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Документ Machine learning(National technical university "Kharkiv polytechnic institute", 2024) Gavrylenko, SvitlanaThe workshops guide contains the necessary material for performing workshops: options for tasks, examples of program texts and report. Intended for students of computer majors at higher educational institutions.Документ Methodical guidelines for the implementation of the course project from the course "Compiler Design Theory"(Національний технічний університет "Харківський політехнічний інститут", 2024) Gavrylenko, Svitlana; Chelak, ViktorResearchers studying the appearance of intelligence on our planet believe that the appearance of language played a decisive role in its development, which allowed not only to express and store knowledge, but also to exchange it. With the creation of computers, there was a need to communicate with such devices, as it turned out to be necessary to give them orders, tasks, and a description of the work that they should perform. For this purpose, special languages began to be developed, which came to be called artificial, in contrast to the natural languages of human communication. Artificial languages should be, on the one hand, convenient and understandable for humans, and on the other hand, they should be perceived by devices. The combination of these requirements in one language turned out to be a difficult task, so there were tools for converting texts from a language understandable to a person to the language of a device. Such tools are called translators. The translator can be of the interpreting or compiling type. In the first case, it is called an interpreter of the input language, and in the second - a compiler. The interpreter sequentially reads the input language propositions, analyzes them and immediately executes them, while the compiler does not execute the language propositions, but builds a program that can later be run to obtain the result. The input of the compiler is a text written in an input language understood by a person, and the result of the compiler's work is a text in a language understood by the device. These methodological instructions consider the construction of a syntactic LR parser, which is one of the stages of the compiler. It is at the stage of syntactic analysis that the largest number of errors in the text of the program is revealed.Документ Compiler design theory(Національний технічний університет "Харківський політехнічний інститут", 2024) Gavrylenko, Svitlana; Petrovska, Inna; Hornostal, OleksiiThe workshops guide contains the necessary material for performing workshops: options for tasks, examples of program texts and report. Intended for students of computer majors at higher educational institutions.Документ Development of method for identification the computer system state based on the decision tree with multidimensional nodes(Запорізький національний технічний університет, 2022) Gavrylenko, Svitlana; Chelak, V. V.; Semenov, S. G.Context. The problem of identifying the state of a computer system is considered. The object of the research is the process of computer system state identification. The subject of the research is the methods of constructing solutions for computer system state identification. Objective. The purpose of the work is to develop a method for decision trees learning for computer system state identification. Method. A new method for constructing a decision tree is proposed, combining the classical model for constructing a decision tree and the density-based spatial clustering method (DBSCAN). The simulation results showed that the proposed method makes it possible to reduce the number of branches in the decision tree, which will increase the efficiency of identifying the state of the computer system. Belonging to hyperspheres is used as a criterion for decision-making, which enables to increase the identification accuracy due to the nonlinearity of the partition plane and to perform a more optimal adjustment of the classifier. The method is especially effective in the presence of initial data with high correlation coefficients, since it combines them into one or more multivariate criteria. An assessment of the accuracy and efficiency of the developed method for identifying the state of a computer system is carried out. Results. The developed method is implemented in software and researched in solving the problem of identifying the state of the functioning of a computer system. Conclusions. The carried out experiments have confirmed the efficiency of the proposed method, which makes it possible to recommend it for practical use in order to improve the accuracy of identifying the state of a computer system. Prospects for further research may consist in the development of an ensemble of decision trees.Документ Method of Identifying the State of Computer System under the Condition of Fuzzy Source Data(2020) Gavrylenko, Svitlana; Chelak, Viktor; Kazarinov, MichaelThe purpose of this work is developing a method for identifying the abnormal state of a computer system based on the Bayes' Fuzzy classifier. It allowed us to create a Fuzzy expert identification system with an unlimited number of controlled indicators that belong to a finite interval. Estimation of informativeness of such indicators does not depend on the type of indicator’s functions and on the rule of their usage in the calculated formula. Introduced criterion allowed to estimate indices of the functioning of computer systems presented indistinctly. The quality of classification was evaluated based on ROC analysis. It was found that the method based on Bayes' Fuzzy expert system is qualitative, and its classification speed is almost independent of quantity indicators. Comparative evaluation of Bayes' Fuzzy classifier with Fuzzy clustering classifier and Fuzzy discriminant classifier are performed. In order to regulate the level of false-positive and false-negative classification, recommendations have been developed to manage the level of sensitivity and specificity of a Fuzzy expert system based on the Bayes classifier.Документ Development of Computer State Identification Method Based on Boosting Ensemble(2021) Chelak, Viktor; Gavrylenko, SvitlanaThis work is about developing a modification of boosting method by using a special preprocessing procedure to improve the accuracy of computer system state identification. The aim of the research is to develop method for detection computer threats, malware, etc. Experimental research have confirmed the effectiveness of the proposed method, which makes it possible to recommend it for practical use in order to improve the accuracy of identifying the state of the computer system. Prospects for further research may be to develop an ensemble of fuzzy decision trees based on the proposed method, optimizing their software implementation.