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IEEEPress
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IEEEPressEditorialBoard
SarahSpurgeon, EditorinChief
JónAtliBenediktssonAndreasMolischDiomidisSpinellis
AnjanBoseSaeidNahavandiAhmetMuratTekalp
AdamDrobot
Peter(Yong)Lian
JeffreyReed ThomasRobertazzi
MachineLearningAlgorithmsforSignalandImageProcessing
Editedby
DeepikaGhai
LovelyProfessionalUniversity,IN
SumanLataTripathi
LovelyProfessionalUniversity,IN
SobhitSaxena
LovelyProfessionalUniversity,IN
ManashChanda
MeghnadSahaInstituteofTechnology,IN
MamounAlazab
CharlesDarwinUniversity,AS
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Setin9.5/12.5ptSTIXTwoTextbyStraive,Chennai,India
Contents
EditorBiography xix
ListofContributors xxi
Preface xxix
Acknowledgments xxxi
SectionIMachineLearningandDeepLearningTechniquesforImageProcessing 1
1ImageFeaturesinMachineLearning 3 AnterpreetK.BediandRameshK.Sunkaria
1.1Introduction 3
1.2FeatureVector 4
1.3Lower-LevelImageFeatures 5
1.3.1Color 5
1.3.1.1ColorHistogram 5
1.3.1.2ColorMoments 6
1.3.1.3ColorCoherenceVector 6
1.3.1.4ColorCorrelogram 6
1.3.2Texture 6
1.3.2.1SignalProcessing-BasedFeatures 6
1.3.2.2StructuralFeatures 7
1.3.2.3Model-BasedFeatures 7
1.3.2.4StatisticalFeatures 7
1.3.3Shape 15
1.3.3.1ShapeFeaturesBasedonBoundary 15
1.3.3.2ShapeFeaturesBasedonRegion 16
1.4Conclusion 16 References 16
2ImageSegmentationandClassificationUsingDeepLearning 19 AbhisekRayandMaheshkumarH.Kolekar
2.1Introduction 19
2.2ImageSegmentation 20
2.2.1TypesofDL-BasedSegmentation 20
2.2.1.1InstanceSegmentationUsingDeepLearning 20
2.2.1.2SemanticSegmentationUsingDeepLearning 20
2.2.2AdvantagesandApplicationsofDL-BasedSegmentation 21
2.2.3TypesandLiteratureSurveyRelatedtoDL-BasedSegmentation 21
2.2.3.1FullyConvolutionModel 21
2.2.3.2CNNwithGraphicalModel 21
2.2.3.3DilatedConvolutionModel 22
2.2.3.4Encoder–DecoderModel 22
2.2.3.5R-CNNBasedModel 23
2.2.3.6MultiscalePyramidBasedModel 24
2.2.3.7RNNBasedModel 25
2.2.3.8GenerativeAdversarialNetwork(GAN)BasedModel 25
2.2.3.9SegmentationModelBasedonAttentionMechanism 26
2.3ImageClassification 27
2.3.1TypesandSchemesinImageClassification 27
2.3.2TypesandLiteratureSurveyRelatedtoDL-BasedImageClassification 28
2.3.2.1CNNBasedImageClassification 28
2.3.2.2CNN–RNNBasedImageClassification 30
2.3.2.3Auto-encoderBasedImageClassification 31
2.3.2.4GANBasedImageClassification 31
2.4Conclusion 32 References 32
3DeepLearningBasedSyntheticApertureRadarImageClassification 37 J.AnilRajandSumamM.Idicula
3.1Introduction 37
3.2LiteratureReview 38
3.3DatasetDescription 38
3.4Methodology 39
3.5ExperimentalResultsandDiscussions 41
3.6Conclusion 43 References 43
4DesignPerspectivesofMulti-taskDeep-LearningModelsandApplications 45 YeshwantSingh,AnupamBiswas,AngshumanBora,DebashishMalakar,SubhamChakraborty,and SumanBera
4.1Introduction 45
4.2DeepLearning 46
4.2.1Feed-ForwardNeuralNetwork 47
4.2.2ConvolutionNeuralNetwork 47
4.2.2.1ConvolutionLayer 47
4.2.2.2PoolingLayer 48
4.2.3RecurrentNeuralNetwork 48
4.3Multi-taskDeep-LearningModels 48
4.3.1ClassificationModels 48
4.3.1.1Multi-attributeRecognitionModelsUsingJointLearningofFeatures 48
4.3.1.2Multi-taskFacialAttributesClassificationModelUsingFeatureFusion 50
4.3.2PredictionModels 50
4.3.2.1Multi-taskingonTime-SeriesData 50
4.3.2.2Multi-stepForecastingonMultivariateTimeSeriesUsingSplitLayers 51
4.3.3MixedModels 52
4.4DesignandImplementation 52
4.4.1Multi-taskLearningMethodsusedforDeepLearning 52
4.4.1.1HardParameterSharing 52
4.4.1.2SoftParameterSharing 53
4.4.2VariousDesignofMulti-taskLearning 53
4.4.2.1DeepRelationshipNetworks 53
4.4.2.2FullyAdaptiveFeatureSharing 54
4.4.2.3Cross-stitchNetworks 54
4.4.2.4WeightingLosseswithUncertainty 54
4.4.2.5TensorFactorizationforMTL–SluiceNetworks 54
4.4.2.6JointMany-TaskModel 55
4.4.3CommonProblemswithDesignandImplementation 55
4.4.3.1CombiningLosses 55
4.4.3.2TuningLearningRates 57
4.4.3.3UsingEstimatesasFeatures 57
4.5Applications 57
4.5.1ImageDomain 57
4.5.2TextDomain 58
4.5.3Others 59
4.6EvaluationofMulti-taskModels 59
4.7ConclusionandFutureDirections 60
Acknowledgment 61
References 61
5ImageReconstructionUsingDeepLearning 65
AneetaChristopher,R.HariKishan,andP.V.Sudeep
5.1Introduction 65
5.2DL-IRMethods 67
5.2.1DL-MMSEMethodsforIRTasks 67
5.2.1.1DL-MMSEMethodsUsingAEs 67
5.2.1.2DL-MMSEMethodsUsingCNNs 68
5.2.2MAPBasedDL-IRMethods 72
5.2.3OtherDL-SRMethods 75
5.2.3.1SupervisedSRTechniques 75
5.2.3.2UnsupervisedSRTechniques 77
5.2.4OtherDL-IRTasks 77
5.3DL-BasedMedicalImageReconstruction 78
5.4Conclusion 81
Acknowledgment 81 References 81
6MachineandDeep-LearningTechniquesforImageSuper-Resolution 89
AshishKumar,SachinSrivastava,andPratikChattopadhyay
6.1Introduction 89
6.1.1Motivation 90
6.1.2ApplicationsofImageSuper-Resolution 90
6.1.2.1SatelliteImaging 91
6.1.2.2MedicalDiagnosis 91
6.1.2.3Surveillance 92
