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E-raamat: Industrial Internet of Things (IIoT): Intelligent Analytics for Predictive Maintenance

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  • Ilmumisaeg: 09-Feb-2022
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  • Keel: eng
  • ISBN-13: 9781119769002
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Industrial Internet of Things (IIoT): Intelligent Analytics for Predictive MaintenanceSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 09-Feb-2022
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119769002
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INDUSTRIAL INTERNET OF THINGS (IIOT) This book discusses how the industrial internet will be augmented through increased network agility, integrated artificial intelligence (AI) and the capacity to deploy, automate, orchestrate, and secure diverse user cases at hyperscale.

Since the internet of things (IoT) dominates all sectors of technology, from home to industry, automation through IoT devices is changing the processes of our daily lives. For example, more and more businesses are adopting and accepting industrial automation on a large scale, with the market for industrial robots expected to reach $73.5 billion in 2023. The primary reason for adopting IoT industrial automation in businesses is the benefits it provides, including enhanced efficiency, high accuracy, cost-effectiveness, quick process completion, low power consumption, fewer errors, and ease of control.

The 15 chapters in the book showcase industrial automation through the IoT by including case studies in the areas of the IIoT, robotic and intelligent systems, and web-based applications which will be of interest to working professionals and those in education and research involved in a broad cross-section of technical disciplines.

The volume will help industry leaders by





Advancing hands-on experience working with industrial architecture Demonstrating the potential of cloud-based Industrial IoT platforms, analytics, and protocols Putting forward business models revitalizing the workforce with Industry 4.0.

Audience

Researchers and scholars in industrial engineering and manufacturing, artificial intelligence, cyber-physical systems, robotics, safety engineering, safety-critical systems, and application domain communities such as aerospace, agriculture, automotive, critical infrastructures, healthcare, manufacturing, retail, smart transports, smart cities, and smart healthcare.
Preface xvii
1 A Look at HoT: The Perspective of IoT Technology Applied in the Industrial Field 1(30)
Ana Carolina Borges Monteiro
Reinaldo Padilha Franca
Rangel Arthur
Yuzo Iano
Andrea Coimbra Segatti
Giulliano Paes Carnielli
Julio Cesar Pereira
