Buch, Englisch, Band 116, 473 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 739 g
Reihe: IFMBE Proceedings
Proceedings of CNIB 2024, November 7-9, 2024, Hermosillo, Sonora, México - Volume 1: Signal Processing And Bioinformatics
Buch, Englisch, Band 116, 473 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 739 g
Reihe: IFMBE Proceedings
ISBN: 978-3-031-82122-6
Verlag: Springer Nature Switzerland
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
- Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
Weitere Infos & Material
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.
