Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approache

An Artificial Intelligence System Based Power Estimation Method for CMOS VLSI CircuitsIntroductionPrevious Work Using BPNN and ANFISTraining and Testing DataPower Estimation Using a Neural NetworkConstruction of a Neural NetworkBPNN Training PhaseBPNN Testing PhaseNetwork ParametersProposed Power Estimation Using ANFIS TechniqueOverview of the Proposed WorkTraining and Checking Used in ANFISDesigning the ANFISResults and DiscussionsBPNN-Based MetodCalculating Prediction ErrorANFIS-Based MethodPerformance EvaluationConclusionReferencesAwareness Alert and Information Analysis in Social Media Networking Using Usage Analysis and Negotiable ApproachIntroductionLiterature SurveyUsage Analysis and Negotiable (UAN) ApproachAge Categorization Usage AnalysisRules Defined for 18-25 Age CategorizationRules Defined for 26–30 Age CategorizationRules Defined for 31–35 Age CategorizationUser View AnalysisLikes and Dislikes ViewsComments Posting CategoryContent SharingUser Interested Test Rate (UITR) Analysis AlgorithmRating Statement AnalysisLike and Dislikes Rating AlgorithmComments PostingContent SharingFeedback AnalysisResult and DiscussionsConclusionReferencesObject Detection and Tracking in Video Using Deep Learning Techniques: A ReviewIntroductionChallenges in Video TrackingFundamentals of Object TrackingObject RepresentationShape RepresentationAppearance RepresentationObject DetectionFrame DifferencingOptical FlowBackground SubtractionObject Classification MethodFeature SelectionEdgesOptical FlowColorTextureObject Tracking TechniquesPointKernel-Based Tracking ApproachSilhouette ApproachIntroduction to Deep and Machine Learning TechniquesDeep Learning vs Machine Learning TechniquesExamplesVehicle DetectionTraining of a Cascade DetectorFeature Types Available for TrainingSupply Positive SamplesSupply Negative ImagesConclusionReferencesFuzzy MCDM: Application in Disease Risk and PredictionFuzzy MCDM: IntroductionLiterature ReviewMethodologiesMathematical Procedure for Applying Fuzzy MCDMFuzzificationFuzzy SetsCrisp SetsMembership FunctionsGeneral Architecture of a Fuzzy MCDM Inference SystemFuzzy If-Then RulesCase StudiesDetection and Risk Prediction of Medical Problem (Heart Disease)Risk prediction of Breast Cancer Using Fuzzy LogicApplication of Fuzzy Logic in Determination of Hard Exudates in Diabetic RetinopathyConclusion and Future ScopeReferencesDeep Learning Approach to Predict and Grade Glaucoma from Fundus Images through Constitutional Neural NetworksIntroductionRelated WorksMaterials and MethodDatabase and Division of ImagesPreprocessing and Down-SamplingDeep Learning Architecture (DLA)Input LayerConvolutional LayerBatch Normalization LayerRectified Linear UnitMax Pooling LayerFully Connected LayerSoftmax LayerMethodologyProposed CNN ArchitectureTraining and Testing SchemesExperimentsPerformance AssessmentDiscussion and ConclusionConflict of InterestREFERENCESA Novel Method for Securing Cognitive Radio Communication Network Using the Machine Learning Schemes and a Rule Based ApproachesIntroductionRelated Works and Existing MethodsBrief Note on Existing MethodsCollaborative Approach Toward Secure Spectrum SensingSequential 0/1 for Strategic Byzantine AttackMalicious Cognitive User Identification Algorithm for SSDF AttackXOR Distance Analysis for Securing CSSAn Enhanced CSS Scheme Based on Evidence TheorySystem ModelSSDF Attack: Reference and Mathematical ExpressionsProposed System ModelA Novel SchemaProposed MethodAn “Improved-apriori” AlgorithmImplementation of the Proposed MethodPerformance Evaluation of the AccuracyDiscourse and Performance RatingBasic Simulation ConfigurationConclusionsReferencesAppendix I: Proof of Error Probability, PеDetection of Retinopathy of Prematurity Using Convolution Neural Network Deepa Dhanaskodi and Poongodi ChenniappanIntroductionRetinopathy of PrematurityCauses of ROPStages of ROPLiterature SurveyProposed AlgorithmConvolution Neural NetworkInput LayerHidden LayerOutput LayerProposed Work Flow for ROP DetectionSurvey and Load Image DataSpecify Training and Validation SetsImage Input LayerConvolution LayerReLU LayerMax Pooling LayerFully Connected LayerSoftmax LayerClassification LayerSpecify Training OptionsClassify Evaluation Images and Calculate AccuracyResults and DiscussionConclusionReferencesImpact of Technology on Human Resource Information System and Achieving Business Intelligence in