Cognitive Engineering for next generation computing(2021)[Prakash et al][9781119711087]

Description

Cover Half-Title Page Series Page Title Page Copyright Page Contents Preface Acknowledgment 1 Introduction to Cognitive Computing 1.1 Introduction: Definition of Cognition, Cognitive Computing 1.2 Defining and Understanding Cognitive Computing 1.3 Cognitive Computing Evolution and Importance 1.4 Difference Between Cognitive Computing and Artificial Intelligence 1.5 The Elements of a Cognitive System 1.5.1 Infrastructure and Deployment Modalities 1.5.2 Data Access, Metadata, and Management Services 1.5.3 The Corpus, Taxonomies, and Data Catalogs 1.5.4 Data Analytics Services 1.5.5 Constant Machine Learning 1.5.6 Components of a Cognitive System 1.5.7 Building the Corpus 1.5.8 Corpus Administration Governing and Protection Factors 1.6 Ingesting Data Into Cognitive System 1.6.1 Leveraging Interior and Exterior Data Sources 1.6.2 Data Access and Feature Extraction 1.7 Analytics Services 1.8 Machine Learning 1.9 Machine Learning Process 1.9.1 Data Collection 1.9.2 Data Preparation 1.9.3 Choosing a Model 1.9.4 Training the Model 1.9.5 Evaluate the Model 1.9.6 Parameter Tuning 1.9.7 Make Predictions 1.10 Machine Learning Techniques 1.10.1 Supervised Learning 1.10.2 Unsupervised Learning 1.10.3 Reinforcement Learning 1.10.4 The Significant Challenges in Machine Learning 1.11 Hypothesis Space 1.11.1 Hypothesis Generation 1.11.2 Hypotheses Score 1.12 Developing a Cognitive Computing Application 1.13 Building a Health Care Application 1.13.1 Healthcare Ecosystem Constituents 1.13.2 Beginning With a Cognitive Healthcare Application 1.13.3 Characterize the Questions Asked by the Clients 1.13.4 Creating a Corpus and Ingesting the Content 1.13.5 Training the System 1.13.6 Applying Cognition to Develop Health and Wellness 1.13.7 Welltok 1.13.8 CaféWell Concierge in Action 1.14 Advantages of Cognitive Computing 1.15 Features of Cognitive Computing 1.16 Limitations of Cognitive Computing 1.17 Conclusion References 2 Machine Learning and Big Data in Cyber-Physical System: Methods, Applications and Challenges 2.1 Introduction 2.2 Cyber-Physical System Architecture 2.3 Human-in-the-Loop Cyber-Physical Systems (HiLCPS) 2.4 Machine Learning Applications in CPS 2.4.1 K-Nearest Neighbors (K-NN) in CPS 2.4.2 Support Vector Machine (SVM) in CPS 2.4.3 Random Forest (RF) in CPS 2.4.4 Decision Trees (DT) in CPS 2.4.5 Linear Regression (LR) in CPS 2.4.6 Multi-Layer Perceptron (MLP) in CPS 2.4.7 Naive Bayes (NB) in CPS 2.5 Use of IoT in CPS 2.6 Use of Big Data in CPS 2.7 Critical Analysis 2.8 Conclusion References 3 HemoSmart: A Non-Invasive Device and Mobile App for Anemia Detection 3.1 Introduction 3.1.1 Background 3.1.2 Research Objectives 3.1.3 Research Approach 3.1.4 Limitations 3.2 Literature Review 3.3 Methodology 3.3.1 Methodological Approach 3.3.2 Methods of Analysis 3.4 Results 3.4.1 Impact of Project Outcomes 3.4.2 Results Obtained During the Methodology 3.5 Discussion 3.6 Originality and Innovativeness of the Research 3.6.1 Validation and Quality Control of Methods 3.6.2 Cost-Effectiveness of the Research 3.7 Conclusion References 4 Advanced Cognitive Models and Algorithms 4.1 Introduction 4.2 Microsoft Azure Cognitive Model 4.2.1 AI Services Broaden in Microsoft Azure 4.3 IBM Watson Cognitive Analytics 4.3.1 Cognitive Computing 4.3.2 Defining Cognitive Computing via IBM Watson Interface 4.3.3 IBM Watson Analytics 4.4 Natural Language Modeling 4.4.1 NLP Mainstream 4.4.2 Natural Language Based on Cognitive Computation 4.5 Representation of Knowledge Models 4.6 Conclusion References 5 iParking—Smart Way to Automate the Management of the Parking System for a Smart City 5.1 Introduction 5.2 Background & Literature Review 5.2.1 Background 5.2.2 Review of Literature 5.3 Research Gap 5.4 Research Problem 5.5 Objectives 5.6 Methodology 5.6.1 Lot Availability and Occupancy Detection 5.6.2 Error Analysis for GPS (Global Positioning System) 5.6.3 Vehicle License Plate Detection System 5.6.4 Analyze Differential Parking Behaviors and Pricing 5.6.5 Targeted Digital Advertising 5.6.6 Used Technologies 5.6.7 Specific Tools and Libraries 5.7 Testing and Evaluation 5.8 Results 5.9 Discussion 5.10 Conclusion References 6 Cognitive Cyber-Physical System Applications 6.1 Introduction 6.2 Properties of Cognitive Cyber-Physical System 6.3 Components of Cognitive Cyber-Physical System 6.4 Relationship Between Cyber-Physical System for Human–Robot 6.5 Applications of Cognitive Cyber-Physical System 6.5.1 Transportation 6.5.2 Industrial Automation 6.5.3 Healthcare and Biomedical 6.5.4 Clinical Infrastructure 6.5.5 Agriculture 6.6 Case Study: Road Management