practical implications for various organizational settings.

This article is devoted to the study of fully connected linear regression models, in which the observed variables contain errors, and the pairs of true variables are interconnected by linear functional dependencies. When estimating fully connected regressions, the main problem is the correct choice of the error variances ratios of the variables. If the choice is made incorrectly, then the fully connected regression estimates will be biased. The purpose of this article is to find the dependence of main parameters possible estimates areas on the possible error variances ratios of the variables in fully connected regressions. For the first time, with the help of matrix algebra elements, the inverse problem is solved - analytical dependences of the error variances ratios of variables on the main parameters are obtained. These dependences make it possible to identify the parameters possible estimates areas in which the necessary condition for the extremum of the objective function is satisfied. It is proved that, under certain conditions, for any error variances ratios of the variables, the parameters estimates always lie inside an open convex polygon located only in one of the orthants of the multidimensional space. In this case, the signs of the estimates always agree with the signs of the corresponding correlation coefficients. A numerical experiment was carried out, confirming the correctness of the results obtained.

In this work, the previously developed approach of parametric identification of dynamic systems with interval parameters is applied to the problem of finding the rate constants of the chemical reaction of naphthalene oxidation. This reaction is of practical importance in the production of plastics and paints and varnishes. The essence of the considered approach lies in the compilation of the objective function in the space of the boundaries of the interval parameters and characterizing the deviation of the model solution from the experimental data. For the objective function, it is possible to construct a gradient and use first-order methods to optimize it. The approach is based on the adaptive interpolation algorithm, which makes it possible to obtain solutions for direct interval problems in the form of explicit parametric sets. The found interval estimates of the rate constants are consistent with the known ones, but at the same time they have a smaller width, which demonstrates the advantage of the approach used.