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E-raamat: New Advanced Society: Artificial Intelligence and Industrial Internet of Things Paradigm

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  • ISBN-13: 9781119884378
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New Advanced Society: Artificial Intelligence and Industrial Internet of Things ParadigmSuurem pilt
  • Formaat: EPUB+DRM
  • Sari: Wiley-Scrivener
  • Ilmumisaeg: 18-Mar-2022
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119884378
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THE NEW ADVANCED SOCIETY Included in this book are the fundamentals of Society 5.0, artificial intelligence, and the industrial Internet of Things, featuring their working principles and application in different sectors.

A 360-degree view of the different dimensions of the digital revolution is presented in this book, including the various industries transforming industrial manufacturing, the security and challenges ahead, and the far-reaching implications for society and the economy. The main objective of this edited book is to cover the impact that the new advanced society has on several platforms such as smart manufacturing systems, where artificial intelligence can be integrated with existing systems to make them smart, new business models and strategies, where anything and everything is possible through the internet and cloud, smart food chain systems, where food products can be delivered to any corner of the world at any time and in any situation, smart transport systems in which robots and self-driven cars are taking the lead, advances in security systems to assure people of their privacy and safety, and smart healthcare systems, where biochips can be incorporated into the human body to predict deadly diseases at early stages. Finally, it can be understood that the social reformation of Society 5.0 will lead to a society where every person leads an active and healthy life.

Audience

The targeted audience for this book includes research scholars and industry engineers in artificial intelligence and information technology, engineering students, cybersecurity experts, government research agencies and policymakers, business leaders, and entrepreneurs.

Sandeep Kumar Panda, PhD is an associate professor in the Department of Data Science and Artificial Intelligence at IcfaiTech (Faculty of Science and Technology), ICFAI Foundation for Higher Education, Hyderabad. His research areas include artificial intelligence, IoT, blockchain technology, cloud computing, cryptography, computational intelligence, and software engineering.

Ramesh Kumar Mohapatra, PhD is an assistant professor in the Department of Computer Science and Engineering, National Institute of Technology, Rourkela, Odisha, India. His research interests include optical character recognition, document image analysis, video processing, secure computing, and machine learning.

Subhrakanta Panda, PhD is an assistant professor in the Department of Computer Science and Information Systems, BITS-PILANI, Hyderabad Campus, Jawahar Nagar, Hyderabad, India. His research interests include social network analysis, cloud computing, security testing, and blockchain.