Henri Alves de Godoy
Elder Carlos Fernandes
1.1 Introduction
2(3)
1.2 Relationship Between Artificial Intelligence and IoT
5(10)
1.2.1 AI Concept
6(4)
1.2.2 IoT Concept
10(5)
1.3 IoT Ecosystem
15(6)
1.3.1 Industry 4.0 Concept
18(1)
1.3.2 Industrial Internet of Things
19(2)
1.4 Discussion
21(2)
1.5 Trends
23(1)
1.6 Conclusions
24(2)
References
26(5)
2 Analysis on Security in IoT Devices-An Overview 31(28)
T. Nalini
T. Murali Krishna
2.1 Introduction
32(1)
2.2 Security Properties
33(1)
2.3 Security Challenges of IoT
34(4)
2.3.1 Classification of Security Levels
35(2)
2.3.1.1 At Information Level
36(1)
2.3.1.2 At Access Level
36(1)
2.3.1.3 At Functional Level
36(1)
2.3.2 Classification of IoT Layered Architecture
37(1)
2.3.2.1 Edge Layer
37(1)
2.3.2.2 Access Layer
37(1)
2.3.2.3 Application Layer
37(1)
2.4 IoT Security Threats
38(5)
2.4.1 Physical Device Threats
39(1)
2.4.1.1 Device-Threats
39(1)
2.4.1.2 Resource Led Constraints
39(1)
2.4.2 Network-Oriented Communication Assaults
39(2)
2.4.2.1 Structure
40(1)
2.4.2.2 Protocol
40(1)
2.4.3 Data-Based Threats
41(2)
2.4.3.1 Confidentiality
41(1)
2.4.3.2 Availability
41(1)
2.4.3.3 Integrity
42(1)
2.5 Assaults in IoT Devices
43(3)
2.5.1 Devices of IoT
43(1)
2.5.2 Gateways and Networking Devices
44(1)
2.5.3 Cloud Servers and Control Devices
45(1)
2.6 Security Analysis of IoT Platforms
46(3)
2.6.1 ARTIK
46(1)
2.6.2 GiGA IoT Makers
47(1)
2.6.3 AWS IoT
47(1)
2.6.4 Azure IoT
47(1)
2.6.5 Google Cloud IoT (GC IoT)
48(1)
2.7 Future Research Approaches
49(5)
2.7.1 Blockchain Technology
51(1)
2.7.2 5G Technology
52(1)
2.7.3 Fog Computing (FC) and Edge Computing (EC)
52(2)
References
54(5)
3 Smart Automation, Smart Energy, and Grid Management Challenges 59(30)
J. Gayathri Monicka
C. Amuthadevi
3.1 Introduction
60(2)
3.2 Internet of Things and Smart Grids
62(5)
3.2.1 Smart Grid in IoT
63(1)
3.2.2 IoT Application
64(2)
3.2.3 Trials and Imminent Investigation Guidelines
66(1)
3.3 Conceptual Model of Smart Grid
67(4)
3.4 Building Computerization
71(10)
3.4.1 Smart Lighting
73(1)
3.4.2 Smart Parking
73(1)
3.4.3 Smart Buildings
74(1)
3.4.4 Smart Grid
75(2)
3.4.5 Integration IoT in SG
77(4)
3.5 Challenges and Solutions
81(2)
3.6 Conclusions
83(1)
References
83(6)
4 Industrial Automation (IIoT) 4.0: An Insight Into Safety Management 89(30)
C. Amuthadevi
J. Gayathri Monicka
4.1 Introduction
89(17)
4.1.1 Fundamental Terms in IIoT
91(8)
4.1.1.1 Cloud Computing
92(1)
4.1.1.2 Big Data Analytics
92(1)
4.1.1.3 Fog/Edge Computing
92(1)
4.1.1.4 Internet of Things
93(1)
4.1.1.5 Cyber-Physical-System
94(1)
4.1.1.6 Artificial Intelligence
95(1)
4.1.1.7 Machine Learning
95(4)
4.1.1.8 Machine-to-Machine Communication
99(1)
4.1.2 Intelligent Analytics
99(1)
4.1.3 Predictive Maintenance
100(1)
