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Multi-Chaos,Fractaland Multi-FractionalArtificial IntelligenceofDifferent ComplexSystems Editedby
YelizKaraca
UniversityofMassachusettsMedicalSchool,Worcester,MA,UnitedStates
DumitruBaleanu
C¸ankayaUniversity,Ankara,TurkeyandInstituteofSpaceSciences,Magurele-Bucharest,Romania
Yu-DongZhang
UniversityofLeicester,Leicester,UnitedKingdom
OsvaldoGervasi
PerugiaUniversity,Perugia,Italy
MajazMoonis
UniversityofMassachusettsMedicalSchool,Worcester,MA,UnitedStates
AcademicPressisanimprintofElsevier 125LondonWall,LondonEC2Y5AS,UnitedKingdom 525BStreet,Suite1650,SanDiego,CA92101,UnitedStates 50HampshireStreet,5thFloor,Cambridge,MA02139,UnitedStates TheBoulevard,LangfordLane,Kidlington,OxfordOX51GB,UnitedKingdom
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ThisbookandtheindividualcontributionscontainedinitareprotectedundercopyrightbythePublisher(otherthanasmay benotedherein).
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TypesetbyTNQTechnologies
1.Introduction
YelizKaracaandDumitruBaleanu
2.Theoryofcomplexity,originand complexsystems
YelizKaraca
1.Introduction 9
2.Theoryofcomplexity,originandcomplex systems 13
2.1Abriefhistoryofcomplexityandthe relatedareasofdifferentcomplex systems 13
2.2Theoriespertainingtocomplexity andtheirhistoricalaccount 14
3.Complexorderprocessestowardmodern scientificpath:fromDarwinandonwards 15
3.1Aconceptualoutline:complexity andcomplexsystems 17
4.Concludingremarksandfuturedirections 17 References 18
3.Multi-chaos,fractalandmultifractionalAIindifferentcomplex systems
YelizKaraca
3.Artificialintelligencewayofthinking, processes,complexityandcomplex systems
4.High-performancecomputingand computationalintelligence applicationswithamulti-chaos perspective
DamianoPerri,MarcoSimonetti, OsvaldoGervasiandSergioTasso
3.High-performancecomputingapproaches tosolvingcomplexproblems
complexproblemsbasedon computationalintelligence
6.Thedilemmaofrespectingprivacyin multi-chaossituations
5.Humanhypercomplexity.Errorand unpredictabilityincomplex multichaoticsocialsystems
PieroDominici
1.Introduction 77
2.Thecomplexityoflivingenergyand livingbeings 78
3.Complicated,complex,and hypercomplexsystems 79
4.Takingastepback:abriefhistoryof complexity 80
5.Anepistemologyoferror 84
6.“Objects”arerelations 85
7.Everythingdependsoneverythingelse 87
8.Cognitivecages 88
9.e ` troppo,otropporavvicinato? 90 References 90
6.Multifractalcomplexityanalysisbaseddynamicmediatext categorizationmodelsbynatural languageprocessingwithBERT YelizKaraca,Yu-DongZhang, AhuDereliDursunandShui-HuaWang
1.Introduction 95
2.Dataandmethodology 99
2.1Complexmediatextdata 99
2.2Fractalcomplexityanalysis 99
2.3Naturallanguageprocessing 103
3.Experimentalresultsanddiscussion 104
4.Conclusionandfuturedirections 111 References 113
7.Mittag-Lefflerfunctionswith heavy-taileddistributions’algorithm basedondifferentbiologydatasets tobefitforoptimummathematical models’strategies DumitruBaleanuandYelizKaraca
1.Introduction 117
1.1Themotivationoftheintegrative methodproposed 119
2.Complexbiologicaldatasetsand methodology 120
2.1Complexbiologicaldatasets 120
2.2Methodology 121
3.Experimentalresultsanddiscussion:computationalapplicationofMittag-Leffler functionbasedonheavy-taileddistributionsfordifferentbiologicaldatasets 123
3.1ComputationalapplicationsforfittingMittag-Lefflerfunctionbasedon
heavy-taileddistributionstothecancercelldataset 124
3.2ComputationalapplicationsforfittingMittag-Lefflerfunctionbasedon heavy-taileddistributionstothe diabetesdataset 125
4.Conclusionandfuturedirections 127 References 131
8.Artificialneuralnetworkmodelingof systemsbiologydatasetsfitbasedon Mittag-Lefflerfunctionswith heavy-taileddistributionsfor diagnosticandpredictiveprecision medicine
YelizKaracaandDumitruBaleanu
1.Introduction 133
1.1Themotivationoftheintegrative methodproposed 135
2.Complexbiologicaldatasetsand methodology 136
2.1Complexbiologicaldatasets 136
2.2Methodology 136
3.Experimentalresultsanddiscussions: artificialneuralnetworkmodelingof complexbiologicaldatasetstobefit basedonMittag-Lefflerfunctionwith heavy-taileddistributionsfordiagnosis andprediction 140
3.1Artificialneuralnetworkmodelingof cancercelldatasetstobefitbased onMittag-Lefflerfunctionwith heavy-taileddistributionsfor diagnosisandprediction 140
3.2Artificialneuralnetworkmodelingof diabetesdatasetstobefitbasedon Mittag-Lefflerfunctionwithheavytaileddistributionsfordiagnosis andprediction 141
4.Conclusionandfuturedirections 146 References 147
9.Computationalfractional-order calculusandclassicalcalculus AIforcomparativedifferentiability predictionanalysesof complex-systems-grounded paradigm
YelizKaracaandDumitruBaleanu
1.Introduction 149
1.1.Themotivationandchallenges oftheintegrativemethod proposed 152
2.Datasetsandmethodology 153
2.1Themodelingofdifferentcomplex datasets 153
2.2Methods 154
2.3Artificialneuralnetworks 156
3.Experimentalresultsanddiscussion 157
3.1Computationalapplicationof Caputofractional-orderderivative models 157
3.2Computationalapplicationof Caputofractional-orderderivative andclassicalderivativemodelsfor comparativepredictionanalysesof cancercellandstrokewithFFBP algorithm 160
4.Conclusionandfuturedirections 162 References 166
10.Patternformationinducedby fractional-orderdiffusivemodelof COVID-19
NaveedIqbalandYelizKaraca
1.Introduction 169 2.Model 171
2.1Stabilityanalysisof E2 j1 ; j2 ; j3 172
3.Spatiotemporalmodel 172
3.1Conditionsforturinginstability 173
4.Weaklynonlinearanalysis 174
5.Numericalsimulation 179
6.Conclusion 182 References 184
11.Prony’sseriesandmodern fractionalcalculus
JordanHristov
1.Introduction 187
2.Prony’smethod 187
3.Exponentialsumsapproximationof functions 188
3.1Exponentialsum approximationfor t b 188
3.2Exponentialsums approximationof Mittag-Lefflerfunction 189
3.3Exponentialsums approximationofthe Kohlrauschfunction 189
4.Fractionaloperatorsinappliedrheology 190
4.1Caputoderivative 190
4.2Caputo-Fabriziofractionaloperator 190
5.Modelinglinearviscoelasticresponses 191
5.1Constitutiveequations:time domain 191
5.2Frequencydomain:sinusoidal responses 192
5.3Responsefunction 192
6.Prony’sseriesinlinearviscoelasticity 192
6.1Example1.completelymonotone responsesasProny’sseriesand relateddiscretespectra 192
6.2Example2:KWWasastressrelaxationfunction 194
6.3Example3.Mittag-Lefflerfunction asstressrelaxationmodulus 195
6.4Example4.TheBagley-Torvik equation 197
7.Finalcomments 198 References 198
12.Achainofkineticequationsof Bogoliubov Born Green Kirkwood Yvonanditsapplication tononequilibriumcomplexsystems Nikolai(Jr)Bogoliubov,Mukhayo YunusovnaRasulova,TohirVohidovichAkramov andUmarbekAvazov
1.Introduction 201
2.Formulationoftheproblem 202
3.ThesolutionoftheBBGKYhierarchyfor many-typeparticlesystems 204
3.1Introduction 204
3.2Formulationandsolutionofthe problem 204
4.DerivationoftheGross Pitaevskii equationfromtheBBGKYhierarchy 206
4.1Formulationoftheproblem 207
4.2Derivationofhierarchyof kineticequationsfor correlationmatrices 207
4.3Forthecase s ¼ 1 209
4.4Anothermethodforderivingthe Gross Pitaevskiiequation 210
5.Summary 211 References 211 Furtherreading 213
13.Hearinglossdetectionincomplex settingbystationarywaveletRenyi entropyandthree-segment biogeography-basedoptimization YabeiLi,JundingSunandChongYao
1.Introduction 215
2.Dataset 216
3.Methods 216
3.1Featureextraction stationary waveletRenyientropy 218
3.2Singlehiddenlayerfeedforward neuralnetwork 219
3.3Three-segmentbiogeography-based optimization 220
4.Implementation 222
5.Measure 222
6.Experimentresultsanddiscussions 224
6.1Statisticalanalysisoftheproposed method 224
6.2Biogeography-basedoptimization versusthree-segmentbiogeography-basedoptimization 224
6.3Optimaldecompositionlevel 224
6.4Comparisontostate-of-the-art approaches 225
7.Conclusions
14.Shannonentropy-basedcomplexity quantificationofnonlinear stochasticprocess:diagnosticand predictivespatiotemporal uncertaintyofmultiplesclerosis subgroups
YelizKaracaandMajazMoonis
1.Introduction 231
2.Materialsandmethods 234
