The New Advanced Society: Artificial Intelligence and Industrial Internet of Things Paradigm

ISBN-13: 9781119824473 / Angielski / Twarda / 2022 / 512 str.

Sandeep Kumar Panda; Ramesh Kumar Mohapatra; Subhrakanta Panda

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Preface xviiAcknowledgments xxiii1 Post Pandemic: The New Advanced Society 1Sujata Priyambada Dash1.1 Introduction 11.1.1 Themes 21.1.1.1 Theme: Areas of Management 21.1.1.2 Theme: Financial Institutions Cyber Crime 31.1.1.3 Theme: Economic Notion 41.1.1.4 Theme: Human Depression 61.1.1.5 Theme: Migrant Labor 71.1.1.6 Theme: Digital Transformation (DT) of Educational Institutions 91.1.1.7 School and Colleges Closures 111.2 Conclusions 12References 122 Distributed Ledger Technology in the Construction Industry Using Corda 15Sandeep Kumar Panda, Shanmukhi Priya Daliyet, Shagun S. Lokre and Vihas Naman2.1 Introduction 162.2 Prerequisites 162.2.1 DLT vs Blockchain 172.3 Key Points of Corda 182.3.1 Some Salient Features of Corda 202.3.2 States 202.3.3 Contract 222.3.3.1 Create and Assign Task (CAT) Contract 222.3.3.2 Request for Cash (RT) Contract 232.3.3.3 Transfer of Cash (TT) Contract 242.3.3.4 Updation of the Task (UOT) Contract 242.3.4 Flows 252.3.4.1 Flow Associated With CAT Contract 252.3.4.2 Flow Associated With RT Contract 262.3.4.3 Flow Associated With TT Contract 262.3.4.4 Flow Associated With UOT Contract 262.4 Implementation 262.4.1 System Overview 272.4.2 Working Flowchart 282.4.3 Experimental Demonstration 292.5 Future Work 352.6 Conclusion 36References 373 Identity and Access Management for Internet of Things Cloud 43Soumya Prakash Otta and Subhrakanta Panda3.1 Introduction 443.2 Internet of Things (IoT) Security 453.2.1 IoT Security Overview 453.2.2 IoT Security Requirements 463.2.3 Securing the IoT Infrastructure 493.3 IoT Cloud 493.3.1 Cloudification of IoT 503.3.2 Commercial IoT Clouds 523.3.3 IAM of IoT Clouds 543.4 IoT Cloud Related Developments 553.5 Proposed Method for IoT Cloud IAM 583.5.1 Distributed Ledger Approach for IoT Security 593.5.2 Blockchain for IoT Security Solution 603.5.3 Proposed Distributed Ledger-Based IoT Cloud IAM 623.6 Conclusion 64References 654 Automated TSR Using DNN Approach for Intelligent Vehicles 67Banhi Sanyal, Piyush R. Biswal, R.K. Mohapatra, Ratnakar Dash and Ankush Agarwalla4.1 Introduction 684.2 Literature Survey 694.3 Neural Network (NN) 704.4 Methodology 714.4.1 System Architecture 714.4.2 Database 714.5 Experiments and Results 714.5.1 FFNN 744.5.2 RNN 764.5.3 CNN 764.5.4 CNN 764.5.5 Pre-Trained Models 794.6 Discussion 794.7 Conclusion 80References 885 Honeypot: A Trap for Attackers 91Anjanna Matta, G. Sucharitha, Bandlamudi Greeshmanjali, Manji Prashanth Kumar and Mathi Naga Sarath Kumar5.1 Introduction 925.1.1 Research Honeypots 935.1.2 Production Honeypots 935.2 Method 945.2.1 Low-Interaction Honeypots 945.2.2 Medium-Interaction Honeypots 955.2.3 High-Interaction Honeypots 955.3 Cryptanalysis 965.3.1 System Architecture 965.3.2 Possible Attacks on Honeypot 975.3.3 Advantages of Honeypots 985.3.4 Disadvantages of Honeypots 995.4 Conclusions 99References 1006 Examining Security Aspect in Industrial-Based Internet of Things 103Rohini Jha6.1 Introduction 1046.2 Process Frame of IoT Before Security 1056.2.1 Cyber Attack 1076.2.2 Security Assessment in IoT 1076.2.2.1 Security in Perception and Network Frame 1086.3 Attacks and Security Assessments in IIoT 1116.3.1 IoT Security Techniques Analysis Based on its Merits 1116.4 Conclusion 116References 1197 A Cooperative Navigation for Multi-Robots in Unknown Environments Using Hybrid Jaya-DE Algorithm 123D. Chandrasekhar Rao7.1 Introduction 1247.2 Related Works 1267.3 Problem Formulation 1307.4 Multi-Robot Navigation Employing Hybrid Jaya-DE Algorithm 1347.4.1 Basic Jaya Algorithm 1347.5 Hybrid Jaya-DE 1367.5.1 Mutation 1367.5.2 Crossover 1367.5.3 Selection 1377.6 Simulation Analysis and Performance Evaluation of Jaya-DE Algorithm 1397.7 Total Navigation Path Deviation (TNPD) 1477.8 Average Unexplored Goal Distance (AUGD) 1487.9 Conclusion 159References 1598 Categorization