4.1.4 Disaster Predication and Safety Management
101(4)
4.1.4.1 Natural Disasters
101(1)
4.1.4.2 Disaster Lifecycle
102(1)
4.1.4.3 Disaster Predication
103(1)
4.1.4.4 Safety Management
104(1)
4.1.5 Optimization
105(1)
4.2 Existing Technology and Its Review
106(4)
4.2.1 Survey on Predictive Analysis in Natural Disasters
106(2)
4.2.2 Survey on Safety Management and Recovery
108(1)
4.2.3 Survey on Optimizing Solutions in Natural Disasters
109(1)
4.3 Research Limitation
110(3)
4.3.1 Forward-Looking Strategic Vision (FVS)
110(1)
4.3.2 Availability of Data
111(1)
4.3.3 Load Balancing
111(1)
4.3.4 Energy Saving and Optimization
111(1)
4.3.5 Cost Benefit Analysis
112(1)
4.3.6 Misguidance of Analysis
112(1)
4.4 Finding
113(1)
4.4.1 Data Driven Reasoning
113(1)
4.4.2 Cognitive Ability
113(1)
4.4.3 Edge Intelligence
113(1)
4.4.4 Effect of ML Algorithms and Optimization
114(1)
4.4.5 Security
114(1)
4.5 Conclusion and Future Research
114(1)
4.5.1 Conclusion
114(1)
4.5.2 Future Research
114(1)
References
115(4)
5 An Industrial Perspective on Restructured Power Systems Using Soft Computing Techniques 119(30)
Kuntal Bhattacharjee
Akhilesh Arvind Nimje
Shanker D. Godwal
Sudeep Tanwar
5.1 Introduction
120(1)
5.2 Fuzzy Logic
121(2)
5.2.1 Fuzzy Sets
121(1)
5.2.2 Fuzzy Logic Basics
122(1)
5.2.3 Fuzzy Logic and Power System
122(1)
5.2.4 Fuzzy Logic-Automatic Generation Control
123(1)
5.2.5 Fuzzy Microgrid Wind
123(1)
5.3 Genetic Algorithm
123(5)
5.3.1 Important Aspects of Genetic Algorithm
124(2)
5.3.2 Standard Genetic Algorithm
126(1)
5.3.3 Genetic Algorithm and Its Application
127(1)
5.3.4 Power System and Genetic Algorithm
127(1)
5.3.5 Economic Dispatch Using Genetic Algorithm
128(1)
5.4 Artificial Neural Network
128(17)
5.4.1 The Biological Neuron
129(1)
5.4.2 A Formal Definition of Neural Network
130(1)
5.4.3 Neural Network Models
131(1)
5.4.4 Rosenblatt's Perceptron
131(1)
5.4.5 Feedforward and Recurrent Networks
132(1)
5.4.6 Back Propagation Algorithm
133(1)
5.4.7 Forward Propagation
133(1)
5.4.8 Algorithm
134(1)
5.4.9 Recurrent Network
135(1)
5.4.10 Examples of Neural Networks
136(2)
5.4.10.1 AND Operation
136(1)
5.4.10.2 OR Operation
137(1)
5.4.10.3 XOR Operation
137(1)
5.4.11 Key Components of an Artificial Neuron Network
138(3)
5.4.12 Neural Network Training
141(1)
5.4.13 Training Types
142(1)
5.4.13.1 Supervised Training
142(1)
5.4.13.2 Unsupervised Training
142(1)
5.4.14 Learning Rates
142(1)
5.4.15 Learning Laws
143(1)
5.4.16 Restructured Power System
144(1)
5.4.17 Advantages of Precise Forecasting of the Price
145(1)
5.5 Conclusion
145(1)
References
146(3)
6 Recent Advances in Wearable Antennas: A Survey 149(32)
Harvinder Kaur
Paras Chawla
6.1 Introduction
150(3)
6.2 Types of Antennas
153(1)
6.2.1 Description of Wearable Antennas
153(1)
