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Advanced Machine Learning and Deep Learning Approaches for Remote Sensing / / edited by Gwanggil Jeon
| Advanced Machine Learning and Deep Learning Approaches for Remote Sensing / / edited by Gwanggil Jeon |
| Pubbl/distr/stampa | [Place of publication not identified] : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2023 |
| Descrizione fisica | 1 online resource (362 pages) |
| Disciplina | 621.3678 |
| Soggetto topico |
Deep learning (Machine learning)
Machine learning Remote sensing |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910734348503321 |
| [Place of publication not identified] : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial Intelligence-Based Learning Approaches for Remote Sensing
| Artificial Intelligence-Based Learning Approaches for Remote Sensing |
| Autore | Jeon Gwanggil |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (382 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology Environmental science, engineering & technology |
| Soggetto non controllato |
pine wilt disease dataset
GIS application visualization test-time augmentation object detection hard negative mining video synthetic aperture radar (SAR) moving target shadow detection deep learning false alarms missed detections synthetic aperture radar (SAR) on-board ship detection YOLOv5 lightweight detector remote sensing image spectral domain translation generative adversarial network paired translation synthetic aperture radar ship instance segmentation global context modeling boundary-aware box prediction land-use and land-cover built-up expansion probability modelling landscape fragmentation machine learning support vector machine frequency ratio fuzzy logic artificial intelligence remote sensing interferometric phase filtering sparse regularization (SR) deep learning (DL) neural convolutional network (CNN) semantic segmentation open data building extraction unet deeplab classifying-inversion method AIS atmospheric duct ship detection and classification rotated bounding box attention feature alignment weather nowcasting ResNeXt radar data spectral-spatial interaction network spectral-spatial attention pansharpening UAV visual navigation Siamese network multi-order feature MIoU imbalanced data classification data over-sampling graph convolutional network semi-supervised learning troposcatter tropospheric turbulence intercity co-channel interference concrete bridge visual inspection defect deep convolutional neural network transfer learning interpretation techniques weakly supervised semantic segmentation |
| ISBN | 3-0365-6084-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910639984703321 |
Jeon Gwanggil
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Cybersecurity Vigilance and Security Engineering of Internet of Everything
| Cybersecurity Vigilance and Security Engineering of Internet of Everything |
| Autore | Naseer Qureshi Kashif |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (229 pages) |
| Altri autori (Persone) |
NeweThomas
JeonGwanggil ChehriAbdellah |
| Collana | Internet of Things Series |
| ISBN | 3-031-45162-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Editor's Note -- Preface -- Part A: Security Threats and Vulnerabilities -- Part B: Security Vigilance and Security Engineering for IoE Networks -- Acknowledgments -- Cyber Skills -- Contents -- About the Authors -- Part I: Security Threats and Vulnerabilities -- Chapter 1: Internet of Everything: Evolution and Fundamental Concepts -- 1.1 Introduction -- 1.2 Enabling Communication Technologies for IoE Networks -- 1.3 IoE Applications -- 1.4 IoE Execution and Implementation Challenges -- 1.5 Acceptance and Sustainability -- 1.6 Proposed Five-Layer Conceptual Model -- 1.6.1 Everything Layer -- 1.6.2 Application Layer -- 1.6.3 Virtual Layer -- 1.6.4 Data Handling Layer -- 1.6.5 Communication Layer -- 1.7 Discussion and Findings -- 1.8 Conclusion -- References -- Chapter 2: Cybersecurity Threats and Attacks in IoE Networks -- 2.1 Introduction -- 2.2 Internet of Everything -- 2.2.1 IoE Components -- 2.2.2 IoE Key Enabling Technologies -- 2.3 IoE Threat Landscape -- 2.3.1 IoE Threat Modeling -- 2.4 IoE Cybersecurity Architecture -- 2.4.1 Cybersecurity Vulnerabilities, Threats, and Attacks in Perception Layer -- 2.4.2 Identity-Based Trust and Privacy Provocation -- 2.4.3 Spoofing and Sybil Attacks -- 2.4.4 Access-Level Attacks -- 2.4.5 Transmitting Data Attacks -- 2.4.6 Attacks Based on Device Property -- 2.4.7 Attacks Based on Adversary Location -- 2.4.8 Attacks Based on Attack Strategy -- 2.4.9 Insecure Initialization and Configuration -- 2.5 Cybersecurity Considerations and Solutions in Perception Layer -- 2.5.1 Cybersecurity Vulnerabilities, Threats, and Attacks in Network Layer -- 2.5.1.1 Security Solutions for Wired Networks -- 2.5.1.2 Security Solutions for Mobile Networks -- 2.5.1.3 Security Solutions for Wireless Networks -- 2.5.2 Cybersecurity Vulnerabilities, Threats, and Attacks in Support/Middleware Layer.
