XLVII Mexican Conference on Biomedical Engineering: Proceedings of CNIB 2024, November 7–9, 2024, Hermosillo,...

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José de Jesús Agustín Flores Cuautle Balam Benítez-Mata

XLVII Mexican Conference on Biomedical Engineering

Proceedings of CNIB 2024, November 7–9, 2024, Hermosillo, Sonora, México - Volume 1: Signal...

José de Jesús Agustín Flores Cuautle, Balam Benítez-Mata, José Javier Reyes-Lagos , Humiko Yahaira Hernandez Acosta

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English

Springer International Publishing AG
04 January 2025

Biomedical engineering; Computer aided manufacture (CAM); Electronics engineering; Computing: general; Signal processing

Series: IFMBE Proceedings

This book reports on cutting-edge research and best practices in the broad field of biomedical engineering. Based on the XLVII Mexican Conference on Biomedical Engineering, CNIB 2024, held on November 7-9, 2024 in Hermosillo, Sonora, México, this first volume of the proceedings covers research topics in biomedical signal processing, computational biology and prosthetics, with applications of artificial intelligence for medical diagnosis, behavioral studies and more. All in all, this book provides a timely snapshot on state-of-the-art achievements in biomedical engineering and current challenges in the field. It addresses both researchers and professionals, and it is expected to foster future collaborations between the two groups, as well as international collaborations.

Edited by: José de Jesús Agustín Flores Cuautle, Balam Benítez-Mata, José Javier Reyes-Lagos, Humiko Yahaira Hernandez Acosta, Gerardo Ames Lastra
Imprint: Springer International Publishing AG
Country of Publication: Switzerland
Volume: 116
Dimensions: Height: 235mm, Width: 155mm,
ISBN: 9783031821226
ISBN 10: 303182122X
Series: IFMBE Proceedings
Pages: 473
Publication Date: 04 January 2025
Audience: Professional and scholarly , College/higher education , Undergraduate , Further / Higher Education
Format: Paperback
Publisher's Status: Active

ML Design in Handwriting Analysis for Prediction of Alzheimer's Disease.- Machine learning for classification of electrooculography signals in the detection of visual fatigue syndrome.- Machine Learning Techniques for Classifying Cardiac Arrhythmias.- A Machine Learning approach to breast cancer detection in mammograms.