6.2.1.1 Microstrip Patch Antenna
153(1)
6.2.1.2 Substrate Integrated Waveguide Antenna
153(1)
6.2.1.3 Planar Inverted-F Antenna
153(1)
6.2.1.4 Monopole Antenna
153(1)
6.2.1.5 Metasurface Loaded Antenna
154(1)
6.3 Design of Wearable Antennas
154(8)
6.3.1 Effect of Substrate and Ground Geometries on Antenna Design
154(5)
6.3.1.1 Conducting Coating on Substrate
154(3)
6.3.1.2 Ground Plane With Spiral Metamaterial Meandered Structure
157(1)
6.3.1.3 Partial Ground Plane
158(1)
6.3.2 Logo Antennas
159(1)
6.3.3 Embroidered Antenna
159(1)
6.3.4 Wearable Antenna Based on Electromagnetic Band Gap
160(1)
6.3.5 Wearable Reconfigurable Antenna
161(1)
6.4 Textile Antennas
162(6)
6.5 Comparison of Wearable Antenna Designs
168(1)
6.6 Fractal Antennas
168(6)
6.6.1 Minkowski Fractal Geometries Using Wearable Electro-Textile Antennas
171(1)
6.6.2 Antenna Design With Defected Semi-Elliptical Ground Plane
172(1)
6.6.3 Double-Fractal Layer Wearable Antenna
172(1)
6.6.4 Development of Embroidered Sierpinski Carpet Antenna
172(2)
6.7 Future Challenges of Wearable Antenna Designs
174(1)
6.8 Conclusion
174(1)
References
175(6)
7 An Overview of IoT and Its Application With Machine Learning in Data Center 181(22)
Manikandan Ramanathan
Kumar Narayanan
7.1 Introduction
181(10)
7.1.1 6LoWPAN
183(2)
7.1.2 Data Protocols
185(4)
7.1.2.1 CoAP
185(2)
7.1.2.2 MQTT
187(1)
7.1.2.3 Rest APIs
187(2)
7.1.3 IoT Components
189(2)
7.1.3.1 Hardware
190(1)
7.1.3.2 Middleware
190(1)
7.1.3.3 Visualization
191(1)
7.2 Data Center and Internet of Things
191(5)
7.2.1 Modern Data Centers
191(1)
7.2.2 Data Storage
191(1)
7.2.3 Computing Process
192(4)
7.2.3.1 Fog Computing
192(2)
7.2.3.2 Edge Computing
194(1)
7.2.3.3 Cloud Computing
194(1)
7.2.3.4 Distributed Computing
195(1)
7.2.3.5 Comparison of Cloud Computing and Fog Computing
196(1)
7.3 Machine Learning Models and IoT
196(3)
7.3.1 Classifications of Machine Learning Supported in IoT
197(2)
7.3.1.1 Supervised Learning
197(1)
7.3.1.2 Unsupervised Learning
198(1)
7.3.1.3 Reinforcement Learning
198(1)
7.3.1.4 Ensemble Learning
199(1)
7.3.1.5 Neural Network
199(1)
7.4 Challenges in Data Center and IoT
199(2)
7.4.1 Major Challenges
199(2)
7.5 Conclusion
201(1)
References
201(2)
8 Impact of IoT to Meet Challenges in Drone Delivery System 203(26)
J. Ranjani
P. Kalaichelvi
V.K.G Kalaiselvi
D. Deepika Sree
K. Swathi
8.1 Introduction
204(5)
8.1.1 IoT Components
204(1)
8.1.2 Main Division to Apply IoT in Aviation
205(1)
8.1.3 Required Field of IoT in Aviation
206(9)
8.1.3.1 Airports as Smart Cities or Airports as Platforms
207(1)
8.1.3.2 Architecture of Multidrone
208(1)
8.1.3.3 The Multidrone Design has the Accompanying Prerequisites
208(1)
8.2 Literature Survey
209(2)
8.3 Smart Airport Architecture
211(4)
8.4 Barriers to IoT Implementation
215(1)
