Browsing by Author "Krasniuk, Svitlana"
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Item Association rules in finance management(European Scientific Platform, 2021-02-26) Krasniuk, Maksym; Краснюк, Максим Тарасович; Krasniuk, SvitlanaItem Development of the fintech industry and fintech technologies under covid-19(European Scientific Platform, 2021-05-07) Krasniuk, Maksym; Краснюк, Максим Тарасович; Tkalenko, Antonina; Krasniuk, SvitlanaItem E-business and e-commerce technologies as an important factor for economic efficiency and stability in the modern conditions of the digital economy (on the example of oil and gas company)(NGO European Scientific Platform, 2022-07-22) Krasniuk, Maksym; Краснюк, Максим Тарасович; Kulynych, Yurii; Tuhaienko, Viktoriia; Krasniuk, SvitlanaThe article proves that a necessary factor for increasing economic efficiency of an oil & gas company is the use of Internet technologies, in particular, in support of management decision-making. The advantages and disadvantages of using internet technologies in the oil & gas industry, its specificities, are studied. Recommendations for each possible direction of application of internet technologies in DSS in the oil & gas company are outlined. The trends, ways of improvement and practical recommendations identified by the authors should be taken into account during further theoretical research and practical implementation (or reengineering) of DSS systems in Ukraine for industrial corporations (ie, not only for oil and gas companies). The obtained results are relevant and applicable not only for local companies and organizations, but also for international applications in the context of global, regional macroeconomic and current national crisis phenomena.Item Economic and mathematical modeling of an oil and gas production company as an integrated complex specific system(Видавнича група «Наукові перспективи», 2022) Krasniuk, Maksym; Краснюк, Максим Тарасович; Kulynych, Yurii; Кулинич, Юрій Михайлович; Hrashchenko, Iryna; Гращенко, Ірина Семенівна; Honcharenko, Svitlana; Гончаренко, Світлана Миколаївна; Krasniuk, Svitlana; Краснюк, Світлана ОлександрівнаThe following factors were studied and taken into account in the process of economic and mathematical modeling of an oil and gas company as a complete complex specific system: a significant inertia of a management object; multi-level management structure; irregularity of a management system of an oil and gas company; a need to decompose the system along the weakest lines of communication "vertically" and "horizontally" and build economic and mathematical models of smaller dimensions for each selected element. Determining an algorithm that connects economic and mathematical models of the technological, tactical and strategic management levels of an oil and gas company is a very difficult task. Economic and mathematical modeling of the technological level of an oil and gas company should contribute to management of functioning of main technological objects of the oil and gas production: to ensure optimal modes of operation under the selected optimality criteria; must take into account the parallel-sequential flow of operations and is the most studied level of an economic and mathematical management of an oil and gas company. Economic and mathematical modeling of the tactical level of management of an oil and gas company as optimality criteria involves maximizing profit and minimizing integral costs; and can be formalized in the form of economic and mathematical models of the following interconnected blocks: geological and industrial, production, transport and economic. Economic and mathematical modeling of the strategic level of an oil and gas company should reflect the strategic goal of the development of a company and the industry. The basis for building an economic and mathematical model of an oil and gas production company is the choice of optimality criterion, which characterizes the entire activity of the company for a certain period of time, in particular, the criterion of maximum profit. However, for making cost-effective decisions in the management of complex systems (an oil and gas production company) in the conditions of a sharp change in market prices, this criterion is impractical. Since economic efficiency does not necessarily mean high profitability, that is, in the conditions of constant changes in the world economy, the most stable position in a long term is the system characterized by maximum efficiency, and not profitability, which also depends on random factors (constant price fluctuations on the energy carrier is the norm of the modern world economy). Objectively, it turned out that the most developed issues of economic and mathematical modeling of the strategic level of management are for processing industries, not extractive ones. Therefore, when building economic and mathematical models of a strategic planning level of an oil and gas production company, researchers face difficulties caused by the specifics of the industry, in particular: