The New Advanced Society E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Wiley-Scrivener
• Sprache: Englisch

Seitenzahl: 652

Veröffentlichungsjahr: 2022

Beschreibung

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.

Leseprobe

Table of Contents

Cover

Title Page

Copyright

Dedication

Preface

Acknowledgments

1 Post Pandemic: The New Advanced Society

1.1 Introduction

1.2 Conclusions

References

2 Distributed Ledger Technology in the Construction Industry Using Corda

2.1 Introduction

2.2 Prerequisites

2.3 Key Points of Corda

2.4 Implementation

2.5 Future Work

2.6 Conclusion

References

3 Identity and Access Management for Internet of Things Cloud

3.1 Introduction

3.2 Internet of Things (IoT) Security

3.3 IoT Cloud

3.4 IoT Cloud Related Developments

3.5 Proposed Method for IoT Cloud IAM

3.6 Conclusion

References

4 Automated TSR Using DNN Approach for Intelligent Vehicles

4.1 Introduction

4.2 Literature Survey

4.3 Neural Network (NN)

4.4 Methodology

4.5 Experiments and Results

4.6 Discussion

4.7 Conclusion

References

5 Honeypot: A Trap for Attackers

5.1 Introduction

5.2 Method

5.3 Cryptanalysis

5.4 Conclusions

References

6 Examining Security Aspects in Industrial-Based Internet of Things

6.1 Introduction

6.2 Process Frame of IoT Before Security

6.3 Attacks and Security Assessments in IIoT

6.4 Conclusion

References

7 A Cooperative Navigation for Multi-Robots in Unknown Environments Using Hybrid Jaya-DE Algorithm

7.1 Introduction

7.2 Related Works

7.3 Problem Formulation

7.4 Multi-Robot Navigation Employing Hybrid Jaya-DE Algorithm

7.5 Hybrid Jaya-DE

7.6 Simulation Analysis and Performance Evaluation of Jaya-DE Algorithm

7.7 Total Navigation Path Deviation (TNPD)

7.8 Average Unexplored Goal Distance (AUGD)

7.9 Conclusion

References

8 Categorization Model for Parkinson’s Disease Occurrence and Severity Prediction

8.1 Introduction

8.2 Applications

8.3 Methodology

8.4 Proposed Models

8.5 Results and Discussion

8.6 Conclusion

References

9 AI-Based Smart Agriculture Monitoring Using Ground-Based and Remotely Sensed Images

9.1 Introduction

9.2 Automatic Land-Cover Classification Techniques Using Remotely Sensed Images

9.3 Deep Learning-Based Agriculture Monitoring

9.4 Adaptive Approaches for Multi-Modal Classification

9.5 System Model

9.6 IEEE 802.15.4

9.7 Analysis of IEEE 802.15.4 for Smart Agriculture

9.8 Experimental Results

9.9 Conclusion & Future Directions

References

10 Car Buying Criteria Evaluation Using Machine Learning Approach

10.1 Introduction

10.2 Literature Survey

10.3 Proposed Method

10.4 Dataset

10.5 Exploratory Data Analysis

10.6 Splitting of Data Into Training Data and Test Data

10.7 Pre-Processing

10.8 Training of Our Models

10.9 Result Analysis

10.10 Conclusion and Future Work

References

11 Big Data, Artificial Intelligence and Machine Learning: A Paradigm Shift in Election Campaigns

11.1 Introduction

11.2 Big Data Reveals the Voters’ Preference

11.3 Deep Fakes and Election Campaigns

11.4 Social Media Bots

11.5 Future of Artificial Intelligence and Machine Learning in Election Campaigns

References

12 Impact of Optimized Segment Routing in Software Defined Networks

12.1 Introduction

12.2 Software-Defined Network

12.3 SDN Architecture

12.4 Segment Routing

12.5 Segment Routing in SDN

12.6 Traffic Engineering in SDN

12.7 Segment Routing Protocol

12.8 Simulation and Result

12.9 Conclusion and Future Work

References

13 An Investigation into COVID-19 Pandemic in India

13.1 Introduction

13.2 Literature Survey

13.3 Technologies Used to Fight COVID-19

13.4 Impact of COVID-19 on Business

13.5 Impact of COVID-19 on Indian Economy

13.6 Data and Result Analysis

13.7 Conclusion and Future Scope

References

14 Skin Cancer Classification: Analysis of Different CNN Models via Classification Accuracy

14.1 Introduction

14.2 Literature Survey

14.3 Methodology

14.4 Models Used

14.5 Simulation Results

14.6 Conclusion

References

15 Route Mapping of Multiple Humanoid Robots Using Firefly-Based Artificial Potential Field Algorithm in a Cluttered Terrain

15.1 Introduction

15.2 Design of Proposed Algorithm

15.3 Hybridization Process of Proposed Algorithm

15.4 Execution of Proposed Algorithm in Multiple Humanoid Robots

15.5 Comparison

15.6 Conclusion

References

16 Innovative Practices in Education Systems Using Artificial Intelligence for Advanced Society

16.1 Introduction

16.2 Literature Survey

16.3 Proposed System

16.4 Results

16.5 Future Enhancements

16.6 Conclusion

References

17 PSO-Based Hybrid Weighted k-Nearest Neighbor Algorithm for Workload Prediction in Cloud Infrastructures

17.1 Introduction

17.2 Literature Survey

17.3 Proposed System

17.4 Experimental Results

17.5 Conclusion

References

18 An Extensive Survey on the Prediction of Bankruptcy

18.1 Introduction

18.2 Literature Survey

18.3 System Architecture and Simulation Results

18.4 Conclusion

References

19 Future of Indian Agriculture Using AI and Machine Learning Tools and Techniques

19.1 Introduction

19.2 Overview of AI and Machine Learning

19.3 Review of Literature

19.4 Application of AI & Machine Learning in Agriculture

19.5 Current Scenario and Emerging Trends of AI and ML in Indian Agriculture Sector

19.6 Opportunities for Agricultural Operations in India

19.7 Conclusion

References

Index

Also of Interest

End User License Agreement

List of Tables

Chapter 2

Table 2.1 DLT vs Blockchain.

