- Häftad (Paperback / softback)
- Antal sidor
- Softcover reprint of the original 1st ed. 2018
- Springer Nature Switzerland AG
- Jotsov, Vladimir (ed.), Kacprzyk, Janusz (ed.), Sgurev, Vassil (ed.)
- 100 Tables, color; 137 Illustrations, color; 45 Illustrations, black and white; XII, 330 p. 182 illu
- Antal komponenter
- 1 Paperback / softback
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Practical Issues of Intelligent Innovations1439Skickas inom 10-15 vardagar.
Fri frakt inom Sverige för privatpersoner.This book presents recent advances in the field of intelligent systems. Composed of fourteen selected chapters, it covers a wide range of research that varies from applications in industrial data science to those in applied science. Today the word INNOVATION is more and more connected with the words INTELLIGENT and SECURITY, as such the book discusses the theory and applications of hot topics such as big data, education applications of robots with different levels of autonomy, knowledge-based modeling and control of complex dynamical systems, sign-based synthesis of behavior, security issues with intelligent systems, innovative intelligent control design, neuromorphic computation, data-driven classification, intelligent modeling and measurement innovations, multisensor data association, personal education assistants, a modern production architecture, study of peer review and scientometrics, intelligent research on bug report data, and clustering non-Gaussian data. The broad and varied research discussed represents the mainstream of contemporary intelligent innovations that are slowly but surely changing the world.
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Non-conventional Control Design by Sigmoid Generated Fixed Point Transformation using Fuzzy Approximation.- Network Flows and Risks.- Personal Assistants in a Virtual Education Space.- Responsive Production in Manufacturing: A Modular Architecture.- Multisensor Data Association by Using the Polar Hough Transform.- Extracting Knowledge and Realizing Services from Bug Report Data.- Scientific Research Funding Criteria: An Empirical Study of Peer Review and Scientometrics.- Clustering Non-Gaussian Data Using Mixture Estimation with Uniform Components.