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E-grāmata: Cognitive Engineering for Next Generation Computing: A Practical Analytical Approach [Wiley Online]

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  • Formāts: 368 pages
  • Izdošanas datums: 13-Apr-2021
  • Izdevniecība: Wiley-Scrivener
  • ISBN-10: 1119711304
  • ISBN-13: 9781119711308
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Cognitive Engineering for Next Generation Computing: A Practical Analytical ApproachPalielināt
  • Formāts: 368 pages
  • Izdošanas datums: 13-Apr-2021
  • Izdevniecība: Wiley-Scrivener
  • ISBN-10: 1119711304
  • ISBN-13: 9781119711308
Citas grāmatas par šo tēmu:
Wiley Online E-booksMore info about Wiley Online
Rokasgrāmatas mājas lapa: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119711308
"The cognitive approach to the IoT provides connectivity to everyone and everything since IoT connected devices are known to increase rapidly. When the IoT is integrated with cognitive technology, performance is improved, and smart intelligence is obtained. Discussed in this book are different types of datasets with structured content based on cognitive systems. The IoT gathers the information from the real time datasets through the internet, where the IoT network connects with multiple devices. This book mainly concentrates on providing the best solutions to existing real-time issues in the cognitive domain. Healthcare-based, cloud-based and smart transportation-based applications in the cognitive domain are addressed. The data integrity and security aspects of the cognitive computing main are also thoroughly discussed along with validated results."--

The cognitive approach to the IoT provides connectivity to everyone and everything since IoT connected devices are known to increase rapidly. When the IoT is integrated with cognitive technology, performance is improved, and smart intelligence is obtained. Discussed in this book are different types of datasets with structured content based on cognitive systems. The IoT gathers the information from the real time datasets through the internet, where the IoT network connects with multiple devices.

This book mainly concentrates on providing the best solutions to existing real-time issues in the cognitive domain. Healthcare-based, cloud-based and smart transportation-based applications in the cognitive domain are addressed. The data integrity and security aspects of the cognitive computing main are also thoroughly discussed along with validated results.

Preface xvii
Acknowledgments xix
1 Introduction to Cognitive Computing
1(48)
Vamsidhar Enireddy
Sagar Imambi
C. Karthikeyan
1.1 Introduction: Definition of Cognition, Cognitive Computing
1(1)
1.2 Defining and Understanding Cognitive Computing
2(4)
1.3 Cognitive Computing Evolution and Importance
6(2)
1.4 Difference Between Cognitive Computing and Artificial Intelligence
8(3)
1.5 The Elements of a Cognitive System
11(6)
1.5.1 Infrastructure and Deployment Modalities
11(1)
1.5.2 Data Access, Metadata, and Management Services
12(1)
1.5.3 The Corpus, Taxonomies, and Data Catalogs
12(1)
1.5.4 Data Analytics Services
12(1)
1.5.5 Constant Machine Learning
13(1)
1.5.6 Components of a Cognitive System
13(1)
1.5.7 Building the Corpus
14(2)
1.5.8 Corpus Administration Governing and Protection Factors
16(1)
