2023 № 3 Сучасні інформаційні системи

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Probabilistic counterfactual causal model for a single input variable in explainability task
(Національний технічний університет "Харківський політехнічний інститут", 2023) Chalyi, Serhii; Leshchynskyi, Volodymyr
The subject of research in this article is the process of constructing explanations in intelligent systems represented as black boxes. The aim is to develop a counterfactual causal model between the values of an input variable and the output of an artificial intelligence system, considering possible alternatives for different input variable values, as well as the probabilities of these alternatives. The goal is to explain the actual outcome of the system's operation to the user, along with potential changes in this outcome according to the user's requirements based on changes in the input variable value. The intelligent system is considered as a "black box." Therefore, this causal relationship is formed using possibility theory, which allows accounting for the uncertainty arising due to the incompleteness of information about changes in the states of the intelligent system in the decision-making process. The tasks involve: structuring the properties of a counterfactual explanation in the form of a causal dependency; formulating the task of building a potential counterfactual causal model for explanation; developing a possible counterfactual causal model. The employed approaches include: the set-theoretic approach, used to describe the components of the explanation construction process in intelligent systems; the logical approach, providing the representation of causal dependencies between input data and the system's decision. The following results were obtained. The structuring of counterfactual causal dependency was executed. A comprehensive task of constructing a counterfactual causal dependency was formulated as a set of subtasks aimed at establishing connections between causes and consequences based on minimizing discrepancies in input data values and deviations in the decisions of the intelligent system under conditions of incomplete information regarding the functioning process of the system. A potential counterfactual causal model for a single input variable was developed. Conclusions. The scientific novelty of the obtained results lies in the proposal of a potential counterfactual causal model for a single input variable. This model defines a set of alternative connections between the values of the input variable and the obtained result based on estimates of the possibility and necessity of using these variables to obtain a decision from the intelligent system. The model enables the formation of a set of dependencies that explain to the user the importance of input data values for achieving an acceptable decision for the user.
Оцінка стану різних сільськогосподарських культур з використанням фрактального аналізу
(Національний технічний університет "Харківський політехнічний інститут", 2023) Пащенко, Руслан Едуардович; Марюшко, Максим В'ячеславович
Відсутність загальних підходів до оцінки стану сільськогосподарських культур за даними ДЗЗ показує, що задача оцінки змін їх стану є до кінця не вирішеною. Предметом дослідження є оцінка стану сільськогосподарських культур різних типів з використанням фрактального аналізу. Об'єктом дослідження є космічні знімки супутника Sentinel-2 сільськогосподарських культур різних типів. Метою є розгляд можливості використання фрактального аналізу космічних знімків сільськогосподарських культур різних типів для визначення змін їх стану. Отримані наступні результати. Проведена оцінки стану різних сільськогосподарських культур (кукурудзи, соняшника, пшениці, ячменя і гречки) на протязі всього періоду вегетації з використанням фрактального аналізу їх космічних знімків. Основою фрактального аналізу космічних знімків є побудова поля фрактальних розмірностей. Показано, що нормальний стан сільськогосподарських культур характеризується збільшенням середніх і мінімальних фрактальних розмірностей (ФР) на початкових фазах вегетації, досягненням найбільших значень ФР на середніх фазах вегетації і знову зменшенням ФР на пізніх фазах вегетації. Визначено, що за величиною середніх ФР можна розподілити полія, засіяні гречкою і кукурудзою та поля, засіяні соняшником, пшеницею і ячменем. Між собою поля, засіяні гречкою і кукурудзою можна розділити за тривалістю найбільших значень ФР, а поля, засіяні соняшником, пшеницею і ячменем між собою за величиною середніх ФР і тривалістю їх найбільших значень розділити практично не можливо. Висновки. Проведені дослідження показали, що фрактальний аналіз космічних знімків дозволяє проводити моніторинг стану сільськогосподарських культур різних типів.
Comparative analysis of the efficiency of various energy storages
(Національний технічний університет "Харківський політехнічний інститут", 2023) Hasanov, Arif; Hashimov, Elshan; Zulfugarov, Bakir
Research relevance This article presents a mathematical solution to the issue of a comparative analysis of various types of energy storage devices and determining the most efficient type of energy storage device for use on an industrial scale. The subject of the study in the article is the most important parameters of seven types of energy storages, the use of which is spreading in the world. The purpose of the work is to obtain an answer to the following question: which of the ubiquitous different types of energy storages is most likely to be the most efficient for the future industrial energy supply? The following tasks are solved in the article: 1) generalization of the collected data; 2) analysis (evaluation) of data using mathematical methods of data analysis. The following research methods are used: comparison, abstraction, axiomatic, analysis, synthesis, formalization and induction. The following results were obtained: among the analyzed energy storages, the best result was shown by a mechanical potential (gravitational) energy storage. Conclusions: If it is planned to use energy storages on an industrial scale in various fields, it should be recognized as expedient to give preference to gravitational devices.
