Soft Computing for Biomedical Applications and Related Topics
(Sprache: Englisch)
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...
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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. Inhaltsverzeichnis zu „Soft Computing for Biomedical Applications and Related Topics “
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.
Bibliographische Angaben
- 2021, 1st ed. 2021, IX, 329 Seiten, 123 farbige Abbildungen, Masse: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Herausgegeben: Vladik Kreinovich, Nguyen Hoang Phuong
- Verlag: Springer, Berlin
- ISBN-10: 3030495388
- ISBN-13: 9783030495381
- Erscheinungsdatum: 30.06.2021
Sprache:
Englisch
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