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Biocompatible materials selection via new supervised learning methods
Published by
LAP LAMBERT Academic Publishing Jan 2019, 2019
ISBN 10: 6139443849
ISBN 13: 9786139443840
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. Seller Inventory # 9786139443840
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Biocompatible materials selection via new supervised learning methods
Published by
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139443849
ISBN 13: 9786139443840
New
Softcover
Seller: moluna, Greven, Germany
Seller rating 4 out of 5 stars
Condition: 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. Seller Inventory # 274023349
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Biocompatible materials selection via new supervised learning methods
Published by
LAP LAMBERT Academic Publishing Jan 2019, 2019
ISBN 10: 6139443849
ISBN 13: 9786139443840
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 112 pp. Englisch. Seller Inventory # 9786139443840
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Biocompatible materials selection via new supervised learning methods
Published by
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139443849
ISBN 13: 9786139443840
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. Seller Inventory # 9786139443840
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Biocompatible materials selection via new supervised learning methods
Published by
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139443849
ISBN 13: 9786139443840
Used
Softcover
Seller: Buchpark, Trebbin, Germany
Seller rating 5 out of 5 stars
Condition: 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. Seller Inventory # 33664481/2
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Biocompatible materials selection via new supervised learning methods
Published by
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139443849
ISBN 13: 9786139443840
Used
Softcover
Seller: Buchpark, Trebbin, Germany
Seller rating 5 out of 5 stars
Condition: 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. Seller Inventory # 33664481/1