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Predictive Intelligence in Biomedical and Health Informatics (Intelligent Biomedical Data Analysis)

By: Ashish Khanna (Edited by) , Nhu Gia Nguyen (Edited by) , Rajshree Srivastava (Edited by) , Siddhartha Bhattacharyya (Edited by)

Extended Catalogue

Ksh 27,050.00

Format: Hardback or Cased Book

ISBN-10: 3110676087

ISBN-13: 9783110676082

Collection / Series: Intelligent Biomedical Data Analysis

Collection Type: Publisher collection

Publisher: De Gruyter

Imprint: De Gruyter

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Oct 12th, 2020

Publication Status: Active

Product extent: 180 Pages

Weight: 454.00 grams

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Predictive Intelligence in Biomedical and Health Informatics focuses on imaging, computer-aided diagnosis and therapy as well as intelligent biomedical image processing and analysis. It develops computational models, methods and tools for biomedical engineering related to computer-aided diagnostics (CAD), computer-aided surgery (CAS), computational anatomy and bioinformatics. Large volumes of complex data are often a key feature of biomedical and engineering problems and computational intelligence helps to address such problems. Practical and validated solutions to hard biomedical and engineering problems can be developed by the applications of neural networks, support vector machines, reservoir computing, evolutionary optimization, biosignal processing, pattern recognition methods and other techniques to address complex problems of the real world.

Predictive Intelligence in Biomedical and Health Informatics focuses on imaging, computer-aided diagnosis and therapy as well as intelligent biomedical image processing and analysis. It develops computational models, methods and tools for biomedical engineering related to computer-aided diagnostics (CAD), computer-aided surgery (CAS), computational anatomy and bioinformatics. Large volumes of complex data are often a key feature of biomedical and engineering problems and computational intelligence helps to address such problems. Practical and validated solutions to hard biomedical and engineering problems can be developed by the applications of neural networks, support vector machines, reservoir computing, evolutionary optimization, biosignal processing, pattern recognition methods and other techniques to address complex problems of the real world.


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