8.4.1 How is the Internet of Things Converting the Aviation Enterprise?
216(1)
8.5 Current Technologies in Aviation Industry
216(2)
8.5.1 Methodology or Research Design
217(1)
8.6 IoT Adoption Challenges
218(1)
8.6.1 Deployment of IoT Applications on Broad Scale Includes the Underlying Challenges
218(1)
8.7 Transforming Airline Industry With Internet of Things
219(3)
8.7.1 How the IoT Is Improving the Aviation Industry
219(1)
8.7.1.1 IoT: Game Changer for Aviation Industry
220(1)
8.7.2 Applications of AI in the Aviation Industry
220(4)
8.7.2.1 Ticketing Systems
220(1)
8.7.2.2 Flight Maintenance
221(1)
8.7.2.3 Fuel Efficiency
221(1)
8.7.2.4 Crew Management
221(1)
8.7.2.5 Flight Health Checks and Maintenance
221(1)
8.7.2.6 In-Flight Experience Management
222(1)
8.7.2.7 Luggage Tracking
222(1)
8.7.2.8 Airport Management
222(1)
8.7.2.9 Just the Beginning
222(1)
8.8 Revolution of Change (Paradigm Shift)
222(1)
8.9 The Following Diagram Shows the Design of the Application
223(1)
8.10 Discussion, Limitations, Future Research, and Conclusion
224(2)
8.10.1 Growth of Aviation IoT Industry
224(1)
8.10.2 IoT Applications-Benefits
225(1)
8.10.3 Operational Efficiency
225(1)
8.10.4 Strategic Differentiation
225(1)
8.10.5 New Revenue
226(1)
8.11 Present and Future Scopes
226(1)
8.11.1 Improving Passenger Experience
226(1)
8.11.2 Safety
227(1)
8.11.3 Management of Goods and Luggage
227(1)
8.11.4 Saving
227(1)
8.12 Conclusion
227(1)
References
227(2)
9 IoT-Based Water Management System for a Healthy Life 229(20)
N. Meenakshi
V. Pandimurugan
S. Rajasoundaran
9.1 Introduction
230(1)
9.1.1 Human Activities as a Source of Pollutants
230(1)
9.2 Water Management Using IoT
231(2)
9.2.1 Water Quality Management Based on IoT Framework
232(1)
9.3 IoT Characteristics and Measurement Parameters
233(2)
9.4 Platforms and Configurations
235(4)
9.5 Water Quality Measuring Sensors and Data Analysis
239(2)
9.6 Wastewater and Storm Water Monitoring Using IoT
241(3)
9.6.1 System Initialization
241(1)
9.6.2 Capture and Storage of Information
241(1)
9.6.3 Information Modeling
241(2)
9.6.4 Visualization and Management of the Information
243(1)
9.7 Sensing and Sampling of Water Treatment Using IoT
244(2)
References
246(3)
10 Fuel Cost Optimization Using IoT in Air Travel 249(32)
P. Kalaichelvi
V. Akila
J. Ranjani
S. Sowmiya
C. Divya
10.1 Introduction
250(2)
10.1.1 Introduction to IoT
250(1)
10.1.2 Processing IoT Data
250(1)
10.1.3 Advantages of IoT
251(1)
10.1.4 Disadvantages of IoT
251(1)
10.1.5 IoT Standards
251(1)
10.1.6 Lite Operating System (Lite OS)
251(1)
10.1.7 Low Range Wide Area Network (LoRaWAN)
252(1)
10.2 Emerging Frameworks in IoT
252(1)
10.2.1 Amazon Web Service (AWS)
252(1)
10.2.2 Azure
252(1)
10.2.3 Brillo/Weave Statement
252(1)
10.2.4 Calvin
252(1)
10.3 Applications of IoT
253(2)
10.3.1 Healthcare in IoT
253(1)
10.3.2 Smart Construction and Smart Vehicles
254(1)
10.3.3 IoT in Agriculture
254(1)
10.3.4 IoT in Baggage Tracking
254(1)
10.3.5 Luggage Logbook
254(1)
10.3.6 Electrical Airline Logbook
254(1)
10.4 IoT for Smart Airports
255(3)
10.4.1 IoT in Smart Operation in Airline Industries
257(1)
10.4.2 Fuel Emissions on Fly
258(1)
10.4.3 Important Things in Findings
258(1)
10.5 Related Work
258(6)
10.6 Existing System and Analysis
264(4)
10.6.1 Technology Used in the System
265(3)
10.7 Proposed System
268(8)
10.8 Components in Fuel Reduction
276(1)
10.9 Conclusion
276(1)
10.10 Future Enhancements
277(1)
References
277(4)
11 Object Detection in IoT-Based Smart Refrigerators Using CNN 281(20)
R. Ashwathan
Y. Asnath Victy Phamila
