Academic Journal
IDENTIFICATION OF AREAS OF CORONAVIRUS COVID-19 INCIDENCE SPREADING BASED ON CLUSTER ANALYSIS METHOD
| Τίτλος: | IDENTIFICATION OF AREAS OF CORONAVIRUS COVID-19 INCIDENCE SPREADING BASED ON CLUSTER ANALYSIS METHOD |
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| Συγγραφείς: | Kseniia Bazilevych, Ievgen Meniailov, Dmytro Chumachenko |
| Πηγή: | Сучасний стан наукових досліджень та технологій в промисловості, Iss 1 (15) (2021) Innovative Technologies and Scientific Solutions for Industries; No. 1 (15) (2021): Innovative Technologies and Scientific Solutions for Industries; 5-13 Современное состояние научных исследований и технологий в промышленности; № 1 (15) (2021): Современное состояние научных исследований и технологий в промышленности; 5-13 Сучасний стан наукових досліджень та технологій в промисловості; № 1 (15) (2021): Сучасний стан наукових досліджень та технологій в промисловості; 5-13 |
| Στοιχεία εκδότη: | Kharkiv National University of Radioelectronics, 2021. |
| Έτος έκδοσης: | 2021 |
| Θεματικοί όροι: | эпидемический процесс, кластерный анализ, neural network, 4. Education, epidemic process, епідемічний процес, COVID-19, 02 engineering and technology, машинне навчання, TA177.4-185, кластерний аналіз, машинное обучение, 7. Clean energy, нейронная сеть, 3. Good health, machine learning, Engineering economy, 0202 electrical engineering, electronic engineering, information engineering, нейронна мережа, cluster analysis |
| Περιγραφή: | Subject: the use of the mathematical apparatus of neural networks for the scientific substantiation of anti-epidemic measures in order to reduce the incidence of diseases when making effective management decisions. Purpose: to apply cluster analysis, based on a neural network, to solve the problem of identifying areas of incidence. Tasks: to analyze methods of data analysis to solve the clustering problem; to develop a neural network method for clustering the territory of Ukraine according to the nature of the epidemic process COVID-19; on the basis of the developed method, to implement a data analysis software product to identify the areas of incidence of the disease using the example of the coronavirus COVID-19. Methods: models and methods of data analysis, models and methods of systems theory (based on the information approach), machine learning methods, in particular the Adaptive Boosting method (based on the gradient descent method), methods for training neural networks. Results: we used the data of the Center for Public Health of the Ministry of Health of Ukraine distributed over the regions of Ukraine on the incidence of COVID-19, the number of laboratory examined persons, the number of laboratory tests performed by PCR and ELISA methods, the number of laboratory tests of IgA, IgM, IgG; the model used data from March 2020 to December 2020, the modeling did not take into account data from the temporarily occupied territories of Ukraine; for cluster analysis, a neural network of 60 input neurons, 100 hidden neurons with an activation Fermi function and 4 output neurons was built; for the software implementation of the model, the programming language Python was used. Conclusions: analysis of methods for constructing neural networks; analysis of training methods for neural networks, including the use of the gradient descent method for the Adaptive Boosting method; all theoretical information described in this work was used to implement a software product for processing test data for COVID-19 in Ukraine; the division of the regions of Ukraine into zones of infection with the COVID-19 virus was carried out and a map of this division was presented. |
| Τύπος εγγράφου: | Article Other literature type |
| Περιγραφή αρχείου: | application/pdf |
| ISSN: | 2524-2296 2522-9818 |
| DOI: | 10.30837/itssi.2021.15.005 |
| Σύνδεσμος πρόσβασης: | http://journals.uran.ua/itssi/article/download/227912/227361 https://doaj.org/article/0002995bb05d407aaf0a3d841bc651a4 https://itssi-journal.com/index.php/ittsi/article/download/254/264 http://journals.uran.ua/itssi/article/view/227912 http://journals.uran.ua/itssi/article/download/227912/227361 https://itssi-journal.com/index.php/ittsi/article/view/254 https://doaj.org/article/0002995bb05d407aaf0a3d841bc651a4 http://journals.uran.ua/itssi/article/view/227912 |
| Rights: | CC BY NC SA |
| Αριθμός Καταχώρησης: | edsair.doi.dedup.....11f80565e5716f4c2258df327c76d2e2 |
| Βάση Δεδομένων: | OpenAIRE |
| ISSN: | 25242296 25229818 |
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| DOI: | 10.30837/itssi.2021.15.005 |