Mathematics and Cybernetics - applied aspects
Journal EEJET
All articles in open access journal which are indexed by Scopus (H-INDEX 36, SJR 0.288, SNIP 0.573)
Development of a methodical approach to assessing the state of hierarchical systems using a meta-heuristic approach
Optimization is a complex process of defining a set of solutions for a wide range of problems, including management decision-making. One of the approaches to increasing the efficiency of solving optimization problems is metaheuristic algorithms. The problem solved in the study is to increase decision-making efficiency in the problems of assessing the state of hierarchical systems while ensuring a given reliability, regardless of its hierarchy. The object of the study is hierarchical systems. The subject of the study is the decision-making process in management problems using an advanced Tasmanian devil algorithm (TDA) and evolving artificial neural networks. A methodical approach using a metaheuristic algorithm is proposed. For TDA training, evolving artificial neural networks are used.
Improving linear programming model for assessing product sales taking into account social responsibility of consumers
The object of this study is the process of analyzing the volume of product sales in the retail market, where producers and consumers with different levels of social responsibility operate. Advancements are aimed at solving the problem of insufficient consideration of the social responsibility of economic entities in modern economic and mathematical models. In the work, the improvement of the linear programming model, intended for the evaluation of sales of alternative variants of goods with different social utility to consumers with different social responsibility, by taking into account some real conditions of making purchases, was carried out. As a result of the study, a modification of the model was proposed that makes it possible to take into account the behavior of buyers who choose a product randomly.
Development of a method for assessing the level of digital competencies of higher education teachers based on the calculation of the multidimensional polytope volume
The object of the study is the method for assessing the level of digital competencies of higher education teachers. Teachers with a high level of digital competencies can create more interactive, interesting and more effective learning materials, which has a positive impact on education quality. The ability to ensure the continuity and quality of the educational process in remote conditions is relevant. To improve the qualification level of teachers, it is necessary to form a measuring system of indicators. A method for calculating the potential for changing the digital competency level was developed and trajectories of forming digital competencies of higher education teachers based on the project-vector methodology were formed. The method was implemented and verified as part of the state project in the Republic of Kazakhstan with the participation of the Higher Education Development National Center and Astana IT University.
Building a fuzzy model for determining the level of social well-being of the population
This paper considers objects that affect the social security of the country. The complexity of such objects makes the development of computer systems in sociological research a difficult algorithmic task because of information uncertainty. Human thinking is based on inaccurate, approximate data, the analysis of which makes it possible to formulate clear decisions. In practice, there are usually no precise mathematical models that describe social objects. In such cases, it is advisable to use fuzzy mathematics as a tool for solving this problem. The main advantage of this approach compared to other artificial intelligence methods is the ability to interpret the results obtained. To assess the level of social well-being of the population, we used the mathematical apparatus of fuzzy set theory and fuzzy inference.
Determining epidemiological patterns in disease identification using mathematical models on machine learning based multilayer structures
The object of the study is epidemiological grouping using the SEIR mathematical model on a machine learning-based multilayer network. The problems in this research are related to managing epidemiological data on a large scale to determine disease patterns and identification such as the number of recovered cases, number of infected cases and number of deaths and demographic factors. In the process, traditional methods make it difficult to carry out processes such as determining patterns and identifying diseases. So, it is necessary to use machine learning and the SEIR (Susceptible-Exposed-Infectious-Recovered) mathematical model which can be integrated with multilayer networks to increase accuracy and effectiveness in identifying diseases and determining patterns. The results obtained from this research are a model that can identify and determine patterns of disease spread in large-scale epidemiological data.
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Synthesis of expert matrices in inductive system-analytical research based on fuzzy logic algorithm
The object of research is the process of inductive modeling of complex systems. The studies that were conducted related to the application of algorithms of the fuzzy logic theory were to coordinate the conclusions of top-level experts in system information-analytical research (SIAR) in the tasks of innovative design. The possibilities of constructing elements of expert matrices of results, as well as evaluating the effectiveness of such applications, are defined. Thanks to this, obtaining formal expert evaluations in numerical form became possible. Experimental studies have confirmed that the proposed approach to the application of fuzzy logic algorithms to the construction of matrices of expert evaluations of SIAR results is quite effective and simple to implement. In addition, this approach fits well into the general paradigm of the Group Method of Data Handling (GMDH). In particular, it was established that the possibility of ?retraining? such a block without significant efforts of professional experts can have a positive result, as well as have a good effect on the economic and time parameters of the research project.
Method for solving distributional problems of mathematical programming under conditions of fuzzy initial data
The object of this study is a large class of mathematical programming problems under conditions of uncertainty of initial data. The formulated object generates a subclass of problems of rational distribution of a limited resource under conditions of initial data described in terms of fuzzy mathematics. The conventional, standardly used method for solving such problems is based on optimization on average. To obtain such a solution, it is sufficient to replace all fuzzy initial data with their modal values in the analytical description of the mathematical model of the corresponding problem. To solve the resulting deterministic problem, one can use known methods of mathematical programming. However, the results of such a solution can be used in practice if the carriers of fuzzy parameters are specified compactly, that is, the intervals of possible values of fuzzy parameters of the problem are small.
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