03046nam a22005655i 4500 978-3-031-51893-5 DE-He213 20251030111752.0 cr nn 008mamaa 240329s2024 sz | s |||| 0|eng d 9783031518935 978-3-031-51893-5 10.1007/978-3-031-51893-5 doi R856-857 MQW bicssc TEC059000 bisacsh MQW thema Machine Learning Applications in Medicine and Biology [electronic resource] / edited by Ammar Ahmed, Joseph Picone. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. V, 168 p. 62 illus., 52 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Introduction -- Signal and Image Analysis (EEG, ECG, MRI) -- Machine Learning -- Data Mining and Classification -- Big Data -- Index. This book combines selected papers from the 2022 IEEE Signal Processing in Medicine and Biology Symposium (IEEE SPMB) held at Temple University. The symposium presents multidisciplinary research in the life sciences. Topics covered include: Signal and image analysis (EEG, ECG, MRI) Machine learning Data mining and classification Big data resources Applications of particular interest at the 2022 symposium included digital pathology, computational biology, and quantum computing. The book features tutorials and examples of successful applications that will appeal to a wide range of professionals and researchers in signal processing, medicine, and biology. Presents an interdisciplinary look at research trends; Promotes collaboration between practitioners and researchers; Includes tutorials and examples of successful applications. . Biomedical engineering. Imaging systems in biology. 68562 Machine learning. 55059 Signal processing. Biomedical Engineering and Bioengineering. Biological Imaging. 68563 Machine Learning. 55059 Biomedical Devices and Instrumentation. Digital and Analog Signal Processing. Ahmed, Ammar. editor. edt http://id.loc.gov/vocabulary/relators/edt 74249 Picone, Joseph. editor. edt http://id.loc.gov/vocabulary/relators/edt 74250 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031518928 Printed edition: 9783031518942 Printed edition: 9783031518959 https://doi.org/10.1007/978-3-031-51893-5 ZDB-2-ENG ZDB-2-SXE 58758 58758