Search Results - computer program testing chapter processing.

Connect to the full text of this electronic book

Table of Contents: “...Introduction xxi -- Assessment Test xxviii -- Chapter 1 Architectural Concepts 1 -- Cloud Characteristics 2 -- Business Requirements 4 -- Existing State 5 -- Quantifying Benefits and Opportunity Cost 6 -- Intended Impact 8 -- Cloud Evolution, Vernacular, and Models 9 -- New Technology, New Options 9 -- Cloud Computing Service Models 10 -- Cloud Deployment Models 12 -- Cloud Computing Roles and Responsibilities 13 -- Cloud Computing Definitions 14 -- Foundational Concepts of Cloud Computing 16 -- Sensitive Data 16 -- Virtualization 16 -- Encryption 16 -- Auditing and Compliance 17 -- Cloud Service Provider Contracts 17 -- Related and Emerging Technologies 18 -- Summary 19 -- Exam Essentials 19 -- Written Labs 20 -- Review Questions 21 -- Chapter 2 Design Requirements 25 -- Business Requirements Analysis 26 -- Inventory of Assets 26 -- Valuation of Assets 27 -- Determination of Criticality 27 -- Risk Appetite 29 -- Security Considerations for Different Cloud Categories 31 -- IaaS Considerations 32 -- PaaS Considerations 32 -- SaaS Considerations 32 -- General Considerations 33 -- Design Principles for Protecting Sensitive Data 33 -- Hardening Devices 33 -- Encryption 35 -- Layered Defenses 35 -- Summary 36 -- Exam Essentials 37 -- Written Labs 37 -- Review Questions 38 -- Chapter 3 Data Classification 43 -- Data Inventory and Discovery 45 -- Data Ownership 45 -- The Data Lifecycle 46 -- Data Discovery Methods 50 -- Jurisdictional Requirements 51 -- Information Rights Management (IRM) 53 -- Intellectual Property Protections 53 -- IRM Tool Traits 57 -- Data Control 59 -- Data Retention 60 -- Data Audit 61 -- Data Destruction/Disposal 63 -- Summary 65 -- Exam Essentials 65 -- Written Labs 66 -- Review Questions 67 -- Chapter 4 Cloud Data Security 71 -- Cloud Data Lifecycle 73 -- Create 74 -- Store 75 -- Use 75 -- Share 75 -- Archive 76 -- Destroy 77 -- Cloud Storage Architectures 78 -- Volume Storage: File-Based Storage and Block Storage 78 -- Object-Based Storage 78 -- Databases 79 -- Content Delivery Network (CDN) 79 -- Cloud Data Security Foundational Strategies 79 -- Encryption 79 -- Masking, Obfuscation, Anonymization, and Tokenization 81 -- Security Information and Event Management 84 -- Egress Monitoring (DLP) 85 -- Summary 86 -- Exam Essentials 86 -- Written Labs 87 -- Review Questions 88 -- Chapter 5 Security in the Cloud 93 -- Shared Cloud Platform Risks and Responsibilities 95 -- Cloud Computing Risks by Deployment Model 97 -- Private Cloud 98 -- Community Cloud 98 -- Public Cloud 100 -- Hybrid Cloud 104 -- Cloud Computing Risks by Service Model 104 -- Infrastructure as a Service (IaaS) 104 -- Platform as a Service (PaaS) 105 -- Software as a Service (SaaS) 106 -- Virtualization 106 -- Threats 107 -- Countermeasure Methodology 109 -- Disaster Recovery (DR) and Business Continuity (BC) 112 -- Cloud-Specific BIA Concerns 112 -- Customer/Provider Shared BC/DR Responsibilities 113 -- Summary 116 -- Exam Essentials 116 -- Written Labs 117 -- Review Questions 118 -- Chapter 6 Responsibilities in the Cloud 123 -- Foundations of Managed Services 126 -- Business Requirements 127 -- Business Requirements: The Cloud Provider Perspective 127 -- Shared Responsibilities by Service Type 133 -- IaaS 133 -- PaaS 133 -- SaaS 133 -- Shared Administration of OS, Middleware, or Applications 134 -- Operating System Baseline Configuration and Management 134 -- Shared Responsibilities: Data Access 136 -- Customer Directly Administers Access 137 -- Provider Administers Access on Behalf of the Customer 137 -- Third-Party (CASB) Administers Access on Behalf of the Customer 137 -- Lack of Physical Access 137 -- Audits 138 -- Shared Policy 142 -- Shared Monitoring and Testing 142 -- Summary 143 -- Exam Essentials 143 -- Written Labs 144 -- Review Questions 145 -- Chapter 7 Cloud Application Security 149 -- Training and Awareness 151 -- Common Cloud Application Deployment Pitfalls 154 -- Cloud-Secure Software Development Lifecycle (SDLC) 156 -- Configuration Management for the SDLC 157 -- ISO/IEC 27034-1 Standards for Secure Application Development 158 -- Identity and Access Management (IAM) 159 -- Identity Repositories and Directory Services 160 -- Single Sign-On (SSO) 161 -- Federated Identity Management 161 -- Federation Standards 162 -- Multifactor Authentication 162 -- Supplemental Security Components 163 -- Cloud Application Architecture 164 -- Application Programming Interfaces 164 -- Tenancy Separation 165 -- Cryptography 165 -- Sandboxing 166 -- Application Virtualization 167 -- Cloud Application Assurance and Validation 167 -- Threat Modeling 167 -- Quality of Service 169 -- Software Security Testing 170 -- Approved APIs 172 -- Software Supply Chain (API) Management 172 -- Securing Open-Source Software 172 -- Application Orchestration 173 -- The Secure Network Environment 174 -- Summary 175 -- Exam Essentials 175 -- Written Labs 176 -- Review Questions 177 -- Chapter 8 Operations Elements 181 -- Physical/Logical Operations 183 -- Facilities and Redundancy 184 -- Virtualization Operations 194 -- Storage Operations 196 -- Physical and Logical Isolation 199 -- Application Testing Methods 200 -- Security Operations Center 201 -- Continuous Monitoring 201 -- Incident Management 202 -- Summary 203 -- Exam Essentials 204 -- Written Labs 204 -- Review Questions 205 -- Chapter 9 Operations Management 209 -- Monitoring, Capacity, and Maintenance 211 -- Monitoring 211 -- Maintenance 213 -- Change and Configuration Management (CM) 217 -- Baselines 218 -- Deviations and Exceptions 218 -- Roles and Process 219 -- Release Management 221 -- IT Service Management and Continual Service Improvement 222 -- Business Continuity and Disaster Recovery (BC/DR) 223 -- Primary Focus 224 -- Continuity of Operations 225 -- The BC/DR Plan 225 -- The BC/DR Kit 227 -- Relocation 228 -- Power 229 -- Testing 230 -- Summary 231 -- Exam Essentials 231 -- Written Labs 232 -- Review Questions 233 -- Chapter 10 Legal and Compliance Part 1 237 -- Legal Requirements and Unique Risks in the Cloud Environment 239 -- Legal Concepts 239 -- US Laws 242 -- International Laws 246 -- Laws, Frameworks, and Standards Around the World 246 -- Information Security Management Systems (ISMSs) 252 -- The Difference between Laws, Regulations, and Standards 254 -- Potential Personal and Data Privacy Issues in the Cloud Environment 254 -- eDiscovery 255 -- Forensic Requirements 256 -- Conflicting International Legislation 256 -- Cloud Forensic Challenges 257 -- Direct and Indirect Identifiers 258 -- Forensic Data Collection Methodologies 258 -- Audit Processes, Methodologies, and Cloud Adaptations 259 -- Virtualization 259 -- Scope 259 -- Gap Analysis 260 -- Restrictions of Audit Scope Statements 260 -- Policies 261 -- Different Types of Audit Reports 261 -- Auditor Independence 262 -- AICPA Reports and Standards 262 -- Summary 263 -- Exam Essentials 264 -- Written Labs 264 -- Review Questions 265 -- Chapter 11 Legal and Compliance Part 2 269 -- The Impact of Diverse Geographical Locations and Legal Jurisdictions 271 -- Policies 272 -- Implications of the Cloud for Enterprise Risk Management 276 -- Choices Involved in Managing Risk 276 -- Risk Management Frameworks 279 -- Risk Management Metrics 281 -- Contracts and Service-Level Agreements (SLAs) 281 -- Business Requirements 284 -- Cloud Contract Design and Management for Outsourcing 284 -- Identifying Appropriate Supply Chain and Vendor Management Processes 285 -- Common Criteria Assurance Framework (ISO/IEC 15408-1:2009) 285 -- CSA Security, Trust, and Assurance Registry (STAR) 286 -- Supply Chain Risk 287 -- Manage Communication with Relevant Parties 288 -- Summary 289 -- Exam Essentials 289 -- Written Labs 289 -- Review Questions 290 -- Appendix A Answers to Written Labs 295 -- Chapter 1: Architectural Concepts 296 -- Chapter 2: Design Requirements 296 -- Chapter 3: Data Classification 297 -- Chapter 4: Cloud Data Security 298 -- Chapter 5: Security in the Cloud 299 -- Chapter 6: Responsibilities in the Cloud 299 -- Chapter 7: Cloud Application Security 300 -- Chapter 8: Operations Elements 300 -- Chapter 9: Operations Management 301 -- Chapter 10: Legal and Compliance Part 1 302 -- Chapter 11: Legal and Compliance Part 2 302 -- Appendix B Answers to Review Questions 303 -- Chapter 1: Architectural Concepts 304 -- Chapter 2: Design Requirements 305 -- Chapter 3: Data Classification 307 -- Chapter 4: Cloud Data Security 308 -- Chapter 5: Security...”
Connect to the full text of this electronic book