OrganizationsIntroductionObjective of the StudyResearch MethodologyLiterature ReviewEvolution of HRIS and the Changing Concept of HR Integration with ITBefore 1945 (Pre World War II)-1960 (Post World War II)–1980 (Legislative Era and Emerging HRM)–1990 (Low-Cost Era and Integration of HR with IT)–2000 (Technology Era and the Emergence of Strategic Human Resource Management)–2010 (Emergence of High Technology and Introduction of Diversified Technological Tools)Onwards (Recent Trends of HRIS)Technology as a Competitive StrategyImplicationsScope of the StudyConclusionReferencesProficient Prediction of Acute Lymphoblastic Leukemia Using Machine Learning AlgorithmIntroductionProblem SolvingProblem StatementSolutionSystem Design and ImplementationImage Preprocessingk-Means ClusteringMaximum iterationEpsilonTypeWatershed SegmentationFeature ExtractionSVM ClassifierPCA Graph for Linear and RBF KernelConclusionReferencesRole of Machine Learning in Social Area NetworksIntroduction to Machine LearningFramework to Develop Machine Learning ModelsTwo Phases of Machine LearningTypes of Machine LearningTypes of Supervised LearningClassificationLinear RegressionLogistic RegressionTypes of Unsupervised LearningDimensionality ReductionFeature SelectionFeature ExtractionMethods of Dimensionality ReductionReinforcement LearningChallenges and Limitations of Machine LearningApplications of MLSocial Area NetworksResearch Areas in Social NetworkingSentimental AnalysisRecommender SystemIntroduction to Rule-Based AlgorithmsExperiment 1: Sentimental AnalysisSample Experimentation ResultsExperiment 2: Movie RecommendationSample Experimentation ResultsConclusion and Future ScopeReferencesBreast Cancer and Machine Learning: Interactive Breast Cancer Prediction Using Naive Bayes AlgorithmIntroduction to Breast CancerTypes of Breast CancerBreast Cancer SymptomsTreating Breast CancerIntroduction to Machine Learning AlgorithmClassification of Machine LearningGrouping Machine Learning Based on the Results ObtainedImplementing Machine LearningRisk Factors of Breast CancerRisk FactorsTNM Staging SystemT CategoryN CategoryM CategoryCancer Staging, Grouping, and GradingStaging and GradingStagingEvaluating and Implementing Naive Bayes AlgorithmAssumptionsBayes TheoremAreas Utilization Naive Bayes AlgorithmsBuilding Primary Modules Using Naive BayesSystem Workflow ArchitectureExploratory Data AnalysisSplitting the DatasetData WranglingData GatheringPreprocessingBuilding the Classification ModelTraining the DataTesting the DatasetSystem WorkflowImplementation of Naive Bayes Algorithm Using Anaconda SoftwareEvaluating the Accuracy of Software ModulesConfusion MatrixBasic TermsComparing the Algorithm with Prediction in the Form of Best Accuracy ResultPrediction Result by AccuracyAccuracy CalculationResults of Testing Using Confusion MatrixConclusionReferencesDeep Networks and Deep Learning AlgorithmsIntroductionDeep LearningWhy Deep LearningThe Perceptron (Neuron)Neural NetworksNetwork TopologiesFeedforward Neural NetworksThe Artificial NeuronGradient DescentThe Backpropagation AlgorithmTensorFlow and Its UseNeurons in Human VisionThe Predictors: Decision TreesLearning Lower-Dimensional RepresentationsConvolutional LayersReinforcement LearningRecurrent Neural NetworksOn the Future of Neural NetworksConclusionReferencesMachine Learning for Big Data Analytics, Interactive and ReinforcementIntroductionBig DataCharacteristics of Big DataThe Big Data RevolutionWhy Is Big Data Analytics Important?Challenges FACED BY Big DataMachine LearningTypes of Machine LearningSupervised LearningUnsupervised LearningSemi-Supervised LearningReinforcement LearningMachine Learning StepsCollection of DataPreparing DataSelecting a ModelTraining ModelEvaluation of the ModelTuning of ParameterMaking Predictions (Matthew Mayo, 2018)Applications of Machine LearningVirtual AssistantsPrognosisVideos SupervisionSocial Media ActivitiesSpam E-mail and Filtering of MalwareSupporting Customers OnlineSearch Engine Result RefiningOnline Fraud DetectionBig Data Using Machine LearningEnter Machine LearningML and Big Data—Real-World ApplicationsImplementing Machine Learning in Big DataEmpowering Big Data and Machine LearningConclusion and the Future of Big Data AnalyticsReferencesFish Farm Monitoring System Using IoT and Machine LearningIntroductionRelated WorkProposed System ModelHardware SectionExperimental SetupResultConclusionFuture WorkReferences
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