Adaptive resource allocation method for data processing and security in cloud environment
(Національний технічний університет "Харківський політехнічний інститут", 2023) Petrovska, Inna; Kuchuk, Heorhii
Subject of research: methods of resource allocation of the cloud environment. The purpose of the research: to develop a method of resource allocation that will improve the security of the cloud environment. At the same time, effective data processing should be achieved. Method characteristics. The article discusses the method of adaptive resource allocation in cloud environments, focusing on its significance for data processing and enhanced security. A notable feature of the method is the consideration of external influences when calculating the characteristics of cloud resource requests and predicting resource requests based on a time series test. The main idea of this approach lies in the ability to intelligently distribute resources while considering real needs, which has the potential to optimize both productivity and confidentiality protection simultaneously. Integrating adaptive resource allocation methods not only improves data processing efficiency in cloud environments but also strengthens mechanisms against potential cyber threats. Research results. To ensure timely resource allocation, the NSGA-II algorithm has been enhanced. This allowed reducing the resolution time of multi-objective optimization tasks by 5%. Additionally, research results demonstrate that effective utilization of various types of resources on a physical machine reduces resource losses by 1.2 times compared to SPEA2 and NSGA-II methods.
Research application of the spam filtering and spammer detection algorithms on social media and messengers
(Національний технічний університет "Харківський політехнічний інститут", 2023) Podorozhniak, Andrii; Liubchenko, Nataliia; Oliinyk, Vasyl; Roh, Viktoriia
In the current era, numerous social networks and messaging platforms have become integral parts of our lives, particularly in relation to work activities, due to the prevailing COVID-19 pandemic and russian war in Ukraine. Amidst this backdrop, the issue of spam and spammers has become more pertinent than ever, with a continuous rise in the incidence of spam within work-related text streams. Spam refers to textual content that is extraneous to a specific text stream, while a spammer denotes an individual who disseminates unsolicited messages for personal gain. The proposed article is devoted to address this scientific and practical challenge of identifying spammers and detecting spam messages within the textual context of any social network or messenger. This endeavor encompasses the utilization of diverse spam detection algorithms and approaches for spammer identification. Four algorithms were implemented, namely a naive Bayesian classifier, Support-vector machine, multilayer perceptron neural network, and convolutional neural network. The research objective was to develop a spam detection algorithm that can be seamlessly integrated into a messenger platform, exemplified by the utilization of Telegram as a case study. The designed algorithm discerns spam based on the contextual characteristics of a specific text stream, subsequently removing the spam message and blocking the spammeruser until authorized by one of the application administrators.
Efficiency of supplementary outputs in siamese neural networks
(Національний технічний університет "Харківський політехнічний інститут", 2023) Melnychenko, Artem; Zdor, Kostyantyn
In the world of image analysis, effectively handling large image datasets is a complex challenge that requires using deep neural networks. Siamese neural networks, known for their twin-like structure, offer an effective solution to image comparison tasks, especially when data volume is limited. This research explores the possibility of enhancing these models by adding supplementary outputs that improve classification and help find specific data features. The article shows the results of two experiments using the Fashion MNIST and PlantVillage datasets, incorporating additional classification, regression, and combined output strategies with various weight loss configurations. The results from the experiments show that for simpler datasets, the introduction of supplementary outputs leads to a decrease in model accuracy. Conversely, for more complex datasets, optimal accuracy was achieved through the simultaneous integration of regression and classification supplementary outputs. It should be noted that the observed increase in accuracy is relatively marginal and does not guarantee a substantial impact on the overall accuracy of the model.
Method of assessing the state of hierarchical objects based on bio-inspired algorithms
(Національний технічний університет "Харківський політехнічний інститут", 2023) Shyshatskyi, Andrii; Stasiuk, Tetiana; Odarushchenko, Elena; Berezanska, Karina; Demianenko, Hanna
Relevance. Nowadays, no state in the world is able to work on the creation and implementation of artificial intelligence in isolation from others. Artificial intelligence technologies are actively used to solve both general and highly specialized tasks in various spheres of society. In the process of assessing (identifying) the state of complex and objects of analysis and management, there is a high degree of a priori uncertainty regarding their state and a small amount of initial data describing them. At the same time, despite the huge amount of information, the degree of non-linearity, illogicality and noisy data is increasing. That is why the issue of improving the efficiency of assessing the condition of complex and objects is an important and urgent issue. The object of research is the objects of analysis. The subject of the research is the identification and forecasting of the analysis objects state with the help of bio-inspired algorithms. In the research, the evaluation and forecasting method was developed using fuzzy cognitive maps and the genetic algorithm. The novelty of the proposed method consists in: taking into account the degree of uncertainty about the object state while calculating the correction factor; adding a correction factor for data noise as a result of distortion of information about the object state; reduction of computing costs while assessing the objects state; creation of a multi-level and interconnected description of hierarchical objects; adjusting the description of the object as a result of changing its current state using a genetic algorithm; the possibility of performing calculations with the original data, which are different in nature and units of measurement. It is advisable to implement the mentioned method in specialized software, which is used to analyze the state of complex technical systems and make decisions.