2.5.3 Cybersecurity Considerations and Solutions in Support/Middleware Layer -- 2.5.4 Cybersecurity Vulnerabilities, Threats, and Attacks in Application Layer -- 2.5.5 Cybersecurity Considerations and Solutions in Application Layer -- 2.5.6 IoE Business Layer Security -- 2.5.7 IoE End-to-End Security -- 2.6 Conclusion -- References -- Chapter 3: Attack Detection Mechanisms for Internet of Everything (IoE) Networks -- 3.1 Introduction -- 3.2 Definition, Elements, and Applications of IoE Networks -- 3.2.1 IoE Network Components -- 3.2.2 Applications of IoE Networks -- 3.3 Challenges and Issues -- 3.4 IoE Security Requirements -- 3.5 Security Attacks in IoE -- 3.6 IoE: Security Vulnerabilities -- 3.7 Security Risks in IoE -- 3.8 Privacy Challenges in IoE -- 3.9 Attack Detection and Countermeasures -- 3.10 Conclusion -- References -- Chapter 4: Cyber-Resilience, Principles, and Practices -- 4.1 Introduction -- 4.2 Building Cyber-Resilience in Industry Using CYBER INTEL -- 4.2.1 Traction Plc. (Selected Use-Case) -- 4.2.2 Cyber-Threat Landscape -- 4.2.3 Data Security and Risk Management -- 4.2.4 Cyber and Data Protection Laws & -- Regulations -- 4.2.5 Governance, Risk and Control - Data Protection -- 4.2.6 NIST Risk Management Framework -- 4.2.7 Incident Response Planning -- 4.2.7.1 Preparation -- 4.2.7.2 Detection and Analysis -- 4.2.7.3 Containment, Eradication and Recovery -- 4.2.7.4 Post Incident Activity -- 4.3 Cybersecurity Compliance -- 4.4 Governance, Risk & -- Compliance - Audit Assurance -- 4.5 Cyber-Resilience -- 4.6 Enhanced Cybersecurity Posture Achieved Using the CYBER INTEL Framework -- 4.7 Conclusion and Future Directions -- References -- Chapter 5: Future Cybersecurity Challenges for IoE Networks -- 5.1 Overview -- 5.2 Introduction -- 5.3 IoE Working Architecture -- 5.3.1 Sensors and Devices -- 5.3.2 Connectivity. 5.3.3 Cloud and Edge Computing -- 5.3.4 Big Data Analytics -- 5.3.5 Artificial Intelligence and Machine Learning -- 5.3.6 Security -- 5.4 Applications of IoE -- 5.5 Security Architecture of IoT as Compared to IoE -- 5.6 Generic Challenges of IoE Network -- 5.7 Cybersecurity Challenges in IoE -- 5.7.1 IoE Network Vulnerabilities -- 5.8 Future Threads to IoE Networks -- 5.9 Conclusion -- References -- Part II: Security Vigilance and Security Engineering for IoE Networks -- Chapter 6: Networking and Security Architectures for IoE Networks -- 6.1 Overview -- 6.2 Internet of Everything -- 6.3 Pillars of IoE -- 6.4 Proposed Security Architecture for IoE Networks -- 6.4.1 Advanced Wire- and Wireless-Based Technologies for IoE Security Architecture -- 6.4.2 IEEE 802.15.4 Medium Access Control (MAC) Superframe Structure for Network Communication -- 6.4.3 IEEE 802.15.6 Medium Access Control (MAC) Superframe Structure for Network Communication -- 6.4.3.1 MAC Superframe Structure of IEEE 802.15.6 -- 6.5 Data Collection, Recognition, and Processing in Multiple Environment of IoE -- 6.6 Diverse Technologies in IoEs -- 6.6.1 Internet of Ad Hoc Network (IoAV) -- 6.6.2 Internet of Vehicular Ad Hoc Network (IoVAN) -- 6.6.3 Internet of Mobile Ad Hoc Networks (IoMANs) -- 6.6.4 Internet of Ambulance (IoA) -- 6.6.5 Internet of Air Traffic (IoAT) -- 6.6.6 Internet of Smart Building (IoSM) -- 6.6.7 Internet of Underwater Things (IoUTs) -- 6.7 Security in IoE Networks -- 6.7.1 Intrusion Detection Systems -- 6.7.2 Authentication -- 6.8 Proposed DIP Architecture to Secure IoE Networks -- 6.9 Conclusion -- References -- Chapter 7: Machine Learning-Based Detection and Prevention Systems for IoE -- 7.1 Overview -- 7.2 Evolution of IoT to IoE -- 7.3 Importance of Machine Learning in IoE -- 7.4 Intrusion Detection and Prevention Systems. 