product stocks for a given field are always limited; a production cost of 1 ton of conventional hydrocarbon fuel from one field increases significantly during its life cycle; it is difficult to determine the degree of detailing of the models. The use of economic and mathematical modeling in managerial processes on the part of the information system of an oil and gas production company faces sociological, political and other limitations, which an experienced manager should take into account when making a final decision. The main task of the oil and gas production complex is to ensure production and a growing renewal of hydrocarbon reserves. The problem of improving quality of a balance of explored reserves should be solved by an oil and gas company by opening new fields, features of which largely determine the specifics of planning, organization of a process and a material and technical base of work, determination of the industrial value of explored reserves, etc. The economic indicators of effectiveness of work on development of discovered reserves should include the amount of capital investments, operating costs, cost of production, profit, profitability, payback period, etc. To determine the total profit of an oil and gas company from the development of reserves, the system of criteria and economic indicators of a subsoil is used. Which is used at all stages of prospecting and development of reserves for: - substantiating economic feasibility of carrying out work on the search and exploration of oil and gas deposits; - establishment of a valuation of recoverable hydrocarbon reserves; - ranking of individual prospective plots into groups according to economic criteria and a sequence of their development; - forecasting of oil and gas prices, taking into account the level of forecasted cumulative specific costs for preparation and development of reserves and expected profit. Making managerial decisions regarding investment projects of an oil and gas company requires determining the value of underground reserves (resources) and is characterized by a set of indicators that generally reflect the comparison of expected (obtained) results with necessary costs for participants in a geological exploration process. In the article, the concept of economic and mathematical modeling of an oil and gas company as a complete complex specific system was further developed. The main factors and limitations are taken into account, the specifics of economicmathematical modeling of oil and gas company management at the technological, tactical and strategic levels are investigated. У процесі економіко-математичного моделювання нафтогазового підприємства як цілісної комплексної специфічної системи досліджувалися та враховувалися такі фактори: значна інертність об’єкта управління; багаторівнева структура управління; невідлагодженість системи управління нафтогазовою компанією; необхідність декомпозиції системи по найслабших лініях зв'язку «по вертикалі» і «горизонталі» і побудови економіко-математичних моделей меншої розмірності для кожного обраного елемента. Визначення алгоритму, що зв’язує економіко-математичні моделі технологічного, тактичного та стратегічного рівнів управління нафтогазовою компанією, є дуже складним завданням. Економіко-математичне моделювання технологічного рівня нафтогазового підприємства має сприяти управлінню функціонуванням основних технологічних об’єктів нафтогазовидобутку: забезпечити оптимальні режими роботи за обраними критеріями оптимальності; має враховувати паралельно-послідовний перебіг операцій і є найбільш вивченим рівнем економіко-математичного управління нафтогазовою компанією. Економіко-математичне моделювання тактичного рівня управління нафтогазовим підприємством як критерій оптимальності передбачає максимізацію прибутку та мінімізацію інтегральних витрат; і можуть бути формалізовані у вигляді економіко-математичних моделей наступних взаємопов’язаних блоків: геолого-промислового, виробничого, транспортно-економічного. Економіко-математичне моделювання стратегічного рівня нафтогазової компанії має відображати стратегічну мету розвитку компанії та галузі. Основою побудови економіко-математичної моделі нафтогазовидобувного підприємства є вибір критерію оптимальності, який характеризує всю діяльність підприємства за певний період часу, зокрема, критерію максимального прибутку. Однак для прийняття економічно ефективних рішень в управлінні складними системами (нафтогазовидобувною компанією) в умовах різкої зміни ринкових цін цей критерій є недоцільним. Оскільки економічна ефективність не обов’язково означає високу рентабельність, тобто в умовах постійних змін у світовій економіці найбільш стабільну позицію в довгостроковій перспективі має система, яка характеризується максимальною ефективністю, а не прибутковістю, яка також залежить від випадкових факторів. (постійні коливання цін на енергоносій є нормою сучасної світової економіки). Об’єктивно виявилося, що найбільш розробленими є питання економіко-математичного моделювання стратегічного рівня управління для переробних