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Extremes in a Changing Climate : Detection, Analysis and Uncertainty / / edited by Amir AghaKouchak, David Easterling, Kuolin Hsu, Siegfried Schubert, Soroosh Sorooshian
| Extremes in a Changing Climate : Detection, Analysis and Uncertainty / / edited by Amir AghaKouchak, David Easterling, Kuolin Hsu, Siegfried Schubert, Soroosh Sorooshian |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Dordrecht : , : Springer Netherlands : , : Imprint : Springer, , 2013 |
| Descrizione fisica | 1 online resource (429 p.) |
| Disciplina | 551.6 |
| Collana | Water Science and Technology Library |
| Soggetto topico |
Atmospheric sciences
Climatology Statistics Civil engineering Atmospheric Sciences Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Civil Engineering |
| ISBN |
1-283-74099-0
94-007-4479-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Statistical Indices for Diagnosing and Detecting Changes in Extremes -- 2. Statistical Methods for Nonstationary Extremes -- 3. Bayesian Methods for Nonstationary Extreme Value Analysis -- 4. Return Periods and Return Levels Under Climate Change -- 5. Multivariate Extreme Value Methods -- 6. Methods of Extreme Value Index and Tail Dependence Estimation -- 7. Stochastic Models of Climate Extremes:Theory and Observations -- 8. Methods of Projecting Future Changes in Extremes -- 9. Climate Variability and Weather Extremes: Model-Simulated and Historical Data -- 10. Uncertainties in Observed Changes in Climate Extremes -- 11. Uncertainties in Projections of Future Changes in Extremes -- 12. Global Data Sets for Analysis of Climate Extremes -- 13. Nonstationarity in Extremes and Engineering Design -- Index. |
| Record Nr. | UNINA-9910437792203321 |
| Dordrecht : , : Springer Netherlands : , : Imprint : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The new advanced society : artificial intelligence and industrial Internet of Things paradigm / / edited by Ke Zhang, Yang Hong, and Amir AghaKouchak
| The new advanced society : artificial intelligence and industrial Internet of Things paradigm / / edited by Ke Zhang, Yang Hong, and Amir AghaKouchak |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2022] |
| Descrizione fisica | 1 online resource (512 pages) |
| Disciplina | 620.0028563 |
| Collana | Wiley-Scrivener Ser. |
| Soggetto topico |
Artificial intelligence - Industrial applications
Internet of things |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-88439-X
1-119-88437-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNINA-9910554858203321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The new advanced society : artificial intelligence and industrial Internet of Things paradigm / / edited by Ke Zhang, Yang Hong, and Amir AghaKouchak
| The new advanced society : artificial intelligence and industrial Internet of Things paradigm / / edited by Ke Zhang, Yang Hong, and Amir AghaKouchak |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2022] |
| Descrizione fisica | 1 online resource (512 pages) |
| Disciplina | 620.0028563 |
| Collana | Wiley-Scrivener Ser. |
| Soggetto topico |
Artificial intelligence - Industrial applications
Internet of things |
| ISBN |
1-119-88438-1
1-119-88439-X 1-119-88437-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNINA-9910643177803321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The new advanced society : artificial intelligence and industrial Internet of Things paradigm / / edited by Ke Zhang, Yang Hong, and Amir AghaKouchak
| The new advanced society : artificial intelligence and industrial Internet of Things paradigm / / edited by Ke Zhang, Yang Hong, and Amir AghaKouchak |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2022] |
| Descrizione fisica | 1 online resource (512 pages) |
| Disciplina | 620.0028563 |
| Collana | Wiley-Scrivener |
| Soggetto topico |
Society 5.0
Artificial intelligence Internet of things |
| ISBN |
1-119-88438-1
1-119-88439-X 1-119-88437-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNINA-9910830688103321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Remote sensing of water-related hazards / / edited by Ke Zhang, Yang Hong, Amir AghaKouchak
| Remote sensing of water-related hazards / / edited by Ke Zhang, Yang Hong, Amir AghaKouchak |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley-American Geophysical Union, , [2022] |
| Descrizione fisica | 1 online resource (269 pages) |
| Disciplina | 551.48 |
| Collana | Geophysical Monograph Ser. |
| Soggetto topico | Hydrological forecasting |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-15914-8
1-119-15913-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910554859303321 |
| Hoboken, New Jersey : , : Wiley-American Geophysical Union, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Remote sensing of water-related hazards / / Ke Zhang, Yang Hong, Amir AghaKouchak, editors
| Remote sensing of water-related hazards / / Ke Zhang, Yang Hong, Amir AghaKouchak, editors |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley and Sons, Inc. |
| Descrizione fisica | 1 online resource (xii, 254 pages) : illustrations (some colour), maps (chiefly colour) |
| Disciplina | 551.48 |
| Collana |
Geophysical monograph
American Geophysical Union |
| Soggetto topico |
Hydrological forecasting
Remote sensing Remote Sensing Technology |
| ISBN |
1-119-15914-8
1-119-15913-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front Matter -- Interdisciplinary Perspectives on Remote Sensing for Monitoring and Predicting Water-Related Hazards / Ke Zhang, Yang Hong, Amir AghaKouchak -- Remote Sensing of Precipitation and Storms. Progress in Satellite Precipitation Products over the Past Two Decades / Guoqiang Tang, Tsechun Wang, Meihong Ma, Wentao Xiong, Feng Lyu, Ziqiang Ma -- Observations of Tornadoes and Their Parent Supercells Using Ground-Based, Mobile Doppler Radars / Howard B Bluestein -- Remote Sensing of Floods and Associated Hazards. Remote Sensing Mapping and Modeling for Flood Hazards in Data-Scarce Areas / Ke Zhang, Zaw Myo Khaing, Zhijia Li -- Multisensor Remote Sensing and the Multidimensional Modeling of Extreme Flood Events / Mengye Chen, Zhi Li, Shang Gao -- A Multisource, Data-Driven, Web-GIS-Based Hydrological Modeling Framework for Flood Forecasting and Prevention / Zhanming Wan, Xianwu Xue, Ke Zhang, Yang Hong, Jonathan J Gourley, Humberto Vergara -- An Ensemble-Based, Remote-Sensing-Driven, Flood-Landslide Early Warning System / Ke Zhang, Guoding Chen, Yi Xia, Sheng Wang -- Detection of Hazard-Damaged Bridges Using Multitemporal High-Resolution SAR Imagery / Wen Liu, Kazuki Inoue, Fumio Yamazaki -- Remote Sensing of Droughts and Associated Hazards. Drought Monitoring Based on Remote Sensing / Xin Li, Ran Tao, Ke Zhang -- Remote Sensing of Vegetation Responses to Drought Disturbances Using Spaceborne Optical and Near-Infrared Sensors / Ke Zhang, Linxin Liu, Yunping Li, Ran Tao -- Recent Advances in Physical Water Scarcity Assessment Using GRACE Satellite Data / Emad Hasan, Aondover Tarhule -- Study of Water Cycle Variation in the Yellow River Basin Based on Satellite Remote Sensing and Numerical Modeling / Meixia Lv, Zhuguo Ma -- Assessing the Impact of Climate Change-Induced Droughts on Soil Salinity Development in Agricultural Areas Using Ground and Satellite Sensors / Dennis L Corwin, Elia Scudiero -- INDEX. |
| Record Nr. | UNINA-9910831197003321 |
| Hoboken, New Jersey : , : John Wiley and Sons, Inc. | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||