2.1Materials 234
2.2Methods 234
2.3k-Nearestneighboranddecision treealgorithms 237
3.Experimentalresults 238
4.Conclusionandfuturedirections 241 References 243
15.ChestX-rayimagedetectionfor pneumoniaviacomplex convolutionalneuralnetworkand biogeography-basedoptimization XiangLi,MengyaoZhaiandJundingSun
16.Facialexpressionrecognitionby DenseNet-121
BinLi
17.Quantitativeassessmentoflocal warmingbasedonurbandynamics
LuciaSaganeiti,AngelaPilogallo, FrancescoScorza,BeniaminoMurgante, ValentinaSantarsieroandGabrieleNole `
18.Managinginformationsecurityrisk andInternetofThings(IoT)impact onchallengesofmedicinal problemswithcomplexsettings: acompletesystematicapproach
EaliStephenNealJoshua, DebnathBhattacharyyaandN.ThirupathiRao
1.Introductiontoinformationsecurity 291
1.1Variousvulnerabilitiesin healthcare 292
2.Informationsecurityinhealthcare 296
2.1Backgroundofhealthinformation privacyandsecurity 296
2.2Stateofinformationsecurity researchinhealthcare 298
2.3Threatstoinformationprivacy 298
3.ImpactofIoTinmedicalproblems 300
3.1InternetofThingsinhealthcare 300
3.2ChallengesofIoTinmedical problems 301
3.3ApplicationsofIoTinhealthcare 302
4.Medicalproblemswithcomplexsettings 303
4.1Thechallengeofinteroperability 303
4.2Keepingupwitholdtechnology 303
4.3User-unfriendlyinterfaces 303
4.4Exacerbatingmalpractice claims 303
4.5Overcomplicatedasset tracking 304
4.6Overallimplementation 304
5.IoTandinformationsecurity 304
5.1UnderstandingtheneedsofIoT security 304
5.2Datainteroperabilityand informationsecurity 305
5.3Informationsecurityissuesof e-health 306
5.4Healthcareinformationsystem withcomplexsettings 306
5.5Providers’perspectiveofregulatory compliance 307
5.6Information-accesscontrol 308
6.Challengesofmedicinalproblemsusing IoT:acasestudy 309 7.Conclusion 309 References 310 19.Anextensivediscussionon utilizationofdatasecurityand bigdatamodelsforresolving healthcareproblems
N.ThirupathiRao,DebnathBhattacharyya andEaliStephenNealJoshua
1.Informationsecurity 311 1.1Confidentiality 311 1.2Integrity 311 1.3Availability 311 1.4Informationsecuritypolicy 312 1.5Informationsecuritymeasures 312 1.6Managinginformationsecurity 312 2.InternetofThings 312 2.1ConnectingwiththeIoT 313 2.2IoTforphysicians 313 2.3IoTforhospitals 313 2.4IoTforhealthinsurancecompanies 314 2.5IoTforpatients 314 2.6Redefininghealthcare 314 3.InformationsecurityandIoT 315 3.1Informationsecuritythreats 315 3.2Informationsecuritythreats? 315
4.DatasecurityandIoTinmedicine 316 4.1BenefitsofIoThealthcare 316 4.2Challengesininformationsecurity andIoTwithrespecttomedicine 317 5.Bigdataanditsapplications 318
6.IoTandbigdataapplicationsin medicine 319
7.Complexsysteminhealthcare 321
8.RoleofIoTandbigdataapplicationsin medicine 323 9.Conclusion 323 References 323
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Listofcontributors TohirVohidovichAkramov,NuclearPhysics,Academy ofSciencesofUzbekistan,Tashkent,Uzbekistan;NationalUniversityofUzbekistan,Tashkent,Uzbekistan
UmarbekAvazov,NuclearPhysics,AcademyofSciences ofUzbekistan,Tashkent,Uzbekistan
DumitruBaleanu,ÇankayaUniversity,Ankara,Turkey; InstituteofSpaceScience,Magurele,Bucharest, Romania
DebnathBhattacharyya,KoneruLakshmaiahEducation Foundation,Vaddeswaram,Guntur,AndhraPradesh, India
Nikolai(Jr)Bogoliubov,SteklovInstituteofMathematics oftheRussianAcademyofSciences,Moscow,Russia
PieroDominici,CHAOS InternationalResearchandEducationProgramme “ComplexHumanAdaptiveOrganizationsandSystems”,PerugiaUniversity,Italy; DepartmentofPhilosophy,Social,HumanandEducationalSciences,UniversityofPerugia,Italy;WAASWorldAcademyofArtandScience,Rome,Italy
AhuDereliDursun,InstituteofSocialSciences,CommunicationStudies,IstanbulBilgiUniversity,Istanbul, Turkey
OsvaldoGervasi,UniversityofPerugia,Perugia,Italy
JordanHristov,UniversityofChemicalTechnologyand Metallurgy,Sofia,Bulgaria
NaveedIqbal,UniversityofHa’il,Ha’il,SaudiArabia
YelizKaraca,UniversityofMassachusettsMedical School,Worcester,MA,UnitedStates
XiangLi,HenanPolytechnicUniversity,Jiaozuo,Henan, PRChina
YabeiLi,HenanPolytechnicUniversity,Jiaozuo,Henan, PRChina
BinLi,HenanPolytechnicUniversity,Jiaozuo,Henan,PR China
MajazMoonis,UniversityofMassachusettsMedical School,Worcester,MA,UnitedStates
BeniaminoMurgante,UniversityofBasilicata,Viadell’AteneoLucano,Potenza,Italy
EaliStephenNealJoshua,Vignan’sInstituteofInformationTechnology(A),Visakhapatnam,AndhraPradesh,India
GabrieleNolè,CNR-IMAA,C.daSantaLojaZona IndustrialeTitoScalo,Potenza,Italy
DamianoPerri,UniversityofFlorence,Firenze,Italy; UniversityofPerugia,Perugia,Italy
AngelaPilogallo,UniversityofBasilicata,Viadell’Ateneo Lucano,Potenza,Italy
N.ThirupathiRao,Vignan’sInstituteofInformation Technology(A),Visakhapatnam,AndhraPradesh, India
MukhayoYunusovnaRasulova,NuclearPhysics,AcademyofSciencesofUzbekistan,Tashkent,Uzbekistan
LuciaSaganeiti,UniversityofL’Aquila,L’Aquila,Italy
ValentinaSantarsiero,UniversityofBasilicata,Viadell’AteneoLucano,Potenza,Italy;CNR-IMAA,C.da SantaLojaZonaIndustrialeTitoScalo,Potenza,Italy
FrancescoScorza,UniversityofBasilicata,Viadell’AteneoLucano,Potenza,Italy
MarcoSimonetti,UniversityofFlorence,Firenze,Italy; UniversityofPerugia,Perugia,Italy
JundingSun,HenanPolytechnicUniversity,Jiaozuo, Henan,PRChina
SergioTasso,UniversityofPerugia,Perugia,Italy
Shui-HuaWang,UniversityofLeicester,Leicester, UnitedKingdom
ChongYao,HenanPolytechnicUniversity,Jiaozuo, Henan,PRChina
MengyaoZhai,HebiPolytechnic,Hebi,Henan,PRChina
Yu-DongZhang,UniversityofLeicester,Leicester, UnitedKingdom
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Preface Multi-Chaos,FractalandMulti-FractionalArti ficial IntelligenceofDifferentComplexSystems isaneditedbook thataddressesdifferentuncertainprocessesinherentinthe complexsystems,attemptingtoprovideglobalandrobust optimizedsolutionsdistinctivelythroughmultifarious methods,technicalanalyses,modeling,optimization processes,numericalsimulations,casestudiesandapplicationsnotexcludingtheoreticalaspectsofcomplexity. Basedonadvancedmathematicalfoundation,ouredited bookforegroundsmultichaos,fractal,multifractional, fractionalcalculus,fractionaloperators,quantum,wavelet, entropy-basedapplicationsandartifi cialintelligence (AI)mathematics-informedanddata-drivenprocesses. Theprimaryfocusandpurpose,herein,isrelatedtothe needsandsolutionsfornewanalyticstrategiesand mathematicalmodelingtoattainaccurate,timelyand optimizedsolutions.
Appealingtoaninterdisciplinarynetworkofscientists andresearcherstodisseminatethetheoryandapplicationof multichaos,fractalandmultifractionalAIofdifferent complexsystemsinmedicine,neurology,mathematics, physics,biology,chemistry,informationtheory,engineering,computerscience,socialsciencesandotherfarreachingdomains,theoverarchingaimistoenablethe provisionofglobalandoptimizedrobustsolutions distinctivelywithaperspectivethroughmultifarious methods,differentfromtheconventionalperspective,as directedtowardparadoxicalsituations,differentuncertain processes,nonlineardynamicsystemsinherentincomplex systems.Elaboratingonthemostintriguingtheoreticalaspects,modelingandapplicationsofmultichaos,fractal, multifractional,fractionalcalculus,fractionaloperators, quantum,wavelet,entropy-basedapplicationsandAI mathematics-informedanddata-drivenprocessesaround thecommonthemeofcomplexityandnonlinearityunder consideration,currentapplications,futuredirectionsand perspectives,limitations,strengthsandopportunitiesare providedinoureditedbookforscientists,researchers, students,andanyonewhoisinterestedintheenigmaof complexity.Theinvaluableinputsof31expertsworldwide specializedinmathematics,physics,biology,chemistry, neurology,informationtheory,computerscience,engineering,appliedsciences,sociology,philosophyand
communication,amongothers,from11countries,aresignificanttoestablishaholisticbodyofworkandspectrum, owingtotheirpersonalcontributionsintheirrespective fi elds.Theeditedbookincludesatotalof19chapters,as hasbeeninspiredbytheaforesaidconsiderations;the chaptersalongthebookareoutlinedintermsoftheir contentasfollows.
