book

Deep Learning for Cognitive Computing Systems

Name: Deep Learning for Cognitive Computing Systems
ISBN: 9783110750614

by M.G. Sumithra, Rajesh Kumar Dhanaraj, Celestine Iwendi, Anto Merline Manoharan

December 2022

Intermediate to advanced

208 pages

6h 51m

English

De Gruyter

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R. Regan, Anto Merline Manoharan, R. Gayathri, K. Kandasamy The Learning of Deep Learning: Overview, Methods, and Applications
IntroductionMachine LearningDeep LearningDeep Learning Versus Machine LearningHow Deep Learning WorksDeep Learning ModelObtaining InformationPreparation of DataTrainingParameter TurningDeep Learning TechniquesConvolutional Neural NetworksDeep Belief NetworksAutoencoder/Stack AutoencoderDeep Learning’s Advantages and DisadvantagesDeep Learning in ActionApplications of Deep LearningVirtual AssistantsVision for Driverless and Autonomous CarsService-Oriented Chat BotsTranslationsFacial RecognitionShopping and EntertainmentMedical Field and PharmaceuticalsConclusion
R. Remya, M.G. Sumithra, M. Krishnamoorthi Foundation of Cognitive Computing
IntroductionApplicationsRetail IndustryLogisticsBanking and FinanceCybersecurityHealthcareEducationChatbotsAnalysis by SentimentFace DetectionRisk ManagementFraud DetectionTop Cognitive Computing CompaniesVantage SoftwareWelltokLifelearnWayblazerEdgeup SportsBright MindedSpark CognitionNumentaExpert SystemCustomer MatrixChallengesConclusion
R. Ranjana, Subha T, S Varsha, N Jothishree, Pravin Kumar Applications and Implications of Artificial Intelligence and Deep Learning in Computer Vision
Background and Introduction to Computer VisionApplication of Computer VisionRetail and ShoppingBehavioral TrackingHealthcareAutonomous VehiclesNeed for Computer VisionAims of a Computer VisionImplications of AI Techniques in Computer VisionHow Does AI’s Computer Vision Work?Deep Learning and Computer VisionConvolutional Neural NetworksExampleSemantic SegmentationRecurrent Neural NetworksMethods Used in Making Computer “See”Deep Learning Algorithm Used in Edge DetectionObject DetectionPattern RecognitionUse Cases for Computer VisionRetail and Security in RetailAutomotiveAgricultureBankingDeep Learning Algorithm for Image SegmentationDeep Learning Algorithms for Object IdentificationRole of Computer Vision in Augmented Reality and Virtual RealityAugmented RealityVirtual RealityChallenges in Building Vision for ComputersConclusionCase Study
R. Regan, P. Rajaram, K. Senthilkumar, N. Khadirkumar A Study of Voice Recognition System Using Deep Learning Techniques
IntroductionNeural Network Architectures for Voice RecognitionNeural Network ArchitectureAutomatic Speech Recognition SystemWhy ASR?Evolution of Voice Recognition SystemsExisting MethodsNatural Language ProcessingHidden Markov ModelsN-GramsArtificial Neural NetworksMel-Frequency Cepstral CoefficientVoice Recognition TechniqueAspects of ASRTyping is Slower than TalkingPersonalizationPerformance and ProductivitySimple to UseMultiple Users’ AccessHands-FreeArchitecture of ASRASR DatasetsCharacteristics of a Successful Voice Recognition SystemThe Applications of Voice Recognition SystemAutomobilesHealthcareSalesMobile PhonesSecurityAdvantages and DisadvantagesConclusion
Oleeviya Babu P, Sobin C. C, Jahfar Ali Building Machine Learning–Based Prediction System for Critical Diseases
IntroductionSickle Cell AnemiaHeart DiseaseBreast CancerLiterature ReviewSickle Cell DiseaseHeart DiseaseBreast CancerProposed MethodSickle Cell DiseaseHeart DiseaseBreast CancerExperimental ResultsSickle Cell DiseaseHeart DiseaseBreast CancerConclusion
Shahab Nadeem Hashmi, Nitesh Bharti, V. M. Manikandan An Overview of Internet of Things and Machine Learning for Smart Healthcare
IntroductionHistoryPresent ScenarioInternet of Things and Machine Learning Approaches in HealthcareBenefits of IoT and Machine Learning in HealthcareVarious IoT Devices in Healthcare DomainRelated Works in IoT SecurityConclusion
Reethika A, Jagadesh T, M S Kanivarshini Nutrition Food Recognition Using Deep Learning Algorithm for Physically Challenged Human Being
IntoductionMaterials and MethodsExperimental ResultsArrangement ArchitectureTraditional Deep Neural NetworkConclusion
K. Renuka Devi, S. Suganyadevi, K. Balasamy Healthcare Data Analysis Using Deep Learning Paradigm
IntroductionThe Concept of Deep Structured LearningBenefits of Deep LearningClassification of DL ApproachSupervised Deep Learning StrategyUnsupervised Deep Learning StrategySemi-Supervised Deep Learning StrategyDeep Learning in Healthcare DomainChallenges Faced by Healthcare SectorThe Availability of Healthcare Systems and EquipmentTremendous Growth of Clinical DataAppropriate Data for Making Better DecisionsLegal ChallengesPrivacy and Security IssueRemedy by Deep Learning in HealthcareDeep Learning – ApplicationsNatural Language ProcessingVirtual AssistantBig DataHealthcareEntertainmentFraud DetectionImage ProcessingConclusion
R. Babu, Prithi Samuel, K. Jayashree Cognitive Authentication for Smart Healthcare System
IntroductionCognitive Authentication System in Smart HealthcareSystem ModelThreat ModelNetwork ModelMethods for Cognitive Authentication System for Smart-Based HealthcareMachine LearningDeep Learning and Neural NetworksBenefits and Limitations of Cognitive Authentication System for Smart-Based HealthcareBenefitsLimitationsResolution Solutions for Cognitive Authentication FrameworkSecurity Authentication Using BrainwavesEEG-Based BiometricsChallenges and Future Research DirectionsConclusion
B. Lakshmanan, S. Priyadharsini, G. Priyanka Cognitive-Inspired Computer Vision Assist System for Diabetic Retinopathy Detection from Fundus Images
IntroductionRelated WorksMaterials and MethodsDataset DescriptionPreprocessingConvolution-Based Small Attention NetworkExperimental Setup and Results DiscussionConclusion and Future Work

Dineshnath G, S. Saraswathi A Novel Deep Belief Neural Network Model for Abstractive Text Summarization
IntroductionResearch ContributionRelated WorksMotivation and ObjectiveResults and DiscussionConclusion
Index
De Gruyter Series Smart Computing Applications

Overview

Cognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing. The integration of deep learning improves the performance of Cognitive computing systems in many applications, helping in utilizing heterogeneous data sets and generating meaningful insights.

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