6.1.2.4VideoEnhancement 92
6.1.3MajorContributionsandOrganizationoftheChapter 92
6.2TraditionalUpsamplingApproaches 93
6.2.1NearestNeighborInterpolation 93
6.2.2BilinearInterpolation 93
6.2.3BicubicInterpolation 93
6.3PrimitiveMachine-Learning-BasedApproaches 94
6.3.1FrequencyDomain 94
6.3.1.1FastFourierTransform 95
6.3.1.2WaveletTransform 95
6.3.2SpatialDomain 96
6.3.2.1IterativeBackProjection 96
6.3.2.2MaximumLikelihoodEstimation 96
6.3.2.3MaximumAPosteriori(MAP)Estimation 97
6.3.2.4Self-Similarity-BasedApproach 98
6.3.2.5Learning-BasedApproach 98
6.3.2.6Sparse-BasedApproach 98
6.4ModernDeep-Learning-BasedApproaches 98
6.4.1Upsampling-BasedClassification 98
6.4.2Network-BasedClassification 100
6.4.2.1LinearNetworks 100
6.4.2.2ResidualNetworks 100
6.4.2.3RecursiveNetworks 101
6.4.2.4ProgressiveReconstructionNetworks 102
6.4.2.5DenselyConnectedNetworks 103
6.4.2.6Attention-BasedNetworks 103
6.4.2.7GAN-BasedNetworks 104
6.4.3DiscussiononDifferentTypesofLossFunctions 105
6.4.3.1PixelLoss 105
6.4.3.2ContentLoss 106
6.4.3.3TextureLoss 106
6.4.3.4AdversarialLoss 106
6.5PerformanceMetricsandComparativeStudyofExistingTechniques 107
6.5.1ObjectiveEvaluation 107
6.5.1.1PeakSignal-to-NoiseRatio(PSNR) 107
6.5.1.2StructuralSimilarityIndexMeasure(SSIM) 108
6.5.2SubjectiveEvaluation 108
6.5.3Datasets 108
6.5.3.1TrainingDataset 108
6.5.3.2TestingDataset 109
6.5.4EvaluationResults 109
6.6SummaryandDiscussions 110 References 111
SectionIIMachineLearningandDeepLearningTechniquesforTextandSpeech Processing 115
7MachineandDeep-LearningTechniquesforTextandSpeechProcessing 117
DasariL.PrasannaandSumanLataTripathi
7.1TextProcessing 117
7.1.1AutomaticTexttoImageGenerationorVice-VersaUsingMachineandDeepLearning 117
7.1.2AutomaticImageCaptionGenerationUsingMachineandDeepLearning 118
7.1.3ManipuriHandwrittenScriptRecognitionUsingMachineandDeepLearning 119
7.1.4NaturalLanguageProcessingUsingMachineandDeepLearning 122
7.2SpeechProcessing 122
7.2.1SmartSignLanguageRecognitionSystemforDeafPeopleUsingDeepLearning 122
7.2.2SmartTextReaderforBlindPeopleUsingMachineandDeepLearning 123
7.2.3TheRoleofDeepLearningParadigminBuildingtheAcousticComponentsofanAutomaticSpeech RecognitionSystem 125
7.3Conclusion 126 References 126
8ManipuriHandwrittenScriptRecognitionUsingMachineandDeepLearning 129 PalungbamR.Chanu
8.1Introduction 129
8.2LiteratureSurvey 130
8.3ProposedWork 131
8.4ExperimentalResultsandDiscussions 132
8.5Conclusion 136 References 136
9ComparisonofDifferentTextExtractionTechniquesforComplexColorImages 139 DeepikaGhaiandNeeluJain
9.1Introduction 139
9.2RelatedWork 140
9.3Edge-BasedandCC-BasedMethods 143
9.3.1Edge-BasedMethodIntroducedbyLiuandSamarabandu[17] 143
9.3.2CC-BasedMethodFormulatedbyGllavataetal.[36] 143
9.4ProposedMethodology 146
9.5ExperimentalResultsandDiscussion 150
9.5.1SampleTestResults 151
9.5.2ComparisonofProposedMethodwithExistingState-of-the-ArtMethods 153
9.6Conclusions 157 Acknowledgment 157 References 157
10SmartTextReaderSystemforPeoplewhoareBlindUsingMachineandDeepLearning 161 ZobeirRaisi,MohamedA.Naiel,GeorgesYounes,PaulFieguth,andJohnZelek
10.1Introduction 161
10.2LiteratureReview 163
10.2.1SmartTextReaderSystemforBlindPeople 163
x Contents
10.2.1.1TextDetection 164
10.2.1.2TextRecognition 168
10.3ExperimentalResults 173
10.3.1Datasets 174
10.3.1.1MJSynth 174
10.3.1.2SynthText 175
10.3.1.3ICDAR03 175
10.3.1.4ICDAR13 175
10.3.1.5ICDAR15 175
10.3.1.6COCO-Text 176
10.3.1.7SVT 176
10.3.1.8SVT-P 176
10.3.1.9IIIT5K-Words 176
10.3.1.10CUT80 176
10.3.2EvaluationMetrics 176
10.3.2.1Detection 176
10.3.2.2Recognition 177
10.3.3EvaluationofTextDetectionTechniques 177
10.3.3.1QuantitativeResults 177
10.3.3.2QualitativeResults 177
10.3.3.3Discussion 179
10.3.4EvaluationofTextRecognitionTechniques 181
10.3.4.1QuantitativeResults 181
10.3.4.2QualitativeResults 181
10.3.4.3Discussion 184
10.3.5OpenInvestigationsforSceneTextDetectionandRecognition 186
10.3.5.1TrainingDatasets 186
10.3.5.2RicherAnnotations 186
10.3.5.3NovelFeatureExtractors 188
10.3.5.4OcclusionHandling 188
10.3.5.5ComplexFontsandSpecialCharacters 188
10.4ConclusionsandRecommendedFutureWork 188 Acknowledgments 189 References 189
11Machine-LearningTechniquesforDeafPeople 201
YoginiD.BoroleandRoshaniRaut
11.1Introduction 201
11.2LiteratureSurvey 202
11.3Objectives 203
11.4ProposedCalculationDepiction 203
11.4.1ReferenceSystem 203
11.5ResourcesandStrategies 206
11.5.1Equipment/Programming 206
11.5.2Topics 206
11.5.3HandlingConditionsandImprovements 207
11.5.4UtilizedConvention 207
11.6Assessment 207
11.7OutcomesandConversations 208
11.8DiscourseCoherence 208
11.9Conclusion 214
References 214
12DesignandDevelopmentofChatbotBasedonReinforcementLearning 219 HemlataM.Jadhav,AltafMulani,andMakarandM.Jadhav
12.1Introduction 219
12.2StudentGuideUsingChatbot 221
12.3ImplementationofChatbotSystem 221
12.3.1Data-FlowDiagram 222
12.3.2Use-CaseDiagram 222