Документ Development and comparative analysis of computer system state identification methods based on ensemble algorithms(Інжиніринг, 2020) Gavrylenko, Svitlana; Sheverdin, IlliaThe scientific novelty of the results obtained consists in creating ensemble methods for classifying the state of a computer system without a teacher and with a teacher. The method based on the "Isolation Forest" algorithm can be used as an express method for analyzing a computer system state. This will allow not only to identify the state of a computer system state, but also to highlight the name of the abnormal processes. This method can also be used to generate labeled data and use it as the source data of the ensemble algorithm with a teacher. The algorithm with a teacher built according to the C4.5 algorithm is more accurate and can be used to refine the result of identifying a computer system state using the method based on the "Isolation Forest" algorithm.Документ Method of computer system state identification based on boosting ensemble with special preprocessing procedure(Національний технічний університет "Харківський політехнічний інститут", 2022) Chelak, Viktor; Gavrylenko, SvitlanaThe subject of the research is methods of identifying the state of the Computer System. The object of research is the process of identifying the state of a computer system for information protection. The aim of the research is to develop the method for identifying the state of a computer system for information protection. This article is devoted to the development of method (boosting ensemble) to increase the accuracy of detecting anomalies in computer systems. Methods used: artificial intelligence methods, machine learning, decision tree methods, ensemble methods. The results were obtained: a method of computer system identification based on boosting ensemble with special preprocessing procedure is developed. The effectiveness of using machine learning technology to identify the state of a computer system has been studied. Experimental researches have confirmed the effectiveness of the proposed method, which makes it possible to recommend it for practical use in order to improve the accuracy of identifying the state of the computer system. Conclusions. According to the results of the research, ensemble classifier of computer system state identification based on boosting was proposed. It was found that the use of the proposed classifier makes it possible to reduce the variance to 10%. In addition, due to the optimization of the initial data, the efficiency of identifying the state of the computer was increased. Prospects for further research may be to develop an ensemble of fuzzy decision trees based on the proposed method, optimizing their software implementation.Документ Development of a method for identifying the state of a computer systemusing fuzzy cluster analysis(Національний технічний університет "Харківський політехнічний інститут", 2020) Gavrylenko, Svitlana; Chelak, Viktor; Hornostal, Oleksii; Vassilev, VelizarThe subject of this article is the study of methods for identifying the state of computer systems. The purpose of the article is to develop a method for identifying the abnormal state of a computer system based on fuzzy cluster analysis. Objective: to analyze methods for identifying the state of computer systems; to conduct research on the selection of source data; to develop a method for identifying the state of a computer system with a small sample or fuzzy source data; to investigate and justify the procedure for comparing fuzzy distances between grouping centers and clustering objects; to develop a software and test. The methodsused in the paper: cluster analysis, fuzzy logic tools. The following resultswere obtained: a method was theoretically substantiated and investigated for identifying the state of a computer system with a small sample or fuzziness of the initial data, which is distinguished by the use of the method based on fuzzy cluster analysis by the refined grouping procedure. To solve the clustering problem, we used a special procedure for comparing fuzzy distances between grouping centers and clustering objects. Software was developed and testing of the developed method was performed. The quality of classification based on the ROC analysis is assessed. Conclusions. The scientific novelty of the results is as follows: a study was conducted on the selection of source data for analysis; a method for identifying the state of a computer system based on fuzzy cluster analysis using a special procedure for comparing fuzzy distances between grouping centers and clustering objects has been developed. This allowed to improve the classification quality to 22 %.Документ The ensemble method development of classification of the computer system state based on decisions trees(Національний технічний університет "Харківський політехнічний інститут", 2020) Gavrylenko, Svitlana; Sheverdin, Illia; Kazarinov, MichaelThe subject of this article is exploration of methods for identifying the status of a computer system.The purpose of the article is development of a method for classifying a computer system anomalous state based on ensemble methods. Task: To investigate the usage of algorithms for building decision trees: REPTree, Random Tree, J48, HoeffdingTree, DecisionStump and bagging and boosting decision tree ensembles to identify a computer system anomalous state by analyzing operating system events. The methods used are artificial intelligence, machine learning and ensemble classification methods. The following results were obtained: the methods of identifying the computer systems anomalous state based on ensemble methods were investigated, namely, bagging, boosting, and classifiers: REPTree, Random Tree, J48, HoeffdingTree, DecisionStump to identify a computer system anomalous state. The different classifiers set and classifiers ensembles were developed. Training and cross-validation on each algorithm was performed. The developed classifiers performance has been evaluated. The research suggests an ensemble method ofa computer system state classifying based on the J48 decision tree algorithm. Conclusions.The scientific novelty of the obtained results consists in creating an ensemble method for classifying the state of a computer system based on a decision tree, which makes it possible to increase the reliability and speed of classification.