Machine Learning Paradigms: Advances in Deep Learning-based Technological Applications PDF
Preview Machine Learning Paradigms: Advances in Deep Learning-based Technological Applications
Learning and Analytics in Intelligent Systems 18 George A. Tsihrintzis Lakhmi C. Jain Editors Machine Learning Paradigms Advances in Deep Learning-based Technological Applications Learning and Analytics in Intelligent Systems Volume 18 Series Editors George A. Tsihrintzis, University of Piraeus, Piraeus, Greece Maria Virvou, University of Piraeus, Piraeus, Greece Lakhmi C. Jain, Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, NSW, Australia; KES International, Shoreham-by-Sea, UK; Liverpool Hope University, Liverpool, UK Themainaimoftheseriesistomakeavailableapublicationofbooksinhardcopy form and soft copy form on all aspects of learning, analytics and advanced intelligentsystemsandrelatedtechnologies.Thementioneddisciplinesarestrongly related and complement one another significantly. Thus, the series encourages cross-fertilization highlighting research and knowledge of common interest. The series allows a unified/integrated approach to themes and topics in these scientific disciplines which will result in significant cross-fertilization and research dissem- ination. To maximize dissemination of research results and knowledge in these disciplines, the series publishes edited books, monographs, handbooks, textbooks and conference proceedings. More information about this series at http://www.springer.com/series/16172 George A. Tsihrintzis Lakhmi C. Jain (cid:129) Editors Machine Learning Paradigms Advances in Deep Learning-based Technological Applications 123 Editors George A.Tsihrintzis LakhmiC. Jain Department ofInformatics University of Technology University of Piraeus Sydney,NSW,Australia Piraeus, Greece LiverpoolHope University Liverpool, UK ISSN 2662-3447 ISSN 2662-3455 (electronic) Learning andAnalytics in Intelligent Systems ISBN978-3-030-49723-1 ISBN978-3-030-49724-8 (eBook) https://doi.org/10.1007/978-3-030-49724-8 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature SwitzerlandAG2020 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseof illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. 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 for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Dedicated to our loving and supporting families George A. Tsihrintzis and Lakhmi C. Jain Foreword Indisputably,theparadigmsofMachineLearninganddeeplearningwithitsplethora ofapplicationsandadiversityoftechnologicalpursuitshavepositionedthemselves attheforefrontofstudiesonintelligentsystems.Itisverylikelythatthisinterestwill growovertime.Evenforanexperiencedresearcher,thebroadspectrumofdirections and a rapidly changing landscape of the discipline create a quest to navigate effi- ciently across current developments and gain a thorough insight into their accom- plishments and limitations. Thisvolume addresses theevident challenges. Professors George A. Tsihrintzis and Lakhmi C. Jain did a superb job by bringingacollectionofcarefullyselectedandcoherentlystructuredcontributionsto studies on recent developments. The chapters have been written by active researchers who reported on their research agendas and timely outcomes of their investigationsbeingpositionedattheforefrontofMachineLearningmethodologies and its existing practices. A set of 16 chapters delivers an impressive spectrum of authoritatively covered timely topics by being focused on deep learning to sensing (3 chapters), Social MediaandInternetofThings(IoT)(2chapters),healthcare(2chapters)focusedon medical imaging and electroencephalography, systems control (2 chapters), and forecasting and prediction (3 chapters). The last part of the book focuses on per- formance analysis of the schemes of deep learning (3 chapters). Invirtueoftheorganizationofthecontributionsandthewaythekeyideashave been exposed, cohesion of exposure retained, a smooth flow of algorithmic developments, and a wealth of valuable and far-reaching applications have been delivered. Deep learning by itself is a well-established paradigm, however when cast in a context of a certain problem, the domain knowledge contributes to its enhancedusage.Thisisacruxofwellthought-outdesignprocessandherethebook offers this important enhancement to the general design strategy. vii viii Foreword Allinall,thebookisawell-roundedvolumewhichwillbeofgenuineinterestto the academic community as well as practitioners with a vital interest in Machine Learning. The Editors should be congratulated on bringing this timely and useful research material to the readers. Prof. Dr. Witold Pedrycz Fellow IEEE University of Alberta Alberta, Canada Systems Research Institute Polish Academy of Sciences Warsaw, Poland Preface Artificial Intelligence [1], in general, and Machine Learning [2], in particular, are scientific areas of very active research worldwide. However, while many researchers continuously report on new findings and innovative applications, skeptics warn us of potentially existential threats to humankind [3, 4]. The debate among zealots and skeptics of Artificial Intelligence is ongoing and only in the future we may seethis issue resolved. Forthe moment, it seems that perhaps Nick Bostrom’s statement constitutes the most reliable view: “…artificial intelligence (AI) is not an ongoing or imminent global catastrophic risk. Nor is it as uncon- troversially a serious cause for concern. However, from a long term perspective, thedevelopmentofgeneralartificialintelligenceexceedingthatofthehumanbrain canbeseenasoneofthemainchallengestothefutureofhumanity(arguably,even as the main challenge)” [5]. Forthemoment,itisalsocertainthataneweraisarisinginhumancivilization, which has been called “the 4th Industrial Revolution” [6,7], and Artificial Intelligence is one of the key technologies driving it. Within the broad discipline of Artificial Intelligence, a sub-field of Machine Learning, called Deep Learning, stands out due to its worldwide pace of growth both in new theoretical results and in new technological application areas (for example,see[8]foran-easy-to-followfirstreadonDeepLearning).Whilegrowing at a formidable rate, Deep Learning is also achieving high scores in successful technological applications and promises major impact in science, technology, and the society. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field,(4)DeepLearninginSystemsControl,(5)DeepLearninginFeatureVector Processing, and (6) Evaluation of Algorithm Performance. ix x Preface This research book is directed toward professors, researchers, scientists, engi- neers, and students in computer science-related disciplines. It is also directed toward readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest. We hope that all of them will find it useful and inspiring in their works and researches. Wearegratefultotheauthorsandreviewersfortheirexcellentcontributionsand visionary ideas. We are also thankful to Springer for agreeing to publish this book initsLearningandAnalyticsinIntelligentSystemsseries.Last,butnotleast,weare grateful to the Springer stafffor their excellent work in producing this book. Piraeus, Greece George A. Tsihrintzis Sydney, Australia Lakhmi C. Jain References 1. E.Rich,K.Knight,S.B.Nair,ArtificialIntelligence,3rdedn.(TataMcGraw-HillPublishing Company,2010) 2. J.Watt,R.Borhani,A.K.Katsaggelos,MachineLearningRefined—Foundations,Algorithms andApplications,2ndedn.(CambridgeUniversityPress,2020) 3. J.Barrat,OurFinalInvention—ArtificialIntelligenceandtheEndoftheHumanEra,Thomas DunnBooks,2013 4. N.Bostrom,Superintelligence—Paths,Dangers,Startegies(OxfordUniversityPress,2014) 5. N. Bostrom, M. M. Ćirković (eds.), Global Catastrophic Risks, (Oxford University Press, 2008),p.17 6. J. Toonders, Data Is the New Oil of the Digital Economy, Wired (https://www.wired.com/ insights/2014/07/data-new-oil-digital-economy/) 7. K.Schwabd,TheFourthIndustrialRevolution—WhatItMeansandHowtoRespond,Foreign Affairs (2015), https://www.foreignaffairs.com/articles/2015-12-12/fourth-industrial-revolution Accessed12December2015 8. J.Patterson,A.Gibson,DeepLearning—APractitioner’sApproach,O’Reilly,2017
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