галузей, а не видобувних. Тому при побудові економіко-математичних моделей рівня стратегічного планування нафтогазовидобувної компанії дослідники стикаються з труднощами, зумовленими специфікою галузі, зокрема: запаси продукції для даного родовища завжди обмежені; собівартість видобутку 1 тонни умовного вуглеводневого палива з одного родовища значно зростає протягом життєвого циклу; складно визначити ступінь деталізації моделей. Використання економіко-математичного моделювання в управлінських процесах з боку інформаційної системи нафтогазовидобувної компанії стикається з соціологічними, політичними та іншими обмеженнями, які досвідчений менеджер повинен враховувати при прийнятті остаточного рішення. Основним завданням нафтогазовидобувного комплексу є забезпечення видобутку та зростаюче відновлення запасів вуглеводнів. Проблема підвищення якості балансу розвіданих запасів нафтогазової компанії повинна вирішуватися шляхом відкриття нових родовищ, особливості яких значною мірою визначають специфіку планування, організації процесу та матеріально-технічної бази роботи, визначення промислове значення розвіданих запасів і т. д. Економічні показники ефективності робіт з розробки виявлених запасів повинні включати обсяг капітальних вкладень, експлуатаційні витрати, собівартість продукції, прибуток, рентабельність, термін окупності і т. д. Для визначення загального прибутку нафтогазової компанії від розробки запасів використовується система критеріїв та економічних показників надр. Який використовується на всіх етапах пошуку та розробки запасів для: - обґрунтування економічної доцільності проведення робіт з пошуку та розвідки родовищ нафти і газу; - встановлення оцінки видобувних запасів вуглеводнів; - ранжування окремих перспективних ділянок на групи за економічними ознаками та послідовність їх освоєння; - прогнозування цін на нафту і газ з урахуванням рівня прогнозованих сукупних питомих витрат на підготовку і розробку запасів та очікуваного прибутку. У статті отримано подальший розвиток концепції економіко-математичного моделювання нафтогазового підприємства як цілісної комплексної специфічної системи. Враховано основні фактори та обмеження, досліджено специфіку економіко-математичного моделювання управління нафтогазовим підприємством на технологічному, тактичному та стратегічному рівнях.Item Efficiency of evolutionary algorithms in solving optimization problems on the example of the fintech industry(NGO European Scientific Platform, 2022-05-27) Kulynych, Yurii; Krasniuk, Maksym; Краснюк, Максим Тарасович; Krasniuk, SvitlanaThe pandemic forced companies to rebuild business processes in an accelerated mode. Now they pay more attention to web products and work with customers in the virtual space. The financial technology market (FinTech) is getting bigger and more diverse every day. Financial news website Market Screener reports that the global FinTech market will be worth $26.5 trillion by 2022, with a compound annual growth rate of 6%. In Europe alone, the use of FinTech increased by 72% in 2020. The competition in this market segment is also growing. In the first eleven months of 2021, more than 26,300 startups have joined the fray, more than double the number of new entrants just three years earlier. As the competition for customer engagement and loyalty heats up, FinTech players need to reach out to a much larger audience optimally distributed across ever-growing geographies. Monitoring and managing business operations is becoming increasingly complex as the number of customer accounts and financial transactions continues to grow. Therefore, more solutions are needed to address the challenges associated with financial IT. Therefore, the focus should be on algorithms and methods that help FinTech companies optimize all stages of their activities, from customer acquisition to payment processing and payout forecasting. In all aspects of a business, there is little room for errors, unexpected failures, or downtime. Performance optimization is the key to success in this industry. The explosion of activity caused by all these companies generates a huge amount of Structured and Unstructured Big Financial Data about customers and payments, as well as information about the underlying business processes. The deep analytics hidden in this data can help companies optimize payment approval rates, transaction costs and reduce the risk of fraud, as well as customer retention and accelerate revenue growth. The above determines the acquisition of competitive advantages not only for FinTech corporations and companies, both regionally and globally, which is especially true in times of crisis. The article comprehensively explores the following topical issues: problems, features and prospects of effective optimization tasks in modern conditions, critical issues of theory and practice of Evolutionary Computations (including financial management), the specifics of effective use of Genetic Algorithms in information systems of FinTech companies. The above trends and peculiarities of the application of