12.3.2.1AttheAdminEnd 223
12.3.2.2AtUserEndforStudent/Parent 223
12.3.3ClassDiagram 223
12.3.4SequenceDiagram 224
12.3.5ActivityDiagram 224
12.3.6StateDiagram 225
12.4DevelopmentofAlgorithmsUsedinChatbotSystem 226
12.4.1StopWordRemovalAlgorithm 226
12.4.2StringSimilarityAlgorithm 226
12.4.3Q-LearningAlgorithm 227
12.5Conclusion 227
References 228
13DNNBasedSpeechQualityEnhancementandMulti-speakerSeparationforAutomatic SpeechRecognitionSystem 231 RamyaandSivaSakthi
13.1Introduction 231
13.2DeepLearning 231
13.2.1RecurrentNeuralNetwork 232
13.2.2LongShort-TermMemory(LSTM)Networks 233
13.2.3ConvolutionalNeuralNetwork 233
13.3SpeechEnhancementandSeparation 234
13.4SpeechEnhancementAlgorithms 234
13.4.1BasicPrinciplesofSpectralSubtraction 234
13.4.1.1SpectralSubtractionUsingOver-Subtraction 235
13.4.1.2NonlinearSpectralSubtraction 235
13.4.2StatisticalModelBasedMethods 236
13.4.2.1Maximum-LikelihoodEstimators 236
13.4.2.2MinimumMeanSquareError(MMSE)Estimator 236
13.4.3SubspaceAlgorithms 237
13.4.3.1DefinitionofSVD 237
13.4.3.2SubspaceDecompositionMethod 238
13.4.3.3EigenValueDecomposition 238
13.5SpeechSeparationAlgorithms 238
13.5.1ClassicalSpeechSeparationAlgorithms 238
13.5.2HarmonicModels 239
13.5.3ComputationalAuditorySceneAnalysis 239
13.5.4Non-negativeMatrixFactorization(NMF) 240
13.5.5GenerativeModels 240
13.6DeepLearningBasedSpeechEnhancement 240
13.6.1MaskApproximation 241
13.6.1.1ComplexIdealRatioMask 241
13.6.1.2IdealBinaryMask 242
13.6.2SignalApproximation 242
13.7DeepLearningBasedSpeechSeparation 242
13.7.1LabelPermutationProblem(LPP) 243
13.7.2DeepClustering 243
13.8ResultsandDiscussions 243
13.9Conclusion 244
References 244
14DesignandDevelopmentofReal-TimeMusicTranscriptionUsingDigitalSignal Processing 247
ThummalaReddychakradharGoud,KonetiChandraSekhar,GannamaniSriram,GadamsettiNarasimha Deva,VuyyuruPrashanth,DeepikaGhai,andSandeepKumar
14.1Introduction 247
14.2RelatedWork 247
14.3MotivationoftheProposedWork 248
14.4MathematicalExpressionsofSignalProcessing 249
14.5ProposedMethodology 250
14.5.1ReadingandVisualization 250
14.5.2SignalProcessing 250
14.5.3MIDIConversion 250
14.5.4FeatureExtraction 253
14.5.5ImageProcessing 254
14.5.6KeyExtraction 256
14.6ExperimentalResultsandDiscussions 257
14.6.1BenchmarkDatabase 257
14.6.2EvaluationParameters 257
14.6.3PerformanceEvaluation 258
14.7Conclusion 260
References 261
SectionIIIApplicationsofSignalandImageProcessingwithMachineLearningandDeep LearningTechniques 263
15RoleofMachineLearninginWristPulseAnalysis 265
SachinKumar,Pooja,SanjeevKumar,andKaranVeer
15.1Introduction 265
15.2Machine-LearningTechniques 267
15.2.1Regression 268
15.2.2Classification 268
15.2.3Clustering 268
15.2.4DimensionalityReduction 269
15.2.5EnsembleMethods 269
15.2.6ArtificialNeuralNetworksandDeepLearning 269
15.2.7ReinforcementLearning 269
15.3PerformanceAnalysisofMLAlgorithms 270
15.4RoleoftheMachineandDeepLearninginWristPulseSignalAnalysis(WPA) 270
15.4.1SupervisedMachineLearninginWPA 270
15.4.2UnsupervisedMachineLearningandReinforcementMachineLearninginWPA 271
15.4.3DeepLearninginWPA 271
15.5DiscussionandConclusion 272 References 274
16AnExplainableConvolutionalNeuralNetwork-BasedMethodforSkin-LesionClassification fromDermoscopicImages 279 BiswarupGanguly,DebangshuDey,andSugataMunshi
16.1Introduction 279
16.1.1Background,Motivation,andLiterature 279
16.1.2MajorContributions 281
16.2MethodsandMaterials 282
16.2.1Pixel-WiseDecomposition 282
16.2.2Layer-WiseRelevanceBack-Propagation 282
16.3ExplainableDeep-Learning(x -DL)FrameworkforDermoscopicImageClassification 283
16.3.1DatasetsandImagePreprocessing 283
16.3.2StructureofConvolutionalNeuralNetwork(CNN) 283
16.3.3TrainingDetailsandSystemImplementation 285
16.4ExperimentalResultsandDiscussion 285
16.4.1AnalysisofLearntSkin-LesionPatternsfrom x -DL 285
16.4.2AblationAnalysisConsideringRegularizationFactor 286
16.4.3ComparativeStudywithOtherCNNModules 287
16.4.4Discussion 289
16.5Conclusion 289 Acknowledgments 289 References 289
17FutureofMachineLearning(ML)andDeepLearning(DL)inHealthcareMonitoring System 293
KanakKumar,KaustavChaudhury,andSumanLataTripathi
17.1Introduction 293
17.1.1ML/DLAlgorithmsforOptimization 294
17.1.2Pre-processingMethods 295
17.2PerformanceAnalysisParameters 299
17.3ObjectivesandMotivation 300
17.4ExistingML/DLTechniquesforHealthcareMonitoringandDiseaseDiagnosis 300
17.5ProposedModel/MethodsforHealthcareMonitoringSystemUsingML/DL 303
17.5.1CaseStudy-I:BreastCancer 304
17.6ExperimentalResultsandDiscussion 305
17.6.1CaseStudy-II:Diabetes 306
17.7Conclusions 310
17.8FutureScope 310 References 311
18UsageofAIandWearableIoTDevicesforHealthcareData:AStudy 315 SwarupNandi,MadhusudhanMishra,andSwanirbharMajumder
18.1Introduction 315
18.2LiteratureReview 315
18.3AI-BasedWearableDevices 316
18.3.1Cloud-AssistedAgent-BasedSmartEnvironment 317