Table of Contents: “...Automated EOR/Chemical Process -- References -- Further Reading -- Chapter Eight: Transitioning to Effective DOF Enabled by Collaboration and Management of Change -- 8.1. ...”
Connect to the full text of this electronic book

Table of Contents: “...ContentsIntroductionChapter 1 Introduction to the CISA examinationThe structure of the CISA examBecoming CertifiedExperience requirementsPassing the ExamCISA Job Practice Domains and task and knowledge statementsISACAs Code of Professional EthicsThe ISACA StandardsContinuous Professional EducationChapter 2: Domain 1The Process of Auditing Information SystemsKnowledge StatementsUnderstanding the Fundamental Business ProcessesControl principles related to controls in information systemsRisk-based audit planning and audit project management techniquesQuality of the internal control frameworkAuditor understanding of the applicable lawsEvidence collection techniquesDomain One exam tipsDomain One - Practice questionsDomain One Review Questions and Hands on ExerciseDomain One - Answers to practice questionsExercise 1 sample answerChapter 3: Domain 2Governance and Management of ITGovernance in GeneralResource ManagementProject Management ToolsAuditors Role in the Project Management ProcessAudit Risk AssessmentAudit PlanningDomain Two - practice questionsDomain Two Review Questions and Hands on ExerciseExercise 2 sample answerDomain 2 Answers to practice questionsChapter 4: Domain 3Information Systems Acquisition, Development and ImplementationSystems AcquisitionSystems DevelopmentSystems ImplementationSystems Maintenance ReviewDomain Three - practice questionsDomain Three Review Questions and Hands on ExerciseExercise 3 sample answerDomain 3 Answers to practice questionsChapter 5: Domain 4 Information Systems Operations, Maintenance and Service ManagementHardwareAuditing Operating SystemsPeopleSystem interfacesChange ManagementAuditing Change ControlDisaster Recovery PlanningAuditing Service DeliveryDomain Four - practice questionsDomain Four Review Questions and Hands on ExerciseExercise 4 sample answerDomain 4 Answers to practice questionsChapter 6: Domain 5 Protection of Information AssetsProtection of information assetsPrivacy principlesDesign, implementation, maintenance, monitoring and reporting of security controlsPhysical access controls for the identification, authentication and restriction of usersLogical access controls for the identification, authentication and restriction of usersRisk and controls associated with virtualization of systemsRisks and controls associated with the use of mobile and wireless devicesEncryption-related techniques and their usesPublic key infrastructure (PKI) components and digital signature techniquesPeer-to-peer computing, instant messaging, and web-based technologiesData classification standards related to the protection of information assetsRisks in end-user computingImplementing a security awareness programInformation system attack methods and techniquesPrevention and detection tools and control techniquesSecurity testing techniquesPenetration testing and Vulnerability scanningForensic investigation and procedures in collection and preservation of the data and evidenceDomain Five - practice questionsDomain Five Review Questions and Hands on ExerciseExercise 5 sample answerDomain 5 Answers to practice questionsChapter 7 Preparing for the ExamAppendicesAppendix A: Glossary of TermsAppendix B: CISA Sample Exam Choose any 150 questionsAppendix C: Sample Exam Answers...”
Connect to the full text of this electronic book

