title: Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ covid-19 Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Implementation of the forecasting model covid-19 based on neural networks
Creators: name: Ð¨ÐµÐ²ÑÐ¾Ð², ÐÑÑÑÐ¼
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
COVID-19
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐµÐºÑÑÑÐµÐ½ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
LSTM
Python
Keras
forecasting
neural network
recurrent neural network

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ covid-19 Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹Â». Ð¦ÐµÐ»ÑÑ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð½Ð¾Ð²Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ COVID-19 Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. Ð ÑÐ²ÑÐ·Ð¸ Ñ ÑÑÐ¸Ð¼ Ð±ÑÐ» ÑÐµÑÐµÐ½ ÑÑÐ´ Ð·Ð°Ð´Ð°Ñ: ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ COVID-19 Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹; ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ ÑÐ¿Ð¾ÑÐ¾Ð±Ñ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»ÐµÐ¹ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¿ÑÐµÐ´Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð´Ð°Ð½Ð½ÑÑ; Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð° Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð° ÑÐ²Ð¾Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ COVID-19 Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ Ð²ÑÑÐ¾ÐºÐ¾ÑÑÐ¾Ð²Ð½ÐµÐ²Ð¾Ð³Ð¾ ÑÐ·ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python Ð¸ ÑÑÐµÐ¹Ð¼Ð²Ð¾ÑÐºÐ° Tensorflow Ð±ÑÐ»Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ñ Ð¸ Ð¾Ð±ÑÑÐµÐ½Ñ Ð´Ð²Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸ Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÐ¼Ð¸ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ°Ð¼Ð¸. ÐÐ»Ñ Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ°ÑÐ¸Ð¸ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÐµÐ¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð±ÑÐ»Ð¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ñ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¾Ð² Ð½Ð° Ð¿ÐµÑÐ¸Ð¾Ð´ Ð² 60 Ð´Ð½ÐµÐ¹, Ð° Ð·Ð°ÑÐµÐ¼ Ð±ÑÐ»Ð¾ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¸ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¸ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ°Ð¼Ð¸ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ð½ÑÑ ÑÐ°Ð±Ð¾Ñ. Ð ÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÑÑÐ°Ð²Ð½Ð¸Ð»Ð¸ÑÑ Ð¿Ð¾ Ð·Ð½Ð°ÑÐµÐ½Ð¸ÑÐ¼ ÑÑÐµÐ´Ð½ÐµÐºÐ²Ð°Ð´ÑÐ°ÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¾ÑÐºÐ»Ð¾Ð½ÐµÐ½Ð¸Ñ Ð¸ ÑÑÐµÐ´Ð½ÐµÐ¹ Ð°Ð±ÑÐ¾Ð»ÑÑÐ½Ð¾Ð¹ Ð¾ÑÐ¸Ð±ÐºÐ¸ Ñ Ð¼Ð¾Ð´ÐµÐ»ÑÑ, Ð¿ÑÐ¸Ð²ÐµÐ´ÐµÐ½Ð½Ð¾Ð¹ Ð² ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ð½Ð¾Ð¹ ÑÑÐ°ÑÑÐµ. ÐÑÐ¾Ð³Ð½Ð¾Ð·Ñ ÑÐ¾Ð±ÑÑÐ²ÐµÐ½Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸, Ð¿ÑÐ¸Ð²ÐµÐ´ÐµÐ½Ð½Ð¾Ð¹ Ð² ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ, Ð²Ð¸Ð·ÑÐ°Ð»ÑÐ½Ð¾ ÑÑÐ¾Ð´ÑÑÑÑ.
description: The subject of graduate qualification work is Â«Implementation of the forecasting model covid-19 based on neural networksÂ». The goal of this work is to develop a new forecasting model COVID-19 based on neural networks. In this connection, several problems were solved: the existing neural network-based COVID-19 prediction models were considered; methods of improving performance of neural network-based models using preprocessing data methods were considered; own implementation of the neural network-based COVID-19 prediction model was proposed and implemented. In the course of work using the high-level Python programming language and the Tensorflow framework, two neural networks with the developed architectures were implemented and trained. To demonstrate the capabilities of the model, forecast values were obtained for a period of 60 days and then the results of the forecasts and accuracy were compared with the results of the reviewed work. The implemented models were compared in terms of root mean square error and mean absolute error values with the model given in the article reviewed. The predictions of the own models and the model given in the considered work visually converge.