Zoia Duriagina

Work place: Lviv Polytechnic National University, Lviv, 70913, Ukraine

E-mail: roman.tkachenko@gmail.com


Research Interests: Medical Informatics, Medical Image Computing


Zoia Duriagina. Prof., Doctor of Technical Sciences, Head of the Department «Applied Materials Science and Materials Engineering», Lviv Polytechnic National University, Ukraine. Prof., dr. hab. of the Lublin Catholic University, Poland.

She has published more than 218 scientific publications, including 5 scientific monographs (2 of them in English), 4 textbooks and 16 patents.

Scientific and research interest - Stainless steels and functional alloys. Surface engineering: application of functional (dielectric, resistive, heat-resistant, protective) coatings. Creating new thermoelectric materials. Investigation the properties of Ti- powders alloys: development of 3D printing technology for the production of aerospace equipment. Biomedical, medical materials and functional coatings.

Professor Duriagina has a several awards: the «Golden Medal of Polish Scientific Society of Materials Science», Gliwice, 2016-2017. Diploma of International expert in the field of materials science, Weihai, China, 2017. Member of the Academy of Higher Education of Ukraine (since 2008). Member of the World Academy of Materials and Manufacturing Engineering (since 2015).

Author Articles
The Combined Use of the Wiener Polynomial and SVM for Material Classification Task in Medical Implants Production

By Ivan Izonin Andriy Trostianchyn Zoia Duriagina Roman Tkachenko Tetiana Tepla Nataliia Lotoshynska

DOI: https://doi.org/10.5815/ijisa.2018.09.05, Pub. Date: 8 Sep. 2018

This document presents two developed methods for solving the classification task of medical implant materials based on the compatible use of the Wiener Polynomial and SVM. The high accuracy of the proposed methodology for solving this task are experimentally confirmed. A comparison of the proposed methods with existing ones: Logistic Regression; Linear SVC; Random Forest; SVC (linear kernel); SVC (RBF kernel); Random Forest + Wiener Polynomial is carried out. The duration of training of all methods that described in work is investigated. The article presents the visualization of all method results for solving this task.

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