S. Balamurugan, PhD is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.
Preface xvii
Acknowledgments xxiii
1 Post Pandemic: The New Advanced Society
1(14)
Sujata Priyambada Dash
1.1 Introduction
1(11)
1.1.1 Themes
2(1)
1.1.1.1 Theme: Areas of Management
2(1)
1.1.1.2 Theme: Financial Institutions Cyber Crime
3(1)
1.1.1.3 Theme: Economic Notion
4(2)
1.1.1.4 Theme: Human Depression
6(1)
1.1.1.5 Theme: Migrant Labor
7(2)
1.1.1.6 Theme: Digital Transformation (DT) of Educational Institutions
9(2)
1.1.1.7 School and Colleges Closures
11(1)
1.2 Conclusions
12(3)
References
12(3)
2 Distributed Ledger Technology in the Construction Industry Using Corda
15(28)
Sandeep Kumar Panda
Shanmukhi Priya Daliyet
Shagun S. Lokre
Vihas Naman
2.1 Introduction
16(1)
2.2 Prerequisites
16(2)
2.2.1 DLT vs Blockchain
17(1)
2.3 Key Points of Corda
18(8)
2.3.1 Some Salient Features of Corda
20(1)
2.3.2 States
20(2)
2.3.3 Contract
22(1)
2.3.3.1 Create and Assign Task (CAT) Contract
22(1)
2.3.3.2 Request for Cash (RT) Contract
23(1)
2.3.3.3 Transfer of Cash (TT) Contract
24(1)
2.3.3.4 Updation of the Task (UOT) Contract
24(1)
2.3.4 Flows
25(1)
23.4.1 Flow Associated With CAT Contract
25(1)
2.3.4.2 Flow Associated With RT Contract
26(1)
2.3.4.3 Flow Associated With TT Contract
26(1)
2.3.4.4 Flow Associated With UOT Contract
26(1)
2.4 Implementation
26(9)
2.4.1 System Overview
27(1)
2.4.2 Working Flowchart
28(1)
2.4.3 Experimental Demonstration
29(6)
2.5 Future Work
35(1)
2.6 Conclusion
36(7)
References
37(6)
3 Identity and Access Management for Internet of Things Cloud
43(24)
Soutnya Prakash Otta
Subhrakanta Panda
3.1 Introduction
44(1)
3.2 Internet of Things (IoT) Security
45(4)
3.2.1 IoT Security Overview
45(1)
3.2.2 IoT Security Requirements
46(3)
3.2.3 Securing the IoT Infrastructure
49(1)
3.3 IoT Cloud
49(6)
3.3.1 Cloudification of IoT
50(2)
3.3.2 Commercial IoT Clouds
52(2)
3.3.3 IAM of IoT Clouds
54(1)
3.4 IoT Cloud Related Developments
55(3)
3.5 Proposed Method for IoT Cloud IAM
58(6)
3.5.1 Distributed Ledger Approach for IoT Security
59(1)
3.5.2 Blockchain for IoT Security Solution
60(2)
3.5.3 Proposed Distributed Ledger-Based IoT Cloud IAM
62(2)
3.6 Conclusion
64(3)
References
65(2)
4 Automated TSR Using DNN Approach for Intelligent Vehicles
67(24)
Banhi Sanyal
Piyush R. Biswal
R.K. Mohapatra
Ratnakar Dash
Ankush Agarwalla
4.1 Introduction
68(1)
4.2 Literature Survey
69(1)
4.3 Neural Network (NN)
70(1)
4.4 Methodology
71(1)
4.4.1 System Architecture
71(1)
4.4.2 Database
71(1)
4.5 Experiments and Results
71(8)
4.5.1 FFNN
74(2)
4.5.2 RNN
76(1)
4.5.3 CNN
76(1)
4.5.4 CNN
76(3)
4.5.5 Pre-Trained Models
79(1)
4.6 Discussion
79(1)
4.7 Conclusion
80(11)
References
88(3)
5 Honeypot: A Trap for Attackers
91(12)
Anjanna Matta
G. Sucharitha
Bandlamudi Greeshmanjali
Manji Prashanth Kumar
Mathi Naga Sarath Kumar
5.1 Introduction
92(2)
5.1.1 Research Honeypots
93(1)
5.1.2 Production Honeypots
93(1)
5.2 Method
94(2)
5.2.1 Low-Interaction Honeypots
94(1)
5.2.2 Medium-Interaction Honeypots
95(1)
5.2.3 High-Interaction Honeypots
95(1)
5.3 Cryptanalysis
96(3)
5.3.1 System Architecture
96(1)
5.3.2 Possible Attacks on Honeypot
97(1)
5.3.3 Advantages of Honeypots
98(1)
5.3.4 Disadvantages of Honeypots
99(1)