Chapter1isthe “Introduction” (byYelizKaracaand DumitruBaleanu),whichprovidesthebasicmotivations underlyingcomplexity,complexitythinkingandtheory alongwiththeimportantroleofcomputationalprocesses withextensiveapplicationsinintegrationwithfractals, multifractals,fractionalmethods,chaos,nonlineardynamicalpropertiesandstochasticelements.Computational technologies,withmachinelearningasthecorecomponent ofAI,isstatedtohavebroaduseandtransformativeimpacts,enablingthetrainingofcomplexdatatoautomateor augmentsomeofthecriticalhumanskills.Thus,itis presentedthatoureditedbookforegroundsmultichaos, fractal,andmultifractionalintheeraofAI,whichrequires theintegrationofadvancedmathematicalmodelsand mathematics-informedframeworksaswellasAIaddressingfractal,fractionalcalculus,fractionaloperators,quantum,wavelet,entropy-basedapplicationsasidefromthe meansofmodeling,technicalanalysesandnumerical simulationsassomeofthemostbroadlyemployedmethods forthesolutionofmultifacetedproblemscharacterizedby nonlinearity,nonregularity,self-similarityandmanyother properties,frequentlyencounteredindifferentcomplex systems.Accordingly,thechapterpresentstheoverarching aimoftheeditedbookofours,itskeyobjectives,motivationalaspectsandthedetailedcontentofallother chapterspresentedherein.
Chapter2entitled “TheoryofComplexity,Origin andComplexSystems” (byYelizKaraca)attemptsto touchonthepossibledimensionsofcomplexsystemsin different fieldswithafocusonorigin-related,historical, evolutionaryandepistemologicalviewpointsofcomplexity bytakingintoconsiderationthevariousmultipleinteracting factorsofsystemswiththegoalofprovidingaglobalunderstandingbetweenvariables,sensitivitytoinitialcontrol, andstrange,nonperiodicandunpredictabletimeevolution. Thedetailedpresentationinthechaptertriestoensurethat
thefoundationforthecomplexsystems’ interpretationscan beexploredindifferentrelatedareasofcomplexity.
Chapter3 “Multichaos,FractalandMultifractionalAI inDifferentComplexSystems” (byYelizKaraca)provides anoverviewincludingmultichaos,fractal,fractionalandAI wayofthinkingwithregardtothesolutionsofthecomplex systemproblemsconcernedwithnaturalandsocialsciences.Ethicaldecision-makingframeworksandstrategies relatedtobigdataandAIapplicationsarealsopresentedin detailtoenableassistancefortheidentificationofthe relatedproblemsindifferentsettingsandthinking methodicallysothattensionsbetweenconflictingaspects canbemanagedsystematically.
Chapter4, “High-PerformanceComputingand ComputationalIntelligenceApplicationswithMultichaos Perspective” (byDamianoPerri,MarcoSimonetti,Osvaldo GervasiandSergioTasso),addressestheexperienceofthe COVID-19pandemic,whichhasacceleratedmanychaotic processesinmodernsocietybesidesrevealingtheneedto understandcomplexprocessestoachievecommonwellbeinginaveryseriousandemergentway.Asetofbest practicesandcasestudies,whichprovideassistancetothe researcherswhilehandlingcomputationallycomplex problems,arepresentedinthechapter,providingageneral sketchofvarioustopics,whichcouldbeofhelptoresearchersanddeveloperstodealwithcomplexandchaotic situationswithinthescopeofmachinelearningandthe issueofprivacyincludingtherecentrelatedregulations.
Chapter5 “HumanHypercomplexity.Errorand UnpredictabilityinComplexMultichaoticSocialSystems” (byPieroDominici)hastheperspectivethattraditional linearmodelsanddeterministicapproachescannolonger becapableoftheanalyzingthedynamicsofunstable dynamics.Thechapterprovidesperspectivesonthe complexityoflivingenergyandlivingbeings,alongwith 12essentialplanesofawareness,thecharacteristicsof complicated,complexandhypercomplexsystems,epistemologyoferrorandcomplexandchaoticcharacteristicsof socialsystems.
Chapter6 “MultifractalComplexityAnalysis-Based DynamicMediaTextCategorizationModelsbyNatural LanguageProcessingwithBERT” (byYelizKaraca, Yu-DongZhang,AhuDereliDursunandShui-HuaWang) addressesthechallengesandcomplexityinherentindigitalbasedcomplexmediatexts.Thestudyputsforththe significanceofthefractalbehaviorwhilearticulatingthe distinguishingqualityofBERTowingtoitscapabilityof classi ficationaccuracyandadaptivenessintointegrated methodologies.
Chapter7(PartI) “Mittag-LefflerFunctionsWith Heavy-TailedDistributions’ AlgorithmBasedonDifferent BiologyDatasetstobeFitforOptimumMathematical Models’ Strategies” (byDumitruBaleanuandYeliz Karaca)addressesthechallengesofintegratingfractional
calculusincasesofcomplexity,whichnecessitatesan effectiveuseofempirical,numerical,experimental,and analyticalmethodstotacklecomplexity.TheproposedintegratedapproachinthischapterusestheMittag Leffl er functionwithtwoparameters (a, fl) forthepurposeof investigatingthedynamicsoftwodiseases:cancercelland diabetes.
Chapter8(PartII) “Arti ficialNeuralNetworkModeling ofSystemsBiologyDatasetsFitBasedonMittag-Leffl er FunctionswithHeavy-TailedDistributionsforDiagnostic andPredictivePrecisionMedicine” (byYelizKaracaand DumitruBaleanu)obtainsthegenerationofoptimum modelstrategiesfordifferentbiologydatasetsalong withtheMittag-Lefflerfunctionswithheavy-taileddistributions.Theintegrativemodelingschemeproposedinthe chapterisconcernedwiththeapplicabilityandreliabilityof thesolutionsobtainedbythetwo-parametricMittag-Leffl er functionswithheavy-taileddistributions.Accordingly,the proposedintegratedapproachinthischapterinvestigates thedynamicsofdiseasesrelatedtobiologicalelements.The applicationofmultilayerperceptron,asoneoftheArti ficial NeuralNetwork(ANN)algorithms,isdirectedforthe diagnosticandpredictivepurposeofthedisease.The contentofthechapterintendstoenablethebuildingof precisemodelstoavoidunpredictablerisksandidentify opportunitiesinnonlinearcomplexsituations,alongwith theintegrationofprecisionmedicine.
Chapter9 “ComputationalFractionalOrderCalculus andClassicalCalculusAIforComparativeDifferentiability PredictionAnalysesofComplexSystems-groundedParadigm” (byYelizKaracaandDumitruBaleanu)intendsto provideanintermediaryfacilitatingfunctionforboththe physiciansandindividualsthroughestablishinganaccurate androbustmodelbasedontheintegrationoffractional ordercalculusandANNintermsofthediagnosticand differentiabilitypredictivepurposeswiththediseases, whichdisplayhighlycomplexproperties.Theintegrative andmultistagedapproachproposedincludestheapplication oftheCaputofractionalderivativewithtwo-parametric Mittag-Lefflerfunctiononthestrokedatasetandcancer celldataset.Thechapterrevealsthatmodelingmany complexsystemscanbepossiblebyfractionalorderderivativesbasedonfractionalcalculusandcomputational complexityisshowntoprovideuswithapplicablesetsof ideasorintegrativeparadigmstounderstandtheintricate propertiesofcomplexsystems.
Chapter10 “PatternFormationInducedbyFractional OrderDiffusiveModelofCOVID-19” (byNaveedIqbal andYelizKaraca)presentstheinvestigationoftheTuring instabilityproducedbyfractionaldiffusioninaCOVID-19 model.Differentialequationswithcomplexorderfractional derivativesenabletheregulationofcomplicatedfractional systems,positiveequilibriumpointshavebeeninitially specifi ed,andRouth-Hurwitzcriteriahavebeenusedto
assessthestabilityofpositiveequilibriumpoint.Local equilibriumpointsandstabilityanalysishavebeen employedto fi ndtheconditionsforTuringinstability.The analysis,byexploringthesystem’sdynamicalbehaviorand thebifurcationpointcenteredonthedeathrate,anticipates toserveasaleverageindifferentdisciplinesconcerning COVID-19modelthroughthelensesofdistinctviewpoints; andwithinthatframework,fractionalcalculusisknownto unfoldthefundamentalmechanismsandmultiscale dynamicphenomena.
Chapter11 “Prony’sSeriesinTimeandFrequency DomainsandRelevantFractionalModels” (byJordan Hristov)dealswithProny’sseriesapproximationofmonotonicallyresponsesinmaterialviscoelasticrheologyand thepossibilitiesofimplementationonthissortofbasis relyingonmodernfractionaloperationswithnonsingular kernels,whichistosaytheCaputo-Fabriziooperator.The chapterprovidestheoriginsofProny’sseriesintimeand frequencydomainstogetherwiththerelevantapproximationandcalculationtechniques.Inthisway,contributions inpuremathematicsandexperimentalaspectsareputforth, whiletheelaborationandapplicationofProny’sseriesare saidtohavetheextensionpossibilitytomodelingproblems emerginginmechanicalengineering,chemicalengineering andotherrelateddisciplines.
Chapter12 “AChainofKineticEquationsofBogoliubov-Born-Green-Kirkwood-YvonandItsApplicationto NonequilibriumComplexSystems” (byNicolai(Jr)Bogoliubov,MukhayoYunusovnaRasulova,TohirAkramovand UmarbekAvazov)isdirectedtothestudyoftheBogoliubovBorn-Green-Kirkwood-Yvonchainofkineticequations (BBGKYchke)anditsapplicationstomodernproblemsof physics.Thechapterhasthefocusontheneedofcreatinga mathematicalapparatusfulfillingtheexistingtheoryofoneparticlesystemsandsystemsmadeupofahugenumberof particles.TwotypesofBBGKYchainsareaddressedfor bothclassicalandquantumparticlesystems.Thesolutionof theBBGKYchqkeforgeneralizedYukawapotential(gYp)is provided,solvingoftheBBGKYchqkewiththegYpfor systemsofmanytypeparticlesisalsoelaboratedon,and theGross-Pitaevskiiequationderivedbasedonthe BBGKYchqkeispresented.
