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  4. 124 — системний аналіз
Ezzel az azonosítóval hivatkozhat erre a dokumentumra forrásmegjelölésben vagy hiperhivatkozás esetén: http://elartu.tntu.edu.ua/handle/lib/48076
Összes dokumentumadat
DC mezőÉrtékNyelv
dc.contributor.advisorГолотенко, Олександр Сергійович-
dc.contributor.advisorHolotenko, Olexandr S.-
dc.contributor.authorJuliet Otojareri, Slyusarenko-
dc.date.accessioned2025-01-30T16:21:53Z-
dc.date.available2025-01-30T16:21:53Z-
dc.date.issued2025-01-30-
dc.date.submitted2025-01-16-
dc.identifier.citationSlyusarenko D. O. Optimization of Mass Service Systems using Ai algorithms : work towards a master's degree : spec. 124 - system analysis / supervisor O. S. Holotenko. Ternopil : Ternopil Ivan Puluj National Technical University, 2025. 51 p.uk_UA
dc.identifier.urihttp://elartu.tntu.edu.ua/handle/lib/48076-
dc.description.abstractThe developed solution expands the capabilities of existing QMS through the use of VA and the development of AI, in particular GPT-3.5, to ensure more effective and informative communication with users. Thus, an effective tool has been developed to automate and improve service processes, which provides: improved interaction with by users, the use of AI to improve responses, efficient data storage and analysis, the possibility of automation thanks to GAS. In the future, it is planned to expand the language model, improve the user interface, add an automatic speech recognition module to support many languages and additional query analysis capabilities, develop algorithms that learn from user responses to provide personalized answers and improve the interaction experience, research and optimize data processing algorithms for faster and more efficient system operation with a large flow of requests. These scientific developments can improve the efficiency, accuracy and user experience of the MSS using VA.uk_UA
dc.description.tableofcontentsINTRODUCTION 6 1. ANALYSIS OF THE SUBJECT AREA 8 1.1. Historical Context for the Evolution of Mass Service Systems and AI's Role in Transformation 8 1.2. Significance in healthcare, transportation, and customer service. 10 1.3. Why optimization of mass service systems using ai algorithms is important 11 1.4. Use of artificial intelligence in mass service systems 12 2. METHODOLOGY OF MACHINE LEARNING AND DATA PROCESSING ALGORITHMS 14 2.1. General Approach to AI-Based Optimization 14 2.2 Data Collection for Optimization 14 2.3 Design Considerations for the AI System 15 2.4. Ethical and Safety Considerations in AI-Optimized Systems 25 3. DEVELOPMENT OF PROGRAM STRUCTURE AND ALGORITHMS 29 3.1. Analysis of modern scientific achievements reading 29 3.2. Development of an optimization method QMO operations using AI-based VA. 30 4 SAFETY OF LIFE, BASIC LABOR PROTECTION 40 4.1. Effects of electromagnetic radiation on the human body 40 4.2 Types of hazards 43 4.3 Road Transport Safety 46 4.4 Conclusions 46 CONCLUSIONS 48 REFERNCES 49uk_UA
dc.language.isoukuk_UA
dc.publisherТернопільський національний технічний університет імені Івана Пулюяuk_UA
dc.subjectsystem analysisuk_UA
dc.subjectvirtual assistantuk_UA
dc.subjectmass service systemuk_UA
dc.subjectartificial assistantuk_UA
dc.subjecttelegram botuk_UA
dc.titleOptimization of Mass Service Systems using Ai algorithmsuk_UA
dc.typeMaster Thesisuk_UA
dc.rights.holder© Juliet Otojareri Slyusarenko, 2025uk_UA
dc.coverage.placenameТНТУ ім. І.Пулюя, ФІС, м. Тернопіль, Українаuk_UA
dc.subject.udc004.04uk_UA
thesis.degree.discipline51-
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dc.relation.references13. Y. Leshchyshyn, L. Scherbak, O. Nazarevych, V. Gotovych, P. Tymkiv and G. Shymchuk, «Multicomponent Model of the Heart Rate Variability Change-point,» 2019 IEEE XVth International Conference on the Perspective Technologies and Methods in MEMS Design (MEMSTECH), Polyana, Ukraine, 2019, pp. 110-113, doi: 10.1109/MEMSTECH.2019.8817379uk_UA
dc.relation.references14. Lytvynenko, S. Lupenko, O. Nazarevych, G. Shymchuk and V. Hotovych, «Mathematical model of gas consumption process in the form of cyclic random process,» 2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT), LVIV, Ukraine, 2021, pp. 232-235, doi: 10.1109/CSIT52700.2021.9648621uk_UA
dc.relation.references15. V. Kozlovskyi, Y. Balanyuk, H. Martyniuk, O. Nazarevych, L. Scherbak and G. Shymchuk, «Information Technology for Estimating City Gas Consumption During the Year,» 2022 International Conference on Smart Information Systems and Technologies (SIST), Nur-Sultan, Kazakhstan, 2022, pp. 1-4, doi: 10.1109/SIST54437.2022.9945786uk_UA
dc.relation.references16. I. Lytvynenko, S. Lupenko, N. Kunanets, O. Nazarevych, G. Shymchuk and V. Hotovych, "Simulation of gas consumption process based on the mathematical model in the form of cyclic random process considering the scale factors", 1st International Workshop on Information Technologies: Theoretical and Applied Problems ITTAP 2021, 16–18 November 2021uk_UA
dc.relation.references17. Approach to gas consumption process forecasting on the basis of a mathematical model in the form of a random cyclic process / Serhii Lupenko, Iaroslav Lytvynenko, Oleg Nazarevych, Grigorii Shymchuk, Volodymyr Hotovych // ICAAEIT 2021, 15-17 December 2021. – Tern. : TNTU, Zhytomyr «Publishing house „Book-Druk“» LLC, 2021. – P. 213–219. – (Mathematical modeling in power engineering and information technologies)uk_UA
dc.relation.references18. Конспект лекцій з дисципліни «Грід-системи та технології хмарних обчислень» для студентів освітніх рівнів «бакалавр», «магістр» / Укладачі : Шимчук Г.В., Маєвський О.В., Назаревич О.Б., Стадник М.А. – Тернопіль : Тернопільський національний технічний університет імені Івана Пулюя, 2016 – 340 сuk_UA
dc.contributor.affiliationТернопільський національний технічний університет імені Івана Пулюя, факультет комп’ютерно-інформаційних систем і програмної інженерії, кафедра комп’ютерних наук, м. Тернопіль, Українаuk_UA
dc.coverage.countryUAuk_UA
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