1.6 Ingesting Data Into Cognitive System
17(2)
1.6.1 Leveraging Interior and Exterior Data Sources
17(1)
1.6.2 Data Access and Feature Extraction
18(1)
1.7 Analytics Services
19(3)
1.8 Machine Learning
22(2)
1.9 Machine Learning Process
24(1)
1.9.1 Data Collection
24(1)
1.9.2 Data Preparation
24(1)
1.9.3 Choosing a Model
24(1)
1.9.4 Training the Model
24(1)
1.9.5 Evaluate the Model
25(1)
1.9.6 Parameter Tuning
25(1)
1.9.7 Make Predictions
25(1)
1.10 Machine Learning Techniques
25(5)
1.10.1 Supervised Learning
25(2)
1.10.2 Unsupervised Learning
27(1)
1.10.3 Reinforcement Learning
27(1)
1.10.4 The Significant Challenges in Machine Learning
28(2)
1.11 Hypothesis Space
30(2)
1.11.1 Hypothesis Generation
31(1)
1.11.2 Hypotheses Score
32(1)
1.12 Developing a Cognitive Computing Application
32(3)
1.13 Building a Health Care Application
35(7)
1.13.1 Healthcare Ecosystem Constituents
35(2)
1.13.2 Beginning With a Cognitive Healthcare Application
37(1)
1.13.3 Characterize the Questions Asked by the Clients
37(1)
1.13.4 Creating a Corpus and Ingesting the Content
38(1)
1.13.5 Training the System
38(1)
1.13.6 Applying Cognition to Develop Health and Wellness
39(1)
1.13.7 Welltok
39(2)
1.13.8 CafeWell Concierge in Action
41(1)
1.14 Advantages of Cognitive Computing
42(1)
1.15 Features of Cognitive Computing
43(1)
1.16 Limitations of Cognitive Computing
44(3)
1.17 Conclusion
47(2)
References
47(2)
2 Machine Learning and Big Data in Cyber-Physical System: Methods, Applications and Challenges
49(44)
Janmenjoy Nayak
P. Suresh Kumar
Dukka Karun Kumar Reddy
Bighnaraj Naik
Danilo Pelusi
2.1 Introduction
50(2)
2.2 Cyber-Physical System Architecture
52(1)
2.3 Human-in-the-Loop Cyber-Physical Systems (HiLCPS)
53(2)
2.4 Machine Learning Applications in CPS
55(15)
2.4.1 K-Nearest Neighbors (K-NN) in CPS
55(3)
2.4.2 Support Vector Machine (SVM) in CPS
58(3)
2.4.3 Random Forest (RF) in CPS
61(2)
2.4.4 Decision Trees (DT) in CPS
63(2)
2.4.5 Linear Regression (LR) in CPS
65(1)
2.4.6 Multi-Layer Perceptron (MLP) in CPS
66(4)
2.4.7 Naive Bayes (NB) in CPS
70(1)
2.5 UseofloTinCPS
70(2)
2.6 Use of Big Data in CPS
72(5)
2.7 Critical Analysis
77(6)
2.8 Conclusion
83(10)
References
84(9)
3 HemoSmart: A Non-Invasive Device and Mobile App for Anemia Detection
93(28)
J.A.D.C.A. Jayakody
E.A.G.A. Edirisinghe
S. Lokuliyana
3.1 Introduction
94(4)
3.1.1 Background
94(2)
3.1.2 Research Objectives
96(1)
3.1.3 Research Approach
97(1)
3.1.4 Limitations
98(1)
3.2 Literature Review
98(3)
3.3 Methodology
101(9)
3.3.1 Methodological Approach
101(1)
3.3.1.1 Select an Appropriate Camera
102(1)
3.3.1.2 Design the Lighting System
102(2)
3.3.1.3 Design the Electronic Circuit
104(1)
3.3.1.4 Design the Prototype
104(1)
3.3.1.5 Collect Data and Develop the Algorithm
104(2)
3.3.1.6 Develop the Prototype
106(1)
3.3.1.7 Mobile Application Development
106(1)
3.3.1.8 Completed Device
107(2)
3.3.1.9 Methods of Data Collection
109(1)
3.3.2 Methods of Analysis
109(1)
3.4 Results
110(2)
3.4.1 Impact of Project Outcomes
110(1)
3.4.2 Results Obtained During the Methodology
111(1)
3.4.2.1 Select an Appropriate Camera
111(1)
3.4.2.2 Design the Lighting System
112(1)
3.5 Discussion
112(4)
3.6 Originality and Innovativeness of the Research
116(1)
3.6.1 Validation and Quality Control of Methods
117(1)
3.6.2 Cost-Effectiveness of the Research
117(1)
3.7 Conclusion
117(4)
References
117(4)