Chapter13 “HearingLossDetectioninComplex SettingbyStationaryWaveletRenviEntropyandThreeSegmentBiogeography-BasedOptimization” (byYabei Li)addresseshearinglosswiththemainobjectiveof improvingtheaccuracyandeffi ciencyofdetectingimages insensorineuralhearinglossthroughanewsolution.To thisend,animprovedfeatureextractionmethodstationary waveletRenvientropyaswellasoptimizationalgorithmfor modelandfeatureextraction,namelythree-segment biogeography-basedoptimizationhavebeenproposed.
Chapter14 “ShannonEntropy-BasedComplexity Quanti ficationofNonlinearStochasticProcess:Diagnostic
andPredictiveSpatio-temporalUncertaintyofMultiple SclerosisSubgroups” (byYelizKaracaandMajazMoonis) aimsatfacilitatingtheaccurateclassi ficationandcourseof threesubgroupsofmultiplesclerosis(MS)(relapsing remittingMS,secondaryprogressiveMS,primaryprogressiveMS),whichisadebilitatingneurologicaldisease. Anentropy-basedfeatureselectionmethod(Shannon entropyandminimumredundancymaximumrelevance)as wellaslineartransformationmethods(principalcomponent analysisandlineardiscriminantanalysis)havebeen applied.Eachnewdatasetobtainedhasbeenaddressedas inputforthetrainingprocedureofk-nearestneighborand decisiontreealgorithms.TheaccuracyratesfortheMS subgroups’ classi ficationhavealsobeenanalyzed comparativelybasedontheoptimizedexperimentalresults, whichdemonstratethatShannonentropy,asadistinctive entropymethod,hasproventobehigherintermsofaccuracycomparedwiththeotherfeatureselectionmethods. Accordingly,anewperspectivewithamultilevelaspecthas beenpresentedtocopewiththecomplexdynamicsystems whereuncertaintyandheterogeneityprevailforcritical decision-makingandmanageabletrackinginmedicineand relevant fields.
Chapter15 “ChestX-rayImageDetectionforPneumoniaviaComplexConvolutionalNeuralNetworkand Biogeography-BasedOptimization” (byXiangLi,MengvaoZhaiandJundingSun)proposesanovelchestX-ray imagedetectionforpneumonia.Thedetectionmodel proposedisreliantonthecombinationofcomplexconvolutionalneuralnetwork(CNN)andbiogeography-based optimization.Ithasbeenproventhatthemodelhas highersensitivityandaccuracyintermsofdetectingthe pneumonia-relatedchestX-rayimageswithadetection performancebeingsigni ficantlybetterthanthatof advancedapproachesincomplexmedicalsettings.The utilizationofBBO,employedastheglobaloptimization algorithmoftherelatedmodel,alsoprovidesthebenefitof optimizingthestridesizeoftheconvolutionkernelonCNN toobtainbetterdetectioneffectswithlessmodeltraining cost.
Chapter16 “ComplexFacialExpressionRecognition byDenseNet-121” (byBinLi)isconcernedwithfacial expressionrecognitionsystem,whichhasgraduallybeen integratedintodifferent fieldsofourliveswiththeadvent ofAIera.Theapplicationprospectsofintelligentface recognitionviacomputertechnologyareverybroad,which canalsobeappliedtothediagnosisoffacialparalysisin medicine.Handlingthecomplexnatureoffacialexpression sinceitinvolvesemodiversityandemotionalcomplexity, thechaptershowsthatfacialexpressionrecognitionisa dif ficulttaskbringingaboutsomeproblemssuchaslow accuracyandpoorgeneralizationabilityofnetworkmodel recognition.Toaddressthesechallenges,theauthorshave proposedaDenseNet-121imagefeatureextractionmethod,
combinedwithCNNforfacialexpressionrecognition.The presentationofanimprovedfaceemotionrecognition systemproposedemployingamethodbasedondensely connectedneuralnetworkalsofacilitatestheavoidingof complexfeatureextractionrequiredbytraditionaldeep learningwhilesavingonthetrainingtime.
Chapter17 “QuantitativeAssessmentofLocalWarmingBasedonComplexUrbanDynamicsUsingRemote SensingTechniques” (byL.Saganeiti,AngelaPilogallo, FrancescoScorza,ValentinaSantarsiero,GabrieleNole andBeniaminoMurgante)isconcernedwithurbangrowth, whichisoneofthecornerstonesofsustainabledevelopmentpoliciesthatrequiretobeimplementedatinitialstates forawell-managedurbanizationprocessandexperience. Thechapterprovidesasimultaneousanalysisofthevariationsoflandsurfacetemperatureandurbanizedenvironmentoveraperiodof15yearswithintworegionsthat differinsize,populationdensity,andgrowthdynamics. Theresearchalsoprovidesanappealingandinnovative contributiontograsptherelationshipsbetweenurban growthspatialpatternsandtheurbanthermalenvironment. Detailedanalysespresentedinthechapterarebeneficialin supportingdecision-makingprocessesunderlyingfuture urbanpoliciesandassessmentofdevelopmentscenarios withregardtoqualityoflife,environmentalsustainability andpreservationofecosystems.
Chapter18 “ManagingInformationSecurityRiskand InternetofThingsImpactonChallengesofMedicinal ProblemswithComplexSettings:aCompleteSystematic Approach” (byEaliStephenNealJoshua,DebnathBhattacharyyaandN.ThirupathiRao),discoversthecrosswayof healthcareandsignificantdata,providingdetailswithrespect toinformationsecurity,differentvulnerabilitiesinhealthcare,databreaches,distributeddenialofserviceassaults, insiderthreats,informationsecurityinhealthcare,health informationprivacyandsecurity,andvariousinformation threatelementsregardingmedicalhealthreports.Thechapter alsopointsouttheimpactofIoTinmedicalproblems,IoTin healthcare,andchallengesinIoTinmedicalproblems.The informationthreatsareoutlinedindetailinthechapter, whichpresentsthechallengesofmedicinalproblemsusing IoTthroughacasestudythatshowstheefficiencyofIoT owingtoexponentiallyincreasingpatientmonitoring(blood pressuremonitoring,glucosemonitoring,andpulserate monitoring)inthehealthcareplans.
Chapter19isentitled “AnExtensiveDiscussiononUtilizationofDataSecurityandBigDataModelsforResolving ComplexHealthcareProblems” (byN.ThirupathiRao,
DebnathBhattacharyyaandEaliStephenNealJoshua),andit isconcernedwiththeutilizationoftechnologyinthehealthcaresettingswithafocusontheemploymentoftheIoT technology,providinganextensiveelaborationofitsopportunities,benefits,impacts,existinggaps,securitythreatsand adaptiveframeworksthatneedtobedeveloped.Thechapter, withupdatedinformationforourcurrenttime,presents detaileddiscussionsonbigdatainhealthcare,information security,confidentiality,integrity,andavailabilityby consideringtherelatedstakeholdersintheareathatarethe physicians,patients,hospitalsandinsurancecompanies.The chapterpresentsthecomplexsystemwithitscomponentsin varioushealthcaredomains,andthisattributeconcernsmany differentdisciplinesincludingbutnotlimitedtomedicine, microbiology,biomedicalengineering,computerscienceand bigdataanalytics.Awarenessintoandefficientmanagement ofallthecomponentsinvolvedisnotedtohavebenefitsforthe patientswhowillbeknowledgeableintermsofpertinent medicalresourcesandfaithinhealthcareprofessionals.In addition,accessintoavarietyofmedicalservicesbasedon technologicaldeviceswillbeofgreatbenefittoallthestakeholdersandcomplexsettings.
Weareoftheopinionandanticipationthatouredited bookwillprovidenewdimensionsintolayersof complexitythinking,momentumtoprogressiveideasinto complexity,complexitythinkingandprocesses,andabove allout-of-the-boxwayofthinkingforeveryoneinterested inthetheory,applicationsandmodelingofcomplexityand differentcomplexsystems.
September,2021
YelizKaraca UniversityofMassachusettsMedicalSchool, Worcester,UnitedStates
DumitruBaleanu ÇankayaUniversity,Ankara,TurkeyandInstituteof SpaceSciences,Magurele-Bucharest,Romania
Yu-DongZhang UniversityofLeicester,Leicester,UnitedKingdom
OsvaldoGervasi UniversityofPerugia,Perugia,Italy
MajazMoonis UniversityofMassachusettsMedicalSchool, Worcester,MA,UnitedStates
Acknowledgment YelizKaraca wouldliketoexpressherdeeprespectand gratitudetoherfamilymembers:hermother,FahriveEkecik Karaca;herfather,EminKaraca;andherbrother,Mehmet Karacaandhisfamilywhohavealwaysprovidedunconditional truelove,offeringallkindsofsupportallthewaythrough.Yeliz Karacaisalsosincerelyindebtedtoherancestor,lategrandfather,HasanHüseyinEkecik,holdingthesuperiorityservice awardbytheTurkishGrandNationalAssemblyforhisbeneficialcontributionsinpublicwelfare,educationandsocial developmentbothatnationalandinternationalscales,whom shehastakenasanesteemedrolemodelinherlife.
DumitruBaleanu wouldliketothankhiswife Mihaela-Cristinaforhercontinuoussupport.
Yu-DongZhang wouldliketoexpresshisacknowledgmenttoallhisfamilymembers,includinghiswifeand son,whosupporthisresearchworkallthetime.
OsvaldoGervasi wouldliketoexpresshisdeepest thanksforthecontinuoussupportinthecourseofhiswork tohiswifeLorellaGiovannelliandhischildrenMarta, AndreaandDamianoandtohisparentsLorettaPucciand AngeloGervasifortheprofoundvaluesthatOsvaldo Gervasiwasabletotransmittohischildren.
MajazMoonis isdeeplygratefultohisfatherProfessor MoonisRazawhotaughtandencouragedtheideaof research,hismotherandwifewhoinalladversitiesstood behindhimandmadeitpossibletocontinuehiswork.