5.4 Conclusions
99(4)
References
100(3)
6 Examining Security Aspect in Industrial-Based Internet of Things
103(20)
Rohini Jha
6.1 Introduction
104(1)
6.2 Process Frame of IoT Before Security
105(6)
6.2.1 Cyber Attack
107(1)
6.2.2 Security Assessment in IoT
107(1)
6.2.2.1 Security in Perception and Network Frame
108(3)
6.3 Attacks and Security Assessments in IIoT
111(5)
6.3.1 IoT Security Techniques Analysis Based on its Merits
111(5)
6.4 Conclusion
116(7)
References
119(4)
7 A Cooperative Navigation for Multi-Robots in Unknown Environments Using Hybrid Jaya-DE Algorithm
123(40)
D. Chandrasekhar Rao
7.1 Introduction
124(2)
7.2 Related Works
126(4)
7.3 Problem Formulation
130(4)
7.4 Multi-Robot Navigation Employing Hybrid Jaya-DE Algorithm
134(2)
7.4.1 Basic Jaya Algorithm
134(2)
7.5 Hybrid Jaya-DE
136(3)
7.5.1 Mutation
136(1)
7.5.2 Crossover
136(1)
7.5.3 Selection
137(2)
7.6 Simulation Analysis and Performance Evaluation of Jaya-DE Algorithm
139(8)
7.7 Total Navigation Path Deviation (TNPD)
147(1)
7.8 Average Unexplored Goal Distance (AUGD)
148(11)
7.9 Conclusion
159(4)
References
159(4)
8 Categorization Model for Parkinson's Disease Occurrence and Severity Prediction
163(28)
Prashant Kumar Shrivastava
Ashish Chaturvedi
Megha Kamble
Megha Jain
8.1 Introduction
164(2)
8.2 Applications
166(7)
8.2.1 Machine Learning in PD Diagnosis
166(3)
8.2.2 Challenges of PD Detection
169(1)
8.2.3 Structuring of UPDRS Score
170(3)
8.3 Methodology
173(5)
8.3.1 Overview of Data Driven Intelligence
173(2)
8.3.2 Comparison Between Deep Learning and Traditional Machine
175(1)
8.3.3 Deep Learning for PD Diagnosis
176(1)
8.3.4 Convolution Neural Network for PD Diagnosis
176(2)
8.4 Proposed Models
178(6)
8.4.1 Classification of Patient and Healthy Controls
178(3)
8.4.2 Severity Score Classification
181(3)
8.5 Results and Discussion
184(3)
8.5.1 Performance Measures
185(2)
8.5.2 Graphical Results
187(1)
8.6 Conclusion
187(4)
References
187(4)
9 AI-Based Smart Agriculture Monitoring Using Ground-Based and Remotely Sensed Images
191(32)
Shounak Chakraborty
Nikumani Choudhury
Indrajit Kalita
9.1 Introduction
192(2)
9.2 Automatic Land-Cover Classification Techniques Using Remotely Sensed Images
194(2)
9.3 Deep Learning-Based Agriculture Monitoring
196(1)
9.4 Adaptive Approaches for Multi-Modal Classification
197(5)
9.4.1 Unsupervised DA
199(1)
9.4.2 Semi-Supervised DA
200(1)
9.4.3 Active Learning-Based DA
201(1)
9.5 System Model
202(2)
9.6 IEEE 802.15.4
204(3)
9.6.1 802.15.4 MAC
204(1)
9.6.2 DSME MAC
205(1)
9.6.3 TSCH MAC
206(1)
9.7 Analysis of IEEE 802.15.4 for Smart Agriculture
207(2)
9.7.1 Effect of Device Specification
207(1)
9.7.1.1 Low-Power
208(1)
9.7.2 Effect of MAC Protocols
208(1)
9.8 Experimental Results
209(3)
9.9 Conclusion & Future Directions
212(11)
References
212(11)
10 Car Buying Criteria Evaluation Using Machine Learning Approach
223(24)
Samdeep Kumar Panda
10.1 Introduction
224(1)
10.2 Literature Survey
225(1)
10.3 Proposed Method
226(1)
10.4 Dataset
227(1)
10.5 Exploratory Data Analysis
227(3)
10.6 Splitting of Data Into Training Data and Test Data
230(2)
10.7 Pre-Processing
232(1)
10.8 Training of Our Models
232(8)
10.8.1 Gaussian Naive Bayes
233(1)
10.8.2 Decision Tree Classifier
234(1)