Evolutionary Computations in general and Genetic Algorithms in particular should be taken into account in further research and practical projects and real projects of effective implementation and use of Data Mining and Artificial Inelligence technologies in FinTech information systems. The obtained results are relevant and applicable not only for local companies, but also for international applications in the context of global, national and regional (not only economic, but also pandemic, military, natural disaster etc) crisis phenomena.Item Evolutionary technologies and genetic algorithms in machine translation(ScientificWorld-NetAkhatAV, 2024) Krasniuk, Maksym; Краснюк, Максим Тарасович; Krasniuk, Svitlana; Краснюк, Світлана І.In general, the application of evolutionary technologies and genetic algorithms in professional translation is a promising direction of development. Overall, the application of evolutionary technologies and genetic algorithms can help improve the translation process and provide more accurate and complete translations. In general, the use of evolutionary technologies and genetic algorithms is an important stage in the development of professional translation and helps to improve the quality of translation and reduce the time required for its execution. However, the use of genetic algorithms and evolutionary technologies should be balanced with other machine learning & mathematical programming & soft computing approaches to maximum improve translation (such as the use of hybrid machine learning, the creation soft LLM, fuzzy inference engine for translation & interpretation etc. [13-15]) and depend on the specific requirements and needs of users.In general, the use of evolutionary technologies and genetic algorithms is a promising innovative direction for improving the quality and efficiency of professional translation, especially in the conditions of streaming semi-structured Big Data [16-18]. Genetic algorithms and evolutionary technologies cannot completely replace human expertise and the performance of tasks by professional translators, but only help them perform their work more efficiently and quickly [19]. Such technologies allow translators to focus on more complex aspects of translation, such as understanding and conveying shades of meaning, while using the support of computer technology to improve translation speed and accuracy.In particular, genetic algorithms can help in solving the problem of choosing the most optimal translation option from a large number of possible options. Also, evolutionary technologies make it possible to improve the quality of translation by automatically adapting the translation to a specific text and its context.It should be noted that the use of evolutionary technologies and genetic algorithms has its limitations and drawbacks. For example, they may be less efficient in solving some types of tasks and require a significant amount of computing resources. Research and development in this field continues, and we can expect new innovative solutions and technologies that will allow even more accurate and efficient translation of texts of different levels of complexity and style.Item Features, problems and prospects of the application of deep machine learning in linguistics(Громадська наукова організація «Всеукраїнська асамблея докторів наук з державного управління», 2023) Krasniuk, Maksym; Краснюк, Максим Тарасович; Krasniuk, Svitlana; Краснюк, Світлана Олександрівна; Honcharenko, Svitlana; Гончаренко, Світлана Миколаївна; Roienko, Liudmyla; Роєнко, Людмила Віталіївна; Denysenko, Vitalina; Денисенко, Віталіна Миколаївна; Liubymova, Nataliia; Любимова, Наталія ВолодимирівнаIn its nascent years, artificial intelligence (AI) largely focused on expert systems based on knowledge in the form of production rules, which solved mainly diagnostic problems, but also design-type problems (using the previously manually collected and formalized knowledge/experience of human experts in specific subject area). However, this type of intelligent systems had numerous drawbacks, including the subjectivity of experts' opinions, which eventually led to their loss of mass popularity [1]. In addition, this technology practically could not be adapted to mass and effective use either in scientific philological research or in practical effective linguistics. As the scale and volume of data has increased, these methods have been replaced by a more controlled and objective data-oriented approach – machine learning [2]. Machine learning is a set of algorithms and methods that helps machines understand the hidden patterns in data and use the structure and essence of these hidden patterns in the data/heuristics to make logical inference/prediction about a specific task. Currently, there is a diverse range of such methods/algorithms, with the help of which machines seek to understand these basic patterns such as association, sequence, classification, clustering, regression prediction, finding anomalies in data [3]. If we systematically consider the history of the