S. Geetha
K. Kalaivani
11.1 Introduction
282(1)
11.2 Literature Survey
283(4)
11.3 Materials and Methods
287(7)
11.3.1 Image Processing
292(1)
11.3.2 Product Sensing
292(1)
11.3.3 Quality Detection
293(1)
11.3.4 Android Application
293(1)
11.4 Results and Discussion
294(5)
11.5 Conclusion
299(1)
References
299(2)
12 Effective Methodologies in Pharmacovigilance for Identifying Adverse Drug Reactions Using IoT 301(20)
Latha Parthiban
Maithili Devi Reddy
A. Kumaravel
12.1 Introduction
302(1)
12.2 Literature Review
302(2)
12.3 Data Mining Tasks
304(4)
12.3.1 Classification
305(1)
12.3.2 Regression
306(1)
12.3.3 Clustering
306(1)
12.3.4 Summarization
306(1)
12.3.5 Dependency Modeling
306(1)
12.3.6 Association Rule Discovery
307(1)
12.3.7 Outlier Detection
307(1)
12.3.8 Prediction
307(1)
12.4 Feature Selection Techniques in Data Mining
308(4)
12.4.1 GAs for Feature Selection
308(1)
12.4.2 GP for Feature Selection
309(1)
12.4.3 PSO for Feature Selection
310(1)
12.4.4 ACO for Feature Selection
311(1)
12.5 Classification With Neural Predictive Classifier
312(7)
12.5.1 Neural Predictive Classifier
313(4)
12.5.2 MapReduce Function on Neural Class
317(2)
12.6 Conclusions
319(1)
References
319(2)
13 Impact of COVID-19 on IIoT 321(28)
K. Priyadarsini
S. Karthik
K. Malathi
M.V.V. Rama Rao
13.1 Introduction
321(5)
13.1.1 The Use of IoT During COVID-19
321(1)
13.1.2 Consumer IoT
322(1)
13.1.3 Commercial IoT
322(1)
13.1.4 Industrial Internet of Things (IIoT)
322(1)
13.1.5 Infrastructure IoT
322(1)
13.1.6 Role of IoT in COVID-19 Response
323(1)
13.1.7 Telehealth Consultations
323(1)
13.1.8 Digital Diagnostics
323(1)
13.1.9 Remote Monitoring
323(1)
13.1.10 Robot Assistance
323(3)
13.2 The Benefits of Industrial IoT
326(3)
13.2.1 How IIoT is Being Used
327(1)
13.2.2 Remote Monitoring
327(1)
13.2.3 Predictive Maintenance
328(1)
13.3 The Challenges of Wide-Spread IIoT Implementation
329(3)
13.3.1 Health and Safety Monitoring Will Accelerate Automation and Remote Monitoring
330(1)
13.3.2 Integrating Sensor and Camera Data Improves Safety and Efficiency
330(1)
13.3.3 IIoT-Supported Safety for Customers Reduces Liability for Businesses
331(1)
13.3.4 Predictive Maintenance Will Deliver for Organizations That Do the Work
332(1)
13.3.5 Building on the Lessons of 2020
332(1)
13.4 Effects of COVID-19 on Industrial Manufacturing
332(3)
13.4.1 New Challenges for Industrial Manufacturing
333(1)
13.4.2 Smarter Manufacturing for Actionable Insights
333(1)
13.4.3 A Promising Future for IIoT Adoption
334(1)
13.5 Winners and Losers-The Impact on IoT/Connected Applications and Digital Transformation due to COVID-19 Impact
335(2)
13.6 The Impact of COVID-19 on IoT Applications
337(4)
13.6.1 Decreased Interest in Consumer IoT Devices
338(1)
13.6.2 Remote Asset Access Becomes Important
338(1)
13.6.3 Digital Twins Help With Scenario Planning
339(1)
13.6.4 New Uses for Drones
339(1)
13.6.5 Specific IoT Health Applications Surge
340(1)
13.6.6 Track and Trace Solutions Get Used More Extensively
340(1)
13.6.7 Smart City Data Platforms Become Key
340(1)
13.7 The Impact of COVID-19 on Technology in General
341(2)
13.7.1 Ongoing Projects Are Paused
341(1)
13.7.2 Some Enterprise Technologies Take Off
341(1)
13.7.3 Declining Demand for New Projects/Devices/Services
342(1)
13.7.4 Many Digitalization Initiatives Get Accelerated or Intensified
342(1)
13.7.5 The Digital Divide Widens
343(1)
13.8 The Impact of COVID-19 on Specific IoT Technologies
343(1)
13.8.1 IoT Networks Largely Unaffected
343(1)
13.8.2 Technology Roadmaps Get Delayed
344(1)