Chapter 3

Table 3.1 Comparison of access control method for IoT.

Chapter 4

Table 4.1 Different datasets on traffic signs.

Table 4.2 Efficiency (in %) results for implemented classifiers.

Table 4.3 Details of best performing architecture.

Table 4.4 Architecture of FFNN.

Table 4.5 Detailed parameters of FFNN.

Table 4.6 Size details on FFNN.

Table 4.7 Architecture of RNN.

Table 4.8 Detailed parameters of RNN.

Table 4.9 Size details on RNN.

Table 4.10 Architecture of CNN.

Table 4.11 Detailed parameters of CNN.

Table 4.12 Size details on CNN.

Table 4.13 Accuracy and loss of improved CNN.

Table 4.14 Architecture of improved CNN.

Table 4.15 Detailed parameters of improved CNN.

Table 4.16 Size details on improved CNN.

Table 4.17 Hyper-parameters of improved CNN.

Table 4.18 Impact of pooling strategy on accuracy of improved CNN.

Table 4.19 Accuracy of various pre trained models.

Table 4.20 Comparative results with state-of-the-art schemes.

Chapter 6

Table 6.1 Security assessment in IoT network frame.

Table 6.2 Security assessment for IIoT.

Chapter 7

Table 7.1 Individual robot performance using Jaya-DE, basic Jaya, and DE.

Table 7.2 Individual robot performance using Jaya-DE and IGWO [26].

Chapter 8

Table 8.1 Feature extraction for training dataset.

Table 8.2 Severity score class rule.

Table 8.3 Performance measures and comparative analysis.

Chapter 10

Table 10.1 Conversion of attributes into numeric values.

Chapter 12

Table 12.1 Link measurement data of the network having 200 number of data.

Table 12.2 Average throughput (bp/S) with different network size.

Table 12.3 Average hop-count with different network size.

Table 12.4 Average link utilization (%) with different network size.

Chapter 13

Table 13.1 Summary of COVID 19 symptoms.

Chapter 14

Table 14.1 Table showing attributes of the dataset.

Table 14.2 Table showing changing size of MaxPool2D(n,n) vs accuracy.

Table 14.3 Table showing changing size of averagePooling2D(n,n) vs accuracy.

Table 14.4 Table showing changing size of conv2D-32-64 layers vs accuracy.

Table 14.5 Table showing accuracy of different activation function.

Table 14.6 Table showing accuracy of different models.

Table 14.7 Model vs accuracy.

Chapter 15

Table 15.1 Path length (cm) deviation between simulation and experimental result...

Table 15.2 Time spent (s) deviation between simulation and experimental result o...

Table 15.3 Path length (cm) deviation between simulation and experimental result...

Table 15.4 Time spent (s) deviation between simulation and experimental result o...

Table 15.5 Path comparison (s) between proposed controller and Neuro-Fuzzy Contr...

Chapter 16

Table 16.1 Score table indicating student’s prior knowledge in a subject.

Table 16.2 Prediction accuracy.

Chapter 17

Table 17.1 Parameter values for performance evaluation.

Table 17.2 Prediction evaluation methods.

Table 17.3 Evaluation metrics values for Google CPU workload.

Table 17.4 Evaluation metrics values for Google memory workload.

Chapter 18

Table 18.1 Findings from different chapters working on imbalance data through re...

Table 18.2 Findings from different chapters working on outlier data through revi...

Table 18.3 Findings from different chapters working on ensembling methods throug...

Chapter 19

Table 19.1 Research contribution on use of AI & machine learning in agriculture.

Table 19.2 Artificial intelligence on the basis of geographical segmentation.

Table 19.3 Yield of major crops per hectare in India (kilogram per hectare).

List of Illustrations

Chapter 2

Figure 2.1 Representation of the “task” state.

Figure 2.2 Representation of the “cash” state.

Figure 2.3 Representation of the CAT contract.

Figure 2.4 Representation of the RT contract.

Figure 2.5 Representation of the TT contract.

Figure 2.6 Representation of the UOT contract.

Figure 2.7 Flow of the CAT contract.

Figure 2.8 Flow of the RT contract.

Figure 2.9 Flow of the TT contract.

Figure 2.10 Flow of the UOT contract.

Figure 2.11 System overview.

Figure 2.12 Working flowchart of the solution.

Figure 2.13 Upon executing gradlew.bat deployNodes.

Figure 2.14 Upon executing build\nodes\runnodes.bat.

Figure 2.15 The ‘Client’ node.

Figure 2.16 The ‘MainContractor’ node.

Figure 2.17 The ‘SubContractor’ node.

Figure 2.18 The ‘Notary’ node.

Figure 2.19 The ‘Client’ node.

Figure 2.20 The ‘MainContractor’ node.

Figure 2.21 The ‘SubContractor’ node.

Figure 2.22 The output on the MainContractor’s node.

Figure 2.23 The output on all the nodes.

Chapter 3

Figure 3.1 Actors of an IoT system.

Figure 3.2 IoT Cloud system architecture.

Figure 3.3 Blockchain empowered IoT Cloud.

Figure 3.4 Proposed model of blockchain-based IoT Cloud for IAM.

Chapter 4

Figure 4.1 Examples from GTSRB.

Figure 4.2 Accuracy graph of FFNN.

Figure 4.3 Loss graph of FFNN.

Figure 4.4 Normalized confusion matrix of FFNN.

Figure 4.5 Accuracy curve of RNN.

Figure 4.6 Loss graphs of RNN.

Figure 4.7 Normalized confusion matrix of RNN.

Figure 4.8 Accuracy graph of CNN.

Figure 4.9 Loss graph of CNN.

Figure 4.10 Normalized confusion matrix of CNN.

Figure 4.11 Accuracy graph of improved CNN.

Figure 4.12 Loss graph of improved CNN.

