Кафедра штучного інтелекту (ШІ)
Постійний URI для цієї колекції
Перегляд
Перегляд Кафедра штучного інтелекту (ШІ) за автором "Filatov, V."
Зараз показано 1 - 5 з 5
Результатів на сторінку
Варіанти сортування
Публікація Good practices of Industry 4.0 in Ukraine(2022) Golovianko, M.; Gryshko, S.; Titova, L.; Filatov, V.There are examples and good practices of using AI and developing Industry 5.0 all over Europe. This desk research is made by the team from Kharkiv National University of Radio Electronics within Erasmus+ JoInME Project “JoInt Multidisciplinary training program on Entrepreneurship in the field of artificial intelligence for industry 5.0” to identify the winning practices from Ukrainian startups or companies working in the Industry 4.0/5.0 and using AI.Публікація On the approach to searching for functional dependences of data in relational systems(ХНУРЕ, 2018) Filatov, V.; Doskalenko, S.The subject matter of the study is information systems built on the basis of relational databases. The goal of the article is to develop a method for re-engineering relational databases that takes into account implicit interrelated functionally dependent data that affect the structure of the logical model. The following results are obtained: the approach to identify previously unknown functional dependencies based on the analysis of a set of relational database data is suggested; the classes of tasks of reengineering relational databases are specified; the stage of developing the target logic diagram which is common for the problems of adaptation and refactoring was studied; the sub-task of checking if the logic diagram of the relational database corresponds to the third normal form within this stage is considered using the synthesis method; it is shown that the solution of this task involves a number of difficulties, in particular, it is necessary to find a set of functional dependencies that are performed on the current instance of the data of a relational database; the approach for finding a set of functional dependencies from an instance of the data of a relational structure is suggested. The direction of further research can be the support of empty values at the stage of identifying functional dependencies as well as the issues of data transfer without any loss from the initial structure of the database to the target data obtained as a result of applying the methods of re-engineering. Conclusions. The approach is suggested to identify previously unknown functional dependencies which are based on the analysis of a set of relational database data. The first step is to get a set of functional dependencies for each relationship. The similar operation for the universal relation of the target database is performed at the second step. At this step, functional dependencies among the attributes of different relationships, that is the interrelationships among the data that were established during the information system operation, can be identified. The method for determining their information novelty is suggested; this method consists in verifying the membership of the functional dependencies of the universal relation while discovering the union of sets of dependencies of individual relations. A promising direction for further research is the development of methods to implement the technology for verifying if the obtained dependencies correspond to the logical model of the domain.Публікація Personalized Adaptation of Learning Environments(2019) Filatov, V.; Yerokhin, A.; Zolotukhin, O.; Kudryavtseva, M.This work is devoted to the development of personalized training systems. A major problem in learning environmens is applying the same approach to all students: teaching materials, time for their mastering, and a training program that is designed in the same way for everyone. Although, each student is individual has his own skills, ability to assimilate the material, his preferences and other. Recently, recommendation systems, of which the system of personalized learning is a part, have become widespread in the learning environment. On the one hand, this shift is due to mathematical approaches, such as machine learning and data mining, that are used in such systems while, on the other hand, the requirements of technological standards "validated" by the World Wide Web Consortium (W3C). According to this symbiosis of mathematical methods and advanced technologies, it is possible to implement a system that has several advantages: identifying current skill levels, building individual learning trajectories, tracking progress, and recommending relevant learning material. The conducted research demonstrates how to make learning environment more adaptive to the users according to their knowledge base, behavior, preferences, and abilities. In this research, a model of a learning ecosystem based on the knowledge and skills annotations is presented. This model is a general model of the all life learning. Second, this thesis focuses on the creation of tools for personalized assessment, recommendation, and advising.Публікація Relational vs non-relational databases(«European Scientific Platform», 2022) Sliusarenko, T.; Filatov, V.; Слюсаренко, Т.; Філатов, В.In this paper the difference between relational and non-relational databases are given.Публікація Synthesis of Semantic Model of Subject Area at Integration of Relational Databases(2019) Filatov, V.; Semenets, V.; Zolotukhin, O.The article considers the problem of synthesizing a semantic model of the subject area at integration of heterogeneous information resources. In this case, emphasis is placed on ensuring the universality of the means of description, without regard to artificial limitations on the data typification and categorization. Two types of logical existence rules are introduced: functional and structural ones, which allow analyzing not only explicitly defined, but also logically deducible information objects, that is, determining the boundary of the subject area. Information about all possible information objects of the subject area makes it possible to determine the area of intersection of the integrable data semantics.