Бионика интеллекта
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Перегляд Бионика интеллекта за автором "Afanasieva, I. V."
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Публікація A neural network approach for the auto matic selection of a complex of rehabilitation exercises(ХНУРЭ, 2021) Butsenko, M. O.; Afanasieva, I. V.; Golian, N. V.; Kameniuk, N.This article is devoted to solving the problem of automatic selection of a set of rehabilitation exercises during injuries, considering the state of the human cardiovascular system through the use of neural networks. To solve this problem, it was necessary to choose one of two classical approaches – multiclass classification or multilabel classification, each of which solves the problem of data classification through its own algorithm, and use the selected neural network architecture to create a software system. While working on this system, it was also necessary to solve certain problems related to each of these approaches (the need for a large sample due to the large number of exercises that the system should recommend) or a specific approach (inability to select multiple exercises at once – for Multiclass Classification, lower productivity and the number of supported programming languages – for Multilabel Classification). Samples of different sizes (from 1 million records and more) were used to train the neural network, which were generated through a self-written program that generated a given number of records and wrote them to a .CSV (commaseparated values) file.Публікація Neural network approach for emotional recognition in text(ХНУРЕ, 2019) Nazarenko, D. S.; Afanasieva, I. V.; Golian, N. V.The article is devoted to one of the most popular trends in the field of IT today – natural language processing, in particular, the extraction of emotions from the text using the neural network approach. The main task was to solve the problem of the high costs of time and human resources for companies to receive feedback from users and process emotional reactions of the second one. That to decide the task it was necessary to make modelling and learn neural network using own architecture based on the backpropagation algorithm that to recognize the emotional component in the text.The emotional component of reviews was used as a metric for evaluating user reactions. It was decided to work with five types of emotions that will help to provide better results. The neural network architecture consists of interconnected layers: embedding, bidirectional LSTM, pooling, dropout layers and two dense layers. For the neural network learning was selected an open dataset consisted of 47,288-tagged posts from Twitter. As a result, the F-measure on the test dataset was 0.62 and which is a worthy indicator in comparison with large business solutionsюПублікація Principles of searching and sorting optimization in social networks using a multi-factor assessment system(ХНУРЕ, 2019) Shopynskyi, M. V.; Golian, N. V.; Afanasieva, I. V.The analysis of social networks, which focuses on the relationship between social entities today is an area of active research. It is a set of tools for research, in particular, in combination with artificial intelligence methods such as machine learning, deep learning. The paper examined the current quality of the assessment of information in social networks, analyzed the methods of searching and sorting information in various social networks, as well as the process of providing recommendations to users. Social media data is an inexhaustible source of research and business opportunities. In general, social media data is information gathered from social networks that shows how users interact with content. Methods of improving search results for personalizing recommendations in social networks are given. These indicators and statistics provide an effective understanding of the strategy of behavior in social networks. The advantages and disadvantages of a multifactor assessment system are considered. The possible ways of integrating the combined system of evaluating information elements by the user to optimize search queries and filtering big data are identified.