7.5 Existing IDPS Solutions Designed for IoE Networks -- 7.6 Attacks in IoE Networks and Its Pretension by Using ML-Based IDPS Systems -- 7.7 Pros and Cons of ML in Detection and Prevention Systems -- 7.8 Conclusion -- References -- Chapter 8: Role of Blockchain for IoE Infrastructures and Applications -- 8.1 Internet of Everything -- 8.2 Introduction to Blockchain -- 8.3 Types of Blockchain -- 8.4 Understanding How Blockchain Works -- 8.5 Role of Blockchain in the Internet of Everything -- 8.6 A Framework for Blockchain in IoE -- 8.7 Conclusion -- References -- Chapter 9: Cybersecurity as a Service -- 9.1 Introduction -- 9.2 CSaaS Functions -- 9.2.1 Security Personnel as a Service -- 9.2.2 Cyber-awareness Training -- 9.2.3 Vulnerability Assessment -- 9.2.4 Periodic Penetration Testing -- 9.2.5 E-mail Security -- 9.2.6 Identity and Access Management -- 9.2.7 Cyber Insurance -- 9.2.8 Incident Response -- 9.2.9 Business Continuity/Disaster Recovery Planning -- 9.2.10 Security Information and Event Management -- 9.2.11 System Patching and Updates -- 9.2.12 Security Standards Compliance -- 9.3 Future of CSaaS -- 9.4 Findings and Suggestions -- 9.5 Conclusion -- References -- Chapter 10: Big Data Analytics for Cybersecurity in IoE Networks -- 10.1 Introduction -- 10.2 Big Data Analytics -- 10.3 Securing IoE with Big Data Analytics -- 10.4 Related Work -- 10.4.1 Big Data Commercial Solutions for Cybersecurity -- 10.5 Processing Methodology Using Big Data -- 10.6 Cybersecurity Architecture Based on Big Data -- 10.7 Data Analytics Architecture for Cybersecurity Applications -- 10.7.1 Indicators Module -- 10.8 Discussion -- 10.9 Conclusion -- References -- Chapter 11: Cybersecurity Standards and Policies for CPS in IoE -- 11.1 Overview -- 11.2 Introduction -- 11.3 Information Security Standards Requirements, Policy, and Elements. 11.3.1 Information Security Policy Elements -- 11.4 Existing IoE Security Standards -- 11.4.1 ISO 27KX - ISO -- 11.4.2 ISO 27001 -- 11.4.3 ISO 27002 -- 11.4.4 ISO 38500 -- 11.4.5 HIPAA -- 11.4.6 GDPR -- 11.4.7 PCI-DSS -- 11.4.8 NIST-800-53 -- 11.4.9 COBIT -- 11.4.10 PRINCE2 -- 11.4.11 NIST CSF -- 11.5 Technical Comparison of the Standards -- 11.6 A Security Framework for IoE Networks -- 11.7 Conclusion -- References -- Chapter 12: Future Privacy and Trust Challenges for IoE Networks -- 12.1 Overview -- 12.2 Internet of Everything -- 12.3 Concepts, Basic Cardinals, Significance -- 12.4 Challenges and Vulnerabilities -- 12.4.1 People Security -- 12.4.2 Data Security -- 12.4.3 Security of Things -- 12.5 Data Trust and Mistrust in IoE -- 12.5.1 Trustful -- 12.5.2 Untruthful -- 12.5.3 Trust Is Critical in IoE -- 12.5.4 Privacy and Trust Issues -- 12.6 Security and Privacy Issues -- 12.7 Open Issues in Research, Future Trends, and Way Forward -- 12.7.1 Challenges -- 12.7.2 Open Research Issues -- 12.8 Conclusions -- References -- Index. |
| Record Nr. | UNINA-9910767586503321 |
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Naseer Qureshi Kashif
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| Cham : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data analytics for internet of things infrastructure / / Rohit Sharma, Gwanggil Jeon, Yan Zhang, editors
| Data analytics for internet of things infrastructure / / Rohit Sharma, Gwanggil Jeon, Yan Zhang, editors |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2023 |
| Descrizione fisica | 1 online resource (xv, 326 pages) : illustrations |
| Disciplina | 004.678 |
| Altri autori (Persone) |
SharmaRohit (Rohit Y.)
