Оглавление
Группа авторов. Cognitive Engineering for Next Generation Computing
Table of Contents
List of Illustrations
List of Table
Guide
Pages
Cognitive Engineering for Next Generation Computing. A Practical Analytical Approach
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
3.3.1.1 Select an Appropriate Camera
3.3.1.2 Design the Lighting System
3.3.1.3 Design the Electronic Circuit
3.3.1.4 Design the Prototype
3.3.1.5 Collect Data and Develop the Algorithm
3.3.1.6 Develop the Prototype
3.3.1.7 Mobile Application Development
3.3.1.8 Completed Device
3.3.1.9 Methods of Data Collection
3.3.2 Methods of Analysis
3.4 Results. 3.4.1 Impact of Project Outcomes
3.4.2 Results Obtained During the Methodology. 3.4.2.1 Select an Appropriate Camera
3.4.2.2 Design the Lighting System
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
4.3.2.1 Evolution of Systems Towards Cognitive Computing
4.3.2.2 Main Aspects of IBM Watson
4.3.2.3 Key Areas of IBM Watson
4.3.3 IBM Watson Analytics
4.3.3.1 IBM Watson Features
4.3.3.2 IBM Watson DashDB
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
7.4.2.1 Rational Method and Perceptual Method
7.4.2.2 Cognitive Computing and Image Understanding
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
11.4.3.1 Security of IoT Sensors
11.4.3.2 Security of Data Generated by Sensors
11.4.3.3 LoWPAN Networks Healthcare Systems and its Attacks
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
12.2.1.1 Privacy-Preserving PDP Schemes
12.2.1.2 Efficient 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
12.3.4.1 System Encoding
12.3.4.1.1 Encoding in Reed–Solomon Codes
12.3.4.1.2 Encoding in Regeneration Codes
12.3.4.2 Decoding
12.3.4.2.1 Decoding in Reed–Solomon Codes
12.3.4.2.2 Decoding in Regeneration Codes
12.3.4.3 Repair and Check
12.3.4.3.1 Repair and Check-in Reed–Solomon Codes
12.3.4.3.2 Repair and Checking Regeneration Codes
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
12.4.6.1 NC-Cloud
12.4.6.2 Open Swift
12.5 Performance
12.6 Conclusion
References
Index
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