13.9 Coronavirus With IoT, Can Coronavirus Be Restrained?
344(1)
13.10 The Potential of IoT in Coronavirus Like Disease Control
345(1)
13.11 Conclusion
346(1)
References
346(3)
14 A Comprehensive Composite of Smart Ambulance Booking and Tracking Systems Using IoT for Digital Services 349(20)
Sumanta Chatterjee
Pabitra Kumar Bhunia
Poulami Mondal
Aishwarya Sadhu
Anusua Biswas
14.1 Introduction
350(3)
14.2 Literature Review
353(3)
14.3 Design of Smart Ambulance Booking System Through App
356(3)
14.4 Smart Ambulance Booking
359(4)
14.4.1 Welcome Page
360(1)
14.4.2 Sign Up
360(1)
14.4.3 Home Page
361(1)
14.4.4 Ambulance Section
361(1)
14.4.5 Ambulance Selection Page
362(1)
14.4.6 Confirmation of Booking and Tracking
363(1)
14.5 Result and Discussion
363(2)
14.5.1 How It Works?
365(1)
14.6 Conclusion
365(1)
14.7 Future Scope
366(1)
References
366(3)
15 An Efficient Elderly Disease Prediction and Privacy Preservation Using Internet of Things 369
Resmi G. Nair
N. Kumar
15.1 Introduction
370(1)
15.2 Literature Survey
371(1)
15.3 Problem Statement
372(1)
15.4 Proposed Methodology
373(9)
15.4.1 Design a Smart Wearable Device
373(1)
15.4.2 Normalization
374(3)
15.4.3 Feature Extraction
377(1)
15.4.4 Classification
378(1)
15.4.5 Polynomial HMAC Algorithm
379(3)
15.5 Result and Discussion
382(8)
15.5.1 Accuracy
382(1)
15.5.2 Positive Predictive Value
382(1)
15.5.3 Sensitivity
383(1)
15.5.4 Specificity
383(1)
15.5.5 False Out
383(1)
15.5.6 False Discovery Rate
383(1)
15.5.7 Miss Rate
383(1)
15.5.8 F-Score
383(7)
15.6 Conclusion
390(1)
References
390
Index 39
R. Anandan, PhD completed his degree in Computer Science and Engineering, is an IBMS/390 Mainframe professional, is recognized as a Chartered Engineer from the Institution of Engineers in India, and received a fellowship from Bose Science Society, India. He is a professor in the Department of Computer Science and Engineering, School of Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. He has published more than 110 research papers in various international journals, authored 9 books in the computer science and engineering disciplines, and has received 13 awards.

G. Suseendran, PhD received his degree in Information Technology-Mathematics from Presidency College, University of Madras, Tamil Nadu, India. He passed away during the production of this book.

Souvik Pal, PhD is an associate professor in the Department of Computer Science and Engineering at Sister Nivedita University (Techno India Group), Kolkata, India. Dr. Pal received his PhD in the field of computer science and engineering. He is the editor/author of 12 books and has been granted 3 patents. He is the recipient of a Lifetime Achievement Award in 2018.

Noor Zaman, PhD completed his degree in IT from University Technology Petronas (UTP) Malaysia. He has authored many research papers in WoS/ISI indexed and impact factor research journals and edited 12 books in computer science.
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