development of computational analytics and analyze its perspective, it becomes clear that deep learning is a further evolution and subdomain of machine learning. Thanks to the emergence of architectures with increased computing power (GPU and TPU) and large sets of semi-structured and unstructured data, specialized architectures and corresponding deep learning algorithms are able to independently learn hidden patterns in linguistic data and even perform generative functionality (Large Language Models). However, recently there has been a growing misconception that deep learning is a competing technology to classical machine learning. Deep learning is not a single possible approach, but rather a class of algorithms and topologies, that can be applied to a wide range of scientific and practical problems (especially in machine linguistics). This article investigates and conducts a comparative analysis not only of this hypothesis, but also presents the results of thorough research on the advantages, problems, and features of effective deep machine learning in philology, namely in machine linguistics. У роки свого зародження штучний інтелект (AI) значною мірою зосереджувався на експертних системах, заснованих на знаннях у вигляді продукційних правил, які вирішували головним чином діагностичні задачі, але і задачі проектного типу (використовуючи заздалегідь вручну зібрані та формалізовані знання/досвід людей-експертів у конкретній предметній області). Однак такий тип інтелектуальних систем мав численні недоліки, в тому числі суб’єктивізм думок експертів, що врешті призвело до того, що вони втратили масову популярність [1]. Крім того, ця технологія практично не могла бути пристосована до масового та ефективного використання ні у наукових філологічних дослідженнях, ні у практичній ефективній лінгвістиці. Зі збільшенням масштабу та обсягу даних ці методи були замінені підходом, більш керованим та орієнтованим на об’єктивні дані – машинним навчанням [2]. Машинне навчання – це набір алгоритмів і інструментів, які допомагають машинам розуміти приховані закономірності в даних і використовувати структуру та суть цих прихованих закономірностей, що лежить в даних/евристиках, для виконання логічного висновку/передбачення щодо певного конкретного завдання. Наразі є різноманітний діапазон таких методів/алгоритмів, за допомогою яких машини прагнуть зрозуміти ці базові закономірності типу асоціації, послідовності, класифікації, кластеризації, прогнозу регресіі, пошуку аномалій в даних [3]. Якщо розглянути системно історію розвитку обчислювальної аналітики та проаналізувати її перспективу, глибинне навчання є подальшою еволюцією і піддоменом машинного навчання. Саме завдяки появі архітектур підвищеної обчислювальної потужності (GPU та TPU) та великим наборам напівструктурованих та неструктурованим даних - спеціалізовані архітектури та відповідні алгоритми глибокого навчання здатні самостійно вивчати приховані шаблони в лінгвістичних даних та виконувати, навіть, генеративний функціонал (Large Language Models). Проте, останнім часом зростає помилкове уявлення, що глибоке навчання є конкурентною технологією для класичного машинного навчання. Глибоке навчання — це не єдиний можливий підхід, а радше клас алгоритмів і топологій, які можна застосувати до широкого спектру наукових проблем та практичних задач (особливо у машинній лінгвістиці). У цій статті досліджено та проведений порівняльний аналіз не тільки щодо цієї гіпотези, але і викладено результати ґрунтовних досліджень щодо переваг, проблем та особливостей ефективного глибокого машинного навчання у філології, а саме у машинній лінгвістиці.Item Hybrid application of decision trees, fuzzy logic and production rules for supporting investment decision making (on the example of an oil and gas producing company)(ACCESS Press, 2022) Krasniuk, Maksym; Краснюк, Максим Тарасович; Hrashchenko, Iryna; Honcharenko, Svitlana; Krasniuk, SvitlanaDuring the last years, in most countries of Eastern Europe (and Ukraine in particular), even a simple reproduction of onshore hydrocarbon reserves was not ensured. Achieving the possible level of self-sufficiency in fuel and energy resources is a fundamental task of national economies, without which the successful implementation of economic, scientific, technical and social programs aimed at ensuring state independence and stability in Europe is impossible. However, the onshore oil and gas industry of the countries of Eastern Europe with significant volumes of unexplored oil and gas resources, with the cost of oil and gas several times lower than world prices, the presence of a significant number of oil and gas industries, drilling and geophysical enterprises, oil refineries, and an extensive network of oil and gas pipelines , highly qualified production teams allows, with their effective use, not only to stabilize, but also to significantly increase the production of oil, gas and condensate in the future. An important reason for the drop in oil and gas production volumes is insufficient management efficiency of the cycle of