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

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