4 Advanced Cognitive Models and Algorithms
121(20)
J. Ramkumar
M. Baskar
B. Amutha
4.1 Introduction
122(1)
4.2 Microsoft Azure Cognitive Model
122(4)
4.2.1 AI Services Broaden in Microsoft Azure
125(1)
4.3 IBM Watson Cognitive Analytics
126(6)
4.3.1 Cognitive Computing
126(1)
4.3.2 Denning Cognitive Computing via IBM Watson Interface
127(1)
4.3.2.1 Evolution of Systems Towards Cognitive Computing
128(1)
4.3.2.2 Main Aspects of IBM Watson
129(1)
4.3.2.3 Key Areas of IBM Watson
130(1)
4.3.3 IBM Watson Analytics
130(1)
4.3.3.1 IBM Watson Features
131(1)
4.3.3.2 IBM Watson DashDB
131(1)
4.4 Natural Language Modeling
132(2)
4.4.1 NLP Mainstream
132(2)
4.4.2 Natural Language Based on Cognitive Computation
134(1)
4.5 Representation of Knowledge Models
134(3)
4.6 Conclusion
137(4)
References
138(3)
5 iParking--Smart Way to Automate the Management of the Parking System for a Smart City
141(26)
J.A.D.C.A. Jayakody
E.A.G.A. Edirisinghe
S.A.H.M. Karunanayaka
E.M.C.S. Ekanayake
H.K.T.M. Dikkumbura
L.A.I.M. Bandara
5.1 Introduction
142(2)
5.2 Background & Literature Review
144(7)
5.2.1 Background
144(1)
5.2.2 Review of Literature
145(6)
5.3 Research Gap
151(1)
5.4 Research Problem
151(2)
5.5 Objectives
153(1)
5.6 Methodology
154(5)
5.6.1 Lot Availability and Occupancy Detection
154(1)
5.6.2 Error Analysis for GPS (Global Positioning System)
155(1)
5.6.3 Vehicle License Plate Detection System
156(1)
5.6.4 Analyze Differential Parking Behaviors and Pricing
156(1)
5.6.5 Targeted Digital Advertising
157(1)
5.6.6 Used Technologies
157(1)
5.6.7 Specific Tools and Libraries
158(1)
5.7 Testing and Evaluation
159(2)
5.8 Results
161(1)
5.9 Discussion
162(2)
5.10 Conclusion
164(3)
References
165(2)
6 Cognitive Cyber-Physical System Applications
167(22)
John A.
Senthilkumar Mohan
D. Maria Manuel Vianny
6.1 Introduction
168(1)
6.2 Properties of Cognitive Cyber-Physical System
169(1)
6.3 Components of Cognitive Cyber-Physical System
170(1)
6.4 Relationship Between Cyber-Physical System for Human-Robot
171(1)
6.5 Applications of Cognitive Cyber-Physical System
172(9)
6.5.1 Transportation
172(1)
6.5.2 Industrial Automation
173(3)
6.5.3 Healthcare and Biomedical
176(2)
6.5.4 Clinical Infrastructure
178(2)
6.5.5 Agriculture
180(1)
6.6 Case Study: Road Management System Using CPS
181(3)
6.6.1 Smart Accident Response System for Indian City
182(2)
6.7 Conclusion
184(5)
References
185(4)
7 Cognitive Computing
189(30)
T. Gunasekhar
Marella Surya Teja
7.1 Introduction
189(2)
7.2 Evolution of Cognitive System
191(2)
7.3 Cognitive Computing Architecture
193(9)
7.3.1 Cognitive Computing and Internet of Things
194(3)
7.3.2 Cognitive Computing and Big Data Analysis
197(3)
7.3.3 Cognitive Computing and Cloud Computing
200(2)
7.4 Enabling Technologies in Cognitive Computing
202(7)
7.4.1 Cognitive Computing and Reinforcement Learning
202(2)
7.4.2 Cognitive Computive and Deep Learning
204(1)
7.4.2.1 Rational Method and Perceptual Method
205(2)
7.4.2.2 Cognitive Computing and Image Understanding
207(2)
7.5 Applications of Cognitive Computing
209(3)
7.5.1 Chatbots
209(1)
7.5.2 Sentiment Analysis
210(1)
7.5.3 Face Detection
211(1)
7.5.4 Risk Assessment
211(1)
7.6 Future of Cognitive Computing
212(2)
7.7 Conclusion
214(5)
References
215(4)
8 Tools Used for Research in Cognitive Engineering and Cyber Physical Systems
219(12)
Ajita Seth
8.1 Cyber Physical Systems
219(1)