18.3.2ImprovedBayesianConvolutionNetwork 317
18.3.3EDL 317
18.4ActivitiesofWearableDevicesinHealthcareSystem 320
18.4.1Women’sHealthfocusedAVASensor 320
18.4.2AliveCor–PersonalEKG 320
18.4.3TempTraq 321
18.4.4BioScarf 322
18.4.5Blinq–WearableRings 322
18.4.6SmartSleepWearable 323
18.4.7BioPatch 323
18.4.8SmartGlasses 324
18.4.9SmartHearingAids 324
18.4.10WirelessPatientMonitoring 325
18.4.11WearableFitnessTracker 325
18.4.12SmartHealthWatch 326
18.4.13WearableECGMonitors 326
18.4.14WearableBloodPressureMonitors 327
18.4.15Biosensors 327
18.5BarrierstoWearable’sAdoption 329
18.5.1Cost 329
18.5.2Designation 329
18.5.3AbsenceofInitiativeUseCase 329
18.5.4LackofaKillerApp 329
18.5.5LimitedFunctionality 329
18.6WearableDevicesConsumers 329
18.7RecentTrendsinWearableTechnology 334
18.7.1WearablesinHealthcare 334
18.7.2WearablesinIndustry 335
18.7.3WearablesinRobotics 335
18.7.4WearablesinDefense 335
18.7.5WearablesinSport 335
18.7.6WearableinCPS 336
18.8Conclusion 336 References 336
19ImpactofIoTinBiomedicalApplicationsUsingMachineandDeepLearning 339 RehabA.Rayan,ImranZafar,HusamRajab,MuhammadAsimM.Zubair,MudasirMaqbool,and SamrinaHussain
19.1Introduction 339
19.1.1ArtificialIntelligenceandMachineLearning 340
19.2HistoryofDLandML 340
19.3MethodsofMLandDLAlgorithmsandClassification 341
19.3.1DeepLearningArchitectures 342
19.3.1.1AutoEncoders 342
19.3.1.2DeepMultilayerPerceptron 342
19.3.1.3DeepAuto-encoders 342
19.3.1.4RestrictedBoltzmannMachineandDeepBeliefNetworks 343
19.3.1.5ConvolutionalNeuralNetworks 343
19.3.2FindingsofAppliedMLandDLTechniques 347
19.4MLandDLApplicationsinBiomedicine 347
19.5DiscussionsofIoT-BasedMLandDLCaseStudiesinBiomedicalSystems 350
19.6OpportunitiesandChallenges 352
19.6.1FutureInsights 353
19.6.2Conclusions 354 References 354
20WirelessCommunicationsUsingMachineLearningandDeepLearning 361 HimanshuPriyadarshi,KulwantSingh,andAshishShrivastava
20.1Introduction 361
20.1.1IRS-EnabledWireless-CommunicationSystems 364
20.2ContributionsofIntelligentReflectingSurfaces(IRS)inWireless-Communication Systems 364
20.2.1IRSAsSignalReflectorinWireless-CommunicationSystem 364
20.2.2IRSAsSignalTransmitterinWireless-CommunicationSystem 365
20.2.3IRSAsSignalReceiverinWireless-CommunicationSystem 365
20.3MeritsofIRS-AidedWireless-CommunicationSystemsforPerformanceEnhancement 365
20.3.1EnhancementintheChannelCapacity 365
20.3.2SavingsontheTransmitPowerofBaseStationinIRS-AidedWireless-Communication System 365
20.3.3ProtectionAgainstEavesdroppingandHighConfidentialityRate 365
20.4IssuesinCollaborationBetweenActiveandPassiveBeamforming 366
20.4.1OverheadonAlgorithmsDuetoTimeandSpaceComplexity 366
20.4.2LackofChannelInformation 366
20.4.3SimplifyingAssumptionsLeadtoUnrealisticSystemModeling 366
20.5ScopeofMachineLearningforIRS-EnabledWireless-Communication Systems 366
20.5.1PathwayAssessmentforCommunicationChannelandSignalDiagnostics 367
20.5.2MachineLearningforPassiveBeamforming 367
20.5.3PreventionofDenialofServiceAttacksandStealthinCommunications 368
20.6Summary 369
Acknowledgment 369 References 369
21ApplicationsofMachineLearningandDeepLearninginSmartAgriculture 371 RanganathanKrishnamoorthy,RanganathanThiagarajan,ShanmugamPadmapriya,IndiranMohan, SundaramArun,andThangarajuDineshkumar
21.1Introduction 371
21.1.1MajorContributionsofSmartAgriculture 371
21.2ConceptofMachineLearning 372
21.2.1TypesofMachineLearning 372
21.2.1.1SupervisedLearning 373
21.2.1.2UnsupervisedLearning 380
21.2.1.3ReinforcementLearning 381
21.3ConceptofDeepLearning 382
21.3.1TypesofDeepLearning 382
21.3.1.1BackPropagation 383
21.3.1.2CNNinAgriculture 383
21.3.1.3RNNinAgriculture 383
21.3.1.4GANinAgriculture 384
21.4SmartAgriculture 384
21.4.1SmartFarming 384
21.4.2PrecisionFarming 385
21.5ComputationMethods 386
21.6SecurityAspectsandIssues 386
21.7ApplicationDomainsinAgriculture 386
21.8CaseStudy 387
21.9AgroSmartCity 387
21.10ConceptofApplicationofMLandDLinSmartAgriculture 388
21.10.1PredictionofPlantDisease 388
21.10.2LocustPrediction 389
21.10.3PlantClassification 389
21.10.4LivestockFarming 389
21.10.5SmartIrrigationSystem 390
21.10.6PestControlPrediction 391
21.10.7SoilManagement 391
21.10.8CropQualityandItsManagement 392
21.10.9WeedProtection 393
21.10.10YieldPrediction 393
21.11ResultsandDiscussion 393
21.12Conclusion 394 References 394
22StructuralDamagePredictionfromEarthquakesUsingDeepLearning 397 ShagunSharma,GhanapriyaSingh,SmitaKaloni,RanjeetP.Rai,andSidhantYadav
22.1Introduction 397
22.2LiteratureReview 398
22.3ProposedMethodology 399
22.3.1Deep-LearningModels 400
22.4ProposedMethodologyforDeep-LearningModels 400
22.4.1One-DimensionalConvolutionalNeuralNetwork(1DCNNs) 400
22.4.2Two-DimensionalConvolutionalNeuralNetwork(2DCNNs) 402
22.4.3LongShort-TermMemoryNetwork(LSTMs) 404
22.5ExperimentalResultsandDiscussions 405
22.5.1Dataset 405
22.5.2ResultsandDiscussions 405
22.6Conclusion 406 References 406