Connect to the full text of this electronic book

Connect to the full text of this electronic book

Connect to the full text of this electronic book

Connect to the full text of this electronic book

Connect to the full text of this electronic book

Table of Contents: “...Structure Data -- 3.2 Base Modeling of Moisture Transport -- 3.3 Structure of the Simulation Program -- References -- Chapter 4 Experimental Investigations with Regard to the Modeling of Moisture Transport in Mortars and Concrete -- 4.1 Preliminary Remarks on Moisture Storage -- 4.2 Concrete Data for the Experimental Investigations -- 4.3 Data on Porosity of the Considered Materials and Influence of Treatments on Porosity -- 4.3.1 MIP Results for Pore Size Distribution and Pore Volume -- 4.3.2 Control of the Carbonation Behavior of the Test Specimens -- 4.3.3 Air-Porosity Content of the Materials Used -- 4.3.4 Drying Methods and Influence of Drying -- 4.3.4.1 Drying Methods -- 4.3.4.2 Possible Influence of Drying on Capillary Water Uptake -- 4.4 Hysteretic Moisture Storage Behavior as Important Issue with Respect to Modeling -- 4.4.1 Adsorption and Desorption Isotherms of the CEMI Reference Material -- 4.4.2 Causes of Differences Between Adsorption and Desorption Isotherms -- 4.4.3 Questions with Respect to Modeling of Storage and Transport Processes -- 4.5 Water Storage Behavior Under Changing Moisture Boundary Conditions with Consideration of the Air-Pore Content -- 4.5.1 Illustration of the Structure-Related Pore Volume Fractions in Relation to the Total Storage Capacity....”
Connect to the full text of this electronic book

Table of Contents: “...GCP artificial intelligence services -- Google Vertex AI -- Google Cloud ML APIs -- Summary -- Further reading -- Chapter 2: Mastering Python Programming -- Technical requirements -- The basics of Python -- Basic Python variables and operations -- Basic Python data structure -- Python conditions and loops -- Python functions -- Opening and closing files in Python -- An interesting problem -- Python data libraries and packages -- NumPy -- Pandas -- Matplotlib -- Seaborn -- Summary -- Further reading -- Part 2: Introducing Machine Learning -- Chapter 3: Preparing for ML Development...”
Connect to the full text of this electronic book

Connect to the full text of this electronic book
Connect to the full text of this electronic book

