Artificial Neural Network Modelling PDF
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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] About this Series The series “Studies in Computational Intelligence” (SCI) publishes new develop- mentsandadvancesinthevariousareasofcomputationalintelligence—quicklyand with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the worldwide distribution, which enable both wide and rapid dissemination of research output. More information about this series at http://www.springer.com/series/7092 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 LibraryofCongressControlNumber:2015960415 ©SpringerInternationalPublishingSwitzerland2016 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authorsortheeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinor foranyerrorsoromissionsthatmayhavebeenmade. Printedonacid-freepaper ThisSpringerimprintispublishedbySpringerNature TheregisteredcompanyisSpringerInternationalPublishingAGSwitzerland 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.
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