Autors: Kirilov, S. M., Todorova, V. I., Nakov, O. N., Mladenov, V. M.
Title: Application of a memristive neural network for classification of covid-19 patients
Keywords: COVID-19, pandemic, tantalum oxide memristor, synapses, neural network, machine learning, unsupervised learning, SARSCoV-2

Abstract: The global pandemic of COVID-19 has affected the lives of millions around the globe. We learn new facts about this coronavirus every day. A contribution to this knowledge is described in the paper and it is related to the employment of memristor neural networks and algorithms that help us analyze patients’ data and determine what patients are at increased risk for developing severe medical conditions once infected with the COVID-19. Efficient separation of potential patients in ill and healthy sub-groups is conducted using software and hardware neural networks, machine learning, and unsupervised clustering. The main purpose of this paper is the application of a memristor-based neural network with tantalum oxide memristor synapses for COVID-19 analysis. Additional experiments with data clustering are conducted. Experiments show that in fact, patients with specific underlying health conditions and indicators are more predisposed to develop severe COVID-19 illness.



    International Journal of Circuits, Systems and Signal Processing, vol. 15, pp. 1282 - 1291, 2021, United States, NAUN, DOI 10.46300/9106.2021.15.138

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    Вид: статия в списание, публикация в реферирано издание, индексирана в Scopus