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Machine Learning in Bio-Signal Analysis and Diagnostic Imaging /

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Dey, Nilanjan, 1984- (Editor ), Borra, Surekha (Editor ), Ashour, Amira, 1975- (Editor ), Shi, Fuqian (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, [2019]
Edición:First edition.
Temas:
Acceso en línea:Texto completo

MARC

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245 0 0 |a Machine Learning in Bio-Signal Analysis and Diagnostic Imaging /  |c edited by Nilanjan Dey, Surekha Borra, Amira S. Ashour, Fuqian Shi. 
250 |a First edition. 
264 1 |a London :  |b Academic Press,  |c [2019] 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
588 0 |a Online resource; title from PDF title page (EBSCO, viewed December 5, 2018). 
520 |a Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers. 
650 0 |a Diagnostic imaging  |x Digital techniques. 
650 0 |a Diseases  |x Reporting. 
650 0 |a Spectrum analysis. 
650 0 |a Machine learning. 
650 0 |a Diagnostic imaging. 
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650 6 |a Apprentissage automatique.  |0 (CaQQLa)201-0131435 
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650 7 |a Spectrum analysis.  |2 fast  |0 (OCoLC)fst01129108 
700 1 |a Dey, Nilanjan,  |d 1984-  |e editor. 
700 1 |a Borra, Surekha,  |e editor. 
700 1 |a Ashour, Amira,  |d 1975-  |e editor. 
700 1 |a Shi, Fuqian,  |e editor. 
776 0 8 |i Print version:  |z 0128160861  |z 9780128160862  |w (OCoLC)1040657108 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780128160862  |z Texto completo