Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment - Softcover

Über die Autorinnen und Autoren

Alma Y. Alanis received a Ph.D. degree in electrical engineering from the Advanced Studies and Research Center of the National Polytechnic Institute (CINVESTAV-IPN), Guadalajara Campus, Mexico, in 2007. Since 2008, she has been with the University of Guadalajara, where she is currently a Chair Professor in the Department of Computer Science. She is also a member of the Mexican National Research System (SNI-3) and the Mexican Academy of Sciences. She has published papers in recognized international journals and conferences, as well as eight international books. She is a Senior Member of the IEEE and a Subject Editor for the Journal of Franklin Institute (Elsevier), IEEE/ASME Transactions on Mechatronics, IEEE Access, IEEE Latin American Transactions, and Intelligent Automation & Soft Computing. In 2013, she received the grant for women in science by L'Oreal-UNESCO-AMC-CONACYT-CONALMEX. In 2015, she received the Marcos Moshinsky Research Award. Since 2008, she has been a member of the Accredited Assessors record RCEA-CONACYT, evaluating a wide range of national research projects, and has served on important national and international project evaluation committees. Her research interests center on neural control, backstepping control, block control, and their applications to electrical machines, power systems, and robotics.

Dr. Oscar D. Sánchez received his Ph.D. degree in Electronic and Computer Science from the University of Guadalajara. He is a researcher in the Computer Department at the University Center for Exact Sciences and Engineering at the University of Guadalajara. Dr. Sánchez’s research interests are modeling and identification of systems, bioinformatics, optimization and prediction with intelligent systems.

Dr. Alonso Vaca González is a physician at the University of Guadalajara. He holds a Master’s Degree in Medical Microbiology, with diplomas in Applied Public Health, Mental Health, Microbiota, Diagnostic Hematology, Occupational Health and Safety, and Health Business Administration. Dr. González was one of the founding members of the Program for Early Detection and Counseling in Sexually Transmitted Infections (PRODOCITS), and is the author of indexed articles, manuals, and book chapters on the subject of microbiology. He has also advised the preparation of theses at the postgraduate level. Dr. González received the "Irene Robledo García" Award for Social Service in 2022. Dr. González carried out research activities at the Specialized Center for Reproduction and Genetics (CERGEN) and at Summa Corporation, evaluating products and possible treatments for people with Diabetes Mellitus. Dr. González is a professor at the Institutional System for Safety, Health and Environment (SISSMA), where he teaches courses on human capital training and skills assessment.

Dr. Marco Perez-Cisneros received his M.Eng. degree from ITESO University, Mexico, and his Ph.D. degree from The University of Manchester, U.K. He currently works as a Professor with the Electro-Photonics Department at University of Guadalajara and has been appointed as the Rector of the University Centre of Exact Sciences and Engineering of the University of Guadalajara. Dr. Perez-Cisneros is a member of the National Research System in Mexico and is a member of the Mexican National Science Academy.

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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.