Diagnosis of systems under conditions of small initial data sampling
(Національний технічний університет "Харківський політехнічний інститут", 2023) Raskin, Lev; Karpenko, Viacheslav; Ivanchykhin, Yuriy; Sokolov, Dmitro
Object of the study is to assess systems state in conditions of a small sample of initial data. Relevance of the problem is as follows. The functioning of a significant number of real objects takes place under conditions of poorly predicted changes in the values of environmental factors affecting system efficiency. The resulting heterogeneity of the results of objects experimental study and the environment of their functioning leads to reduction in sample size. At the same time, the standard requirements regarding the correspondence of the number of experiments and the number of coefficients of regression equation determining system state are not met. Purpose of the study is to develop methods for assessing systems state operating in a changing environment, in conditions of small sample of initial data. Tasks to be solved to achieve the goal: the first is the equivalent transformation of the set of observed initial data forming a passive experiment in aggregate into an active experiment, which corresponds to an orthogonal plan; the second is the construction of a truncated orthogonal representative sub-plan of the general orthogonal plan obtained as a result of solving the first problem. Research methods: statistical methods of experimental data processing, regression analysis, method for solving a triaxial boolean assignment problem. The results obtained: orthogonal representative subplan of the complete factorial experiment being formed makes it possible to calculate a truncated regression equation containing all the influencing factors and their interactions. Analysis of the coefficients of this equation by known methods makes it possible to cut off its insignificant elements.
EXCEL-орієнтовані процедури визначення ентропії функції розподілу та її відносної параметричної чутливості (еластичності) в умовах двосторонніх обмежень на область значень неперервної випадкової величини
(Національний технічний університет "Харківський політехнічний інститут", 2023) Гадецька, Світлана Вікторівна; Дубницький, Валерій Юрійович; Кушнерук, Юрій Іонович; Ходирєв, Олександр Іванович
Мета роботи. Розробка EXCEL-орієнтованого калькулятора для обчислення ентропії та її еластичності для функцій розподілу за умови обмеженої області визначення неперервної випадкової величини. Предмет дослідження. Функції щільності ймовірності та їх ентропії за умови двосторонніх обмежень на область визначення можливих значень випадкових величин. Методи дослідження. Алгоритмічний та чисельний аналіз процедур отримання чисельних значень ентропії функцій щільності неперервних випадкових величин за умови двобічних обмежень на область її визначення. Отримані результати. В роботі запропоновано EXCEL-орієнтований калькулятор для обчислення ентропії та її еластичності для функцій розподілу за умови обмеженої області визначення неперервної випадкової величини. Всі використані в роботі функції розподілу розподілені на три категорії залежно від того, в який формі подана ентропія та її еластичність. До першої категорії включено функції розподілу, для яких ентропія та її еластичність можуть бути визначені в аналітичній формі. До другої категорії включено функції розподілу, для яких ентропія може бути визначена в аналітичній формі, а її еластичність – в табличній. До третьої категорії включено функції розподілу, для яких ентропія та її еластичність можуть бути визначені в табличній формі.
Acceleration of boolean gene regulatory networks analysis using FPGA
(Національний технічний університет "Харківський політехнічний інститут", 2023) Vasylchenkov, Oleg; Salnikov, Dmytro; Karaman, Dmytro
Gene expression does not occur arbitrarily and spontaneously, it obeys certain patterns that can be expressed as a connected graph or network. The disclosure of these patterns requires a large amount of experimental research and accumulation of necessary statistical information. Then this information is subjected to mathematical processing, which involves significant computing resources and takes a lot of time. Boolean networks are often used as the basis for building mathematical models in those calculations. Recently, models based on Boolean networks have increasingly grown in size and complexity causing increased demands on traditional software solutions and computing tools. Field-programmable gate arrays (FPGAs) are a powerful and reconfigurable platform for implementing efficient and high-performance computing. The use of FPGA will significantly speed up the process of calculating sequential chains of gene states, both through the use of hardware acceleration in the calculation of logical dependencies, and through the implementation of an array of parallel computing cores, each of which can perform its own individual task. Another solution that can significantly simplify the work of researchers of gene regulation networks is the creation of a universal computing architecture that will allow dynamic reconfiguration of its internal structure when the task or logical dependencies for the current Boolean network change. Such a solution will relieve the researcher of the need to perform the entire set of actions for the technological preparation of a new FPGA configuration, from making changes to the HDL code that describes the network to uploading the updated configuration to the hardware accelerator. The article discusses how to use FPGA for the implementation and modeling of arbitrary Boolean networks, describes the concept of a universal reconfigurable architecture of a logical dependency calculating core for an arbitrary Boolean network and proposes a practical implementation of such a calculating core for modeling gene regulation networks.