Artificial Neural Network Modelling

468 Pages·2016·13.85 MB·English

by  Subana Shanmuganathan

Studies in Computational Intelligence 628
Subana Shanmuganathan
Sandhya Samarasinghe Editors
Artificial
Neural
Network
Modelling
Studies in Computational Intelligence
Volume 628
Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
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Subana Shanmuganathan
Sandhya Samarasinghe
Editors
fi
Arti cial Neural Network
Modelling
123
Editors
Subana Shanmuganathan Sandhya Samarasinghe
Schoolof Computer andMathematical Department ofInformatics andEnabling
Sciences Technologies
Auckland University of Technology LincolnUniversity
Auckland Christchurch
NewZealand NewZealand
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN978-3-319-28493-4 ISBN978-3-319-28495-8 (eBook)
DOI 10.1007/978-3-319-28495-8
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Contents
Artificial Neural Network Modelling: An Introduction . . . . . . . . . . . . . 1
Subana Shanmuganathan
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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Sandhya Samarasinghe
Artificial Neural Networks as Models of Robustness
in Development and Regeneration: Stability of Memory
During Morphological Remodeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Jennifer Hammelman, Daniel Lobo and Michael Levin
A Structure Optimization Algorithm of Neural Networks
for Pattern Learning from Educational Data . . . . . . . . . . . . . . . . . . . . 67
Jie Yang, Jun Ma and Sarah K. Howard
Stochastic Neural Networks for Modelling Random
Processes from Observed Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Hong Ling, Sandhya Samarasinghe and Don Kulasiri
Curvelet Interaction with Artificial Neural Networks . . . . . . . . . . . . . . 109
Bharat Bhosale
Hybrid Wavelet Neural Network Approach . . . . . . . . . . . . . . . . . . . . . 127
Muhammad Shoaib, Asaad Y. Shamseldin, Bruce W. Melville
and Mudasser Muneer Khan
Quantification of Prediction Uncertainty in Artificial Neural
Network Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
K.S. Kasiviswanathan, K.P. Sudheer and Jianxun He
v
vi Contents
Classifying Calpain Inhibitors for the Treatment
of Cataracts: A Self Organising Map (SOM) ANN/KM
Approach in Drug Discovery. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
I.L. Hudson, S.Y. Leemaqz, A.T. Neffe and A.D. Abell
Improved Ultrasound Based Computer Aided Diagnosis System
for Breast Cancer Incorporating a New Feature of Mass Central
Regularity Degree (CRD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
Ali Al-Yousef and Sandhya Samarasinghe
SOM Clustering and Modelling of Australian Railway
Drivers’ Sleep, Wake, Duty Profiles. . . . . . . . . . . . . . . . . . . . . . . . . . . 235
Irene L. Hudson, Shalem Y. Leemaqz, Susan W. Kim, David Darwent,
Greg Roach and Drew Dawson
A Neural Approach to Electricity Demand Forecasting. . . . . . . . . . . . . 281
Omid Motlagh, George Grozev and Elpiniki I. Papageorgiou
Development of Artificial Intelligence Based Regional Flood
Estimation Techniques for Eastern Australia . . . . . . . . . . . . . . . . . . . . 307
Kashif Aziz, Ataur Rahman and Asaad Shamseldin
Artificial Neural Networks in Precipitation Nowcasting:
An Australian Case Study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325
Benjamin J.E. Schroeter
Construction of PM Concentration Surfaces Using Neural
x
Evolutionary Fuzzy Models of Type Semi Physical Class . . . . . . . . . . . 341
Alejandro Peña and Jesús Antonio Hernández
Application of Artificial Neural Network in Social
Media Data Analysis: A Case of Lodging Business
in Philadelphia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369
Thai Le, Phillip Pardo and William Claster
Sentiment Analysis on Morphologically Rich Languages:
An Artificial Neural Network (ANN) Approach . . . . . . . . . . . . . . . . . . 377
Nishantha Medagoda
Predicting Stock Price Movements with News Sentiment:
An Artificial Neural Network Approach. . . . . . . . . . . . . . . . . . . . . . . . 395
Kin-Yip Ho and Wanbin (Walter) Wang
Modelling Mode Choice of Individual in Linked Trips
with Artificial Neural Networks and Fuzzy Representation. . . . . . . . . . 405
Nagesh Shukla, Jun Ma, Rohan Wickramasuriya, Nam Huynh
and Pascal Perez
Contents vii
Artificial Neural Network (ANN) Pricing Model for Natural
Rubber Products Based on Climate Dependencies . . . . . . . . . . . . . . . . 423
Reza Septiawan, Arief Rufiyanto, Sardjono Trihatmo,
Budi Sulistya, Erik Madyo Putro and Subana Shanmuganathan
A Hybrid Artificial Neural Network (ANN) Approach
to Spatial and Non-spatial Attribute Data Mining: A Case
Study Experience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443
Subana Shanmuganathan
fi
Arti cial Neural Network Modelling:
An Introduction
Subana Shanmuganathan
Abstract While scientists from different disciplines, such as neuroscience, medi-
cine and high performance computing, eagerly attempt to understand how the
human brain functioning happens, Knowledge Engineers in computing have been