23Machine-LearningandDeep-LearningTechniquesinSocialSciences 409 HutashanV.BhagatandManminderSingh
23.1Introduction 409
23.1.1MachineLearning 409
23.1.2DeepLearning 411
23.1.3SocialDataAnalysis 411
23.1.4Machine-LearningProcess 412
23.1.5Machine-LearningTerminology 413
23.2Machine-LearningandDeep-LearningTechniques 414
23.2.1SupervisedLearningTechniques 414
23.2.2UnsupervisedLearningTechniques 415
23.2.3ReinforcementLearningTechniques 415
23.2.4Deep-LearningTechniques 415
23.3SocialSciencesApplicationsUsingMachine-LearningandDeep-LearningTechniques 416
23.3.1Education 416
23.3.2Economics 417
23.3.3Marketing 418
23.3.4MiscellaneousApplications 420
23.4Conclusion 421 References 421
24GreenEnergyUsingMachineandDeepLearning 429
R.SenthilKumar,S.Saravanan,P.Pandiyan,K.P.Suresh,andP.Leninpugalhanthi
24.1Introduction 429
24.1.1SolarEnergy 430
24.1.1.1Photovoltaic(PV)Cell 430
24.1.2WindEnergy 432
24.1.2.1WindEnergyConversionSystem(WECS) 433
24.1.2.2BasicEquationofWindPower 433
24.1.2.3WindEnergySiteSelection 433
24.1.3Hydropower 434
24.1.3.1WorkingPrincipleofHydropowerPlant 434
24.2MLAlgorithmsforGreenEnergy 435
24.2.1ForecastingRenewable-EnergyGenerationUsingML 435
24.2.1.1Solar-EnergyGeneration 435
24.2.1.2WindPowerGeneration 435
24.2.1.3HydroPowerGeneration 436
24.3ManagingRenewable-EnergyIntegrationwithSmartGrid 437
24.3.1Supply–DemandBalancing 437
24.3.2GridManagementandOperations 437
24.3.3Grid-DataManagement 438
24.4DLModelsforRenewableEnergy 438
24.4.1SolarEnergy 439
24.4.2EnergyfromtheWind 439
24.4.3TechniquesofDL 439
24.4.3.1ConvolutionalNeuralNetwork(CNN) 439
24.4.3.2RestrictedBoltzmannMachine(RBM) 440
24.4.3.3Auto-Encoder 441
24.5Conclusion 442 References 442
25LightDeepCNNApproachforMulti-LabelPathologyClassificationUsingFrontalChest X-Ray 445 SouidAbdelbaki,SoufieneB.Othman,FarisAlmalki,andHediSakli
25.1Introduction 445
25.2RelatedWork 446
25.3MaterialsandMethod 447
25.3.1MobileNetV2 447
25.3.2ModelArchitecture 448
25.4ProposedMethodology 449
25.4.1DatasetPreparationandPreprocessing 449
25.5ResultandDiscussions 452
25.6Conclusion 455 References 455
Index 459
EditorBiography
Dr.DeepikaGhai receivedherPh.DintheareaofsignalandimageprocessingfromPunjabEngineeringCollege, Chandigarh.ShereceivedherM.TechinVLSIDesign&CADfromThaparUniversity,Patiala,andB.Techin electronicsandcommunicationsengineeringfromRayatInstituteofEngineeringandTechnology,Ropar.Sheisan AssistantProfessoratLovelyProfessionalUniversitywithmorethan8years’academicexperience.Shereceived theDr.C.B.GuptaAwardin2021atLovelyProfessionalUniversity.Shehaspublishedmorethan30research papersinrefereedjournalsandconferences.Shehasworkedasasessionchair,conferencesteeringcommittee member,editorialboardmember,andreviewerininternational/nationalIEEEjournalsandconferences.Shehas alsopublishededitedbook“HealthInformaticsandTechnologicalSolutionsforCoronavirus(COVID-19)”inCRC Taylor&Francis.SheisassociatedasalifememberoftheIndianScienceCongress.Herareaofexpertiseincludes signalandimageprocessing,biomedicalsignalandimageprocessing,andVLSIsignalprocessing.
Dr.SumanLataTripathi receivedherPh.D.intheareaofmicroelectronicsandVLSIfromMNNIT,Allahabad. ShereceivedherM.TechinelectronicsengineeringfromUPTechnicalUniversity,Lucknow,andB.TechinelectricalengineeringfromPurvanchalUniversity,Jaunpur.In2022shehasworkedasaremotepost-docresearcher atNottinghamTrentUniversity,London,UK.SheisaProfessoratLovelyProfessionalUniversityandhasmore than19years’academicexperience.Shehaspublishedmorethan72researchpapersinrefereedIEEE,Springer, Elsevier,andIOPsciencejournalsandconferences.Shehasalsobeenawarded13Indianpatentsand2copyrights.Shehasorganizedseveralworkshops,summerinternships,andexpertlecturesforstudents.Shehasworked asasessionchair,conferencesteeringcommitteemember,editorialboardmember,andpeerreviewerininternational/nationalIEEE,Springer,Wileyjournalsandconferences,etc.Shereceivedthe“ResearchExcellence Award”in2019and“ResearchAppreciationAward”in2020,2021atLovelyProfessionalUniversity,India.She receivedthebestpaperatIEEEICICS-2018.Shehaseditedandauthoredmorethan15booksindifferentareas ofelectronicsandelectricalengineering.ShehaseditedworksforElsevier,CRCTaylorandFrancis,Wiley-IEEE Press,NovaScience,AppleAcademicPress,etc.Sheisalsoworkingasabookserieseditorfor“SmartEngineering Systems”andaconferenceserieseditorfor“ConferenceProceedingsSeriesonIntelligentSystemsforEngineering Designs”withCRCPress.Sheistheguesteditorofaspecialissuein“CurrentMedicalImaging”BenthamScience. Sheisaseniormember,IEEE,fellowatIETE,andlifememberatISCandiscontinuouslyinvolvedindifferent professionalactivitiesalongwithacademicwork.Herareaofexpertiseincludesmicroelectronicsdevicemodeling andcharacterization,lowpowerVLSIcircuitdesign,VLSIdesignoftesting,andadvancedFETdesignforIoT, embeddedsystemdesign,reconfigurablearchitecturewithFPGAs,andbiomedicalapplications.
