Smart Innovation, Systems and Technologies

Multidisciplinary Approaches to Neural Computing

Editors: Esposito, A., Faundez-Zanuy, M., Morabito, F.C., Pasero, E. (Eds.)

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  • 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
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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.

Table of contents (36 chapters)

Table of contents (36 chapters)
  • Redefining Information Processing Through Neural Computing Models

    Esposito, Anna (et al.)

    Pages 3-7

  • A Neural Approach for Hybrid Events Discrimination at Stromboli Volcano

    Esposito, Antonietta M. (et al.)

    Pages 11-21

  • Fully Automatic Multispectral MR Image Segmentation of Prostate Gland Based on the Fuzzy C-Means Clustering Algorithm

    Rundo, Leonardo (et al.)

    Pages 23-37

  • Integrating QuickBundles into a Model-Guided Approach for Extracting “Anatomically-Coherent” and “Symmetry-Aware” White Matter Fiber-Bundles

    Cauteruccio, Francesco (et al.)

    Pages 39-46

  • Accurate Computation of Drude-Lorentz Model Coefficients of Single Negative Magnetic Metamaterials Using a Micro-Genetic Algorithm Approach

    Sgrò, Annalisa (et al.)

    Pages 47-55

Buy this book

eBook n/a
  • ISBN 978-3-319-56904-8
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
Hardcover n/a
  • ISBN 978-3-319-56903-1
  • Free shipping for individuals worldwide
Softcover n/a
  • ISBN 978-3-319-86031-2
  • Free shipping for individuals worldwide
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Bibliographic Information

Bibliographic Information
Book Title
Multidisciplinary Approaches to Neural Computing
Editors
  • Anna Esposito
  • Marcos Faundez-Zanuy
  • Francesco Carlo Morabito
  • Eros Pasero
Series Title
Smart Innovation, Systems and Technologies
Series Volume
69
Copyright
2018
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG, part of Springer Nature
eBook ISBN
978-3-319-56904-8
DOI
10.1007/978-3-319-56904-8
Hardcover ISBN
978-3-319-56903-1
Softcover ISBN
978-3-319-86031-2
Series ISSN
2190-3018
Edition Number
1
Number of Pages
XV, 388
Number of Illustrations
124 b/w illustrations
Topics