Association Rules in Finance Management

Krasnyuk, Maxim; Krasniuk, Svitlana

doi:10.36074/logos-26.02.2021.v1.01

Cited by 14 publications

(5 citation statements)

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“…The rule-oriented subsystem is the main one in the knowledge-oriented HYBRID DSS of an oil and gas production company, in fact, other subsystems provide it with analyses, assessments, and knowledge. Namely, the final product of the system: recommendations for making management decisions and is carried out by a rule-oriented subsystem (Krasnyuk et al, 2021). In general, science-based conclusions were obtained regarding the knowledge-oriented intelligent HYBRID DSS architecture of an oil and gas company, the basic model of knowledge representation (production), features of the inference mechanism (direct logical inference), conflict resolution procedure (product compilation method), etc.…”

Section: Introductionmentioning

confidence: 99%

Intelligent management of an innovative oil and gas producing company under conditions of the modern system crisis

Krasnyuk¹,

Hrashchenko²,

Goncharenko³

et al. 2023

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This publication presents the part of the research results and practical results obtained by the authors regarding the hybrid use of economic-mathematical modelling, knowledge-oriented decision support technology of an oil and gas production company using fuzzy logical inference. The purpose of this research is the development of theoretical provisions of modelling and knowledge-oriented decision support means at the macro level of oil and gas production companies. The purpose of the work determined the solution of the following tasks: -development of science-based recommendations regarding the architecture of a knowledge-oriented DSS of an oil and gas company, the basic model of knowledge presentation, features of the logical conclusion mechanism, etc.; -development of a complex system of economic and mathematical support for decision-making at the macro level of an oil and gas production company in modern economic conditions. The object of the study is the oil and gas production industry. The subject of the research is information processes, economic-mathematical models and knowledge-oriented methods and means of supporting the adoption of management decisions at the strategic level on economic and production issues of the domestic oil and gas production project. Methods/Approach: Economic and mathematical methods, methods of artificial intelligence, methods of logical generalization, expert evaluations and situational approach are used to solve the tasks set in the work. Results:The main scientific result of the work consists in the creation of the concept that allows creating a hybrid DSS of an oil and gas company on the basis of the developed systems of economic and mathematical decision-making support at the macro level of an oil and gas production company, focused on knowledge of technology and intelligent technologies. Conclusions: The scientific, theoretical and applied practical solutions proposed in this publication are universal for implementation by both state and private oil and gas resident and non-resident companies for emerging markets, however, in order for a specific oil and gas company to obtain special additional competitive advantages over others, additional industry-specific Big Data Analysis of collected and stored heuristics, expertise and project development are required.

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Section: Introductionmentioning

confidence: 99%

Intelligent management of an innovative oil and gas producing company under conditions of the modern system crisis

Krasnyuk¹,

Hrashchenko²,

Goncharenko³

et al. 2023

ACCESS

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“…the national bank, unofficial restrictions on transit from neighboring states, the exchange rate of the national currency, etc.) 5. the classified sample for training the model should be verified, according to the possibility of scheme bankruptcies, or bankruptcies through re-registration of companies [4]; 6. to achieve a higher accuracy of the results of such models, it is necessary to systematically (at least once a financial year) and critically revise the set of indicators and retrain the "coefficients" of their weighting influence with targeting the strategic goals of shareholders.…”

mentioning

confidence: 99%

Results of Analysis of Machine Learning Practice for Training Effective Model of Bankruptcy Forecasting in Emerging Markets

Krasnyuk¹,

Tkalenko²,

Krasniuk³

2021

Advanced Discoveries of Modern Science: Experience, Approaches and Innovations Band1

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“…There are various assumptions that over time FinTech companies will be able to compete with classical banks, as they are more technological and innovative, accessible to a wider range of people, are able to quickly adapt to market fluctuations, a wide presence in social networks and social media [2]. The strengths of classical banks are knowledge of clients, compliance with requirements, trust, security and experience of credit institutions.…”

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confidence: 99%