Overview

This book presents innovative intelligent techniques, with an emphasis on their biomedical applications. Although many medical doctors are willing to share their knowledge – e.g. by incorporating it in computer-based advisory systems that can benefit other doctors – this knowledge is often expressed using imprecise (fuzzy) words from natural language such as “small,” which are difficult for computers to process. Accordingly, we need fuzzy techniques to handle such words. It is also desirable to extract general recommendations from the records of medical doctors’ decisions – by using machine learning techniques such as neural networks. The book describes state-of-the-art fuzzy, neural, and other techniques, especially those that are now being used, or potentially could be used, in biomedical applications. Accordingly, it will benefit all researchers and students interested in the latest developments, as well as practitioners who want to learn about new techniques. 

Full Product Details

Author:   Vladik Kreinovich ,  Nguyen Hoang Phuong
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2021
Volume:   899
Weight:   0.522kg
ISBN:  

9783030495381

ISBN 10:   3030495388
Pages:   329
Publication Date:   30 June 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Multiagent Smart Communication Based on CI Technology.- Develop a Prediction Model for Nonmelanoma Skin Cancer Using Deep Learning in EHR data.- VectorDefense: Vectorization as a Defense to Adversarial Examples.- Artificial Intelligence in Infection Control—Healthcare Institutions Need Intelligent Information and Communication Technologies for Surveillance and Benchmarking.- Why some Non-Classical Logics are more Studied?.- Why h-index?.- Accuracy of Data Fusion: Interval (and Fuzzy) Case.- Imputing Missing Values: Reinforcement Bayesian Regression and Random Forest.- From Machine Learning to Knowledge-based Decision Support—A Predictive-Model-Markup-Language-to-Arden-Syntax Transformer for Decision Trees.

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