10.8.3 Tuning the Model
235(1)
10.8.4 Karnough Nearest Neighbor Classifier
236(1)
10.8.5 Tuning the Model
237(1)
10.8.6 Neural Network
238(1)
10.8.7 Tuning the Model
239(1)
10.9 Result Analysis
240(4)
10.9.1 Confusion Matrix
240(1)
10.9.2 Gaussian Naive Bayes
241(1)
10.9.3 Decision Tree Classifier
242(1)
10.9.4 Karnough Nearest Neighbor Classifier
242(1)
10.9.5 Neural Network
242(1)
10.9.6 Accuracy Scores
243(1)
10.10 Conclusion and Future Work
244(3)
References
244(3)
11 Big Data, Artificial Intelligence and Machine Learning: A Paradigm Shift in Election Campaigns
247(16)
Md. Safiullah
Neha Parveen
11.1 Introduction
248(1)
11.2 Big Data Reveals the Voters' Preference
249(5)
11.2.1 Use of Software Applications in Election Campaigns
251(1)
11.2.1.1 Team Joe App
252(1)
11.2.1.2 Trump 2020
252(1)
11.2.1.3 Modi App
253(1)
11.3 Deep Fakes and Election Campaigns
254(2)
11.3.1 Deep Fake in Delhi Elections
254(2)
11.4 Social Media Bots
256(3)
11.5 Future of Artificial Intelligence and Machine Learning in Election Campaigns
259(4)
References
259(4)
12 Impact of Optimized Segment Routing in Software Defined Network
263(26)
Amrutanshu Panigrahi
Bibhuprasad Sahu
Satya Sobhan Panigrahi
Ajay Kumar Jena
Md. Sahil Khan
12.1 Introduction
264(2)
12.2 Software-Defined Network
266(2)
12.3 SDN Architecture
268(2)
12.4 Segment Routing
270(2)
12.5 Segment Routing in SDN
272(2)
12.6 Traffic Engineering in SDN
274(1)
12.7 Segment Routing Protocol
275(2)
12.8 Simulation and Result
277(1)
12.9 Conclusion and Future Work
278(11)
References
283(6)
13 An Investigation into COVID-19 Pandemic in India
289(18)
Shubhangi V. Urkude
Vijaykutnar R. Urkude
S. Vairachilai
Sandeep Kumar Panda
13.1 Introduction
289(6)
13.1.1 Symptoms of COVID-19
292(1)
13.1.2 Precautionary Measures
292(2)
13.1.3 Ways of Spreading the Coronavirus
294(1)
13.2 Literature Survey
295(1)
13.3 Technologies Used to Fight COVID-19
296(3)
13.3.1 Robots
296(1)
13.3.2 Drone Technology
297(1)
13.3.3 Crowd Surveillance
297(1)
13.3.4 Spraying the Disinfectant
298(1)
13.3.5 Sanitizing the Contaminated Areas
298(1)
13.3.6 Monitoring Temperature Using Thermal Camera
298(1)
13.3.7 Delivering the Essential Things
298(1)
13.3.8 Public Announcement in the Infected Areas
298(1)
13.4 Impact of COVID-19 on Business
299(1)
13.4.1 Impact on Financial Markets
299(1)
13.4.2 Impact on Supply Side
299(1)
13.4.3 Impact on Demand Side
300(1)
13.4.4 Impact on International Trade
300(1)
13.5 Impact of COVID-19 on Indian Economy
300(1)
13.6 Data and Result Analysis
300(4)
13.7 Conclusion and Future Scope
304(3)
References
304(3)
14 Skin Cancer Classification: Analysis of Different CNN Models via Classification Accuracy
307(16)
Poonam Biswal
Monali Saha
Nishtha Jaiswal
Minakhi Rout
14.1 Introduction
307(1)
14.2 Literature Survey
308(2)
14.3 Methodology
310(2)
14.3.1 Dataset Preparation
310(1)
14.3.2 Dataset Loading and Data Pre-Processing
311(1)
14.3.3 Creating Models
312(1)
14.4 Models Used
312(1)
14.5 Simulation Results
313(8)
14.5.1 Changing Size of MaxPool2D(n,n)
314(1)
14.5.2 Changing Size of AveragePool2D(n,n)
314(1)
14.5.3 Changing Number of con2d(32n-64n) Layers
315(1)
14.5.4 Changing Number of con2d-32*n Layers
315(3)
14.5.5 ROC Curves and MSE Curves
318(3)
14.6 Conclusion