Dr.SobhitSaxena receivedhisPh.D.fromIITRoorkeeintheareaofnanotechnology.HedidhisM.Techin VLSIandB.E.inelectronicsandcommunicationengineering.Hisareaofexpertiseincludesnanomaterialsynthesisandcharacterization,electrochemicalanalysisandmodeling,andsimulationofCNT-basedinterconnectsfor VLSIcircuits.HehasdesignedanewhybridsystemofLi-ionbatteriesandsupercapacitorsforenergystorageapplications.HeworkedasaSEM(scanningelectronmicroscopy)operatorforfouryearsagainstMHRDfellowship. Hehasavastteachingexperienceofmorethan14yearsinvariouscollegesanduniversities.Currently,heis
workingasanAssociateProfessorintheSchoolofElectronicsandElectricalEngineering,LovelyProfessional University.Hehasbeenawardedthe“PerfectAward”fourtimesinconsecutiveyearsforachieving100%result. Hehaspublishedmorethan10researchpapersinSCI/Scopusindexedjournalsandabout20papersinreputed internationalconferences/non-indexedjournals.Hehasfiledthreepatents,publishedaneditedbook“Advanced VLSIDesignandTestabilityIssues”withCRCPress,andtwobookchapters.Hehasalsopublishedoneauthored book, DigitalVLSIDesignandSimulationwithVerilog,withWiley.HeisanIEEEmemberandarevieweratvariousrefereedSCI/Scopusindexedjournalsandconferenceproceedings.Healsohasindustrialexposureintwo differentcompaniesrelatedtomanufacturing(PCB)andbroadbandcommunication.
Dr.ManashChanda graduatedinelectronicsandcommunicationengineeringfromKalyaniGovt.EngineeringCollegein2005.HeobtainedhisM.TechdegreeinVLSIandmicroelectronicsfromJadavpurUniversity.He completedhisPh.DinengineeringfromETCEDept.,JadavpurUniversity,in2018.Atpresent,heisworkingas anAssistantProfessorintheDepartmentofECE,MeghnadSahaInstituteofTechnology,sinceFebruary2006. HeisamemberofIEEEandiscurrentlyamemberofIEEEElectronDeviceSocietyandSolidStateCircuitSociety.Dr.Chandaistheco-founderofIEEEStudentBranchandEDMSITStudentBranchChapter.Atpresent, heistheChapterAdvisorofEDMeghnadSahaInstituteofTechnologyStudentBranchChapter.Also,heisthe ViceChairmanofEDKolkataChapter.HeservedastheSecretaryofIEEEEDMSITSBCfromJanuary2018to December2019.Hehaspublishedmorethan65refereedresearchpapersandconferenceproceedings.Hiscurrent researchinterestspansaroundthestudyofanalyticalmodelingofsub100-nmMOSFETsandnanodevicesconsideringquantummechanicaleffects,low-powerVLSIdesigns,SPICEmodelingofnanoscaledevices,memory designs,etc.HehaspublishedpapersinrefereedinternationaljournalsofreputedpublisherslikeIEEE,Elsevier, IET,Springer,Wiley,tonameafew.Heisthereviewerofmanyreputedinternationaljournalsandconferences likeIEEETCAS,IEEETVLSI,IEEETED,SolidStateCircuits(Elsevier),JournalofComputationalElectronics (Springer),InternationalJournalofNumericalModeling:ElectronicNetworks,DevicesandFields(Wiley),InternationalJournalofElectronics(TaylorandFrancis),etc.HeistherecipientofUniversityGoldmedalinM.Tech fromJadavpurUniversityin2008.OneofhisprojectswasselectedintheTop10VLSIprojectdesigncategory (includingB.TechandM.Tech)alloverINDIA,organizedbyCADENCEDESIGNCONTEST,BANGALORE, Indiain2010.
Dr.MamounAlazab isanassociateprofessorattheCollegeofEngineering,IT,andEnvironment,andthe InauguralDirectoroftheNTAcademicCentreforCyberSecurityandInnovation(ACCI)atCharlesDarwinUniversity,Australia.Heisacyber-securityresearcherandpractitionerwithindustryandacademicexperience.His researchismultidisciplinaryandfocusesoncybersecurityanddigitalforensicsofcomputersystemswithafocus oncybercrimedetectionandprevention.Hehaspublishedmorethan300researchpapers(>90%inQ1andin thetop10%ofjournalarticles,andmorethan100inIEEE/ACMTransactions)and15authored/editedbooks.He receivedseveralawardsincludingtheNTYoungTallPoppy(2021)fromtheAustralianInstituteofPolicyandScience(AIPS),IEEEOutstandingLeadershipAward(2020),theCDUCollegeofEngineering,ITandEnvironment ExceptionalResearcherAwardin(2020)and(2021),and4BestResearchPaperAwards.Heisrankedintop2%of world’sscientistsinthesubfielddisciplineofArtificialIntelligence(AI)andNetworking&Telecommunications (StanfordUniversity).Hewasrankedinthetop10%of30kcybersecurityauthorsofalltime.ProfessorAlazab wasnamedinthe2022ClarivateAnalyticsWebofSciencelistofHighlyCitedResearchers,whichrecognizeshim asoneoftheworld’smostinfluentialresearchersofthepastdecadethroughthepublicationofmultiplehighly citedpapersthatrankinthetop1%bycitationsforfieldandyearinWebofScience.Hedeliveredmorethan120 keynotespeeches,chaired56nationaleventsandmorethan90internationalevents;onprogramcommitteesfor 200conferences.HeservesastheAssociateEditorof IEEETransactionsonComputationalSocialSystems, IEEE TransactionsonNetworkandServiceManagement (TNSM), ACMDigitalThreats:ResearchandPractice,Complex &IntelligentSystems.