System Using CPS 6.6.1 Smart Accident Response System for Indian City 6.7 Conclusion References 7 Cognitive Computing 7.1 Introduction 7.2 Evolution of Cognitive System 7.3 Cognitive Computing Architecture 7.3.1 Cognitive Computing and Internet of Things 7.3.2 Cognitive Computing and Big Data Analysis 7.3.3 Cognitive Computing and Cloud Computing 7.4 Enabling Technologies in Cognitive Computing 7.4.1 Cognitive Computing and Reinforcement Learning 7.4.2 Cognitive Computing and Deep Learning 7.5 Applications of Cognitive Computing 7.5.1 Chatbots 7.5.2 Sentiment Analysis 7.5.3 Face Detection 7.5.4 Risk Assessment 7.6 Future of Cognitive Computing 7.7 Conclusion References 8 Tools Used for Research in Cognitive Engineering and Cyber Physical Systems 8.1 Cyber Physical Systems 8.2 Introduction: The Four Phases of Industrial Revolution 8.3 System 8.4 Autonomous Automobile System 8.4.1 The Timeline 8.5 Robotic System 8.6 Mechatronics References 9 Role of Recent Technologies in Cognitive Systems 9.1 Introduction 9.1.1 Definition and Scope of Cognitive Computing 9.1.2 Architecture of Cognitive Computing 9.1.3 Features and Limitations of Cognitive Systems 9.2 Natural Language Processing for Cognitive Systems 9.2.1 Role of NLP in Cognitive Systems 9.2.2 Linguistic Analysis 9.2.3 Example Applications Using NLP With Cognitive Systems 9.3 Taxonomies and Ontologies of Knowledge Representation for Cognitive Systems 9.3.1 Taxonomies and Ontologies and Their Importance in Knowledge Representation 9.3.2 How to Represent Knowledge in Cognitive Systems? 9.3.3 Methodologies Used for Knowledge Representation in Cognitive Systems 9.4 Support of Cloud Computing for Cognitive Systems 9.4.1 Importance of Shared Resources of Distributed Computing in Developing Cognitive Systems 9.4.2 Fundamental Concepts of Cloud Used in Building Cognitive Systems 9.5 Cognitive Analytics for Automatic Fraud Detection Using Machine Learning and Fuzzy Systems 9.5.1 Role of Machine Learning Concepts in Building Cognitive Analytics 9.5.2 Building Automated Patterns for Cognitive Analytics Using Fuzzy Systems 9.6 Design of Cognitive System for Healthcare Monitoring in Detecting Diseases 9.6.1 Role of Cognitive System in Building Clinical Decision System 9.7 Advanced High Standard Applications Using Cognitive Computing 9.8 Conclusion References 10 Quantum Meta-Heuristics and Applications 10.1 Introduction 10.2 What is Quantum Computing? 10.3 Quantum Computing Challenges 10.4 Meta-Heuristics and Quantum Meta-Heuristics Solution Approaches 10.5 Quantum Meta-Heuristics Algorithms With Application Areas 10.5.1 Quantum Meta-Heuristics Applications for Power Systems 10.5.2 Quantum Meta-Heuristics Applications for Image Analysis 10.5.3 Quantum Meta-Heuristics Applications for Big Data or Data Mining 10.5.4 Quantum Meta-Heuristics Applications for Vehicular Trafficking 10.5.5 Quantum Meta-Heuristics Applications for Cloud Computing 10.5.6 Quantum Meta-Heuristics Applications for Bioenergy or Biomedical Systems 10.5.7 Quantum Meta-Heuristics Applications for Cryptography or Cyber Security 10.5.8 Quantum Meta-Heuristics Applications for Miscellaneous Domain References 11 Ensuring Security and Privacy in IoT for Healthcare Applications 11.1 Introduction 11.2 Need of IoT in Healthcare 11.2.1 Available Internet of Things Devices for Healthcare 11.3 Literature Survey on an IoT-Aware Architecture for Smart Healthcare Systems 11.3.1 Cyber-Physical System (CPS) for e-Healthcare 11.3.2 IoT-Enabled Healthcare With REST-Based Services 11.3.3 Smart Hospital System 11.3.4 Freescale Home Health Hub Reference Platform 11.3.5 A Smart System Connecting e-Health Sensors and Cloud 11.3.6 Customizing 6LoWPAN Networks Towards IoT-Based Ubiquitous Healthcare Systems 11.4 IoT in Healthcare: Challenges and Issues 11.4.1 Challenges of the Internet of Things for Healthcare 11.4.2 IoT Interoperability Issues 11.4.3 IoT Security Issues 11.5 Proposed System: 6LoWPAN and COAP Protocol-Based IoT System for Medical Data Transfer by Preserving Privacy of Patient 11.6 Conclusion References 12 Empowering Secured Outsourcing in Cloud Storage Through Data Integrity Verification 12.1 Introduction 12.1.1 Confidentiality 12.1.2 Availability 12.1.3 Information Uprightness 12.2 Literature Survey 12.2.1 PDP 12.2.2 POR 12.2.3 HAIL 12.2.4 RACS 12.2.5 FMSR 12.3 System Design 12.3.1 Design Considerations 12.3.2 System Overview 12.3.3 Workflow 12.3.4 System Description 12.4 Implementation and Result Discussion 12.4.1 Creating Containers 12.4.2 File Chunking 12.4.3 XORing Partitions 12.4.4 Regeneration of File 12.4.5 Reconstructing a Node 12.4.6 Cloud Storage 12.5 Performance 12.6 Conclusion References Index EULA