Recurrent Neural Network Model Architecture for Detecting a Tendency to Atypical Behavior Of Individuals by Text Posts

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dc.contributor.authorSobko, О.
dc.contributor.authorMazurets, О.
dc.contributor.authorDidur, V.
dc.contributor.authorChervonchuk, I.
dc.contributor.authorМазурець, Олександр Вікторович
dc.date.accessioned2024-12-10T18:18:18Z
dc.date.available2024-12-10T18:18:18Z
dc.date.issued2024
dc.description.abstractThe architectures for two recurrent neural network models that are the input data of the method for detecting a tendency to atypical behavior in a text message were presented. The architecture of the RNN model for detecting a tendency to atypical behavior has 2 outputs responsible for the presence of a tendency to atypical behavior or the absence of a tendency to atypical behavior, and the architecture of the RNN model for determining the type of mental disorder provides 10 outputs, each of which corresponds to the type of mental disorder that may affect the tendency to atypical behavior.
dc.identifier.citationSobko О., Mazurets О., Didur V., Chervonchuk I. Recurrent Neural Network Model Architecture for Detecting a Tendency to Atypical Behavior Of Individuals by Text Posts. Theoretical and Practical Aspects of Modern Research. Proceedings of XXVI International scientific and practical conference. June 5-7, 2024. International Scientific Unity. Ottawa, Canada. 2024. Pp. 113-117.
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/17212
dc.language.isoen
dc.titleRecurrent Neural Network Model Architecture for Detecting a Tendency to Atypical Behavior Of Individuals by Text Posts
dc.typeСтаття
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