The new advanced society : artificial intelligence and industrial Internet of Things paradigm / / edited by Ke Zhang, Yang Hong, and Amir AghaKouchak

• Pubblicazione: Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2022]; ©2022
• Descrizione fisica: 1 online resource (512 pages)
• Disciplina: 620.0028563
• Soggetto topico: Society 5.0; Artificial intelligence; Internet of things
• Persona (resp. second.): HongYang <1973->; ZhangKe (Professor); AghaKouchakAmir
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- 1 Post Pandemic: The New Advanced Society -- 1.1 Introduction -- 1.1.1 Themes -- 1.1.1.1 Theme: Areas of Management -- 1.1.1.2 Theme: Financial Institutions Cyber Crime -- 1.1.1.3 Theme: Economic Notion -- 1.1.1.4 Theme: Human Depression -- 1.1.1.5 Theme: Migrant Labor -- 1.1.1.6 Theme: Digital Transformation (DT) of Educational Institutions -- 1.1.1.7 School and College Closures -- 1.2 Conclusions -- References -- 2 Distributed Ledger Technology in the Construction Industry Using Corda -- 2.1 Introduction -- 2.2 Prerequisites -- 2.2.1 DLT vs Blockchain -- 2.3 Key Points of Corda -- 2.3.1 Some Salient Features of Corda -- 2.3.2 States -- 2.3.3 Contract -- 2.3.3.1 Create and Assign Task (CAT) Contract -- 2.3.3.2 Request for Cash (RT) Contract -- 2.3.3.3 Transfer of Cash (TT) Contract -- 2.3.3.4 Updation of the Task (UOT) Contract -- 2.3.4 Flows -- 2.3.4.1 Flow Associated With CAT Contract -- 2.3.4.2 Flow Associated With RT Contract -- 2.3.4.3 Flow Associated With TT Contract -- 2.3.4.4 Flow Associated With UOT Contract -- 2.4 Implementation -- 2.4.1 System Overview -- 2.4.2 Working Flowchart -- 2.4.3 Experimental Demonstration -- 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 -- 3.2.1 IoT Security Overview -- 3.2.2 IoT Security Requirements -- 3.2.3 Securing the IoT Infrastructure -- 3.3 IoT Cloud -- 3.3.1 Cloudification of IoT -- 3.3.2 Commercial IoT Clouds -- 3.3.3 IAM of IoT Clouds -- 3.4 IoT Cloud Related Developments -- 3.5 Proposed Method for IoT Cloud IAM -- 3.5.1 Distributed Ledger Approach for IoT Security -- 3.5.2 Blockchain for IoT Security Solution. ; 3.5.3 Proposed Distributed Ledger-Based 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 -- 4.4.1 System Architecture -- 4.4.2 Database -- 4.5 Experiments and Results -- 4.5.1 FFNN -- 4.5.2 RNN -- 4.5.3 CNN -- 4.5.4 CNN -- 4.6 Discussion -- 4.7 Conclusion -- References -- 5 Honeypot: A Trap for Attackers -- 5.1 Introduction -- 5.1.1 Research Honeypots -- 5.1.2 Production Honeypots -- 5.2 Method -- 5.2.1 Low-Interaction Honeypots -- 5.2.2 Medium-Interaction Honeypots -- 5.2.3 High-Interaction Honeypots -- 5.3 Cryptanalysis -- 5.3.1 System Architecture -- 5.3.2 Possible Attacks on Honeypot -- 5.3.3 Advantages of Honeypots -- 5.3.4 Disadvantages of Honeypots -- 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 -- 6.2.1 Cyber Attack -- 6.2.2 Security Assessment in IoT -- 6.2.2.1 Security in Perception and Network Frame -- 6.3 Attacks and Security Assessments in IIoT -- 6.3.1 IoT Security Techniques Analysis Based on its Merits -- 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 -- 7.4.1 Basic Jaya Algorithm -- 7.5 Hybrid Jaya-DE -- 7.5.1 Mutation -- 7.5.2 Crossover -- 7.5.3 Selection -- 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 -- 8.2.1 Machine Learning in PD Diagnosis -- 8.2.2 Challenges of PD Detection -- 8.2.3 Structuring of UPDRS Score -- 8.3 Methodology -- 8.3.1 Overview of Data Driven Intelligence -- 8.3.2 Comparison Between Deep Learning and Traditional Machine -- 8.3.3 Deep Learning for PD Diagnosis -- 8.3.4 Convolution Neural Network for PD Diagnosis -- 8.4 Proposed