parallel business processes of the oil and gas company: field exploration, their arrangement and development, production and sale of oil and gas. The solution is the application of effective economic-mathematical modeling at the strategic level of management and the use of knowledge-oriented decision-making support tools as an integral component of the complex information system of an oil and gas company. Objectives: Therefore, the issues of: development of a complex system of economic and mathematical support for making fair and timely investment decisions at the macro level of an oil and gas production company, effective application of knowledge-oriented hybrid methods and technologies are becoming particularly relevant. Methods/Approach: The paper uses a mathematical apparatus of the method of fuzzy logic, decision trees, data mining, knowledge-oriented decision support, theory of investment management and expertise in the field of management of oil&gas exploration and production local and international investment projects. Results: first proposed the decision tree diagram of the effective investment management process of a oil and gas company in the search for hydrocarbons in modern economic conditions is proposed; received further development of the principles of hybrid application of intelligent technologies and knowledge-oriented basis and the problem of handling uncertainty while supporting investment decisions of an oil and gas company; first proposed two related prognostic models are proposed: the seismic impact model and a drilling impact model; first proposed two algorithms/models based on economic-mathematical modeling with elements of fuzzy knowledge to support decision-making of the tender&controlling committee of oil&gas production company. Conclusions: Based on the foregoing, it can be concluded that it is efficient to use developed by authors hybrid, knowledge-oriented investment decision support for oil and gas production projects in Ukraine and other countries of Eastern Europe.Item Innovative management information system in post-crisis economic conditions on emerging markets (on the example of the oil and gas industry)(Mezinárodní Ekonomický Institut s.r.o., 2023) Krasniuk, Maksym; Краснюк, Максим Тарасович; Kulynych, Yurii; Hrashchenko, Iryna; Krasniuk, Svitlana; Honcharenko, Svitlana; Chernysh, TetianaThe formation and development of the oil and gas industry in any region (country) of the world is primarily related to the volume of forecast hydrocarbon resources, the state of explored hydrocarbon reserves and is determined by a number of technological, economic, organizational, political and other factors. Ukraine is no exception – one of the oldest oil and gas producing countries in the world. Before starting the analysis, we need to define some terms that we will use in this subsection: - proven hydrocarbon reserves are known volumes of hydrocarbons that can be profitably extracted using existing technology; - unexplored traditional resources – oil and gas resources that are explored by oil and gas companies with developed technologies and that can be profitably extracted/developed using the existing traditional practice of hydrocarbon development; - unconventional resources – oil and gas resources that exist outside well-defined traps; - resources obtained due to the growth of deposits – resourcesthat are expected to be added to the explored reserves of the deposit due to: physical expansion of the boundaries of the deposit, development of new horizons, more careful calculation and evaluation of deposit reserves based on mining experience and changes in the relationship between price and costs; application of new technologies and methods of search, development, extraction of hydrocarbons and processing of relevant information. To a large extent, the listed factors depend on innovations, which are difficult to predict. In addition, these factors are complex and interrelated, and therefore difficult to analyze individually. Thus, the assessment of the possible future increase in reserves should be based on the empirical projection of past patterns.Item Intelligent management of an innovative oil and gas producing company under conditions of the modern system crisis(ACCESS Press, 2023-09) Krasniuk, Maksym; Краснюк, Максим Тарасович; Hrashchenko, Iryna; Honcharenko, Svitlana; Krasniuk, Svitlana; Kulynych, YuriiThis 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.Item Intelligent technologies in hybrid corporate dss (on the example of Ukraine oil&gas production company)(Ліга-Прес, 2022) Krasniuk, Maksym; Краснюк, Максим Тарасович; Honcharenko, Svitlana; Krasniuk, SvitlanaOil and gas companies in order to maintain their efficiency in the conditions of: liberalization of markets, globalization, increased competition, reduction of consumer loyalty, constant variation in oil and gas prices, further development of Industry 4.0 and the Big Data factor, growing costs for drilling and completion – must have a flexible environment of information technologies that