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Artificial Neural Network Modelling

  • Book
  • © 2016

Overview

Editors:
  1. Subana Shanmuganathan

    1. School of Computer and Mathematical, Auckland University of Technology (, Auckland, New Zealand

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    You can also search for this editor in PubMed   Google Scholar

  2. Sandhya Samarasinghe

    1. Dept of Informatics & Enabling Tech, Lincoln University, Lincoln, New Zealand

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    You can also search for this editor in PubMed   Google Scholar

  • Recent research in Artificial Neural Network Modelling
  • Contains stimulating papers presented in the Advances and Applications in Artificial Neural Network (ANN) session at the 20th International Congress on Modelling and Simulation (MODSIM2013) held at the Adelaide Convention Centre in Adelaide, South Australia in 2013
  • Covers theoretical aspects as well as a few more recent novel ANN applications in natural, environmental and biological systems for modelling small, large and complex systems
  • Featured topics include: Genetic programming, supervised and unsupervised ANNs and fuzzy representations
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Computational Intelligence (SCI, volume 628)

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About this book

This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems.

It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation,  Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems.  The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.    

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Table of contents (21 chapters)

  1. Front Matter

    Pages i-vii
    Download chapter PDF

  2. Artificial Neural Network Modelling: An Introduction

    • Subana Shanmuganathan
    Pages 1-14

  3. Order in the Black Box: Consistency and Robustness of Hidden Neuron Activation of Feed Forward Neural Networks and Its Use in Efficient Optimization of Network Structure

    • Sandhya Samarasinghe
    Pages 15-43

  4. Artificial Neural Networks as Models of Robustness in Development and Regeneration: Stability of Memory During Morphological Remodeling

    • Jennifer Hammelman, Daniel Lobo, Michael Levin
    Pages 45-65

  5. A Structure Optimization Algorithm of Neural Networks for Pattern Learning from Educational Data

    • Jie Yang, Jun Ma, Sarah K. Howard
    Pages 67-82

  6. Stochastic Neural Networks for Modelling Random Processes from Observed Data

    • Hong Ling, Sandhya Samarasinghe, Don Kulasiri
    Pages 83-107

  7. Curvelet Interaction with Artificial Neural Networks

    • Bharat Bhosale
    Pages 109-125

  8. Hybrid Wavelet Neural Network Approach

    • Muhammad Shoaib, Asaad Y. Shamseldin, Bruce W. Melville, Mudasser Muneer Khan
    Pages 127-143

  9. Quantification of Prediction Uncertainty in Artificial Neural Network Models

    • K. S. Kasiviswanathan, K. P. Sudheer, Jianxun He
    Pages 145-159

  10. Classifying Calpain Inhibitors for the Treatment of Cataracts: A Self Organising Map (SOM) ANN/KM Approach in Drug Discovery

    • I. L. Hudson, S. Y. Leemaqz, A. T. Neffe, A. D. Abell
    Pages 161-212

  11. Improved Ultrasound Based Computer Aided Diagnosis System for Breast Cancer Incorporating a New Feature of Mass Central Regularity Degree (CRD)

    • Ali Al-Yousef, Sandhya Samarasinghe
    Pages 213-233

  12. SOM Clustering and Modelling of Australian Railway Drivers’ Sleep, Wake, Duty Profiles

    • Irene L. Hudson, Shalem Y. Leemaqz, Susan W. Kim, David Darwent, Greg Roach, Drew Dawson
    Pages 235-279

  13. A Neural Approach to Electricity Demand Forecasting

    • Omid Motlagh, George Grozev, Elpiniki I. Papageorgiou
    Pages 281-306

  14. Development of Artificial Intelligence Based Regional Flood Estimation Techniques for Eastern Australia

    • Kashif Aziz, Ataur Rahman, Asaad Shamseldin
    Pages 307-323

  15. Artificial Neural Networks in Precipitation Nowcasting: An Australian Case Study

    • Benjamin J. E. Schroeter
    Pages 325-339

  16. Construction of PMx Concentration Surfaces Using Neural Evolutionary Fuzzy Models of Type Semi Physical Class

    • Alejandro Peña, Jesús Antonio Hernández
    Pages 341-368

  17. Application of Artificial Neural Network in Social Media Data Analysis: A Case of Lodging Business in Philadelphia

    • Thai Le, Phillip Pardo, William Claster
    Pages 369-376

  18. Sentiment Analysis on Morphologically Rich Languages: An Artificial Neural Network (ANN) Approach

    • Nishantha Medagoda
    Pages 377-393

  19. Predicting Stock Price Movements with News Sentiment: An Artificial Neural Network Approach

    • Kin-Yip Ho, Wanbin (Walter) Wang
    Pages 395-403

  20. Modelling Mode Choice of Individual in Linked Trips with Artificial Neural Networks and Fuzzy Representation

    • Nagesh Shukla, Jun Ma, Rohan Wickramasuriya, Nam Huynh, Pascal Perez
    Pages 405-422
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Editors and Affiliations

  • School of Computer and Mathematical, Auckland University of Technology (, Auckland, New Zealand

    Subana Shanmuganathan

  • Dept of Informatics & Enabling Tech, Lincoln University, Lincoln, New Zealand

    Sandhya Samarasinghe

Bibliographic Information

  • Book Title: Artificial Neural Network Modelling

  • Editors: Subana Shanmuganathan, Sandhya Samarasinghe

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-319-28495-8

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing Switzerland 2016

  • Hardcover ISBN: 978-3-319-28493-4Published: 12 February 2016

  • Softcover ISBN: 978-3-319-80363-0Published: 30 March 2018

  • eBook ISBN: 978-3-319-28495-8Published: 03 February 2016

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: VII, 472

  • Number of Illustrations: 63 b/w illustrations, 124 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

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