Figure 4.13 Confusion matrix of improved CNN.

Chapter 5

Figure 5.1 Honeypot network.

Figure 5.2 Honeypot architecture.

Chapter 6

Figure 6.1 Basic model of IoT frames.

Figure 6.2 IoT application.

Figure 6.3 Attack difficulties in IoT environment.

Figure 6.4 (a), (b), (c) Security works on IIoT.

Figure 6.5 Applications of IIoT.

Figure 6.6 IoT security areas.

Figure 6.7 Advantages of several techniques.

Figure 6.8 No. of research work in year wise.

Chapter 7

Figure 7.1 Classification for robot navigation planning.

Figure 7.2 Taxonomy of approaches.

Figure 7.3 Illustration of navigation path of a robot with waypoints.

Figure 7.4 Illustration for navigation planning of robot for path smoothness.

Figure 7.5 The structure of Pioneer P3dx mobile robot in different views.

Figure 7.6 Model of the environment with two robots and eight static obstacles.

Figure 7.7 MRN employing Jaya-DE.

Figure 7.8 MRN employing basic Jaya.

Figure 7.9 MRN employing DE.

Figure 7.10 AUGD versus number of iteration employing Jaya-DE, basic Jaya algori...

Figure 7.11 Model of the environment with eight robots and 11 static obstacles.

Figure 7.12 MRN employing Jaya-DE with eight robots and 11 obstacles.

Figure 7.13 MRN planning employing IGWO [26].

Figure 7.14 AUGD versus number of iteration employing Jaya-DE and IGWO [26].

Figure 7.15 Relative performance of Jaya-DE and IGWO [26] in terms of ANPT and A...

Chapter 8

Figure 8.1 Comparison between two techniques: (a) traditional machine learning, ...

Figure 8.2 Architecture of CNN.

Figure 8.3 Logistic regression confusion matrix.

Figure 8.4 KNN confusion matrix.

Figure 8.5 SVM confusion matrix.

Figure 8.6 SVM kernel method confusion matrix.

Figure 8.7 Naïve bayes confusion matrix.

Figure 8.8 Decision tree confusion matrix.

Figure 8.9 Random forest confusion matrix.

Figure 8.10 ANN confusion matrix.

Figure 8.11 Performance evaluation.

Chapter 9

Figure 9.1 System model.

Figure 9.2 Superframe structure.

Figure 9.3 DSME multi-superframe structure.

Figure 9.4 DSME CAP-reduction.

Figure 9.5 Illustrative example of TSCH slotframes.

Figure 9.6 Simulation topology.

Figure 9.7 Throughput comparison.

Figure 9.8 Latency comparison.

Figure 9.9 Energy consumption comparison.

Figure 9.10 Channel utilization comparison.

Chapter 10

Figure 10.1 Flow chart of methodology applied.

Figure 10.2 Basic information about the DataFrame.

Figure 10.3 Conversion data type of the columns.

Figure 10.4 Converted data type into int64 format from object format.

Figure 10.5 Pair plot of all columns in our dataset with respective to label col...

Figure 10.6 Correlation values of every pair of columns.

Figure 10.7 Splitting our dataset.

Figure 10.8 Data before converting the data type.

Figure 10.9 Data after changing the data type.

Figure 10.10 Building Gaussian model.

Figure 10.11 Result of Gaussian Naïve Bayes model.

Figure 10.12 Basic structure of a tree.

Figure 10.13 Decision tree model.

Figure 10.14 Tuning of hyper-parameter for decision tree model.

Figure 10.15 Plotting accuracy values of decision tree model at different depth ...

Figure 10.16 Brief idea of working of KNN.

Figure 10.17 Building the KNN model.

Figure 10.18 Obtaining KNN algorithm accuracies changing the n_neighbor values.

Figure 10.19 Accuracies of KNN model by changing the n_neighbor values.

Figure 10.20 Neural network set up.

Figure 10.21 Accuracy test by changing the number of layers.

Figure 10.22 Accuracy vs layers plot for neural network classifier.

Figure 10.23 Producing a confusion matrix.

Figure 10.24 Confusion matrix representation for Gaussian Naïve Bayes model.

Figure 10.25 Confusion matrix representation for tuned decision tree model.

Figure 10.26 Confusion matrix representation for KNN model.

Figure 10.27 Confusion matrix representation for neural networks.

Chapter 11

Figure 11.1 An overview of the relationship between artificial intelligence (AI)...

Figure 11.2 Big data analytics and visualization. Source: [8].

Figure 11.3 App Store Preview. Source: [14].

Figure 11.4 An image of deep fake and impersonating examples of Barak Obama. Sou...

Figure 11.5 AI bring Mona Lisa’s looks from different angle. Source: [17].

Figure 11.6 Social media bot uses. Source: [20].

Figure 11.7 Social Media Bots Signature Behaviors. Source: [20].

Figure 11.8 Visualization of the spread through social media of an article false...

Chapter 12

Figure 12.1 SDN infrastructure and abstraction.

Figure 12.2 Software defined network architecture.

Figure 12.3 SR based on software defined network.

Figure 12.4 Delay for the communication using six different path.

Figure 12.5 Path loss rate for the communication using six different path.

Figure 12.6 Energy consumption corresponding to the packet error rate during the...

Figure 12.7 Load balancing with different network size.

Figure 12.8 Maximum utilization with different network size.

Figure 12.9 Average throughput in bps with different network size.

Figure 12.10 Average hop-count in bps with different network size.

Figure 12.11 Average link utilization (%) with different network size.

Chapter 13

Figure 13.1 Timeline of COVID-19 pandemic in India.

Figure 13.2 Primary symptoms of COVID-19.

Figure 13.3 Precautionary measures for spreading of COVID-19.

Figure 13.4 Different ways of spreading the coronavirus.

Figure 13.5 COVID 19 timeline for top five countries in the world.

Figure 13.6 Number of active COVID 19 cases in top 10 cities in India.

