Registration Number
0225U003907,
(0120U102913)
, R & D reports
Title
Systems of express expertise and decision support based on methods and means of artificial intelligence in the digital economy of Ukraine
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Узагальнення результатів проведеного дослідження, участь у підготовці навчального посібника та заключного звіту
Head
Lahovskyi Volodymyr V., Кандидат економічних наук
Registration Date
09-09-2025
Organization
State Tax University
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Develop methodological and practical approaches for identifying and assessing the quality of food and light industry products. The systems for fast expertise and decision support that based on these approaches, are proposed. Develop expert systems to help older people interact with information technologies of financial institutions of Ukraine.
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Keywords: artificial intelligence, decision support systems, express analysis, product quality, digital economy, machine learning, computer vision, neural networks.
The object of the research is the processes of identification and quality assessment of food and light industry products, as well as express-expertise systems based on artificial intelligence in the context of the digital economy of Ukraine.
The objective of the work is to develop methodological and practical approaches for the identification and quality assessment of products in these industries and, based on them, to create systems for express-expertise.
The research methods included system and comparative analysis, machine learning methods, computer vision, signal processing, deep learning, as well as methods of software systems design and statistical data processing.
During the research, the current state of the product quality control problem was determined, along with the specific requirements in Ukraine and worldwide, and the challenges and opportunities of the digital economy for modernizing these systems. The international experience in applying AI was investigated; methods of machine and deep learning, image and signal processing, as well as the principles of building expert systems and decision support systems were analyzed. Software tools such as Python, TensorFlow, and PyTorch were also analyzed.
Methodological approaches to AI-based product quality assessment, a methodology for data collection and preparation, models for express identification, the architecture of an integrated system, and prototypes of its key modules were developed.
The feasibility of using AI to create express-expertise systems was substantiated. The choice of specific machine learning methods and architectural solutions was also justified, and the effectiveness of the proposed approaches was proven.
Product Description
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Hladchenko Oksana V.
Horbovyi Artur Yu.
Hryshchenko Svitlana M.
Kovda Maksym V.
Korotun Artem R.
Lahovskyi Volodymyr V.
Lahovskyi Oles V.
Lutsenko Nadiia O.
Nizhehorodtsev Vladyslav O.
Omelchuk Anton A.
Pohorielovska Iryna D.
Ratushniak Tetiana V.
Redych Oleksandr V.
Filatov Kostiantyn Yu.
Filonenko Mykhailo M.
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2025-09-09