ListofContributors
SouidAbdelbaki DepartmentofElectricalEngineering MACSResearchLaboratoryRL16ES22 NationalEngineeringSchoolofGabes GabesUniversity Gabes Tunisia
FarisAlmalki DepartmentofComputerEngineering CollegeofComputersandInformationTechnology
TaifUniversity
Taif KingdomofSaudiArabia
J.AnilRaj DepartmentofElectronicsandCommunication MuthootInstituteofTechnologyandScience
Kochi Kerala India and DepartmentofComputerScience CochinUniversityofScienceandtechnology
Kochi Kerala India
SundaramArun DepartmentofElectronicsandCommunication Engineering JerusalemCollegeofEngineering Chennai India
AnterpreetK.Bedi DepartmentofElectricalandInstrumentation Engineering ThaparinstituteofEngineeringandTechnology
Patiala Punjab India
SumanBera DepartmentofComputerScienceandEngineering NationalInstituteofTechnology
Silchar Assam India
HutashanV.Bhagat DepartmentofComputerScienceandEngineering SantLongowalInstituteofEngineeringand Technology
Longowal
Sangrur India
AnupamBiswas
DepartmentofComputerScienceandEngineering NationalInstituteofTechnology
Silchar Assam
India
AngshumanBora DepartmentofComputerScienceandEngineering NationalInstituteofTechnology
Silchar Assam
India
YoginiD.Borole DepartmentofE&TCEngineering GHRaisoniCollegeofEngineeringandManagement SPPUPuneUniversity
Pune India
SubhamChakraborty DepartmentofComputerScienceandEngineering NationalInstituteofTechnology
Silchar Assam
India
KonetiChandraSekhar SchoolofElectronicsandElectricalEngineering LovelyProfessionalUniversity Phagwara
Punjab
India
PratikChattopadhyay DepartmentofComputerScienceandEngineering IndianInstituteofTechnology(BanarasHindu University)
Varanasi
India
PalungbamR.Chanu ElectronicsandCommunicationEngineering NITNagaland Chumukedima
Nagaland
KaustavChaudhury ElectronicsandCommunicationEngineering HeritageInstituteofTechnology Anandapur
Kolkata
India
AneetaChristopher DepartmentofElectronicsandCommunication Engineering NationalInstituteofTechnologyCalicut
Calicut
Kerala
India
DebangshuDey DepartmentofElectricalEngineering JadavpurUniversity
Kolkata
WestBengal
India
ThangarajuDineshkumar DepartmentofElectronicsandCommunication Engineering KongunaduCollegeofEngineeringandTechnology
Trichy
India
PaulFieguth VisionImageProcessingLab DepartmentofSystemsDesignEngineering UniversityofWaterloo
Waterloo Canada
BiswarupGanguly DepartmentofElectricalEngineering MeghnadSahaInstituteofTechnology MaulanaAbulKalamAzadUniversityofTechnology WestBengal
India
DeepikaGhai
SchoolofElectronicsandElectricalEngineering
LovelyProfessionalUniversity Phagwara
Punjab
India
R.HariKishan
DepartmentofElectronicsandCommunication Engineering
NationalInstituteofTechnologyCalicut
Calicut
Kerala India
SamrinaHussain DepartmentofDrugDesignandPharmacology UniversityofCopenhagen
Denmark
SumamM.Idicula
DepartmentofArtificialIntelligenceandDataScience
MuthootInstituteofTechnologyandScience
Kochi
Kerala India
HemlataM.Jadhav
ElectronicsandTelecommunicationDepartment
MarathwadaMitraMandal’sCollegeofEngineering
Pune
India
MakarandM.Jadhav
ElectronicsandTelecommunicationDepartment
NBNSinhgadSchoolofEngineering
Pune
India
NeeluJain
ElectronicsandCommunicationEngineering Department
PunjabEngineeringCollege(DeemedtobeUniversity) Chandigarh
India
SmitaKaloni DepartmentofCivilEngineering NationalInstituteofTechnology
Uttarakhand
India
MaheshkumarH.Kolekar DepartmentofElectricalEngineering IndianInstituteofTechnology
Patna
Bihar
India
RanganathanKrishnamoorthy CentrefornonlinearSystems ChennaiInstituteofTechnology
Chennai
India
AshishKumar DepartmentofComputerScienceandEngineering IndianInstituteofTechnology(BanarasHindu University)
Varanasi
India
KanakKumar
ElectronicsEngineeringDepartment IEEEMember,IndianInstituteofTechnology (BanarasHinduUniversity)
Varanasi
India
SachinKumar DepartmentofInstrumentationandControl Engineering
DrBRAmbedkarNationalInstituteofTechnology Jalandhar
India
SandeepKumar DepartmentofElectronicsandCommunications SreyasInstituteofEngineeringandTechnology
Hyderabad
Telangana
India
xxiv ListofContributors
SanjeevKumar DepartmentofBioMedicalApplications(BMA) CentralScientificInstrumentsOrganisation (CSIO)-CSIR Chandigarh
India
P.Leninpugalhanthi DepartmentofEEE SriKrishnaCollegeofTechnology Coimbatore TamilNadu
India
SwanirbharMajumder DepartmentofInformationTechnology TripuraUniversity Agartala
Tripura
India
DebashishMalakar DepartmentofComputerScienceandEngineering NationalInstituteofTechnology Silchar Assam
India
MudasirMaqbool DepartmentofPharmaceuticalSciences UniversityofKashmir
Hazratbal
Srinagar
India
MadhusudhanMishra DepartmentofECE NERIST
Nirjuli ArunachalPradesh
India
IndiranMohan DepartmentofComputerscienceandEngineering PrathyushaEngineeringCollege
Chennai India
AltafMulani ElectronicsandTelecommunicationDepartment SKNSCOE Pandharpur
India
SugataMunshi DepartmentofElectricalEngineering JadavpurUniversity
Kolkata
WestBengal
India
SwarupNandi DepartmentofInformationTechnology TripuraUniversity
Agartala
Tripura
India
MohamedA.Naiel VisionImageProcessingLab DepartmentofSystemsDesignEngineering UniversityofWaterloo
Waterloo Canada
GadamsettiNarasimhaDeva SchoolofElectronicsandElectricalEngineering LovelyProfessionalUniversity
Phagwara
Punjab
India
SoufieneB.Othman DepartmentofTelecom,PRINCELaboratory Research,IsitCom,HammamSousse,HigherInstitute ofComputerScienceandCommunicationTechniques UniversityofSousse
Sousse
Tunisia
ShanmugamPadmapriya DepartmentofComputerScienceEngineering LoyolaInstituteofTechnology
Chennai India
P.Pandiyan DepartmentofEEE
KPRInstituteofEngineeringandTechnology Coimbatore TamilNadu
India
Pooja DepartmentofInstrumentationandControl Engineering
DrBRAmbedkarNationalInstituteofTechnology Jalandhar,India
DasariL.Prasanna DepartmentofElectronicsandCommunication Engineering LovelyProfessionalUniversity Phagwara
Punjab India
VuyyuruPrashanth
SchoolofElectronicsandElectricalEngineering LovelyProfessionalUniversity Phagwara
Punjab
India
HimanshuPriyadarshi DepartmentofElectricalEngineering ManipalUniversityJaipur Jaipur
India
RanjeetP.Rai DepartmentofElectronicsEngineering NationalInstituteofTechnology Uttarakhand
India
ZobeirRaisi VisionImageProcessingLab DepartmentofSystemsDesignEngineering UniversityofWaterloo
Waterloo
Canada
ListofContributors
HusamRajab DepartmentofTelecommunicationsandMedia Informatics BudapestUniversityofTechnologyandEconomics