Figure 13.7 State wise number of active, recovered and death cases in India.

Figure 13.8 Number of recovered cases in India.

Figure 13.9 Number of death cases in India.

Chapter 14

Figure 14.1 Graphical model of model 1.

Figure 14.2 Graphical model of model 2.

Figure 14.3 Graphical model of ResNet50.

Figure 14.4 Graph showing change of accuracy with respect to various parameters.

Figure 14.5 Bar graph showing accuracy of different models.

Figure 14.6 ROC curve for model 1.

Figure 14.7 ROC curve for model 2.

Figure 14.8 ROC curve for model 3 (ResNet50).

Figure 14.9 MSE curve showing mean square error during training of model 1.

Figure 14.10 MSE curve showing mean square error during training of model 2.

Figure 14.11 MSE curve showing mean square error during training of model 3 (Res...

Chapter 15

Figure 15.1 Path generation due to the attraction and repulsion force of goal an...

Figure 15.2 Flowchart of firefly algorithm for robot path planning.

Figure 15.3 Flowchart of firefly algorithm for robot path planning.

Figure 15.4 Pseudocode of firefly algorithm for robot path planning.

Figure 15.5 Architecture of dining philosopher controller for solving the confli...

Figure 15.6 Architecture of proposed controller for robot path planning.

Figure 15.7 Simulation result of robot path planning using FA based APF controll...

Figure 15.8 Experimental result of robot path planning using FA-based APF contro...

Figure 15.9 Comparison between proposed controller and existing controller based...

Chapter 16

Figure 16.1 Flow diagram of the proposed system.

Figure 16.2 K-Means algorithm on datasets.

Chapter 17

Figure 17.1 Model of Wavelet Neural Network.

Figure 17.2 Hybrid-PSO with WkNN algorithm.

Figure 17.3 Algorithm of PHWkNN.

Figure 17.4 Predicted with actual CPU workload for PHWkNN algorithm.

Figure 17.5 Predicted with actual Memory workload for PHWkNN algorithm.

Figure 17.6 Evaluation metrics values for Google CPU workload.

Figure 17.7 Evaluation metrics values for Google memory workload.

Figure 17.8 Performance Evaluation under CPU workload.

Figure 17.9 Performance evaluation under memory workload.

Chapter 18

Figure 18.1 Architecture of a bankruptcy prediction model.

Figure 18.2 Comparison of accuracy and F1-score obtained from Adaboost and XGBoo...

Figure 18.3 Comparison of accuracy and F1-score obtained from bagging based mode...

Figure 18.4 Comparison of accuracy and F1-score obtained from single classifier ...

Figure 18.5 RoC curve obtained by applying RF, DT, NN, MV.

Figure 18.6 RoC curve obtained from boosting based ensemble models.

Figure 18.7 RoC curve obtained from bagging based ensemble models.

Figure 18.8 Roc curve obtained from XGBoosting model.

Chapter 19

Figure 19.1 Crop yields, World 1961 to 2018. https://ourworldindata.org/grapher/...

Figure 19.2 Relationship between AI, ML and deep learning.

Figure 19.3 AI in agriculture market. Source: <https...

Figure 19.4 Agriculture advisories issued on simple mobile phone.

Figure 19.5 Satellite used for sharing data and information. Source: https://www...

Figure 19.6 Ecosystem useful for precision agriculture. Source:...

Figure 19.7 Use of water for various purposes in india. Source: Sharma, R. Bhara...

Guide

Cover

Table of Contents

Title Page

Copyright

Dedication

Preface

Acknowledgments

Begin Reading

Index

Also of Interest

End User License Agreement

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Scrivener Publishing

100 Cummings Center, Suite 541J

Beverly, MA 01915-6106

Artificial Intelligence and Soft Computing for Industrial Transformation

Series Editor: Dr. S. Balamurugan ([email protected])

Scope: Artificial Intelligence and Soft Computing Techniques play an impeccable role in industrial transformation. The topics to be covered in this book series include Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks, Fuzzy Logic, Genetic Algorithms, Particle Swarm Optimization, Evolutionary Algorithms, Nature Inspired Algorithms, Simulated Annealing, Metaheuristics, Cuckoo Search, Firefly Optimization, Bio-inspired Algorithms, Ant Colony Optimization, Heuristic Search Techniques, Reinforcement Learning, Inductive Learning, Statistical Learning, Supervised and Unsupervised Learning, Association Learning and Clustering, Reasoning, Support Vector Machine, Differential Evolution Algorithms, Expert Systems, Neuro Fuzzy Hybrid Systems, Genetic Neuro Hybrid Systems, Genetic Fuzzy Hybrid Systems and other Hybridized Soft Computing Techniques and their applications for Industrial Transformation. The book series is aimed to provide comprehensive handbooks and reference books for the benefit of scientists, research scholars, students and industry professional working towards next generation industrial transformation.

Publishers at Scrivener

Martin Scrivener ([email protected])

Phillip Carmical ([email protected])

The New Advanced Society

Artificial Intelligence and Industrial Internet of Things Paradigm

Edited by

Sandeep Kumar Panda

IcfaiTech (Faculty of Science and Technology) ICFAI Foundation for Higher Education, Hyderabad, Telangana, India

Ramesh Kumar Mohapatra

Department of Computer Science and Engineering, National Institute of Technology, Rourkela, Odisha, India

Subhrakanta Panda

Department of Computer Science and Information Systems, Birla Institute of Technology, Pilani, Hyderabad Campus, Telangana, India

and

S. Balamurugan

Director-Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India

This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

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10 9 8 7 6 5 4 3 2 1

Dedication

Dedicated to my sisters Susmita, Sujata, Bhaina Sukanta, my nephew Surya Datta, my wife Itishree (Leena), my son Jay Jagdish (Omm), my late father Jaya Gopal Panda and late mother Pranati Panda.