enables seamless and efficient sharing of knowledge throughout the company and along the entire value chain. One of the elements of this task is the effective use of hybrid knowledge-oriented decision support systems (DSS). This, in particular, determines the future stage of complex author's research – the development of a complex perforating knowledge management policy of an oil and gas company, the key tool for the implementation of which will be the hybrid, knowledge-oriented DSS considered in this publication. The form of knowledge representation has a significant impact on the characteristics and properties of a knowledge-oriented system. Therefore, based on the specifics of the exploration and development of oil and gas fields and the main advantages of the rule-oriented model of the DSS knowledge base, it is possible to conclude that it is necessary to use the KB rule-oriented basis for the DSS of an oil and gas production company. The rule-oriented subsystem is the main one in the knowledge-oriented 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 is carried out by a rule-oriented subsystem. In general, scientifically based conclusions were obtained regarding the knowledgeoriented architecture of the intellectual DSS of an oil and gas company, the basic model of knowledge presentation (production), the features of the mechanism of logical conclusion (direct logical conclusion), the conflict resolution procedure (a method of ordering products), etc. In the oil and gas industry, DSS built according to a hybrid approach have the greatest application potential – which are a powerful tool for solving complex specific problems of an oil and gas company. Therefore, in this work, the principles of hybrid application of intelligent technologies and the knowledge-oriented basis of DSS of an oil and gas company were further developed.Item Knowledge discovery and data mining of structured and unstructured business data: problems and prospects of implementation and adaptation in crisis conditions(NGO European Scientific Platform, 2022-04-29) Krasniuk, Maksym; Краснюк, Максим Тарасович; Kulynych, Yurii; Krasniuk, SvitlanaIn modern conditions of the development of the global economy and in connection with the emergence of new branches of economic activity in the field of IT, the phenomenon of Structured and Unstructured Big Data – the use of Data Science for advanced in-depth analysis of data and knowledge in all possible modes – leads to competitive advantages for corporations and institutions, both at the regional and interstate levels, which is especially relevant in the context of the current macroeconomic and military crisis. The following topical issues are systematically investigated in the article: current status and prospects for further development of Knowledge Discovery in Data Base (KDD), problems and critical issues of theory and practice of Data Mining, the specifics of effective use of Knowledge Discovery in DB (Data Base) in the current crisis in Ukraine. The above trends and features of the KDD market should be taken into account in further theoretical research and practical implementation or reengineering of KDD systems in Ukraine. The obtained results are relevant and applicable not only for local companies and organizations, but also for international applications in the context of global, regional macroeconomic and current national crisis phenomena.Item Methodology of effective application of economic-mathematical modeling as the key component of the multi-crisis adaptive management (on the example of companies in the logistics industry)(Миколаївський національний аграрний університет, 2021) Krasniuk, Maksym; Краснюк, Максим Тарасович; Kulynych, Yuri; Кулинич, Юрій Михайлович; Tkalenko, Antonina; Ткаленко, Антоніна Миколаївна; Krasniuk, Svitlana; Краснюк, Світлана ОлександрівнаAbstract. Introduction. The modern economy is characterized not only by processes of increasing globalization and competition in markets, mobility and internationalization of resources, rapid development of innovative information technologies, but also by rapid spread of crisis phenomena in the middle and between national economies - all these factors put forward new requirements for effective corporate management [1, 2]. Purpose. Thus, for the sake of stability, not only in the current global market competition, but especially in the context of multi-level crisis phenomena [3], all efficient companies (including logistic) should be able to adapt quickly and efficiently to changes, i.e., they must be systemically adaptive with using economic and mathematical modeling of crisis program, forecasting the results of crisis management. Results. The two main groups of results will provide an adequate, systemic multi-modal response to a variety of crisis events and situations. The process of assembling and configuring the elements of the crisis program (plan) is inherently an open, heuristic, intellectual process of making managerial decisions and scenarios. The intellectual and professional abilities