Budapest
Hungary
Ramya DepartmentofElectronicsandCommunication Engineering
SriRamakrishnaEngineeringCollege AnnaUniversity(Autonomous) Coimbatore
India
RoshaniRaut DepartmentofInformationTechnology PimpriChinchwadCollegeofEngineering
Pune
India
AbhisekRay DepartmentofElectricalEngineering IndianInstituteofTechnology Patna
Bihar
India
RehabA.Rayan DepartmentofEpidemiology HighInstituteofPublicHealth
AlexandriaUniversity
Alexandria
Egypt
ThummalaReddychakradharGoud SchoolofElectronicsandElectricalEngineering LovelyProfessionalUniversity Phagwara
Punjab
India
ListofContributors
HediSakli
DepartmentofElectricalEngineering MACSResearchLaboratoryRL16ES22 NationalEngineeringSchoolofGabes GabesUniversity Gabes
Tunisia and EITAConsulting5RueduChantdesoiseaux Montesson France
S.Saravanan DepartmentofEEE SriKrishnaCollegeofTechnology Coimbatore
TamilNadu
India
R.SenthilKumar DepartmentofEEE SriKrishnaCollegeofTechnology Coimbatore
TamilNadu
India
ShagunSharma DepartmentofElectronicsEngineering NationalInstituteofTechnology Uttarakhand
India
AshishShrivastava FacultyofEngineeringandTechnology ShriVishwakarmaSkillUniversity Gurgaon
India
GhanapriyaSingh DepartmentofElectronicsEngineering NationalInstituteofTechnology Uttarakhand
India
KulwantSingh DepartmentofElectronicsandCommunication Engineering ManipalUniversityJaipur Jaipur
India
ManminderSingh DepartmentofComputerScienceandEngineering SantLongowalInstituteofEngineeringand Technology
Longowal Sangrur
India
YeshwantSingh DepartmentofComputerScienceandEngineering NationalInstituteofTechnology
Silchar Assam
India
SivaSakthi DepartmentofBiomedicalEngineering SriRamakrishnaEngineeringCollege AnnaUniversity(Autonomous) Coimbatore
India
GannamaniSriram SchoolofElectronicsandElectricalEngineering LovelyProfessionalUniversity Phagwara Punjab
India
SachinSrivastava DepartmentofComputerScienceandEngineering IndianInstituteofTechnology(BanarasHindu University)
Varanasi India
P.V.Sudeep
DepartmentofElectronicsandCommunication Engineering
NationalInstituteofTechnologyCalicut Calicut
Kerala India
RameshK.Sunkaria DepartmentofElectronics&Communication Engineering
DrBRAmbedkarNationalInstituteofTechnology Jalandhar
Punjab India
K.P.Suresh DepartmentofEEE
SriKrishnaCollegeofTechnology Coimbatore TamilNadu
India
RanganathanThiagarajan DepartmentofInformationTechnology PrathyushaEngineeringCollege
Chennai
India
SumanLataTripathi SchoolofElectronics&ElectricalEngineering LovelyProfessionalUniversity Phagwara
Punjab
India
KaranVeer DepartmentofInstrumentationandControl Engineering
DrBRAmbedkarNationalInstituteofTechnology Jalandhar
India
SidhantYadav DepartmentofElectronicsEngineering NationalInstituteofTechnology Uttarakhand
India
GeorgesYounes
VisionImageProcessingLab DepartmentofSystemsDesignEngineering UniversityofWaterloo
Waterloo
Canada
ImranZafar DepartmentofBioinformaticsandComputational Biology
VirtualUniversityofPakistan
Lahore
Punjab
Pakistan
JohnZelek VisionImageProcessingLab DepartmentofSystemsDesignEngineering UniversityofWaterloo
Waterloo
Canada
MuhammadAsimM.Zubair DepartmentofPharmaceutics
TheIslamiaUniversityofBahawalpur
Pakistan
Preface
Machinelearning(ML)algorithmsforsignalandimageprocessingaidthereaderindesigninganddeveloping real-worldapplicationstoanswersocietalandindustrialneedsusingadvancesinMLtoaidandenhancespeech signalprocessing,imageprocessing,computervision,biomedicalsignalprocessing,textprocessing,etc.Itincludes signalprocessingtechniquesappliedforpre-processing,featureextraction,sourceseparation,ordatadecompositionstoachieveMLtasks.ItwilladvancethecurrentunderstandingofvariousMLanddeeplearning(DL) techniquesintermsoftheirabilitytoimproveupontheexistingsolutionswithaccuracy,precisionrate,recall rate,processingtime,orotherwise.Whatismostimportantisthatitaimstobridgethegapamongtheclosely relatedfieldsofinformationprocessing,includingML,DL,digitalsignalprocessing(DSP),statistics,kerneltheory,andothers.Italsoaimstobridgethegapbetweenacademicians,researchers,andindustriestoprovidenew technologicalsolutionsforhealthcare,speechrecognition,objectdetectionandclassification,etc.Itwillimprove uponthecurrentunderstandingaboutdatacollectionanddatapre-processingofsignalsandimagesforvarious applications,implementationofsuitableMLandDLtechniquesforavarietyofsignalsandimages,aswellas possiblecollaborationtoensuresuccessfuldesignaccordingtoindustrystandardsbyworkinginateam.Itwill behelpfulforresearchersanddesignerstofindoutkeyparametersforfutureworkinthisarea.Theresearchers workingonMLandDLtechniquescancorrelatetheirworkwithreal-lifeapplicationsofsmartsignlanguage recognitionsystem,healthcare,smartblindreadersystem,text-to-imagegeneration,orviceversa.
ThebookwillbeofinteresttobeginnersworkinginthefieldofMLandDLusedforsignalandimageanalysis, interdisciplinaryinitsnature.Writtenbywell-qualifiedauthors,withworkcontributedbyateamofexpertswithin thefield,theworkcoversawiderangeofimportanttopicsasfollows:
● Speechrecognition,imagereconstruction,objectdetectionandclassification,andspeechandtextprocessing.
● Healthcaremonitoring,biomedicalsystems,andgreenenergy.
● Realapplicationsandexamples,includingasmarttextreadersystemforblindpeople,asmartsignlanguage recognitionsystemfordeafpeople,handwrittenscriptrecognition,real-timemusictranscription,smartagriculture,structuraldamagepredictionfromearthquakes,andskinlesionclassificationfromdermoscopicimages.
● HowvariousMLandDLtechniquescanimprovetheaccuracy,precisionraterecallrate,andprocessingtime.
Thiseasy-to-understandyetincrediblythoroughreferenceworkwillbeinvaluabletoprofessionalsinthefield ofsignalandimageprocessingwhowanttoimprovetheirwork.Itisalsoavaluableresourceforstudentsand researchersinrelatedfieldswhowanttolearnmoreaboutthehistoryandrecentdevelopmentsinthisfield.
Acknowledgments
AlleditorswouldliketothanktheSchoolofElectronicsandElectricalEngineering,LovelyProfessional University,Phagwara,India;DepartmentofElectronicsandCommunicationEngineering,MeghnadSaha InstituteofTechnology,Kolkata;andCollegeofEngineering,ITandEnvironmentatCharlesDarwinUniversity, Australia;theMinistryofEducationoftheRepublicofKoreaandtheNationalResearchFoundationofKorea (NRF-2021S1A5A2A03064391);forprovidingnecessarysupportforcompletingthisbook.Theauthorswould alsoliketothanktheresearcherswhohavecontributedtheirchapterstothisbook.