Overview
- Provides fundamental insights for cross-fertilization: machine learning, artificial neural networks (ANNs) (algorithms and models), social and biometric data for applications in human–computer interactions, and neural networks-based approaches to industrial processes
- Identifies features from dynamic realistic signal exchanges and invariant machine representations to automatically identify, detect, analyze, and process them in related applications
- Simplifies automatic signal processing and its exploitation in realistic applications devoted to improving the quality of life of the end users
- Features contributions from computer science, physics, psychology, statistics, mathematics, electrical engineering, and communication science
- Includes supplementary material: sn.pub/extras
Part of the book series: Smart Innovation, Systems and Technologies (SIST, volume 69)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
About this book
This book presents a collection of contributions in the field of Artificial Neural Networks (ANNs). The themes addressed are multidisciplinary in nature, and closely connected in their ultimate aim to identify features from dynamic realistic signal exchanges and invariant machine representations that can be exploited to improve the quality of life of their end users.
Mathematical tools like ANNs are currently exploited in many scientific domains because of their solid theoretical background and effectiveness in providing solutions to many demanding tasks such as appropriately processing (both for extracting features and recognizing) mono- and bi-dimensional dynamic signals, solving strong nonlinearities in the data and providing general solutions for deep and fully connected architectures. Given the multidisciplinary nature of their use and the interdisciplinary characterization of the problems they are applied to – which range from medicine to psychology, industrial and social robotics, computer vision, and signal processing (among many others) – ANNs may provide a basis for redefining the concept of information processing. These reflections are supported by theoretical models and applications presented in the chapters of this book.
This book is of primary importance for: (a) the academic research community, (b) the ICT market, (c) PhD students and early-stage researchers, (d) schools, hospitals, rehabilitation and assisted-living centers, and (e) representatives of multimedia industries and standardization bodies.
Similar content being viewed by others
Keywords
Table of contents (36 chapters)
-
Front Matter
-
Introduction
-
Front Matter
-
-
Algorithms
-
Front Matter
-
-
ANN Applications
-
Front Matter
-
Editors and Affiliations
Bibliographic Information
Book Title: Multidisciplinary Approaches to Neural Computing
Editors: Anna Esposito, Marcos Faudez-Zanuy, Francesco Carlo Morabito, Eros Pasero
Series Title: Smart Innovation, Systems and Technologies
DOI: https://doi.org/10.1007/978-3-319-56904-8
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-56903-1Published: 06 September 2017
Softcover ISBN: 978-3-319-86031-2Published: 07 August 2018
eBook ISBN: 978-3-319-56904-8Published: 28 August 2017
Series ISSN: 2190-3018
Series E-ISSN: 2190-3026
Edition Number: 1
Number of Pages: XV, 388
Number of Illustrations: 124 b/w illustrations
Topics: Computational Intelligence, Artificial Intelligence, Mathematical Models of Cognitive Processes and Neural Networks, Signal, Image and Speech Processing, User Interfaces and Human Computer Interaction, Biometrics