Isbn: 9786139443840 - biocompatible materials selection via new supervised learning methods (7 resultados)

Taschenbuch. Condición: Neu. Biocompatible materials selection via new supervised learning methods | Tetiana Tepla (u. a.) | Taschenbuch | 112 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786139443840 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.…

Condición: Sehr gut. 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.…

Condición: Hervorragend. Zustand: Hervorragend | 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.…

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 112 pp. Englisch.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Tepla TetianaDr Tetiana Tepla, PhD in Materials Science, an associate professor at Lviv Polytechnic National University, Ukraine. Dr Ivan Izonin, PhD in Artificial Intelligence, an assistant professor at Lviv Polytechnic National Uni.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 112 pp. Englisch.…