successful in making use of the brain models thus far discovered to introduce
heuristicsintocomputationalalgorithmicmodelling.Gainingfurtherunderstanding
onhumanbrain/nervecellanatomy,structure,andhowthehumanbrainfunctions,
isdescribedtobesignificantespecially,todevisetreatmentsforpresentlydescribed
as incurable brain and nervous system related diseases, such as Alzheimer’s and
epilepsy. Despite some major breakthroughs seen over the last few decades neu-
roanatomists and neurobiologists of the medical world are yet to understand how
we humans think, learn and remember, and how our cognition and behaviour are
linked. In this context, the chapter outlines the most recent human brain research
initiatives following which early Artificial Neural Network (ANN) architectures,
components, related terms and hybrids are elaborated.
1 Introduction
Neuroanatomists and Neurobiologists of the medical world are yet to discover the
exactstructure andtherealprocessingthattakesplaceinhumannervecellsandto
biologically model the human brain. This is despite the breakthroughs made by
research ever since the human beings themselves began wondering how their own
thinkingabilityhappens.Morerecently,therehasbeensomemajorinitiativeswith
unprecedented funding, that emphasise the drive, to accelerate research into
unlockingthemysteriesofhumanbrain’suniquefunctioning.Oneamongsuchbig
fundingprojectsistheHumanBrainProject(HBP)initiatedin2013.TheHBPisa
European Commission Future and Emerging Technologies Flagship that aims to
S.Shanmuganathan(&)
AucklandUniversityofTechnology,Auckland,NewZealand
e-mail:[email protected]
©SpringerInternationalPublishingSwitzerland2016 1
S.ShanmuganathanandS.Samarasinghe(eds.),ArtificialNeural
NetworkModelling,StudiesinComputationalIntelligence628,
DOI10.1007/978-3-319-28495-8_1
2 S.Shanmuganathan
understand what makes the brain unique, the basic mechanisms behind cognition
and behaviour, how to objectively diagnose brain diseases, and to build new
technologies inspired by how the brain computes. There are 13 subprojects
(SPs) within this ten-year one-billion pound HBP programme. The scientists
involved in the HBP accept that the current computer technology is insufficient to
simulate complex brain functioning. However, they are hopeful of having suffi-
ciently powerful supercomputers to begin the first draft simulation of the human
brain within a decade. It is surprising that despite the remarkable and ground
breaking innovations achieved in computing leading to transformations never seen
in human development, even the modern day’s most powerful computers still
struggle to do things that humans find instinctive. “Even very young babies can
recognise their mothers but programming a computer to recognise a particular
personispossiblebutveryhard.”[1].Hence,SP9scientistsofHBPareworkingon
developing“neuromorphiccomputers-machines”thatcanlearninasimilarmanner
to how the brain functions. The other major impediment in this regard is the
humongous amount of data that will be produced, which is anticipated to require
massive amount of computing memory. Currently, HBP scientists of The
SpiNNakerprojectattheUniversityofManchesterarebuildingamodel,whichwill
mimic1%ofbrainfunction.Unlockingbrainfunctioningsecretsinthismanneris
anticipatedtoyieldmajorbenefitsininformationtechnologyaswell.Theadventof
neuromorphic computers and knowledge could lead to the production of computer
chips with specialised cognitive skills that truly mimic those of the human brain,
such as the ability to analyse crowds, or decision-making on large and complex
datasets.Thesedigitalbrainsshouldalsoallowresearcherstocomparehealthyand
diseased brains within computer models [2].
Meanwhile, across the Atlantic, the unveiling of Brain Research Through
Advancing Innovative Neurotechnologies—or BRAIN in the USA by President
Obama took place in 2013 [3]. This was announced to keep up with the brain
researchinitiated inEurope.The BRAIN projectwas said tobegin in2014 and be
carriedoutbybothpublicandprivate-sectorscientiststomapthehumanbrain.The
President announced an initial $100 m investment to shed light on how the brain
works and to provide insight into diseases such as Alzheimer’s, Parkinson’s, epi-
lepsy and many more. At the White House inauguration, President Obama said:
“Thereisthisenormousmysterywaiting tobeunlocked,andtheBRAIN initiative
willchangethatbygivingscientiststhetoolstheyneedtogetadynamicpictureof
thebraininactionandtobetterunderstandhowwethinkandlearnandremember.
And that knowledge will be transformative.” In addition, the US President as well
pointed out a lack of research in this regard, “As humans we can identify galaxies
lightyearsaway,wecanstudyparticlessmaller thantheatom,butwestillhaven’t
unlocked the mystery of the 3 lb of matter that sits between our ears,” [3].
With that introduction to contemporary research initiatives to unlock unique
human brain functioning, Sect. 2 looks at the early brain models in knowledge
engineering following which initial ANN models and their architectures are elab-
orated. In the final section some modern day ANN hybrids are outlined.