Table of Contents: “...Preface xv List of Figures xxv List of Tables xxxiii List of Contributors xxxvii List of Abbreviations xli 1 Convolutional Neural Networks in Internet of Things: A Bibliometric Study 1 1.1 Introduction 2 1.2 Related Work 3 1.3 Research Questions 4 1.4 Literature Review 5 1.5 Overview of Bibliometric Analysis 6 1.6 Methodology for Bibliometric Analysis 7 1.6.1 Database Collection 7 1.6.2 Methods for Data Extraction 7 1.6.3 Year-Wise Publications 8 1.6.4 Network Analysis of Citations 9 1.6.4.1 Citation analysis of countries 9 1.6.4.2 Citation analysis of organizations 9 1.6.4.3 Citation analysis of authors 10 1.6.4.4 Source citation analysis 12 1.6.4.5 Citation analysis of documents 14 1.6.5 Co-occurrence Analysis for KEYWORDS/HOT RESEARCH AREAS 15 1.6.5.1 Co-occurrence for all keywords 17 1.6.5.2 Co-occurrence for author keywords 19 1.6.5.3 Co-occurrence for index keywords 19 1.7 Limitations and Future Work 20 1.8 Conclusion 24 References 24 2 Internet of Things Enabled Convolutional Neural Networks: Applications, Techniques, Challenges, and Prospects 27 2.1 Introduction 28 2.1.1 Contribution of the Chapter 31 2.1.2 Chapter Organization 31 2.2 Application of Artificial Intelligence in IoT 31 2.3 Convolutional Neural Networks and its Architecture 32 2.3.1 CNN Based on Spatial Exploration 37 2.3.2 Depth of CNNs 38 2.3.3 Multi-Path of CNNs 38 2.3.4 Width for Multi-Connection CNNs 38 2.3.5 Feature-Map Exploitation for CNN 38 2.3.6 The CNN Channels for Exploitation 39 2.3.7 Attention Exploration for CNN 39 2.4 The CNN Techniques in IoT Environment 41 2.4.1 Intelligence Healthcare System 43 2.4.2 Intelligence Learning System 44 2.4.3 Smart City 45 2.4.4 Agriculture 46 2.4.5 Meteorology 47 2.4.6 Biometrics Applications 47 2.4.7 E-Commerce and E-Business 48 2.5 Challenges of Applicability of IoT-Enabled CNN in Various 48 2.6 Conclusion and Future Direction 52 References 53 3 Convolutional Neural Network-Based Models for Speech Denoising and Dereverberation: Algorithms and Applications 65 3.1 Introduction 66 3.2 Signal Model and Problem Formulation 69 3.2.1 Signal Model 69 3.2.2 Feature Extraction 69 3.2.3 Problem Formulation 70 3.3 One-stage CNN-based Speech Enhancement 71 3.3.1 The Architecture of GCT-Net 71 3.3.2 Gated Linear Units 73 3.3.3 S-TCMs 73 3.3.4 Framework Details 74 3.3.5 Loss Function 76 3.4 Multi-Stage CNN-based Speech Enhancement 77 3.4.1 Framework Structure 78 3.4.2 Loss Function 78 3.5 Experimental Setup 79 3.5.1 Datasets 79 3.5.2 Parameter Configuration 80 3.6 Results and Analysis 80 3.6.1 Spectrograms 81 3.6.2 PESQ Scores 84 3.6.3 ESTOI scores 85 3.6.4 SDR 86 3.6.5 Subjective Listening Test 88 3.7 Discussions and Conclusions 90 References 91 4 Edge Computing and Controller Area Network (CAN) for IoT Data Classification using Convolutional Neural Network 97 4.1 Introduction 98 4.1.1 Internet of Things (IoT) 99 4.1.2 Emotional Classification 100 4.1.3 Applications 102 4.2 Literature Review 104 4.3 System Design 107 4.3.1 Featured Image Formation 107 4.3.2 CNN Classification 110 4.4 Result and Discussion 115 4.5 Conclusion 119 References 120 5 Assistive Smart Cane for Visually Impaired People Based on Convolutional Neural Network (CNN) 125 5.1 Introduction 126 5.2 Literature Review 127 5.3 Proposed Methodology 132 5.3.1 Assistive Algorithm 132 5.3.2 Data Acquisition 132 5.3.3 Device Architecture 133 5.3.4 Arduino and Its Interfacing 134 5.3.5 Power 135 5.3.6 Memory 136 5.3.7 Deep Convolutional Neural Network (CNN) 136 5.3.8 Alex-Net Architecture 137 5.3.9 Xception Model 137 5.3.10 Visual Geometry Group (VGG16,19) 138 5.3.11 Residual Neural Network (ResNet) 139 5.3.12 Inception (V2, V3, InceptionResNet) 139 5.3.13 MobileNet 139 5.3.14 DenseNet 140 5.3.15 Experimental Results Analysis 140 5.4 Conclusion and Future Directions 143 References 144 6 Application of IoT-Enabled CNN for Natural Language Processing 149 6.1 Introduction 150 6.2 Related Work 152 6.3 IoT-Enabled CNN for NLP 154 6.4 Applications of IoT-Enabled CNN for NLP 154 6.4.1 Home Automation 157 6.4.2 Boon for Disabled People 159 6.5 Applications of IoT-Enabled CNN 161 6.5.1 Smart Farming 161 6.5.2 Smart Infrastructure 166 6.6 Challenges in NLP-Based IoT Devices and Solutions 166 6.7 Conclusion 170 References 171 7 Classification of Myocardial Infarction in ECG Signals Using Enhanced Deep Neural Network Technique 179 7.1 Introduction 180 7.2 Related Work 181 7.3 The Normal ECG Signal 