Sandeep Kumar Panda

Preface

The primary goal of an advanced society, also known as Society 5.0, is to create a human-centric society in which economic progress and social problems are balanced by implementing a system that integrates cyberspace and physical space. That is, a society which aims to create a better social and economic model by adapting the technological innovations of Industry 4.0. Society 5.0 is a huge societal transformation plan that visualizes a “super-smart society.” It is a follow-up to Industry 4.0, where “information” was the predominant factor but cross-sectional knowledge sharing was not adequate, making cooperation among different sectors difficult. Also, finding the information needed from among information overflow is a tedious task, thereby limiting the scope of actions due to various factors like lack of skills, different abilities of those doing the work, etc. In Industry 4.0, data is accessed from the cloud and operations like searching, retrieving and analyzing data happen over the internet, with the burden of analysis being carried by humans. Whereas, in Society 5.0, people, systems and things will all be connected and the vast amounts of data from sensors will be collected in real time, accumulated and analyzed using artificial intelligence (AI), and the resultant analyses fed back to humans in different forms. Society 5.0 balances economic advancements with the resolution of social problems by incorporating the latest technological advancements like big data, AI, the internet of things (IoT) and robotics in all industrial and societal activities. Of course, Industry 4.0 will be a major component of Society 5.0, but is not the only component—it is also about citizens, organizations, stakeholders, academia and so on. In short, using the technological advancements to provide solutions to better the lives of humans is what an advanced society all about. Some salient features of an advanced society are problem-solving and value-adding, bringing out divergent abilities, decentralization, resilience, sustainability and environmental harmony.

A 360-degree view of the different dimensions of this 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.

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. In order to meet these objectives, accomplished writers in the field have contributed the 19 chapters summarized below.

–

Chapter 1

discusses areas of management, cybercrime in the financial sector, human depression, and school/college closures. Moreover, the constraints posed by returning migrant workers and the remedial measures devised to overcome them, and how to build a new advanced society in a post-COVID-19 era are also discussed.

–

Chapter 2

elaborates on the clients, architects, contractors, material suppliers, etc., in the construction industry. The complex supply chain of globally manufactured construction products has to be managed for the sake of meeting quality requirements and customer satisfaction. But, the lack of accountability in the construction industry sometimes leads to various types of errors, delays, and even accidents at some stages. This chapter introduces the key to ending these disputes with the help of Corda, a distributed ledger platform for permissioned networks inspired by blockchain technology. This helps in maintaining transparency among the actors involved in this industry, thus avoiding any miscommunication.

–

Chapter 3

analyzes the identity and access management challenges in the IoT, followed by a proposal of a cloud identity management model for the IoT using distributed ledger technology.

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Chapter 4

elucidates the development of an efficient deep neural network (DNN) with a reduced number of parameters to make it real-time implementable. The architecture was implemented on German traffic sign recognition benchmark (GTSRB) dataset. Four variations of neural network architectures—feedforward neural network (FFNN), radial basis function neural network (RBNN), convolutional neural network (CNN), and recurrent neural network (RNN)—are designed. The various hyperparameters of the architectures—batch size, number of epochs, momentum, initial learning rate—are tuned to achieve the best results.

–

Chapter 5

deals with the basic aspects of honeypots, their importance in modern networks, types of honeypots, their level of interaction, and their advantages and disadvantages. Furthermore, this chapter also discusses how honeypots enhance the security architecture of a network.

–

Chapter 6

provides an in depth review of the necessity for security in IoT platforms and applications of the industrial internet of things (IIoT). Over the past decade, cyberattacks have mostly occurred on IoT devices; therefore, cybersecurity is introduced to deal with these cyberattacks. Furthermore, one of the chief attack modes in the IIoT are botnets and denial-of-service attacks. These attacks happen in several ways, and once they have occurred it is hard to predict and stop them. This chapter highlights many suggestions from diverse authors, which are detailed in tabular form.

–

Chapter 7

proposes an efficient navigation controller embedding hybrid Jaya-DE algorithm to obtain the optimum path of an individual robot. The efficiency of the proposed navigation controller was evaluated through simulation. The outcomes of the simulation revealed the efficacy of the suggested controller in monitoring the robots towards achieving a safe and optimal path. The strength of the suggested controller was further verified with a similar problem framework. The potency of the proposed controller can be seen from the outcome in resolving the navigation of mobile robots as compared to its competitor.

–

Chapter 8

discusses a study conducted for diagnosing Parkinson’s disease using different machine learning (ML) algorithms for categorization and severity prediction through the measure of 16 voice and eight kinematic features accomplished with various archives. The dataset included 40 people with Parkinson’s disease and healthy patients generated with the help of spiral drawings and voice readings. Of the various ML algorithms used for estimating, the highest accuracy (94.87%) was demonstrated by ANN, while Naïve Bayes was the least precise (71.79%). The work also predicted a severity score by suggesting some scientific measures with a prototype dataset.

–

Chapter 9

discusses the challenges faced in the development of a multi-sensor classification system and their possible solutions. Smart agriculture in rural areas can largely benefit from the low-power, low-cost sensors and aerial devices to sense (soil, temperature, salinity, water, light, insects, pests) and exchange data/images for monitoring and controlling crops.

–

Chapter 10

builds a classification model that classifies whether a customer is going to buy a car with specific features. This research work consisted of four ML models and an analysis of their results. These classifying models were Gaussian Naïve Bayes, decision tree, Karnough Nearest Neighbors and neural networks. The author also attempted to find the best hyperparameter value to obtain the best result from these models. These results are used to compare the accuracies of every model and decide the best model for use in real-time prediction. Here, the author was predicting whether a customer was going to buy a car or not buy a car with particular features available in it. Hence, for this prediction the best accuracy we got was 97.4%, which was given by the decision tree classifier. Also, the neural network had about the same prediction accuracy. Therefore, this ML model can be used by a firm to determine whether or not a new car with specific features will sell well or by a customer wanting to know whether a particular car will be bought by other customers as well.

