Tag: регресія

Defining the gender of the author of the short text by machine learning methods

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. Defining the gender of the author of the short text by machine learning methods//Science online: International Scientific e-zine - 2019. - №11. - https://nauka-online.com/en/publications/tehnicheskie-nauki/2019/11/opredelenie-pola-avtora-korotkogo-teksta-metodami-mashinnogo-obucheniya/

Annotation: This article discusses the use of machine learning methods for classifying texts by the author's gender in the example of short stories written in Russian. The algorithm and process of data preparation were demonstrated, training and testing of the Bayesian classifier were performed to distinguish the gender of the author of the text. The results of determining the gender of the authors are presented and conclusions are drawn about the advantages and disadvantages of the presented approach.

Ефективність використання нейромережевих моделей для прогнозування руху цін, акцій компаній на ринку

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and . Ефективність використання нейромережевих моделей для прогнозування руху цін, акцій компаній на ринку//Science online: International Scientific e-zine - 2019. - №6. - https://nauka-online.com/en/publications/informatsionnye-tehnologii/2019/6/efektivnist-vikoristannya-nejromerezhevih-modelej-dlya-prognozuvannya-ruhu-tsin-aktsij-kompanij-na-rinku/

Annotation: (Українська) У роботі були розглянуті моделі прогнозування фінансових часових рядів. Проаналізовано методи стандартного інтелектуального аналізу даних та його взаємодія з методами обчислювального інтелекту для вирішення задач прогнозування.

Cryptocurrency forecast based on fundamental analysis

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. Cryptocurrency forecast based on fundamental analysis//Science online: International Scientific e-zine - 2018. - №5. - https://nauka-online.com/en/publications/tehnicheskie-nauki/2018/5/prognozirovanie-kursa-kriptovalyut-na-osnove-fundamentalnogo-analiza/

Annotation: A scheme has been imposed, which allows forecasting cryptocurrency rate with accuracy of 20-50% and predicting trend with accuracy up to 00%. It is focused on fundamental analysis, based on the activity of Twitter users.

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