8.2 Introduction: The Four Phases of Industrial Revolution
220(1)
8.3 System
221(1)
8.4 Autonomous Automobile System
221(2)
8.4.1 The Timeline
222(1)
8.5 Robotic System
223(2)
8.6 Mechatronics
225(6)
References
228(3)
9 Role of Recent Technologies in Cognitive Systems
231(34)
V. Pradeep Kumar
L. Pallavi
Kolla Bhanu Prakash
9.1 Introduction
232(4)
9.1.1 Definition and Scope of Cognitive Computing
232(1)
9.1.2 Architecture of Cognitive Computing
233(1)
9.1.3 Features and Limitations of Cognitive Systems
234(2)
9.2 Natural Language Processing for Cognitive Systems
236(5)
9.2.1 Role of NLP in Cognitive Systems
236(2)
9.2.2 Linguistic Analysis
238(2)
9.2.3 Example Applications Using NLP With Cognitive Systems
240(1)
9.3 Taxonomies and Ontologies of Knowledge Representation for Cognitive Systems
241(7)
9.3.1 Taxonomies and Ontologies and Their Importance in Knowledge Representation
242(1)
9.3.2 How to Represent Knowledge in Cognitive Systems?
243(4)
9.3.3 Methodologies Used for Knowledge Representation in Cognitive Systems
247(1)
9.4 Support of Cloud Computing for Cognitive Systems
248(6)
9.4.1 Importance of Shared Resources of Distributed Computing in Developing Cognitive Systems
248(1)
9.4.2 Fundamental Concepts of Cloud Used in Building Cognitive Systems
249(5)
9.5 Cognitive Analytics for Automatic Fraud Detection Using Machine Learning and Fuzzy Systems
254(2)
9.5.1 Role of Machine Learning Concepts in Building Cognitive Analytics
255(1)
9.5.2 Building Automated Patterns for Cognitive Analytics Using Fuzzy Systems
255(1)
9.6 Design of Cognitive System for Healthcare Monitoring in Detecting Diseases
256(3)
9.6.1 Role of Cognitive System in Building Clinical Decision System
257(2)
9.7 Advanced High Standard Applications Using Cognitive Computing
259(3)
9.8 Conclusion
262(3)
References
263(2)
10 Quantum Meta-Heuristics and Applications
265(34)
Kolla Bhanu Prakash
10.1 Introduction
265(2)
10.2 What is Quantum Computing?
267(1)
10.3 Quantum Computing Challenges
268(3)
10.4 Meta-Heuristics and Quantum Meta-Heuristics Solution Approaches
271(2)
10.5 Quantum Meta-Heuristics Algorithms With Application Areas
273(26)
10.5.1 Quantum Meta-Heuristics Applications for Power Systems
277(4)
10.5.2 Quantum Meta-Heuristics Applications for Image Analysis
281(1)
10.5.3 Quantum Meta-Heuristics Applications for Big Data or Data Mining
282(3)
10.5.4 Quantum Meta-Heuristics Applications for Vehicular Trafficking
285(1)
10.5.5 Quantum Meta-Heuristics Applications for Cloud Computing
286(1)
10.5.6 Quantum Meta-Heuristics Applications for Bioenergy or Biomedical Systems
287(1)
10.5.7 Quantum Meta-Heuristics Applications for Cryptography or Cyber Security
287(1)
10.5.8 Quantum Meta-Heuristics Applications for Miscellaneous Domain
288(3)
References
291(8)
11 Ensuring Security and Privacy in IoT for Healthcare Applications
299(16)
Anjali Yeole
D.R. Kalbande
11.1 Introduction
299(1)
11.2 Need of IoT in Healthcare
300(3)
11.2.1 Available Internet of Things Devices for Healthcare
301(2)
11.3 Literature Survey on an IoT-Aware Architecture for Smart Healthcare Systems
303(3)
11.3.1 Cyber-Physical System (CPS) for e-Healthcare
303(1)
11.3.2 IoT-Enabled Healthcare With REST-Based Services
304(1)
11.3.3 Smart Hospital System
304(1)
11.3.4 Freescale Home Health Hub Reference Platform
305(1)
11.3.5 A Smart System Connecting e-Health Sensors and Cloud