of the manager (specialist, auditor, controller) performing this task play a significant role in the development and implementation of effective crisis management plans and programs. This determines the importance of the personal factor and experience in preparing for decision-making in open problems, determines the requirements for the selection and organization of work in the ad-hoc mode of internal and external experts ( auditors, controllers). However, a study of the management practice of logistics companies in emerging markets (especially in the context of the current global pandemic) showed a significant and negative potential impact of subjective factors on decisions of the management of logistics companies, their auditors and controllers (cheating, corruption, raiding, inattention, industrial espionage, etc.). To limit the influence of the above subjective factors, the authors conducted research in the field of economic and mathematical modeling on two main tasks: development of a methodology and model for the formation of a crisis plan for a logistics company in the context of a possible multi-level crisis and development of methodology and improvement of models for predicting the results of a possible implementation of an crisis three-level adaptive program for a logistics company, taking into account the influence of the current multi-level crisis features and conditions. The results obtained can be used by logistics companies on emerging markets of developing countries and under the influence of similar political, macroeconomic and pandemic crisis phenomena (for example, including many countries in Africa, most countries in Eastern Europe, some countries of Latin America, some countries in the Middle East, and some countries in Southeast Asia). Conclusions. The obtained results are relevant and applicable not only for local logistic companies, but also for international applications in the context of projected glo bal macroeconomic and current national crisis phenomena. Сучасна економіка характеризується не тільки процесами посилення глобалізації та конкуренції на ринках, мобільністю та інтернаціоналізацією ресурсів, швидким розвитком інноваційних інформаційних технологій, а й швидким поширенням кризових явищ у середині та між національними економіками – усі ці фактори впливають на нові вимоги до ефективного корпоративного управління. З’ясовано, що процес складання та налаштування елементів антикризової програми (плану) є відкритим, евристичним, інтелектуальним процесом прийняття управлінських рішень та сценаріїв. Доведено, що інтелектуальні та професійні здібності керівника (спеціаліста, аудитора, контролера) відіграють значну роль у розробці та впровадженні ефективних антикризових планів та програм управління. Це визначає важливість особистісного фактора та досвіду підготовки до прийняття рішень у відкритих проблемах, формує вимоги до відбору та організації роботи в режимі ad-hoc внутрішніх та зовнішніх експертів (аудиторів, контролерів). У процесі дослідження з’ясовано, що практика управління логістичними компаніями на ринках, що розвиваються (особливо в контексті поточної глобальної пандемії) показала значний та негативний потенційний вплив суб’єктивних факторів на рішення керівництва логістичних компаній, їх аудиторів та контролерів (шахрайство, корупція, рейдерство, неуважність, промислове шпигунство тощо). Щоб обмежити вплив вищезазначених суб’єктивних факторів, проведено дослідження в галузі економіко-математичного моделювання щодо двох основних задач: розробка методології та моделі формування антикризового плану логістичної компанії в контексті можливої багаторівневої кризи та методології й вдосконалення моделей прогнозування результатів впровадження антикризової багаторівневої адаптивної програми для логістичної компанії з урахуванням впливу нинішніх багаторівневих кризових особливостей та умов. Отримані результати є актуальними та практичними не лише для місцевих логістичних компаній, а й для міжнародного застосування у контексті прогнозованих глобальних макроекономічних та поточних національних кризових явищ.Item Modern practice of machine learning in the aviation transport industry(European Scientific Platform, 2021-04-30) Krasniuk, Maksym; Краснюк, Максим Тарасович; Krasniuk, SvitlanaItem Problems and prospects of logistic information corporate systems on emerging markets in crisis phenomena(Mezinárodní Ekonomický Institut s.r.o., 2022) Krasniuk, Maksym; Краснюк, Максим Тарасович; Honcharenko, Svitlana; Tuhaienko, V.; Krasniuk, SvitlanaItem Relevanz des wahlfaches" Wissensdetektion in unstrukturierten daten" bei der ausbildung von mastern der technischen und humanitären fachrichtungen(European Scientific Platform, 2021) Krasniuk, Maksym; Краснюк, Максим Тарасович; Motsiuk, Tetiana; Krasniuk, SvitlanaItem Results of analysis of machine learning practice for training effective model of bankruptcy forecasting in emerging markets(European Scientific Platform, 2021-04-09) Krasniuk, Maksym; Краснюк, Максим Тарасович; Tkalenko, Antonina; Krasniuk, Svitlana