Pattern Recognition Techniques Applied to Biomedical Problems Pattern Recognition Techniques Applied to Biomedical Problems
STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health

Pattern Recognition Techniques Applied to Biomedical Problems

    • ‏39٫99 US$
    • ‏39٫99 US$

وصف الناشر

This book covers pattern recognition techniques applied to various areas of biomedicine, including disease diagnosis and prognosis, and several problems of classification, with a special focus on—but not limited to—pattern recognition modeling of biomedical signals and images.  
Multidisciplinary by definition, the book’s topic blends computing, mathematics and other technical sciences towards the development of computational tools and methodologies that can be applied to pattern recognition processes. 

In this work, the efficacy of such methods and techniques for processing medical information is analyzed and compared, and auxiliary criteria for determining the correct diagnosis and treatment strategies are recommended and applied.  
Researchers in applied mathematics, the computer sciences, engineering and related fields with a focus on medical applications will benefit from this book, as well as professionals witha special interest in state-of-the-art pattern recognition techniques as applied to biomedicine.

النوع
علم وطبيعة
تاريخ النشر
٢٠٢٠
٢٩ فبراير
اللغة
EN
الإنجليزية
عدد الصفحات
٢٣١
الناشر
Springer International Publishing
البائع
Springer Nature B.V.
الحجم
٢٦
‫م.ب.‬
Bioinformatics and Biomedical Engineering Bioinformatics and Biomedical Engineering
٢٠٢٢
Bioinformatics and Biomedical Engineering Bioinformatics and Biomedical Engineering
٢٠٢٠
Biomedical and Computational Biology Biomedical and Computational Biology
٢٠٢٣
Bioinformatics Research and Applications Bioinformatics Research and Applications
٢٠٢١
GeNeDis 2016 GeNeDis 2016
٢٠١٧
Advances in Artificial Intelligence, Computation, and Data Science Advances in Artificial Intelligence, Computation, and Data Science
٢٠٢١
Causality: The p-adic Theory Causality: The p-adic Theory
٢٠٢٥
Practical Statistical Learning and Data Science Methods Practical Statistical Learning and Data Science Methods
٢٠٢٤
Innovative Integrals and Their Applications II Innovative Integrals and Their Applications II
٢٠٢٤
Sustainable Statistical and Data Science Methods and Practices Sustainable Statistical and Data Science Methods and Practices
٢٠٢٤
Trends and Challenges in Cognitive Modeling Trends and Challenges in Cognitive Modeling
٢٠٢٣
Algebra without Borders – Classical and Constructive Nonassociative Algebraic Structures Algebra without Borders – Classical and Constructive Nonassociative Algebraic Structures
٢٠٢٣