Biocompatible materials selection via new supervised learning methods

Tetiana Tepla, Ivan Izonin, Zoia Duriagina

ISBN 10: 6139443849 / ISBN 13: 9786139443840

Published by LAP LAMBERT Academic Publishing, 2019

About this Item

Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | The development of modern high-tech branches of medicine, including orthopaedics, traumatology and dentistry, places high demands on the quality of materials. The study of the processes occurring in the design of biocompatible material and the manufacture of medical products from it, as well as the ability to manage them, contribute to the production of a material with specified properties. So, the task of the optimal biocompatible material selection for medical usage is a complex task that we solved using Artificial Intelligence tools. In the book, authors describe an improved approach to the development of supervised learning methods for high-precision biocompatible materials selection. The general idea of these methods is a compatible use of the Kolmogorov-Gabor polynomial and machine learning algorithms. This polynomial allows increasing the dimension of the input dataset, which in turn increases the likelihood of correct materials classification. Machine learning algorithms are used as fast tools for finding the coefficients of this polynomial. Experimental studies have shown high classification accuracy using the proposed approach.

Seller Inventory # 33664481/2

Bibliographic Details

• Title: Biocompatible materials selection via new supervised learning methods
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 6139443849
• ISBN 13: 9786139443840
• Binding: Soft cover
• Condition: Sehr gut
• Seller catalogs:
  • Bücher

About this title

Synopsis

The development of modern high-tech branches of medicine, including orthopaedics, traumatology and dentistry, places high demands on the quality of materials. The study of the processes occurring in the design of biocompatible material and the manufacture of medical products from it, as well as the ability to manage them, contribute to the production of a material with specified properties. So, the task of the optimal biocompatible material selection for medical usage is a complex task that we solved using Artificial Intelligence tools. In the book, authors describe an improved approach to the development of supervised learning methods for high-precision biocompatible materials selection. The general idea of these methods is a compatible use of the Kolmogorov-Gabor polynomial and machine learning algorithms. This polynomial allows increasing the dimension of the input dataset, which in turn increases the likelihood of correct materials classification. Machine learning algorithms are used as fast tools for finding the coefficients of this polynomial. Experimental studies have shown high classification accuracy using the proposed approach.