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2020. No. 3 Vol.14
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Modeling of social and economic systems
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7–23
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The solution of the housing problem for many decades has been and remains one of the most important tasks facing the nation.. The problem of modeling the value of residential properties is becoming more and more urgent, since a high-quality forecast makes it possible to reduce risks, both for government bodies and for realtors specializing in the purchase and sale of residential properties, as well as for ordinary citizens who buy or sell apartments. Predictive models allow us to get an adequate assessment of both the current and future situation on the residential property market, to identify trends in the cost of housing and the factors influencing these changes. This involves both the qualitative characteristics of the particular property, and the general condition and the dynamics of the real estate market. Russia is characterized by significant differences in the level of development of regions, therefore, by differences in trends of supply and demand prices for real estate. Valuation of residential properties at the regional level is particularly important, since all of the above determines the social and economic importance of this problem. This article presents a comprehensive model for estimating the value of residential properties in the secondary housing market of Moscow using decision tree methods and ordinal logistic regression. A predictive model of the level of housing comfort was built using the CRT decision tree method. The results of this forecast are used as input information for an ordinal logistic regression model for estimating the value of residential properties in the secondary market of Moscow. Testing the model on real data showed the high predictive ability of the model we generated. |
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24–34
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In modern logistics and supply chain management, the task of inventory management is paramount. The total costs of the enterprise and consequently, its profit, directly depend on the accuracy of calculating the volumes and terms of orders. In this work, the problem of increasing the accuracy of calculating the economic order quantity for a product was solved by involving additional information about the known quantile of a given level of the distribution function of the volume of product’s demand. The quantile information was used to recalculate the annual demand for the product, based on a modified estimator of the sales expectation for the period. The modified estimator is asymptotically unbiased, normal, and more accurate than the traditional sample mean in the sense of mean squared error. New formulas for calculating the economic order quantity and its confidence interval were presented and tested on real data on the monthly sales volumes of goods of a large retail store network over two years. It is shown that the classic way of mean calculation led to an underestimation of the volume of the economic order quantity, which in turn increased the risk of a shortage, and hence a drop in the quality of logistics services. The new calculation method also showed that the period between orders should be one day shorter. The work is practically significant; according to its results, recommendations are given to the enterprise. |
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35–53
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The relevance of developing knowledge-based systems used to support innovative processes for creating products and services is related to the objective need to reduce the life cycle of products under the influence of modern digital technologies in developing network enterprises. Well-known research results in the field of model-oriented design of products, processes, systems and enterprises do not fully provide semantic interoperability in the interaction of stakeholders in the innovation process. The aim of this work is to build a knowledge-based system architecture that implements semantic interoperability of network enterprise participants at various stages of the product lifecycle. The work is based on the use of a model-oriented approach to building a digital thread at all stages of the product lifecycle, an ontological approach to semantic modeling of a distributed knowledge base and a multi-agent approach to organizing interaction between interested participants in the innovation process. The paper proposes a functional architecture of a knowledge-based system that includes modules for planning the innovation process, forming product value characteristics and functional requirements, construction and value chain design. A multi-level system of ontologies of the innovation process is also developed and its application in the work of functional modules that provide access to associated knowledge bases is described. The development of knowledge-based systems based on the results obtained will allow us to find the best design solutions for the configuration of products and corresponding value chains due to the possible iteration of the innovation process and increasing the semantic interoperability of network enterprise stakeholders. |
Decision making and business intelligence
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54–66
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Currently the use of bots, i.e., auto-accounts in social networks which are managed with special programs but disguised as ordinary users, has serious consequences. For example, bots have been used to influence political elections, distort information on the Internet and manipulate prices on the stock exchange. Many research teams concerned with the detection of such accounts have made use of machine learning methods. However, the practical results of detecting social network bots indicate significant limitations because the methodological tools used have language limitation and ineffective criteria for detection. This article presents improved countermeasures in a methodological approach to develop a universal social network account classifier for minimizing the average risk of errors in bot detection. The application of an assembly of classifiers united by a data adaptation criterion and results from the variance of each model found the formation of a universal classifier for social network accounts. The main results obtained by the authors consist of the criteria system and the categorical (nominal) features transformation approach for the formation of the special ensemble of classifiers. In practice, use of the ensemble of classifiers allows us to increase the effectiveness of bot detection compared to other approaches. |
Data analysis and intelligence systems
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67–81
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This paper proposes a model of the impact of technology on the standard of living based on fuzzy linear regression. The Human Development Index (HDI) was chosen as a dependent variable as an indicator of the health and well-being of the population. The explanatory variables are the Network Readiness Index (NRI), which measures the impact of information and communication technologies (ICT) on society and the development of the nation, and the Global Innovation Index (GII), which measures the driving forces of economic growth. The analysis is based on data for 2019 for four groups of countries with different levels of GDP per capita. For developed countries, the positive and balanced impact of innovation and ICT on living standards has been confirmed. For two groups of developing countries (upper and lower middle income), the GII coefficient was found to be negative. A more in-depth analysis showed that this is due to the state of political and social institutions. This fact means that without a simultaneous increase in the maturity of institutions, stimulation of other areas of innovative development (education, knowledge and technology, infrastructure) leads to a decrease in the quality of life. |
Information systems and technologies in business
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82–95
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Modern models and methods for evaluating complex systems are associated with hierarchical socio-economic systems (HSES) implemented on the basis of software systems (expert systems and decision support systems) and used at the regional and municipal levels of government. As usual, such systems have the functionality of analytics and building scenario variants for the development of research objects. However, they do not give quantified values of the state and impact factors at which the complex system under consideration can come to a given state. At the same time, the question of determining such a set state associated with the construction of standards (expected values) for elements, classes or levels of the HSES is still open. In some cases, to make an informed decision it is sufficient to obtain aggregated quantitative estimates and recommendations concerning the further functioning of the research object. This article presents the author’s approach, which allows us to evaluate the functioning of hierarchical socio-economic systems and provides expert opinions for making management decisions implemented on the basis of the EFRA software package. The algorithm includes stages of analysis and synthesis-stages of the basic method of system analysis. The novelty of the proposed approach is the possibility of taking into account the specific conditions of the status and impact of complex systems that provides an opportunity to build their own standard. Additionally, the procedures of standardization and normalization (reduction to a scale from 0 to 1) make it possible to avoid the influence of different units of measurement of results of operation and economies of scale. On the example of regions of the Central Federal district according to data for 2014–2017, estimates of the use of information and telecommunications technologies by the population were obtained, and the optimization problem was solved for the Tula Region, on the basis of which directions related to increasing the region’s readiness for digitalization were proposed. |
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