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Chapter1 Introduction YelizKaraca1 andDumitruBaleanu2, 3
1UniversityofMassachusettsMedicalSchool,Worcester,MA,UnitedStates; 2ÇankayaUniversity,Ankara,Turkey; 3InstituteofSpaceScience, Magurele,Bucharest,Romania
Complexity,havingexistedsinceantiquity,entailsthe understandingofthecomplexcomponents’ origin,with meticulouscomputationsandcausalprocesses.Nonlinearity,self-organization,adaptation,synchronization,noise, ahighnumberofdescriptivevariablesordimensions involvedinthedescriptionofdifferentialequationsystems, andreactiontoresponsesintheexternalenvironmentare someofthenumerouscharacteristicsofacomplexsystem inwhichmultipleinteractionsemerge.Alongtheselines, complexitythinkingandtheory,oneofthebasicpremises ofwhichistheacknowledgingoftheexistenceofahidden ordertothebehaviorandevolutionofcomplexsystems, requiresahorizonthattakesthesubtleandhiddenpropertiesofdifferentdomainsintoaccount,necessitatingtheir ownmeansofoptimizedsolutionsandapplicability. BearinginmindthequotebyStephenHawking: Ithinkthe next[21st]centurywillbethecenturyofcomplexity is criticallysigni ficantnotonlyforthiserabutalsoforonwards.Accordingly,theideaofcomplexityisstatedtobe partofanewunifyingframeworkforscienceandarevolutioninourunderstandingofsystemsthebehaviorof whichhasprovedtobedif ficultintermsofprediction, managementandcontrol.
Inacomplexsystem,differentandmultiplewaysneed tobecontemplatedfortheprovisionofsolutionsand sortingouttheproblems.Thesystemislikelytochange dependingontheseselections,whichshowsusthecomplex systems’ adaptiveness.And,themoreinsightisdeveloped, theanswerstotheproblemskeepchangingwhichenables morelearningintheprocess.Giventhis,modernscience hasembarkedontheattemptsforathorough,holistic, multifacetedandaccurateinterpretationofnaturaland physicalphenomena,whichhasproventoprovidesuccessfulmodelsfortheanalysisofcomplexsystemsand harnessingofcontroloverthevariousrelatedprocesses. Computationalcomplexity,inthisregard,comestothe foregroundbyprovidingtheapplicablesetsofideasand/or
integrativeparadigmstorecognizeandunderstandthe intricatepropertiesanddynamicsofmanydifferentcomplexsystems.Thelensesofsuchtransformativethinkingin conjunctionwithmathematics-informedframeworks encompasschaos,fractalandmulti-fractionalwaysaswell astheindispensableincorporationoftechnology,with Arti ficialIntelligence,asafar-reachingleg,whichareall essentiallyrequiredtobecapableofaddressingandtacklingcomplexitymanifestingchaotic,nonlinear,anddynamiccharacteristics.
Chaosreferstoirregularandunpredictablebehavior characterizedbysensitiverelianceoninitialconditions.The tendencyofnaturetowardpatternformation,iterationand creationoforderoutofchaosallpointtothegenerationof expectationsofpredictability.Chaosanditsstudyinconsortwiththeadvancesinscienti ficrealmareimportant rootsofmodernstudyofcomplexsystemsthatdisplay dynamic,nonlinear,openqualitiesandinterconnection withtheenvironmentconstitutingmanyinteractingcomponents,withnewunanticipatedpatternsemerging.Chaos, inthiscontext,issaidtohavesomehowstrictdefinitions portrayinganonlinearworld,addressingdeterministic systemswithtrajectoriesdivergingexponentiallyintime, whichisalsoamongthepropertiesofbehaviorsincomplex systems.Inmathematicsandphysics,chaostheoryis concernedwiththenonlineardynamicalsystems’ behavior, whichundercertaincircumstancesexhibitsaphenomenon referredtoaschaosmarkedbysensitivitytoinitial conditions.
Fractalsarealsocomponentsofdynamicsystems,being theimagesthereof,drivenbyrecursion,whichistosaythe imageofchaos.Accordingly,fractalsareusedformodeling structureswherepatternsrecurrepeatedlyanddescribe randomorchaoticphenomena.Forthehandlingof complexsystems,theconceptofprogressivesmoothness on finerscalesmaynotalwaysprovetobeusefulasa startingpointfrommathematicalpointofview.This
acknowledgmentisimportantasafundamentalchangein outlookwhentraditionalgeometrystudyingtheproperties ofobjectsandspaceswithintegraldimensionsisnotuseful. Effectivefractionaldimensionsofobjects,namedasfractals,areintegratedintoanintegraldimensionspace.Being never-endingpatterns,fractalscanbecurvesorgeometric figures,witheachpartappearingtobethesameasthewhole pattern,whichiscalledself-similaritybroughtaboutbya processorfunction’siterativerepetition.Fractalsare,in otherwords,imagesofdynamicsystemsdrivenbyrecursion,namelytheimageofchaos.Fractalsareemployedto modelstructuresinwhichpatternsrecurinarepeatedway andtodescriberandomorchaoticphenomena.
Theadventofincreasingcapacityofcomputational processesinnumericalmethods,interestinfractionalderivativeequations(FDEs)hasbeenontherisetobeableto representcomplexphysicalcourseswheredynamicsmay notbeasaccuratelydetectedthroughclassicaldifferential equations.Fractionaldynamics,inthisregard,refersto suchsystemsforwhichderivativesandintegralsoffractionalordersareemployedtodescribeobjectslikelytobe characterizedbypower-lawnonlocality,fractalproperties, orlong-rangedependence.Forthisreason,fractional-order systemmodelcanberegardedasakeyfordescribingthe systemperformanceinabetterway,withpredictivereliabilityandapplicability.Inviewoftheseconceptsand challenges,itisimportantnottodisregarddatareliability, chaosthinkingandprocesses,fractalthinkingandprocesses,aswellasartifi cialintelligencewayofthinkingand processesaroundcomplexityasthecommonthemeunder consideration.Therelatedcomputationalprocesseswith broadapplicationsinintegrationwithfractals,multifractals,fractionalmethods,chaos,nonlineardynamical properties,stochasticelementsandsoforthcanprovide systematicoptimizedsolutions.Furthermore,computationaltechnologies,withmachinelearningasthecore componentofAI,enjoythebroaduseandtransformative impactsenablingustotraincomplexdatatoautomateor augmentsomeofthecriticalhumanskills.Hence,the crosscuttingnatureofAIprovidesmotivationalpowerto formulizeresearchinasystematicway.Arti ficialneural networks(ANNs),whicharenetworksofcomputersystems inspiredbythehumanbrainandbiologicalneuralnetworks havethecapabilityoflearningandmodelingcomplex, dynamicandnonlinearrelationships.Asthesimplification, abstractionandsimulationofthehumanbrain,ANNsalso reflecttherelatedfundamentalcharacteristicsofthiscomplexorgan.Thus,optimizedsolutionsneedtobeconceived andappliedinafacilitatingwayandefficientlywithsome requireddegreeof flexibility,too.Consideringtheimpact ofdatatechnologiesvis-à-visallaspectsofconditionsof moderneraandlife,itbecomeshighlyimportantto establishabalancebetweendatauseandethicalmatters. Computationaltechnologiesindifferentcomplexsystems
basedonmathematical-driveninformedframeworkscan enablethegenerationofmorerealistic,applicable,adaptive modelsopentolearningand flexibilityundertransient, dynamic,chaoticandever-evolvingconditionsofdifferent complexsystems.
Toputitdifferently,complexityalongwithallthe variationsinnetworksandsystemsdemonstratesthatthe decisionsmadearenotbasedononesingleparameterper se,butalsoonmultiplenumbersofparameterswithhiddenandsubtleinformationbeingatstake.Tothisend, multifariousadaptivemethodswithinmathematicsinformedframeworkshavegainedprominenceforthe optimizedsolutionofcomplexproblems.Thiswillenable ustoensurethatsolutionisnotsuper ficialorpretentious butreliable,robust,andsmoothenablingthemaintenance ofquality,sustainabilityandmeritocracy.
Theoverarchingaimofthisbookistoaddresstheneed concerningnovelanalyticstrategiesandmathematical modelingtoachievereliableandoptimizedglobalsolutions withregardtoMulti-chaos,Fractal,andMulti-fractionalin theeraofArti ficialIntelligence,whichrequirestheindispensableintegrationofadvancedmathematicalmodelsand AIforamuchsmarterlevelofblendedsystemsincomplex settings.Appealingtoaninterdisciplinarynetworkofscientistsandresearcherstodisseminatethetheoryand applicationofMulti-chaos,Fractal,andMulti-fractionalAI ofDifferentComplexSystemsinmedicine,neurology, mathematics,physics,biology,chemistry,information theory,engineering,computerscience,socialsciencesand otherfar-reachingdomains,theprimaryfocusistoenable theprovisionofglobalandoptimizedrobustsolutions distinctivelywithaperspectivethroughmultifarious methods,differentfromtheconventionalperspective,as directedtowardparadoxicalsituations,differentuncertain processes,nonlineardynamicsystemsinherentincomplex systems.
Basedontheseideasandconsideration,theprominent objectivesofoureditedbookcanbeoutlinedasfollows:
- Constructingandpresentingamultifariousapproachfor criticaldecision-makingprocessesembodyingparadoxesanduncertainty,
- Combiningtheoryandapplicationswithregardto multi-chaos,fractalandmulti-fractionalAIofdifferent complexsystemsandmany-bodysystems,
- Enablingtheprovisionofglobalandoptimizedrobust solutionsdistinctivelywithaperspectivethrough multifariousmethodsandmathematics-informed frameworks,asdifferentfromtheconventional perspective,
- Providinganoutlookdirectedtowardthepredictionand managementofparadoxicalsituations,differentuncertainprocesses,andnonlineardynamiccomponents inherentinagivencomplexsystem,
- Facilitatingthedisseminationoftheoryandapplication ofmulti-chaos,fractal,andmulti-fractionalAIin differentcomplexsystemsofvariousareas,
- Establishingabalancebetweendatauseandethical matterswhileemployingcomputationaltechnologies indifferentcomplexsystemsofnumerousdomains,
- Actingasabridgebetweenapplicationofadvanced computationalmathematicalmethodsandAIbasedon comprehensiveanalysesandbroadtheories.
