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Applied Intelligent Decision Making in Machine Learning


: Data Stream Mining for Big DataIntroductionResearch Issues in Data Stream MiningFiltering and Counting in a Data StreamBloom FiltersCounting the Frequency of Items in a StreamCount Unique Items in a Data StreamSampling from Data StreamsConcept Drift Detection in Data StreamsCauses of Concept DriftHandling Concept DriftCUSUM AlgorithmThe Higia AlgorithmDynamic Weighted Majority AlgorithmDiscussionReferencesIntroductionLiterature ReviewMaterials and MethodsOverall ML Model Development ProcessData CollectionIris DatasetSoybean Aphid DatasetWeed Species DatasetShape Features ExtractionData CleaningFeature SelectionFilter MethodsWrapper MethodsEmbedded MethodsRelief AlgorithmsData SplittingThe ML MethodsLinear Discriminant Analysisk-Nearest NeighborEvaluation of ML MethodsConfusion MatrixAccuracyPrecisionRecallF-scoreResults and DiscussionResults of Evaluated Features from the DatasetSelected Features from the DatasetDataset Test of Normality for Model SelectionSoybean Aphid IdentificationFeatures RankingThe LDA Model and EvaluationWeed Species ClassificationFeatures RankingThe kNN Model and EvaluationComparison of Results with the Standard Iris DataConclusionsAcknowledgmentsReferences: A Multi-Stage Hybrid Model for Odia Compound Character RecognitionIntroductionGeneral OCR StagesStructural SimilarityProjection Profile and Kendall Rank Correlation Coefficient MatchingLocal Frequency DescriptorGeneral Regression Neural Network (GRNN)Proposed MethodExperimentsDataset CreationExperimental SetupResults and DiscussionConclusion and Future ScopeReferences: Development of Hybrid Computational Approaches for Landslide Susceptibility Mapping Using Remotely Sensed Data in East Sikkim, IndiaIntroductionStudy Materials and MethodologyArea of Research StudyMULTI-COLINEARITY ASSESSMENT (MCT)Affecting FactorsLandslide Inventory Map (LIM)MethodologyHybrid Biogeography-Based OptimizationHybridization with Differential EvolutionThe DE/BBO AlgorithmLocal-DE/BBOSelf-Adaptive DE/BBOValidation of ModelsShortly Structured MethodologyResults and DiscussionImportance of the Conditioning Factors on the Occurrences of LandslidesApplication of Hybrid Biogeography-Based Optimization for Landslide Susceptibility AssessmentConclusionReferences: Domain-Specific Journal Recommendation Using a Feed Forward Neural NetworkIntroductionLiterature SurveyContent-Based Recommendation System for Domain-Specific PapersScraping and Data Integration (Challenges and Solutions for Data Collection)Limitations on the Size of the Query ResultsFixed LimitsPaginationDynamic ContentsAccess LimitationsMasked URL ParametersRobot Recognition and Reverse Turing TestsChanging the Content of Request HeadersSelecting Appropriate Cookie SettingsRequests and Different Time IntervalsAltering the IP AddressData CurationThe Complexity of the Integration OperationPhase 1: Identifying Candidate JournalsPhase 2: Ranking Candidate JournalsExperimental Results and DiscussionsConfigurationsResult AnalysisConclusion and Future WorkReferences: Forecasting Air Quality in India through an Ensemble Clustering TechniqueIntroductionRelated WorksAir Quality PredictionEnsemble ModelingVariants of Ensemble ModelsEnsemble ClusteringDataset DescriptionsMethodologyFinal Cluster LabelingMETIS FunctionMETIS AlgorithmPhasesAdvantagesEXPERIMENTAL RESULTSSilhouette CoefficientCalinski-Harabasz IndexDavies-Bouldin IndexConclusionReferences: An Intelligence-Based Health Biomarker Identification System Using Microarray AnalysisIntroductionExisting KnowledgeClassification ModelApproaches for Feature SelectionShuffled Frog-Leaping Algorithm and Particle Swarm Optimization (SFLA-PSO)The Advantage of SFLAAlgorithm for BSFLA-PSOExperimental Result AnalysisDataset Considered for This ExperimentNormalizationDetails of Classifiers Used in This Experimental Study and Evaluation MetricsResult AnalysisPerformance of Proposed BSFLA-PSO with Prostate DatasetPerformance of Proposed BSFLA-PSO with Leukemia DatasetPerformance of Proposed BSFLA-PSO with ALL/AML DatasetPerformance of Proposed BSFLA-PSO with ADCA Lung DatasetPerformance of Proposed BSFLA-PSO with CNS DatasetConclusionReferences: Extraction of Medical Entities Using a Matrix-Based Pattern-Matching MethodIntroductionBackgroundMethodologyDatasetProposed MethodText Pre-ProcessingTrained Matrix FormationTest Matrix FormationPattern MatchingPruning Non-Medical ConceptsSystem EvaluationResults and DiscussionConclusions and Future WorkAcknowledgmentsReferences: Supporting Environmental Decision Making Application of Machine Learning Techniques to Australia’s EmissionsIntroductionData and MethodologyDataMethodologyDecision TreesRandom ForestsExtreme Gradient BoostingSupport Vector RegressionData Division and the Experimental EnvironmentOptimization of HyperparametersParameter Tuning for the DT, RF, and XGBoost AlgorithmsParameter Tuning for the SVR AlgorithmPerformance MetricsResults and DiscussionDevelopment and Validation of the DT ModelDevelopment and Validation of the RF ModelDevelopment and Validation of the XGBoost ModelDevelopment and Validation of the SVR ModelPerformance Evaluation of ModelConcluding RemarksReferences: Prediction Analysis of Exchange Rate Forecasting Using Deep Learning-Based Neural Network ModelsIntroductionMethodologyPerformance MeasureData PreparationResults and SimulationsFor Sliding Window Size 7For Sliding Window Size 10For Sliding Window Size 13ConclusionReferences: Optimal Selection of Features Using Teaching-Learning-Based Optimization Algorithm for ClassificationIntroductionRelated WorkBasic TechnologyProposed ModelResult AnalysisConclusionReferences: An Enhanced Image Dehazing Procedure Using CLAHE and a Guided FilterIntroductionLiterature SurveyWhite Balance (WB)CLAHEGFProposed MethodologyDataset Collection and AnalysisImage Quality Assessment CriteriaPeak Signal-to-Noise Ratio and Mean Squared ErrorEntropyStructural Similarity IndexContrast GainExperimental Results and DiscussionConclusion and Future ScopeReferences
 
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