–

Chapter 11

examines the use of AI and ML in political campaigns. It is divided into three sections—the first section explores internet penetration and the influence of social media on the Indian Lok Sabha election; the second section explores the forms of deepfake and automated social media bots and their use during the election campaign; and the final section explores the future of AI and ML in the election campaign in India.

–

Chapter 12

attempts to explain the impact of segment routing (SR) in software-defined networking (SDN). For this, the authors implemented three algorithms known as multi-objective particle swarm optimization (MOPSO), advanced MOPSO (A-MOSPO) and minimum interference routing algorithm (MIRA) on a Waxman network topology created randomly having 100 nodes. For performance evaluation, MATLAB and parameters such as throughput, link utilization, and delay were taken as the key parameters for evaluating the above protocols in an SDN environment.

–

Chapter 13

discusses the symptoms of COVID-19, precautionary measures against it, ways of spreading the corona-virus, and technologies used to fight it. Also discussed is the impact of COVID-19 on business, financial markets, supply-side and demand-side economics, and international trade on the Indian economy.

–

Chapter 14

discusses the convolutional neural network (CNN) used for detecting skin cancer and compares the accuracy of the model by applying a vast dataset by varying the parameters, such as number of layers, activation functions, etc., to find the best suitable parameters for CNN to design the classifier that could give the best accuracy while classifying the images of the seven types of skin cancer.

–

Chapter 15

presents the hybrid outcome of the firefly algorithm (FA) and artificial potential field (APF) algorithm for humanoid control, which is preferred in the present study for navigational tasks.

–

Chapter 16

proposes a system that considers the student’s academic and behavioral characteristics. The data collected can help faculty members gain a better understanding of a student’s level of knowledge and personality. Based on the information collected, students are grouped into clusters using k-means clustering and a suitable partner is selected for group activities using Irving’s algorithm to enable active learning.

–

Chapter 17

discusses how the workload prediction in cloud environments improves proper utilization of resources so that service level agreement remains at a stable level. Hence, the particle swarm optimization (PSO)-based hybrid wavelet weighted k-nearest neighbors (PHWkNN) algorithm is proposed to predict workload in the cloud data center.

–

Chapter 18

includes a survey for predicting bankruptcy, in which it was concluded that preprocessed datasets have a better prediction outcome and that ensemble models are more powerful for bankruptcy prediction as compared to the single models.

–

Chapter 19

aims to provide a comprehensive review of the research done with respect to the application of AI and ML in the agriculture domain and the key strategies adopted by leading companies like Deere & Company (John Deere, US), Microsoft Corporation, Descartes Labs, ec2ce (Spain), etc., in the agricultural market. The chapter also discusses the current scenario and emerging trends of AI and ML in the Indian agriculture sector. Next, it demonstrates how the application of these technologies has bright prospects in Indian agriculture and can impact the agricultural market in the long term, and how the technological support will boost the agricultural economy by creating new opportunities in agriculture’s operational environments. Finally, it studies the barriers in the application of AI and ML in the Indian context.

The topics presented in each chapter are unique to this book and are based on the unpublished work of the contributing authors. In editing this book, we attempted to bring into discussion all the new trends and experiments for creating an advanced society. We believe this book is ready to serve as a reference for larger audiences such as system architects, practitioners, developers and researchers.

Sandeep Kumar PandaICFAI Foundation for Higher Education (IFHE),Deemed to be University,Hyderabad, Telangana, IndiaJanuary 2022

Acknowledgments

The preparation of this edited book was like a journey that we had undertaken for several months. We wish to express our heartfelt gratitude to our families, friends, colleagues, and well-wishers for their constant support throughout this journey. We express our gratitude to all the chapter contributors, who allowed us to quote their remarks and work in this book. In particular, we would like to acknowledge the hard work of authors and their cooperation during the revisions of their chapters. We would also like to acknowledge the valuable comments of the reviewers which have enabled us to select these chapters out of the so many chapters we received and also improve the quality of the chapters. We wish to acknowledge and appreciate the Scrivener Publishing, Wiley team for their continuous support throughout the entire process of publication. Our gratitude is extended to the readers, who gave us their trust, and we hope this work guides and inspires them.

1Post Pandemic: The New Advanced Society

Sujata Priyambada Dash

Department of Management, Mesra, Ranchi, India

Abstract

The World Health Organization (WHO) declared the COVID-19 outbreak as a global public health emergency of international concern. The pandemic has increased the suffering of humanity enormously. Loss of income and employment opportunities is the massive adverse effect of the pandemic. Due care needs to be taken by the top-level management of every sector to understand the adverse effect and causes or problems and to build the measures to overcome from the pandemic. The researcher had attempted and discussed the themes viz., areas of management, financial institutions cyber-crime, economic notion, human depression, school and colleges closures, returning of migrant laborers to identify the constraints and to come up with the remedial measures to overcome those constraints and how to build a new advanced society of Post COVID-19 era.

Keywords: Areas of management, financial institutions cyber-crime, economic notion, human depression, school and colleges closures, emotional intelligence, ikigai, migrant labor

1.1 Introduction

On December 31, 2019, the World Health Organization (WHO) contacted China about media reports of a cluster of viral pneumonia in Wuhan, later attributed to a coronavirus disease by the name, SARS-CoV-2 and now referred to as COVID-19 [1]. The WHO proclaimed the virus to be an international public health emergency (PHEIC), by January 30, 2020, a month later, the highest alert that the organization would sound. The pandemic was well underway in thirty more days and had spread to more than 70 countries and territories on six continents, and about 90,000 confirmed COVID-19 cases were registered worldwide [2].

The purpose of this analysis is to minimize this opportunity by understanding what went wrong with the coronavirus pandemic’s early national and international responses, as long as a roadmap for improved preparedness and implementation by countries and the multilateral framework for potential surfs of the current pandemic, and when the subsequent pandemic menace will inexorably occur. Before it becomes a tragedy, this study aims to avoid the next global health challenge. The pandemic of COVID-19 by far could still develop in unpredictable ways. It is already clear to detect, contain and quickly respond to and prevent the blowout of possibly unsafe new infectious diseases, preparation and early execution are important. The capability to marshal early action rest on the preparation for the worst-case situation of a severe pandemic by nations and global organizations and the readiness to execute on that preparedness.