321(2)
References
321(2)
15 Route Mapping of Multiple Humanoid Robots Using Firefly-Based Artificial Potential Field Algorithm in a Cluttered Terrain
323(28)
Abhishek Kumar Kashyap
Anish Pandey
Dayal R. Parhi
15.1 Introduction
324(4)
15.2 Design of Proposed Algorithm
328(11)
15.2.1 Mechanism of Artificial Potential Field
328(1)
15.2.1.1 Potential Field Generated by Attractive Force of Goal
329(2)
15.2.1.2 Potential Field Generated by Repulsive Force of Obstacle
331(1)
15.2.2 Mechanism of Firefly Algorithm
332(3)
15.2.2.1 Architecture of Optimization Problem Based on Firefly Algorithm
335(2)
15.2.3 Dining Philosopher Controller
337(2)
15.3 Hybridization Process of Proposed Algorithm
339(1)
15.4 Execution of Proposed Algorithm in Multiple Humanoid Robots
339(5)
15.5 Comparison
344(2)
15.6 Conclusion
346(5)
References
346(5)
16 Innovative Practices in Education Systems Using Artificial Intelligence for Advanced Society
351(22)
D.C. Vinutha
S. Kavyashree
C.P. Vijay
G.T. Raju
16.1 Introduction
352(1)
16.2 Literature Survey
353(6)
16.2.1 AI in Auto-Grading
354(2)
16.2.2 AI in Smart Content
356(1)
16.2.3 AI in Auto Analysis on Student's Grade
356(1)
16.2.4 AI Extends Free Intelligent Tutoring
357(2)
16.2.5 AI in Predicting Student Admission and Drop-Out Rate
359(1)
16.3 Proposed System
359(9)
16.3.1 Data Collection Module
360(4)
16.3.2 Data Pre-Processing Module
364(1)
16.3.3 Clustering Module
364(2)
16.3.4 Partner Selection Module
366(2)
16.4 Results
368(2)
16.5 Future Enhancements
370(1)
16.6 Conclusion
370(3)
References
371(2)
17 PSO-Based Hybrid Weighted k-Nearest Neighbor Algorithm for Workload Prediction in Cloud Infrastructures
373(22)
N. Yamuna
J. Antony Vijay
B. Gomathi
17.1 Introduction
374(1)
17.2 Literature Survey
375(4)
17.2.1 Machine Learning
378(1)
17.3 Proposed System
379(6)
17.3.1 Load Aware Cloud Computing Model
379(1)
17.3.2 Wavelet Neural Network
379(1)
17.3.3 Evaluation Using LOOCV Model
380(1)
17.3.4 K-Nearest Neighbor (k-NN) Algorithm
381(1)
17.3.5 Particle Swarm Optimization (PSO) Algorithm
382(1)
17.3.6 HWkNN Optimization Algorithm Based on PSO
383(1)
17.3.7 PSO-Based HWkNN (PHWkNN) Load Prediction Algorithm
384(1)
17.4 Experimental Results
385(5)
17.5 Conclusion
390(5)
References
391(4)
18 An Extensive Survey on the Prediction of Bankruptcy
395(52)
Sasmita Manjari Nayak
Minakhi Rout
18.1 Introduction
395(2)
18.2 Literature Survey
397(41)
18.2.1 Data Pre-Processing
397(1)
18.2.1.1 Balancing of Imbalanced Dataset
397(13)
18.2.1.2 Outlier Data Handling
410(8)
18.2.2 Classifiers
418(4)
18.2.3 Ensemble Models
422(16)
18.3 System Architecture and Simulation Results
438(1)
18.4 Conclusion
438(9)
References
443(4)
19 Future of Indian Agriculture Using AI and Machine Learning Tools and Techniques
447(26)
Manoj Kumar
Pratibha Maurya
Rinki Verma
19.1 Introduction
448(2)
19.2 Overview of AI and Machine Learning
450(2)
19.3 Review of Literature
452(4)
19.4 Application of AI & Machine Learning in Agriculture
456(4)
19.5 Current Scenario and Emerging Trends of AI and ML in Indian Agriculture Sector
460(5)
19.6 Opportunities for Agricultural Operations in India
465(1)
19.7 Conclusion
466(7)
References
467(6)
Index 473
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