183 7.3.1 ECG Features 186 7.3.2 12-Lead ECG System 186 7.4 Proposed Methodology 187 7.4.1 Phase I: Pre-Processing 188 7.4.2 Phase II: Feature Extraction 189 7.4.3 Phase III: Feature Selection 190 7.5 ECG Classification Using Deep Learning Techniques 191 7.5.1 CNN 192 7.5.2 LSTM 195 7.5.3 Enhanced Deep Neural Network (EDN) 197 7.6 Experimental Results 202 7.6.1 Performance Evaluation 203 7.6.2 Evaluation Metrics 204 7.7 Results and Discussion 206 7.8 Conclusion 209 References 210 8 Automation Algorithm for Labeling of Oil Spill Images using Pre-trained Deep Learning Model 213 8.1 Introduction 214 8.2 Related Work 216 8.2.1 Image Annotation Algorithm 216 8.2.2 Semantic Segmentation 218 8.3 Proposed Method 219 8.3.1 Image Pre-Processing 219 8.3.2 Semantic Segmentation 222 8.3.3 Automation Algorithm 226 8.4 Performance Measures 229 8.4.1 Evaluation of Segmentation Models 229 8.5 Conclusion 234 References 235 9 Environmental Weather Monitoring and Predictions System Using Internet of Things (IoT) Using Convolutional Neural Network 239 9.1 Introduction 240 9.1.1 Types of Weather Forecasting 243 9.1.1.1 Computer Forecasting 243 9.1.1.2 Synoptic Forecasting 243 9.1.1.3 Persistence Forecasting 243 9.1.1.4 Statistical Forecasting 244 9.2 Literature Review 244 9.3 System Design 246 9.4 Result and Discussion 251 9.4.1 Dataset 251 9.5 Conclusion 255 References 256 10 E-Learning Modeling Technique and Convolution Neural Networks in Online Education 261 10.1 Introduction 262 10.2 Literature Review 265 10.3 Discussion 270 10.3.1 Definition of ML and AI 271 10.3.2 Definition of ML and AI in KKU EL 272 10.3.3 ML Classifications for KKU EL 272 10.3.4 The Benefits of ML and AI in KKU EL 275 10.3.5 ML and AI are Transforming the EL Scenario in KKU 276 10.3.6 Customized EL Content 279 10.3.7 Resource Allocation 279 10.3.8 Automate Content Delivery and Scheduling Process 279 10.3.9 Improve KKU EL Return on Investment 279 10.3.10 Improve Learner Motivation 280 10.3.11 Online Training Programs 280 10.4 Results 282 10.4.1 ExL 285 10.4.2 EER 285 10.4.3 OnT 286 10.4.4 AC 286 10.4.5 AG and M 286 10.4.6 CC 287 10.4.7 CSL 287 10.4.8 SLS 287 10.5 Conclusion 288 References 289 11 Quantitative Texture Analysis with Convolutional Neural Networks 297 11.1 Introduction to Transfer Learning with Convolutional Neural Networks 298 11.1.1 The ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) 299 11.1.2 Transfer Learning Strategies 299 11.2 Texture Analysis 300 11.2.1 Textures in Nature and the Built Environment 300 11.2.2 Traditional Approaches to Texture Analysis 301 11.2.2.1 Statistical methods 301 11.2.2.2 Structural methods 303 11.2.2.3 Spectral methods 303 11.2.2.4 Modeling approaches 303 11.2.3 More Recent Approaches to Texture Analysis 303 11.2.4 Learning Approaches to Texture Analysis 303 11.2.4.1 Vocabulary-based approaches 303 11.2.4.2 Deep learning approaches 304 11.3 Methodology of Texture Analysis with Convolutional Neural Networks 304 11.3.1 Overall Analytical Methodology 304 11.3.2 Traditional Algorithms 305 11.3.2.1 Gray level co-occurrence matrices (GLCM) 305 11.3.2.2 Local binary patterns (LBPs) 307 11.3.2.3 Textons 307 11.3.3 Deep Learning Algorithms 309 11.4 Case Study 1: Voronoi Simulated Material Textures 309 11.4.1 Voronoi Simulation of Material Textures 309 11.4.2 Comparative Analysis of Convolutional Neural Networks and Traditional Algorithms 309 11.5 Case Study 2: Textures in Flotation Froths 312 11.5.1 Froth Image Analysis in Flotation 312 11.5.2 Recognition of Operational States with Convolutional Neural Networks 314 11.6 Case Study 3: Imaged Signal Textures 317 11.6.1 Treating Signals as Images 317 11.6.2 Monitoring of Stock Prices by the Use of Convolutional Neural Networks 318 11.7 Discussion 320 11.8 Conclusion 321 References 322 12 Internet of Things Based Enabled Convolutional Neural Networks in Healthcare 329 12.1 Introduction 330 12.2 Internet of Things Application in the Healthcare Systems 332 12.2.1 Internet of Things Operation in Healthcare Systems 333 12.2.2 Internet of...”
Connect to the full text of this electronic book

Connect to the full text of this electronic book

Connect to the full text of this electronic book

Connect to the full text of this electronic book

Connect to the full text of this electronic book

Connect to the full text of this electronic book

Connect to the full text of this electronic book

Connect to the full text of this electronic book