In order to counter the pandemic, the leadership areas considering the healthcare system and the police management system played a very important role. This research has explored the digital world that, including risk and crime, has transformed almost every aspect of our lives. Again, the focus of this research was on economic activities that have become vulnerable to society. Most importantly the feelings, emotions, and sentiments of the people are predominant. Thus, the study describes Emotional Intelligence (EI) to get rid of depression as one of the remedial measures to manage the situation more self-confidently, enjoy the work passionately and to carry out positive boldness. The study indicated a very challenging task faced by the government to tackle the unemployed migrant laborers and measures to provide employability in due course of time is discussed. Thereafter, the biggest challenging theme deliberated is the digital transformation of educational institutions that paves the way towards technology which is the enabler. Through technology the users’ (students and teachers) experience has become intuitive and innovative. Human Development Index (HDI) has become the priority of every educational institutions. Lastly, ‘ikigai’ which means ‘reasons for being’ is discussed so that during the closure of schools and colleges, students and teachers will remain happy, will bring fulfilment, and live a longer life.

1.1.1 Themes

1.1.1.1 Theme: Areas of Management

Problems: Management is a theory which is very old and traditional. The theory of management always percolates to all subordinates working in a particular organisation. Be it HR, Finance, Marketing, Operation, IT or Entrepreneurial Development, management has become a very serious topic in all these fields over the years. It is almost a year now and the world is fighting with COVID-19, a pandemic which has swept the entire world. This has brought the concept of Management in the forefront where every country is trying to prepare their health system and other support systems to counter the pandemic.

Overcome

The main role in healthcare is played by the Doctors, Nurses, Ward Boy. The work of receptionists, ICCU, ICU support staff, water and electricity suppliers should not be ignored [3]. All these employees have managed to counter the pandemic and inflow of patients and are managing to do so. Recently, the world has faced a new COVID-19 strain, which has worsened the situation further. Once again, the common man is prone to a new strain. All of us need to adhere to more precautionary measures or the SOPs declared by the government. The Police Management System is another system which has also played a significant role in this pandemic.

The police staff were keeping the people at home. People are advised not to wander here and there. The policemen are harsh in this process, which must be individually controlled through proper counseling. In this difficult situation, it has become very difficult to have strong mental acumen, but it is very important to look after well-being. The policemen were only catching the common man who are under severe stressful pandemic situation.

1.1.1.2 Theme: Financial Institutions Cyber Crime

Problems: Cyber-crime has become a major concern for financial institutions in the 21st century. Including risk and crime, the digital world has changed almost every part of our lives. An easy target for crime is financial institutions. In the current digital age, to improve trustworthiness, it is imperative that financial institutions increase investments in protection, fraud prevention, consumer education, and personal information privacy. Financial fraud has evolved rapidly since the pandemic. In India, financial cybercrime, identity theft, account acquisition, synthetic fraud, ransom ware and social engineering are rising [4]. Crypto jacking and botnets are the main ways of cyberattacks. The booming adoption of mobile applications, digital payments, and the growing rate of financial fraud in India are significantly associated. While Indians have embraced digital transactions, as social engineering and phishing email are extensive, they have yet to learn the dos and don’ts to share personal data. To tackle the overall scale of these assaults, the value of consistent security hygiene remains vital. That is why financial institutions need to take a layered approach to cyber protection and fraud prevention, including banks, credit unions, brokerage and payment firms.

Overcome

Organizations should regularly scan the internet for fraudulent application;

An integrated security framework must be introduced by organizations, where each aspect is programmed to interact in real time with all the others;

Identity and track all mobile and IoT devices;

To combat things like Crypto jacking, one essential approach is to involve maintaining a comprehensive inventing of devices through third generation network access controls and then base lining their behavior; and

Continue to inform clients about their legitimate banking applications, such as online “Validation of passwords” or “account validation” methods used by scammers and phishes. So, it is predominant to train the employees to avoid cybercrimes.

1.1.1.3 Theme: Economic Notion

Problems: COVID-19, a colossal and unpredictable pandemic, has already had and will continue to have an economic impact. Throughout the globe, the wheel of whole economic processes has been stopped and has now become vulnerable to society. Humans are unable to handle this issue and the situation is getting worse and worse every day. The pandemic’s overall effects on economic activity and GDP depend on the severity and length of the pandemic. The influence of COVID-19 dominates throughout the world, experiencing and witnessing the parallel spread of stock market volatility, economic development stagnation, and growth, so it would certainly have a profound impact on the way people live [5]. Scientists, researchers, teachers, medical fraternity have been working round the clock for serving the society, work at home is gaining momentum, and lockdown perplexed the people to a great extent. This meticulous and unusual pandemic occurrence affects individuals physically and mentally. Many individuals have encountered a mild to moderate spectrum of stress, anxiety and depression reactions. It is important to evaluate these types of economic reactions and to take the necessary corrective steps to pave the economy during post-pandemic period. The entire world is typically at the verge of recessions such as real recession, policy recession, and financial crisis [6]. The latest pandemic has struck the world’s economy so hard. Few sectors will develop by leaps and bounds, although there will be no more conventional industries. It would be like placing old wine in a new bottle to establish the essence and pattern of new business. There will be a steady turnover in products and services, but there will certainly be a shift in their distribution model, Industry 4.0, telemedicine, robotics, deep learning, machine learning, and artificial tools. To do so, a coordinated collection of fiscal, monetary, and structural steps must be considered by nations and ways of returning from lockdown must be explored.

Overcome

Under COVID-19, depending on a variety of factors, such as the degree to which demand will be delayed, there is a need for a recovery path back to growth, measure of industry 4.0 application and structural damage intensity.