305(1)
11.3.6 Customizing 6L0WPAN Networks Towards IoT-Based Ubiquitous Healthcare Systems
305(1)
11.4 IoT in Healthcare: Challenges and Issues
306(4)
11.4.1 Challenges of the Internet of Things for Healthcare
306(2)
11.4.2 IoT Interoperability Issues
308(1)
11.4.3 IoT Security Issues
308(1)
11.4.3.1 Security of IoT Sensors
309(1)
11.4.3.2 Security of Data Generated by Sensors
309(1)
11.4.3.3 LoWPAN Networks Healthcare Systems and its Attacks
309(1)
11.5 Proposed System: 6L0WPAN and COAP Protocol-Based IoT System for Medical Data Transfer by Preserving Privacy of Patient
310(2)
11.6 Conclusion
312(3)
References
312(3)
12 Empowering Secured Outsourcing in Cloud Storage Through Data Integrity Verification
315(20)
C. Saranya Jothi
Carmel Mary Belinda
N. Rajkumar
12.1 Introduction
315(1)
12.1.1 Confidentiality
316(1)
12.1.2 Availability
316(1)
12.1.3 Information Uprightness
316(1)
12.2 Literature Survey
316(3)
12.2.1 PDP
316(1)
12.2.1.1 Privacy-Preserving PDP Schemes
317(1)
12.2.1.2 Efficient PDP
317(1)
12.2.2 POR
317(1)
12.2.3 HAIL
318(1)
12.2.4 RACS
318(1)
12.2.5 FMSR
318(1)
12.3 System Design
319(5)
12.3.1 Design Considerations
319(1)
12.3.2 System Overview
320(1)
12.3.3 Workflow
320(1)
12.3.4 System Description
321(1)
12.3.4.1 System Encoding
321(1)
12.3.4.2 Decoding
322(1)
12.3.4.3 Repair and Check
323(1)
12.4 Implementation and Result Discussion
324(6)
12.4.1 Creating Containers
324(1)
12.4.2 File Chunking
324(2)
12.4.3 XORing Partitions
326(1)
12.4.4 Regeneration of File
326(1)
12.4.5 Reconstructing a Node
327(1)
12.4.6 Cloud Storage
327(1)
12.4.6.1 NC-Cloud
327(2)
12.4.6.2 Open Swift
329(1)
12.5 Performance
330(2)
12.6 Conclusion
332(3)
References
333(2)
Index 335
Kolla Bhanu Prakash is Professor and Research Group Head for Artificial Intelligence and Data Science Research Group in CSE Department, K L University, Andhra Pradesh, India. He received his MSc and MPhil in Physics from Acharya Nagarjuna University and his ME and PhD in Computer Science & Engineering from Sathyabama University, Chennai, India. Dr. Prakash has 14+ years of experience working in academia, research, and teaching. He has published multiple SCI journal articles as well as been granted 5 patents.

G. R. Kanagachidambaresan received his BE degree in Electrical and Electronics Engineering from Anna University in 2010; ME in Pervasive Computing Technologies in Anna University in 2012, and his PhD in Anna University Chennai in 2017. He is currently an associate professor, Department of CSE, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology.

Srikanth Vemuru is a professor in the Department of Computer Science and Engineering, K L University. He received his PhD degree from Acharya Nagarjuna University (ANU) in 2011. He has more than 17 years of academic experience and in the software industry, and has published more than over 60 research papers in SCI journals and flagship conferences.

Vamsidhar Enireddy is an associate professor in CSE Department, K L University, Andhra Pradesh, India. He received his PhD from JNTU Kakinada, India. Dr. Enireddy has 17+years of experience working in academia, research, and teaching. He has authored over 28 research papers in various national and international journals and conferences as well as been granted 3 patents and 1 patent filed.
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