Видання НТУ "ХПІ"
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Документ Mental health support application based on aritificial intelligence(Національний технічний університет "Харківський політехнічний інститут", 2024) Yelovets, Oleksandr; Arzubov, Mykola; Chelak, Viktor; Pidbutska, Nina; Panfilov, YuryThe current state of human mental health is considered and its content is defined. It describes a program that you install on your gadget to stabilize your mental health. A review and comparison of existing analogues of the mental state support application was carried out, a description of the system and algorithms used to create the mental state support application: speech recognition, dynamic time transformation, artificial neural networks, hidden Markov models, the final speech recognition algorithm. Analysis of affordability is also important, because it affects the possibility of using applications by a wide range of users. a description of the system and algorithms used to create an application for mental state support is presented. The proposed technologies are intertwined with a deep understanding of the human psyche in the context of creating an application for mental stabilization. For further development, you can improve the quality of the theory presented in the application, the speed and efficiency of artificial intelligence, and also constantly add new functions to the application.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.