Accordingly,eachchapterofthiseditedbookaddresses differentuncertainprocessesinherentinthecomplexsystemsandattemptstoprovideaccurate, flexible,global,and robustoptimizedsolutionsdistinctively,withaperspective throughtherelatedmultifariousmethods fitforthecontent. Tothisend,thiseditedbookofoursforegroundsMultichaos,FractalandMulti-fractionalintheeraofArti ficial Intelligence,whichdefinitelyrequirestheintegrationof advancedmathematicalmodelsandmathematics-informed frameworksaswellasAIaddressingfractal,fractional calculus,fractionaloperators,quantum,wavelet,entropybasedapplicationsapartfromthemeansofmodeling, technicalanalyses,andnumericalsimulationsassomeof themostextensivelyusedmethodsforthesolutionof relatedmultifacetedproblemscharacterizedbynonlinearity,nonregularity,self-similarityandmanyotherproperties,frequentlyencounteredindifferentcomplexsystems. Motivatedbytheaforementionedconsiderations,the contentofthechaptersalongwiththeirnovelaspectsare outlinedasfollows.
Chapter2 entitled “TheoryofComplexity,Originand ComplexSystems” (byYelizKaraca)attemptstoencompassthepossibledimensionsofcomplexsystemsin different fieldsfocusingonorigin-related,historical, evolutionaryandepistemologicalviewpointsofcomplexity withthegoalofprovidingaglobalunderstandingthereof, takingintoconsiderationthevariousmultipleinteracting factorsofsystems.Inaddition,throughthepresentationof complexorderprocessestowardmodernscientificpath,it aimstounderstandtherelatedconditionsanddemandsfor handlingcomplexproblemsofthe21stcenturyandonwards.It,furthermore,intendstoelaborateonaccountsof past,presentandfutureindifferentcomplexsystems, whichcanhelpusadoptadeeperunderstandingand implementthestepsalongtheway.Byprovidingthe complexorderprocessestowardmodernscientifi cpath, fromDarwinandonwards,aconceptualoutlineisalso presentedalongwiththedetailsofcomplexityandcomplex systems.Complexsystems,complexitythinkingandtheory,infact,canbroadenthehorizonandscopeofmodern wayofthinking,whichneedstodependontransitionfrom evolutionarydimensionasarevolutionarystageandasa newparadigmfornaturalsciencesandsocialsciences. Therefore,thecharacterization,definition,analysisand
understandingofcomplexsystemsincludeapowerful relationbetweenvariables,sensitivitytoinitialcontrolas wellasstrange,nonperiodicandunpredictabletimeevolution.Overall,thisdetailedpresentationaimstoensure thatthefoundationforthecomplexsystems’ interpretations canbeexploredindifferentrelatedareasofcomplexity.
Chapter3 named “Multi-chaos,FractalandMultifractionalAIinDifferentComplexSystems” (byYeliz Karaca)providesanoverviewthatincludesmulti-chaos, fractal,fractionalandArti ficialIntelligence(AI)wayof thinkingregardingthesolutionofthecomplexsystem problemsconcernedwithnaturalandsocialsciences. Moreover,ethicaldecision-makingframeworksandstrategiesrelatedtobigdataandAIapplicationsareprovidedin detailforthepurposeofenablingassistancetoidentifythe relatedproblemsindifferentsettingsandthinking methodicallyinorderthattensionsbetweenconflicting aspectscanbemanagedinasystematicway.Datareliabilityandcomplexity,chaosthinkingandprocessesand complexity,fractalthinkingandprocessesandcomplexity, fractionalthinkingandprocessesandcomplexity, fi nally, AIwayofthinkingandprocessesandcomplexityare amongthepointselaboratedinthechapter.Thus,the chapterisdirectedtowardmodernscientificthinkingwhich hastoadoptthesystemicproperties,addressingthemby revealingthespontaneousprocessespertainingtoselforganizationinadynamicalsysteminastatefarfromthe equilibriumpointandclosetothedisequilibriumpointwith noexistenceofanexternalforceactingonthesystem.This wayofthinking,naturally,posesachallengeagainst reductionistwayofthinkingandthedichotomybetween thenaturalworldandsocialworld,byconsideringthe conceptsaroundcomplexity,evolutionandorderindetail.
Chapter4 named “HighPerformanceComputingand ComputationalIntelligenceApplicationswithMulti-chaos Perspective ” (byDamianoPerri,MarcoSimonetti, OsvaldoGervasiandSergioTasso)addressestheexperienceoftheCOVID-19pandemicwhichhasactually acceleratedmanychaoticprocessesinmodernsocietybesidespronouncingtheurgetounderstandcomplexprocessestoachievecommonwell-beinginaveryseriousand emergentway.Themaincontributionofthechapteris directedtothesetofbestpracticesandcasestudies,which provideassistancetotheresearcherswhilehandling computationallycomplexproblems.Byanalyzingdifferent technologiesandapplications,complexphenomenaare soughttobeunderstoodintheenvironmentwithever increasingcomplexitybearinginminddifferentelements suchastechnology,algorithmsandchanginglifestyles, whilestrivingtoachievemaximumefficiencyaswellas outcomesbesidesprotectingtheintegrityofindividuals’ personaldataand,aboveall,respectingthehumanbeingas awhole.Thechapterconsidersthatallthesechallenges imposearadicalchangeinmanydifferentareas,including
onesrelatedtocomputationalresources,whichmakesit veryimportanttomanagecomplexproblemsbroughtabout bymulti-chaoticsituations.Onesectionofthechapterison computationalintelligence,withthedescriptionofsomeof thetechniquesthatenabletheaccelerationofcomplex problems’ resolutionbyexploitingthepotentialprovided bymachinelearningtechniques(likeMulti-layerPerceptronandConvolutionalNeuralNetwork)thatcanattain dimensionswhichusedtobeunimaginableinthepast.The chapteralsodealswiththefeaturesofaquantumcomputer, whichcanprocessdataatarateexponentiallyfasterthana classicalcomputer.Takentogether,thechapterprovidesa generalsketchofvarioustopicswhichcouldbeofhelpto researchersanddeveloperstodealwithcomplexand chaoticsituationswithinthescopeofmachinelearningand theissueofprivacyincludingtherecentrelated regulations.
Chapter5 bearsthetitleof “HumanHypercomplexity, ErrorandUnpredictabilityinComplexMulti-ChaoticSocialSystems” (byPieroDominici),whichhastheoutlook thattraditionallinearmodelsanddeterministicapproaches cannolongerbecapableoftheanalysisofreality’sunstabledynamics.Thechapterprovidesperspectivesonthe complexityoflivingenergyandlivingbeings;12essential planesofawareness;thecharacteristicsofcomplicated, complexandhypercomplexsystems;epistemologyoferror aswellascomplexandchaoticcharacteristicsofsocial systems.Theauthorofthechapterprovidesinsightsinto theambivalentnatureofcomplexity,cognitive,subjective, social,ecologicalandethicalaspectsofcomplexity includinglinguisticsandcommunicationaswellasa “cultureofcommunication.” Giventhat,hypercomplexity isnotanoption;butafactoflife.However,theproblematicsisrelatedtotheconditionthatwehavenotbeen trainedandeducatedtorecognizeit,muchlesstoinhabitit. Thus,itisimportanttobearinmindthatcomplexityisa structuralcharacteristicofhumangroups,relations,social systemsandthebiologicalworld.
Chapter6 entitled “MultifractalComplexityAnalysisBasedDynamicMediaTextCategorizationModelsby NaturalLanguageProcessingwithBERT” (byYeliz Karaca,Yu-DongZhang,AhuDereliDursunandShui-Hua Wang)addressesthechallengesandcomplexitypertaining tomediatexts.Duetopropertieslikebeingunstructured, noisyandnonstandard,accurateconveyanceofmeaning becomesproblematicandagainstthisbackground,the studyaimsatensuringregularityandself-similaritywithin thedigital-basedcomplexmediatextbymulti-fractal methods,whicharemultifractalBayesian,multifractal regularizationandmultifractalwaveletshrinkage.BidirectionalEncoderRepresentationsfromTransformers(BERT) astheNaturalLanguageProcessing(NLP)methodis
employedtoattaintheaccurateclassi ficationandcategorizationofthewordswithintextsinthedataset.Therelated stepsoftheintegrativemethodproposedinthestudyincludesregularityenhancementbytheapplicationofthe threeaforementionedmultifractalmethodstothetext dataset.Byobtainingthesignificant,self-similarandregularattributes,newdatasetsweregeneratedwiththe respectiveapplicationofthemultifractalmethods.Subsequently,BERT,astheNLPtechnique,wasemployedtothe textdatasetandthethreenewdatasetswereobtainedforthe classi ficationpurposes.Inthisway,accurateworddetection withinthetextforthecategoryclassi ficationwasensured fortheanalyses.Theanalysisresultsforthetextdatasetand thenewdatasetswerecomparedbyBERTandthemost optimalresultcouldbeattainedbymultifractalBayesian method.Thestudyenunciatesthesignificanceofthe behavioralpatternsoffractalwhilesettingforththe distinctivequalityofBERTowingtoitscapabilityof classi ficationaccuracyandadaptivenessintointegrated methodologies.