Model for Parkinson's Disease Occurrence and Severity Prediction 163Prashant Kumar Shrivastava, Ashish Chaturvedi, Megha Kamble and Megha Jain8.1 Introduction 1648.2 Applications 1668.2.1 Machine Learning in PD Diagnosis 1668.2.2 Challenges of PD Detection 1698.2.3 Structuring of UPDRS Score 1708.3 Methodology 1738.3.1 Overview of Data Driven Intelligence 1738.3.2 Comparison Between Deep Learning and Traditional Machine 1758.3.3 Deep Learning for PD Diagnosis 1768.3.4 Convolution Neural Network for PD Diagnosis 1768.4 Proposed Models 1788.4.1 Classification of Patient and Healthy Controls 1788.4.2 Severity Score Classification 1818.5 Results and Discussion 1848.5.1 Performance Measures 1858.5.2 Graphical Results 1878.6 Conclusion 187References 1879 AI-Based Smart Agriculture Monitoring Using Ground-Based and Remotely Sensed Images 191Shounak Chakraborty, Nikumani Choudhury and Indrajit Kalita9.1 Introduction 1929.2 Automatic Land-Cover Classification Techniques Using Remotely Sensed Images 1949.3 Deep Learning-Based Agriculture Monitoring 1969.4 Adaptive Approaches for Multi-Modal Classification 1979.4.1 Unsupervised DA 1999.4.2 Semi-Supervised DA 2009.4.3 Active Learning-Based DA 2019.5 System Model 2029.6 IEEE 802.15.4 2049.6.1 802.15.4 MAC 2049.6.2 DSME MAC 2059.6.3 TSCH MAC 2069.7 Analysis of IEEE 802.15.4 for Smart Agriculture 2079.7.1 Effect of Device Specification 2079.7.1.1 Low-Power 2089.7.2 Effect of MAC Protocols 2089.8 Experimental Results 2099.9 Conclusion & Future Directions 212References 21210 Car Buying Criteria Evaluation Using Machine Learning Approach 223Samdeep Kumar Panda10.1 Introduction 22410.2 Literature Survey 22510.3 Proposed Method 22610.4 Dataset 22710.5 Exploratory Data Analysis 22710.6 Splitting of Data Into Training Data and Test Data 23010.7 Pre-Processing 23210.8 Training of Our Models 23210.8.1 Gaussian Naïve Bayes 23310.8.2 Decision Tree Classifier 23410.8.3 Tuning the Model 23510.8.4 Karnough Nearest Neighbor Classifier 23610.8.5 Tuning the Model 23710.8.6 Neural Network 23810.8.7 Tuning the Model 23910.9 Result Analysis 24010.9.1 Confusion Matrix 24010.9.2 Gaussian Naïve Bayes 24110.9.3 Decision Tree Classifier 24210.9.4 Karnough Nearest Neighbor Classifier 24210.9.5 Neural Network 24210.9.6 Accuracy Scores 24310.10 Conclusion and Future Work 244References 24411 Big Data, Artificial Intelligence and Machine Learning: A Paradigm Shift in Election Campaigns 247Md. Safiullah and Neha Parveen11.1 Introduction 24811.2 Big Data Reveals the Voters' Preference 24911.2.1 Use of Software Applications in Election Campaigns 25111.2.1.1 Team Joe App 25211.2.1.2 Trump 2020 25211.2.1.3 Modi App 25311.3 Deep Fakes and Election Campaigns 25411.3.1 Deep Fake in Delhi Elections 25411.4 Social Media Bots 25611.5 Future of Artificial Intelligence and Machine Learning in Election Campaigns 259References 25912 Impact of Optimized Segment Routing in Software Defined Network 263Amrutanshu Panigrahi, Bibhuprasad Sahu, Satya Sobhan Panigrahi, Ajay Kumar Jena and Md. Sahil Khan12.1 Introduction 26412.2 Software-Defined Network 26612.3 SDN Architecture 26812.4 Segment Routing 27012.5 Segment Routing in SDN 27212.6 Traffic Engineering in SDN 27412.7 Segment Routing Protocol 27512.8 Simulation and Result 27712.9 Conclusion and Future Work 278References 28313 An Investigation into COVID-19 Pandemic in India 289Shubhangi V. Urkude, Vijaykumar R. Urkude, S. Vairachilai and Sandeep Kumar Panda13.1 Introduction 28913.1.1 Symptoms of COVID-19 29213.1.2 Precautionary Measures 29213.1.3 Ways of Spreading the Coronavirus 29413.2 Literature Survey 29513.3 Technologies Used to Fight COVID-19 29613.3.1 Robots 29613.3.2 Drone Technology 29713.3.3 Crowd Surveillance 29713.3.4 Spraying the Disinfectant 29813.3.5 Sanitizing the Contaminated Areas 29813.3.6 Monitoring Temperature Using Thermal Camera 29813.3.7 Delivering the Essential Things 29813.3.8 Public Announcement in the Infected Areas 29813.4 Impact of COVID-19 on Business 29913.4.1 Impact on Financial Markets 29913.4.2 Impact on Supply Side 29913.4.3 Impact on Demand Side 30013.4.4 Impact on International Trade 30013.5 Impact of COVID-19 on Indian Economy 30013.6 Data and Result Analysis 30013.7 Conclusion and Future Scope 304References 30414 Skin Cancer Classification: Analysis of Different CNN Models via Classification Accuracy 307Poonam Biswal, Monali Saha, Nishtha Jaiswal and Minakhi Rout14.1 Introduction 30714.2 Literature Survey 30814.3 Methodology 31014.3.1 Dataset Preparation 31014.3.2 Dataset Loading and Data Pre-Processing 31114.3.3 Creating Models 31214.4 Models Used 31214.5 Simulation Results 31314.5.1 Changing Size of MaxPool2D(n,n) 31414.5.2 Changing Size of AveragePool2D(n,n) 31414.5.3 Changing Number of con2d(32n-64n) Layers 31514.5.4 Changing Number of con2d-32*n Layers 31514.5.5 ROC Curves and MSE Curves 31814.6 Conclusion 321References 32115 Route Mapping of Multiple Humanoid Robots Using Firefly-Based Artificial Potential Field Algorithm in a Cluttered Terrain 323Abhishek Kumar Kashyap, Anish Pandey and Dayal R. Parhi15.1 Introduction 32415.2 Design of Proposed Algorithm 32815.2.1 Mechanism of Artificial Potential Field 32815.2.1.1 Potential Field Generated by Attractive Force of Goal 32915.2.1.2 Potential Field Generated by Repulsive Force of Obstacle 33115.2.2 Mechanism of Firefly Algorithm 33215.2.2.1 Architecture of Optimization Problem Based on Firefly Algorithm 33515.2.3 Dining Philosopher Controller 33715.3 Hybridization Process of Proposed Algorithm 33915.4 Execution of Proposed Algorithm in Multiple Humanoid Robots 33915.5 Comparison 34415.6 Conclusion 346References 34616 Innovative Practices in Education Systems Using Artificial Intelligence for Advanced Society 351Vinutha D.C., Kavyashree S., Vijay C.P. and G.T. Raju16.1 Introduction 35216.2 Literature Survey 35316.2.1 AI in Auto-Grading 35416.2.2 AI in Smart Content 35616.2.3 AI in Auto Analysis on Student's Grade 35616.2.4 AI Extends Free Intelligent Tutoring 35716.2.5 AI in Predicting Student Admission and Drop-Out Rate 35916.3 Proposed System 35916.3.1 Data Collection Module 36016.3.2 Data Pre-Processing Module 36416.3.3 Clustering Module 36416.3.4 Partner Selection Module 36616.4 Results 36816.5 Future Enhancements 37016.6 Conclusion 370References 37117 PSO-Based Hybrid Weighted k-Nearest Neighbor Algorithm for Workload Prediction in Cloud Infrastructures 373N. Yamuna, J. Antony Vijay and B. Gomathi17.1 Introduction 37417.2 Literature Survey 37517.2.1 Machine Learning 37817.3 Proposed System 37917.3.1 Load Aware Cloud Computing Model 37917.3.2 Wavelet Neural Network 37917.3.3 Evaluation Using LOOCV Model 38017.3.4 k-Nearest Neighbor (k-NN) Algorithm 38117.3.5 Particle Swarm Optimization (PSO) Algorithm 38217.3.6 HWkNN Optimization Algorithm Based on PSO 38317.3.7 PSO-Based HWkNN (PHWkNN) Load Prediction Algorithm 38417.4 Experimental Results 38517.5 Conclusion 390References 39118 An Extensive Survey on the Prediction of Bankruptcy 395Sasmita Manjari Nayak and Minakhi Rout18.1 Introduction 39518.2 Literature Survey 39718.2.1 Data Pre-Processing 39718.2.1.1 Balancing of Imbalanced Dataset 39718.2.1.2 Outlier Data Handling 41018.2.2 Classifiers 41818.2.3 Ensemble Models 42218.3 System Architecture and Simulation Results 43818.4 Conclusion 438References 44319 Future of Indian Agriculture Using AI and Machine Learning Tools and Techniques 447Manoj Kumar, Pratibha Maurya and Rinki Verma19.1 Introduction 44819.2 Overview of AI and Machine Learning 45019.3 Review of Literature 45219.4 Application of AI & Machine Learning in Agriculture 45619.5 Current Scenario and Emerging Trends of AI and ML in Indian Agriculture Sector 46019.6 Opportunities for Agricultural Operations in India 46519.7 Conclusion 466References 467Index 473

O autorze

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, Machine Learning.Subhrakanta Panda, PhD is an assistant professor in the department of Computer Science and Information Systems, BITS-PILANI, Hyderabad Campus, Jawahar Nagar, Shameerpet Mandal, Hyderabad, INDIA. His research interests include Social Network Analysis, Cloud Computing, Security Testing, 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.