Models -- 8.4.1 Classification of Patient and Healthy Controls -- 8.4.2 Severity Score Classification -- 8.5 Results and Discussion -- 8.5.1 Performance Measures -- 8.5.2 Graphical Results -- 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 -- 9.4.1 Unsupervised DA -- 9.4.2 Semi-Supervised DA -- 9.4.3 Active Learning-Based DA -- 9.5 System Model -- 9.6 IEEE 802.15.4 -- 9.6.1 802.15.4 MAC -- 9.6.2 DSME MAC -- 9.6.3 TSCH MAC -- 9.7 Analysis of IEEE 802.15.4 for Smart Agriculture -- 9.7.1 Effect of Device Specification -- 9.7.1.1 Low-Power -- 9.7.2 Effect of MAC Protocols -- 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 -- 10.8.1 Gaussian Naïve Bayes -- 10.8.2 Decision Tree Classifier -- 10.8.3 Tuning the Model -- 10.8.4 Karnough Nearest Neighbor Classifier -- 10.8.5 Tuning the Model -- 10.8.6 Neural Network -- 10.8.7 Tuning the Model -- 10.9 Result Analysis. ; 10.9.1 Confusion Matrix -- 10.9.2 Gaussian Naïve Bayes -- 10.9.3 Decision Tree Classifier -- 10.9.4 Karnough Nearest Neighbor Classifier -- 10.9.5 Neural Network -- 10.9.6 Accuracy Scores -- 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 -- 11.2.1 Use of Software Applications in Election Campaigns -- 11.2.1.1 Team Joe App -- 11.2.1.2 Trump 2020 -- 11.2.1.3 Modi App -- 11.3 Deep Fakes and Election Campaigns -- 11.3.1 Deep Fake in Delhi Elections -- 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 -- 13.1.1 Symptoms of COVID-19 -- 13.1.2 Precautionary Measures -- 13.1.3 Ways of Spreading the Coronavirus -- 13.2 Literature Survey -- 13.3 Technologies Used to Fight COVID-19 -- 13.3.1 Robots -- 13.3.2 Drone Technology -- 13.3.3 Crowd Surveillance -- 13.3.4 Spraying the Disinfectant -- 13.3.5 Sanitizing the Contaminated Areas -- 13.3.6 Monitoring Temperature Using Thermal Camera -- 13.3.7 Delivering Essential Things -- 13.3.8 Public Announcement in the Infected Areas -- 13.4 Impact of COVID-19 on Business -- 13.4.1 Impact on Financial Markets -- 13.4.2 Impact on Supply Side -- 13.4.3 Impact on Demand Side -- 13.4.4 Impact on International Trade -- 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 -- 14.3.1 Dataset Preparation -- 14.3.2 Dataset Loading and Data Pre-Processing -- 14.3.3 Creating Models -- 14.4 Models Used -- 14.5 Simulation Results -- 14.5.1 Changing Size of MaxPool2D(n,n) -- 14.5.2 Changing Size of AveragePool2D(n,n) -- 14.5.3 Changing Number of con2d(32n-64n) Layers -- 14.5.4 Changing Number of con2d-32*n Layers -- 14.5.5 ROC Curves and MSE Curves -- 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 -- 15.2.1 Mechanism of Artificial Potential Field -- 15.2.1.1 Potential Field Generated by Attractive Force of Goal -- 15.2.1.2 Potential Field Generated by Repulsive Force of Obstacle -- 15.2.2 Mechanism of Firefly Algorithm -- 15.2.2.1 Architecture of Optimization Problem Based on Firefly Algorithm -- 15.2.3 Dining Philosopher Controller -- 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 -- 16.2.1 AI in Auto-Grading -- 16.2.2 AI in Smart Content -- 16.2.3 AI in Auto Analysis on Student's Grade -- 16.2.4 AI Extends Free Intelligent Tutoring -- 16.2.5 AI in Predicting Student Admission and Drop-Out Rate -- 16.3 Proposed System -- 16.3.1 Data Collection Module -- 16.3.2 Data Pre-Processing Module -- 16.3.3 Clustering Module -- 16.3.4 Partner Selection Module. ; 16.4 Results.
• Sommario/riassunto: 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.
• Titolo autorizzato: The new advanced society
• ISBN: 1-119-88438-1; 1-119-88439-X; 1-119-88437-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910830688103321
• Lo trovi qui: Univ. Federico II
• Serie: Wiley-Scrivener