Chapter7 (PartI)entitled “Mittag-Leffl erFunctionswith Heavy-tailedDistributions ’ AlgorithmbasedonDifferent BiologyDatasetstobeFitforOptimumMathematical Models’ Strategies ” (byDumitruBaleanuandYelizKaraca) ismotivatedbythechallengeofintegratingfractionalcalculusincasesofcomplexity,whichrequiresaneffectiveuse ofempirical,numerical,experimentalandanalytical methodstotacklecomplexity.Oneofthemostnoteworthy toolsinthefractionalcalculuscontextisnotedtobethe Mittag-Leffler(ML)functionswhosedistributionshave extensiveapplicationdomainswhiledealingwithirregular andnonhomogeneousenvironmentsforthesolutionsof dynamicproblems.Theproposedintegratedapproachinthis chapteraddressestheMittag-Leffl er(ML)functionwithtwo parametersforthepurposeofinvestigatingthedynamicsof twodiseases:cancercellanddiabetes.Thefollowingarethe stepsofthestudy:MLfunctionwithtwoparameterswas appliedtothebiologicaldatasets,namelythecancercell datasetanddiabetesdataset.Itwasaimedtoobtainnew datasets(ml_cancercelldatasetandml_diabetesdataset) withsigni ficantattributesforthediagnosis,prognosisand classi ficationofdiseases.Next,heavy-taileddistributions, whichareMittag-Leffl erdistribution,Paretodistribution, CauchydistributionandWeibulldistribution,wereapplied tothenewdatasetsobtained.Thecomparisonofthemwas donerelatingtotheperformancesbyemployingthelog likelihoodvalueandtheAkaikeInformationCriterion (AIC).Followingthesesteps,theMLfunctionsthatrepresentthecancercellanddiabetesdatawereidentifi edsothat thetwoparameters Ea;b ðzÞ whichyieldtheoptimumvalue basedonthedistributions fitcouldbefound.By findingthe mostsignificantattributeswithheavy-taileddistributions
(TheMittag-Lefflerdistribution,Paretodistribution,Cauchy distributionandWeibulldistribution)basedonMittagLefflerfunctionwithtwoparameters ða; bÞ,thediagnosis, prognosis,andclassi ficationofthediseaseswereensuredin thechapter.Theintegrativeschemeproposedalongwiththe optimalstrategicalmeanswerefortheaccurateandrobust mathematicalmodels’ strategiesconcerningthediagnosis andprogressofthediseases.Accordingly,theresultsobtaineddemonstratethattheintegrativeapproachwith Mittag-Lefflerwithheavy-taileddistributionalgorithmis applicable, fittingverywelltotherelateddatawiththe robustparametervaluesobservedandestimatedintransient chaoticandunpredictablesettings.
Chapter8 (PartII)hasthetitle “Arti ficialNeural NetworkModelingofSystemsBiologyDatasetsFitBased onMittag-Leffl erFunctionswithHeavy-tailedDistributions forDiagnosticandPredictivePrecisionMedicine” (byYeliz KaracaandDumitruBaleanu),whichobtainsthegeneration ofoptimummodelstrategiesfordifferentbiologydatasets alongwiththeMittag-Lefflerfunctionswithheavy-tailed distributions.Theintegrativemodelingschemeproposed inthechapterisconcernedwiththeapplicabilityandreliabilityofthesolutionsobtainedbythetwo-parametric Mittag-Lefflerfunctionswithheavy-taileddistributions. Accordingly,theproposedintegratedapproachinthis chapterinvestigatesthedynamicsofdiseasesrelatedto biologicalelements.Emerginginthedifferentsolutionsof varyingcomplexbiologicalsystems,theMLfunctionwith twoparameterswasappliedtothebiologicaldataset, namelycancercellanddiabetesandthenewdatasetswere generated.Theheavy-taileddistributions(TheMittagLefflerdistribution,Paretodistribution,CauchydistributionandWeibulldistribution)wereappliedtothenew datasetsobtainedwiththeircomparisonperformedinrelationtotheperformances(byemployingtheloglikelihood valueandtheAkaikeInformationCriterion(AIC)).ML functionsthatrepresentthecancercellanddiabetesdata wereidenti fiedsothatthetwoparameters Ea;b ðzÞ yielding theoptimumvaluebasedonthedistributions fitcouldbe found.Subsequently,MultilayerPerceptron(MLP),asone oftheANNalgorithms,wasappliedforthediagnosticand predictivepurposeofthediseaserelatedtotheoptimized MLfunctionsthatrepresentthecancercellanddiabetes datasetsobtainedandtheperformancesoftheMLfunctions withheavy-taileddistributionswerecomparedwithANN trainingfunctions(Levenberg-Marquart,BayesRegularizationandBFGS-Quasi-Newton).Theresultsbasedon mathematicalmodelsdemonstratethattheintegrative approachwithMittag-Leffl erandANNapplicationsis applicableandalso fi tsverywelltotherelateddatawiththe robustparametervaluesobservedandestimated.TheintegrationofANNwiththeself-organizationandself-learning capabilityinpatternidentificationandrecognitionalong
withtherationalthinkingandactingabilitywhilemaking inferencesanddecisionsbasedonpastexperiencehasalso beenshowntobecritical.SinceAIenablesthebuildingof precisemodelstoavoidunpredictablerisksandidentify opportunitiesinnonlinearcomplexsituations,itsintegration inprecisionmedicineisalsoforegroundedinthischapter.
Chapter9 named “ComputationalFractional-Order CalculusandClassicalCalculusAIforComparative DifferentiabilityPredictionAnalysesofComplex-systemsgroundedParadigm” (byYelizKaracaandDumitru Baleanu)aimstoprovideanintermediaryfacilitating functionbothforthephysiciansandindividualsthrough establishinganaccurateandrobustmodelbasedonthe integrationoffractional-ordercalculusandANNfor thediagnosticanddifferentiabilitypredictivepurposeswith thediseaseswhichdisplayhighlycomplexproperties.The integrativeandmulti-stagedapproachproposedinthe chapterincludestheapplicationoftheCaputofractional derivativewithtwo-parametricMittag-Lef flerfunctionon thestrokedatasetandcancercelldataset.Theestablishing ofnewfractionalmodelswithvaryingdegreesisperformed andthereasonwhytheMittag-Lef flerfunctionhasbeen optedisforitsdistributionsofextensiveapplicationdomains,whichcanenableittohandleirregularandheterogeneousenvironmentsforthesolutionofdynamic problems.Subsequently,thenewdatasetsrelatedtocancer cellandstrokewereobtainedbyemployingCaputofractionalderivativewiththetwo-parametricMittag-Leffler function.Furthermore,classicalcalculusisappliedtothe rawdatasets;andtheperformanceofthenewdatasetsas obtainedfromtheCaputofractionalderivativewiththe two-parametricMittag-Lefflerfunction,thedatasetsobtainedfromtheclassicalcalculusapplicationandtheraw datasetsiscomparedbyusingFeedForwardBackPropagation(FFBP),asoneofthealgorithmsofANN.Asperthe accuracyrateresultsobtained,theFFBPapplication,the suitabilityoftheCaputofractional-orderderivativemodel forthediseaseshasbeendemonstrated.Theexperimental resultsobtainedbythischapteralsopointtotheapplicabilityofthecomplex-systems-groundedparadigmscheme ashasbeenproposed.Itshouldalsobenotedthatmodeling manycomplexsystemscanbepossiblebyfractional-order derivativesbasedonfractionalcalculussothatrelated synthesescanberealizedrobustlyandeffectively.Consequently,computationalcomplexityisshowntoprovideus withapplicablesetsofideasorintegrativeparadigmsto recognizeandunderstandtheintricatepropertiesofcomplexsystems.
Entitled “PatternFormationInducedbyFractional-order DiffusiveModelofCOVID-19,” Chapter10(byNaveed IqbalandYelizKaraca)providestheinvestigationofthe Turinginstabilityproducedbyfractionaldiffusionina COVID-19model.Consideringthatdifferential
equationswithcomplexorderfractionalderivativesenable theregulationofcomplicatedfractionalsystems,positive equilibriumpointshavebeeninitiallyspeci fiedandRouthHurwitzcriteriaareusedfortheassessmentofthepositive equilibriumpoint’sstability.Localequilibriumpointsand stabilityanalysishavebeenemployedto fi ndtheconditions forTuringinstability.Theanalysis,bylookingintothe system’sdynamicalbehaviorandthebifurcationpoint centeredonthedeathrate,aimstoserveasaleveragefor furtherstudiesindifferentdisciplinesconcerningCOVID19modelthroughthelensesofdistinctviewpoints.The resultsoftheanalysesrevealthehighlycomplexconnectionbetweenCOVID-19andfractionalorderdiffusion,the turingbifurcationpoint,andweaklynonlinearanalysisused inthefractional-orderdynamicsdiscussedinthechapter. TheTuringbifurcationpointandweaklynonlinearanalysis usedthroughoutthecomplexfractional-orderdynamics handledinthechapterareparticularlyrelevantexperimentallyandcomputationallysincetherelatedeffectscan beexaminedandutilizedinnumerousmathematical, chemical,andecologicalmodels,alongwithengineering, computerscience,bioengineering,informationscience, appliedsciencesandvirologyaswellasotherrelatedareas. Withinthisscale,fractionalcalculusisknowntounfoldthe fundamentalmechanismsandmulti-scaledynamicphenomenainbiologicaltissues.Theresultsofthechapterare importantintermsofshowingthat,onaquantitativebasis, theycanbeextendedtoavarietyofstatistical,physical, engineering,biologicalandfurtherrelatedmodels.
Chapter11whosetitleis “Prony’sSeriesinTimeand FrequencyDomainsandRelevantFractionalModels” (by JordanHristov)addressesProny’sseriesapproximationof monotonicalresponsesinmaterialviscoelasticrheologyas wellasthepossibilitiesofimplementationonsuchabasis dependingonmodernfractionaloperationswithnonsingularkernels,namelytheCaputo-Fabriziooperator.The chapteralsoprovidestheoutlineoftheoriginsofProny’s seriesintimeandfrequencydomainsalongwiththerelevantapproximationandcalculationtechniques.Theresults ofthestudyexposethemutualrelationshipsbetweenthe operatorswithsingularandnonsingularkernels.The chaptershedslightonwhattypeofoperatorsareapplicable inmodels fittingandmodelingtheirexperimentaldata.In thisway,contributionsinpuremathematicsandexperimentalaspectsareputforth.Consequently,theelaboration andapplicationofProny’sseriesaresaidtobeextendedto modelingproblemsemerginginmechanicalengineering, chemicalengineeringaswellasotherrelateddisciplines.