It is very much relevant to plan the three broad situations such as V-shaped which describes the classical real economy shock, a displacement of output, but growth rebounds eventually. Annual growth rates in this scenario could completely absorb the shock. We think it is plausible and seems to be optimistic amid today’s gloom.

The ugly sibling of V-shaped is U-shaped, the shock continues, while the initial growth direction is resumed, there is some continuous loss of output. But in order to make this the simple scenario, we had to see more evidence of the real harm of the virus.

The other condition is L-shaped, which is a really ugly and weak V and U relationship. In order to materialize, we will have to believe in COVID-19's ability to do significant structural harm. i.e., to break anything on the supply side of the economy—the labor market, the break-down of the creation of capital, or the role of productivity. That’s hard to imagine, even with assumptions that are negative [7].

1.1.1.4 Theme: Human Depression

Problems: Employees have lost their jobs in this pandemic. Very pathetic and worse situations have aroused. Employees have become fruit and vegetables sellers on the roadside, in order to run their livelihood, have chosen this path to provide food and shelter to their family. These common people must be managed. During lockdown no individual is allowed to leave their place of living; keeping an individual in their home results in isolation leads to stress, anxiety, fear, and loneliness. Many people have committed suicide. Moreover, these days the families are mostly nuclear, dual-income-groups that prevail in our society where both the parents are working to meet the day-today expenses of their families. Their children feel very lonely and social life get hampered. They were more inclined towards watching television and playing indoor games, used to computers for their entertainment. Less interactions with relatives and friends have become prevalent. During this prevailing pandemic, the situation has gone downhill. Therefore, it is very important to hold promise and harness the emotions to lead a better life in future.

Overcome

To overcome the above situations, different theories have evolved with the concept Emotional Intelligence (EI) and it has become imperative to welcome this new and path-breaking concept into three different theories. Goleman’s Competency model [8] and the trait theory by Bar-On [9] and the ‘ability’ model by Mayer and Salovey [10] are the most prominent approaches proposed in this study.

Employees are very dynamic. Due to the dynamic nature (emotions, feelings and sentiments) of the employees, proper care and considerations should be taken by the employer. Employees need to be handled in a very tactful manner. The concept of Emotional Intelligence (EI) is aroused here. It is the most important driver of leadership and personal achievement and also the best success predictor. EI is a best practice of corporate management technique. Goleman also emphasizes on IQ which are the requisites for the new aspirants in the procurement process. In order to build congenial rapport with superiors, subordinates and colleagues, managerial skills are of utmost importance [11]. With good IQ, the new aspirant will be well placed and EI helps him for the survival and makes him successful in the organization [12–14]. For better results, EI in a company is an important part of negotiating the dynamics and dilemmas of the relationship between employees and customers. Some causal factors are (a) The speedy growth and heavy emphasis on AI and ML have also echoed the importance of integrating digital transformation with human strengths such as empathy and emotional intelligence, (b) As more successful and innovative workers are happier, leaders with a higher EQ get better results. Emotionally intelligent leaders discover what works for each person instead of pushing a one-size-fits-all approach and adapt their strategy accordingly. Happier workers make higher profits from happier customers, and (c) Improves relationships where relationships with people are important; improves communication with people, improves empathy abilities, behaves with honesty, allows one to gain respect from others, enhances career projections, handles variation more confidently, enjoys the job enthusiastically, feels optimistic and positive in attitude, decreases levels of stress, increases creativity, and to learn from mistakes. Thus, organizations need to promote emotional intelligence at the work place.

Bar-On emphasizes more on EQ. He stated that a person becomes more competent, faces difficulties and handles all the pressures of everyday life. Thus, EI significantly contributes to the happiness and well-being of a person’s life [15, 16].

Mayer and Salovey introduced EI as an ‘ability’ model. He stated broad three skills as (i) social skills where a person could able to perceive, respond, and express emotions accurately to others; (ii) emotions can be regulated effectively by a person who could be adaptive to the prevailing situation or influence others and lastly (iii) emotions are utilized in a constructive manner where people with EI solve complex problems and promote intellectual growth.

1.1.1.5 Theme: Migrant Labor

Problem: Migrant labor continuously returned to their native places in India due to unemployment and COVID-19 pandemic. It was a very challenging task faced by the government to take a very large number of migrant laborers to their native places in this pandemic. The migrant labor came out of their native places because of unemployability. Again, they are facing the same difficulty of finding employment, once they come home. Industries cannot be shut down for an indefinite period as this move affects the nation’s economy to a very large extent. In the Unlock-1 industries have started along with other establishments which are going to result into generation of employment and revenue for them. The labor which has gone back to their native places may not be interested in coming back again in this pandemic.

Overcome

Major employment can be related with agriculture for the migrant labors. The government should make trained societies and teams, who can train villagers to encourage organic farming, food processing, etc. Plants producing organic manure can be setup in rural areas. This organic manure can be easily made available to the local farmers at a lower cost and can be a good source of income for rural people. For large scale requirements of food processing industries, contract farming of vegetables, fruits, food grains can be introduced in rural area.

The state which are endowed with a wide variety of medicinal plants and herbs are very good market potential. A tie or Memorandum of Understanding (MoU) can be done with the pharmaceutical companies for these medicinal plants and herbs.

Floriculture activities can be taken up in rural areas to employ migrant labor as this activity is a highly labor intensive one. Floriculture is having a good market potential both in domestic market as well as in the export market [17].

Dairying industry can be developed in rural areas as per the climatic condition that suits to the area [18].

Migrant labor can also be employed in sericulture, pisciculture and piggery which can be undertaken in rural areas. Some other allied agroforestry activities like social forestry where trees like Eucalyptus and poplar trees can be grown in rural areas which can both bring revenue and employment generation. The rest of the migrant labor can be absorbed in cottage industry and small-scale industry.

Instant employment generation has been possible only in construction sector like road construction, building construction, etc. The government can tie up with other states and organizations where there is a requirement of skilled and unskilled labor.

1.1.1.6 Theme: Digital Transformation (DT) of Educational Institutions

Problem: