Name: Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment (en Inglés)
SKU: 63294775
Price: 584402.00 COP
Availability: InStock
Author: Alma Y Alanis; Oscar D Sánchez; Alonso Vaca Gonzalez; Marco Perez Cisneros
ISBN: 9780443223419

portada Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment (en Inglés)

Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment (en Inglés)

Formato

Libro Físico

Editorial

Morgan Kaufmann

Autor

Alma Y Alanis; Oscar D Sánchez; Alonso Vaca Gonzalez; Marco Perez Cisneros

Idioma

Inglés

N° páginas

152

Encuadernación

Tapa Blanda

ISBN13

9780443223419

N° edición

Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment (en Inglés)

Alma Y Alanis; Oscar D Sánchez; Alonso Vaca Gonzalez; Marco Perez Cisneros (Autor) · Morgan Kaufmann · Tapa Blanda

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Reseña del libro "Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment (en Inglés)"

Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment focuses on bioinspired techniques such as modeling to generate control algorithms for the treatment of diabetes mellitus. The book addresses the identification of diabetes mellitus using a high-order recurrent neural network trained by an extended Kalman filter. The authors also describe the use of metaheuristic algorithms for the parametric identification of compartmental models of diabetes mellitus widely used in research works such as the Sorensen model and the Dallaman model. In addition, the book addresses the modeling of time series for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia using deep neural networks. The detection of diabetes mellitus in the early stages or when current diagnostic techniques cannot detect glucose intolerance or prediabetes is proposed, carried out by means of deep neural networks present in the literature. Readers will find leading-edge research in diabetes identification based on discrete high-order neural networks trained with an extended Kalman filter; parametric identification of compartmental models used to describe diabetes mellitus; modeling of data obtained by continuous glucose-monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia; and screening for glucose intolerance using glucose-tolerance test data and deep neural networks. Application of the proposed approaches is illustrated via simulation and real-time implementations for modeling, prediction, and classification.