Chapter12isentitled “AChainofKineticEquationsof Bogoliubov-Born-Green-Kirkwood-YvonandItsApplicationtoNonequilibriumComplexSystems” (byNicolai(Jr) Bogoliubov,MukhayoRasulova,TohirAkramovand UmarbekVazov)whichisdevotedtothestudyofthe Bogoliubov-Born-Green-Kirkwood-Yvonchainofkinetic
equations(BBGKYchke)anditsapplicationstomodern problemsofphysics.Thechapterfocusesontheneedof creatingamathematicalapparatuswhichfulfi llstheexistingtheoryofone-particlesystemsandsystemsmadeupof ahugenumberofparticles.Auniqueobjectwhichsatisfies therelatedconditionsistheBBGKYchkeasobtainedfrom theLiouvilleequationformanyparticles.Twotypesof BBGKYchainsareaddressedforbothclassicaland quantumparticlesystems.And,incontrastwiththeLiouvilleequation,theBBGKYchkehascollisionintegrals.The firstapproximationcoincideswiththewell-knownBoltzmann,VlasovandLandauequations,whilethelastequationsprovidethedescriptionoftheevolutionofoneortwo particlesinmodernphysics.Inthechapter,theexampleof quantummany-particlesystemshasbeenprovided,which showshowtheuseoftheBBGKYchqke,one-particle problemscanbegeneralizedforthecaseofnonequilibriumsystemsthatconsistofinteractingparticleswithina kinetictheoryframework.Thechapterconcernssuch nonequilibriumparticlesystemsinteractingwiththe generalizedYukawapotentialaswell.Overall,thesolution oftheBBGKYchqkeforgeneralizedYukavapotential (gYp)isprovided,andsolvingoftheBBGKYchqkewith thegYpforsystemsofmanytypeparticlesiselaboratedon. Finally,theGross-Pitaevskyequationisderivedbasedon theBBGKYchqke.
Chapter13named “HearingLossDetectioninComplex SettingbyStationaryWaveletRényiEntropyandThreesegmentBiogeography-basedOptimization” (byYabeiLi andJundingSun)addressesanotherhealthproblemwhich ishearinglossthatdecreasesthelifequalityofindividuals. Themainobjectiveoftheresearchistoimprovetheaccuracyandefficiencyofdetectingimagesinsensorineural hearinglossthroughanewsolution.Thechapterincludes theproposalofanimprovedfeatureextractionmethod stationarywaveletRényientropy(SWRE)aswellasoptimizationalgorithmformodelandfeatureextraction, namelythree-segmentbiogeography-basedoptimization (3SBBO).Itisnotedthatthecurrenthearinglossdetection methodshaveonlya fixedschemeoffeatureextraction processandoptimizationmostlyforclassi fiers.Theexperimentsconducteddemonstratehighratesofsensitivities, whichcorroboratethefactthattheapproachadoptedinthe researchhasattainedastate-of-the-artperformanceandcan beappliedinthediagnosisofhearingloss.
Chapter14entitled “ShannonEntropy-based ComplexityQuantificationofNonlinearStochasticProcess:DiagnosticandPredictiveSpatio-temporalUncertaintyofMultipleSclerosisSubgroups” (byYelizKaraca andMajazMoonis)considersthegrowthofcomplexity, whichinmorenonlinearandcomplicatedinstances, evolveswithincreasinginformationandentropyina monotonousway.Complexdynamiccharacteristicsof systemsbasedonentropyrequireadetailedspeci fication
andsynthesisoftheintricateelementsasthesystemgets moreandmorecomplex.Thus,thechaptercarriestheaim offacilitatingtheaccurateclassi ficationandcourseofthree subgroupsofMultipleSclerosis(MS),namelyRelapsing Remitting(RRMS),SecondaryProgressiveMS(SPMS), PrimaryProgressiveMS(PPMS),whichisadebilitating neurologicaldisease.Forthisparticularaim,anentropybasedfeatureselectionmethod(ShannonEntropyand MinimumRedundancyMaximumRelevance)aswellas lineartransformationmethods(PrincipalComponent AnalysisandLinearDiscriminantAnalysis)wereappliedto theMSdataset,fromwhichfournewdatasetswithsignificantattributesweregenerated.Inaddition,eachnew datasetobtainedwasaddressedasinputforthetraining procedureofk-NearestNeighbor(k-NN)anddecisiontree algorithms.Finally,theaccuracyratesfortheMSsubgroups’ classi ficationwereanalyzedcomparativelybased ontheoptimizedexperimentalresultswhichdemonstrate thatShannonEntropy,asadistinctiveentropymethod, provedtobehigherintermsofaccuracycomparedtothe otherfeatureselectionmethods.Thechapter,therefore, intendstopointanewperspective,withamulti-level aspect,forcriticaldecision-makingandmanageable trackinginmedicineandrelevant fields,whichallneedto copewiththecomplexdynamicsystemsinwhichuncertaintyandheterogeneityprevail.
Chapter15entitled “ChestX-rayImageDetectionfor PneumoniaviaComplexConvolutionalNeuralNetwork andBiogeography-basedOptimization” (byXiangLi, MengyaoZhaiandJundingSun)proposesanovelchestXrayimagedetectionforpneumonia,whichisstatedtobea leadingreasonfordeathamongchildrenandaffl ictthe elderlyworldwide.Thedetectionmodelproposedbythe authorsisbasedonthecombinationofcomplexconvolutionalneuralnetwork(CNN)andbiogeography-based optimization(BBO).Itisproventhatthemodelhas highersensitivityandaccuracyintermsofdetectingthe pneumonia-relatedchestX-rayimageswithadetection performancebeingsignificantlybetterthanthatof advancedapproacheswithincomplexmedicalsettings.The utilizationofBBO,whichisemployedastheglobaloptimizationalgorithmoftherelatedmodel,hasthebenefitof optimizingthestridesizeoftheconvolutionkernelonCNN toobtainbetterdetectioneffectswithlessmodeltraining cost.
Chapter16entitled “ComplexFacialExpression RecognitionbyDenseNet-121” (byBinLi)isonfacial expressionrecognitionsystem,whichhasgraduallybeen integratedintodifferent fieldsofourliveswiththeadvent ofartifi cialintelligenceera.Theapplicationprospectsof intelligentfacerecognitionviacomputertechnologyare veryextensive,andcanalsobeappliedtothediagnosisof facialparalysisinthe fieldofmedicine.Thechapterhandlesthecomplexnatureoffacialexpressionsinceit
involvesemodiversityandemotionalcomplexityand makesthepointthatfacialexpressionrecognitionisa dif ficulttaskwhichmayalsobringaboutsomeproblems suchaslowaccuracyandpoorgeneralizationabilityof networkmodelrecognition.Toaddresssuchchallenges,the authorofthechapterhasproposedaDenseNet-121image featureextractionmethod,combinedwithconvolutional neuralnetwork(CNN)forfacialexpressionrecognition. Forthis,theprincipleandmethodthatthismodelcan quicklyandaccuratelyrecognizehumanfacialexpressions havebeenanalyzed.Afterward,theexperimentalanalysis hasbeencarriedout.Theexperimentalresultsprovethat thenetworkmodelproposedhashighprecisionand robustnesswithagoodabilityofgeneralization.Thepresentationofanimprovedfaceemotionrecognitionsystem proposedemployingamethodbasedondenselyconnected neuralnetworkalsohelpsinavoidingcomplexfeature extractionrequiredbytraditionaldeeplearningandalso savingthetrainingtime.
Chapter17isnamed “QuantitativeAssessmentofLocal WarmingBasedonComplexUrbanDynamicsUsing RemoteSensingTechniques” (byL.Saganeiti,Angela Pilogallo,FrancescoScorza,ValentinaSantarsiero,GabrieleNolèandBeniaminoMurgante),whichisconcerned withurbangrowth,whichisoneofthecornerstonesof sustainabledevelopmentpoliciesthatrequiretobeputinto practiceatinitialstatesforawell-managedurbanization processandexperience.Demographicqualitiesandcity’s growthaswellastheconsequentneedtodensifyurban aggregatesarenotedtobeincreasinglyconflictingwiththe themeoflivabilityofurbanspacesandtheservicesthey provideforthewell-beingofthecitizens.Accordingly,the chapterprovidesasimultaneousanalysisofthevariations oflandsurfacetemperatureandurbanizedenvironment overaperiodof15yearswithintworegionsthatdifferin size,populationdensity,andgrowthdynamics.Theresults revealamuchmoremarkedincreaseinallofthesecomponentsasregardsminimumtemperaturesinareaswhere urbanizationhasbeenmatchedbyadecreaseinthenumber ofaggregates.Theresearch,aspresentedinthischapter, providesanappealingandinnovativecontributiontounderstandtherelationshipsbetweenurbangrowthspatial patternsandtheurbanthermalenvironment.Detailedanalyseswiththissortofapproachpresentedinthechapterare consideredtobebeneficialinsupportingdecision-making processesthatunderliefutureurbanpoliciesandassessmentofdevelopmentscenarioswithregardtoqualityof life,environmentalsustainabilityandpreservationof ecosystems.
Chapter18isentitled “ManagingInformationSecurity RiskandInternetofThings(IoT)ImpactonChallengesof MedicinalProblemswithComplexSettings:AComplete SystematicApproach” (byEaliStephenNealJoshua, DebnathBhattacharyyaandN.ThirupathiRao),which