JeonGwanggil ZhangYan |
| Collana | Internet of Things Series |
| ISBN | 3-031-33808-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Key Features -- Contents -- About the Editors -- Big Data in Cloud Today: A Comprehensive Survey -- 1 Introduction -- 2 Characteristics of Big Data -- 3 Classification of Big Data -- 4 Importance of Big Data -- 5 Examples for Big Data -- 6 Tools and Techniques -- 7 Big Data Analytics and Its Benefits -- 8 Cloud Computing -- 9 Working of Cloud Computing -- 10 Conclusion -- References -- Cloud of Things Platform for a Water Meter Network -- 1 Introduction -- 2 Related Work and Problem Motivation -- 2.1 Related Work -- 2.2 IoT Paradigm -- 2.3 Problem Motivation -- 3 Proposed IoT Architecture -- 3.1 System Model -- 3.2 Methodology -- 3.3 Development of Active Switch -- 3.4 Development of an Active Sensor -- 3.5 Integration -- 4 Simulation and Result -- 4.1 Setup Process -- 4.2 Analysis -- 5 Conclusion -- References -- Online Newspaper Development within the Internet of Things Environment: The Role of Computer-Mediated Communication -- 1 Computer-Mediated Communication -- 2 Scholarly Information Related to CMC -- 3 CMC and Development of Online Newspapers -- 3.1 Advantages of Online Newspapers -- 3.2 Delivery of Online News and Information -- 3.3 CMC Journalism Is a Better Option -- 4 Interactivity with News Through CMC and Issues Within IoT Environment -- 4.1 CMC as a Tool for Organizations and Governments to Spread Information and News -- 4.2 The Use of Social Media and Its Social Outcomes Concerning CMC -- 4.3 Emerging Issues Related to the Use of CMC -- 4.4 Reconfiguration of Territorially and Interest-Based Associations -- 4.5 Introduction of New Artifacts and Their Social Outcomes -- 4.6 The Mutual Shaping of Consumers and Technologies -- 5 Conclusion -- References -- FATS (Fuzzy Authentication to Provide Trust-Based Security) in VANET to Mitigate Black Hole Attack -- 1 Introduction -- 2 VANET Architecture.
3 Attacks and Threats Generated in VANET -- 3.1 Selfish Node Attack -- 3.2 Jellyfish Attack -- 3.3 Data Flooding Attack -- 3.4 Black Hole Attack -- 4 Prominent Issues Caused by a Black Hole Node -- 5 Fuzzy Logic and Its Role in the Proposed Approach -- 5.1 Introduction About Fuzzy Logic -- 5.2 Mamdani Fuzzy Inference System -- 5.2.1 Max-Min Inference Method -- 5.2.2 Max-product inference method -- 6 Fuzzy Logic Trust-Based Authentication Schemes in VANET -- 7 Proposed Algorithm FATS (Fuzzy Authentication to Provide Trust-Based Security) for Black Hole Attack Detection -- 7.1 Pseudocode for Providing a Communication Link to the New Node -- 7.2 Formation of Fuzzy Rules Using Mamdani Inference System in MATLAB -- 8 Implementation of FATS -- 9 Conclusion -- References -- AI-Based Chatbot Agents as Drivers of Purchase Intentions: An Interdisciplinary Study -- 1 Introduction -- 2 Conceptual Background and Development of Hypothesis -- 3 Informational Support of Chatbots and Predicting Purchase Intentions -- 4 Trust, Emotional Credibility, and Predicting Purchase Intentions -- 5 Research Gap -- 6 Objectives -- 7 Methods -- 7.1 Sampling Framework and Questionnaire Design -- 7.2 Measures -- 8 Research Tools and Techniques -- 9 Measurement Model -- 10 Analysis of Structural Model -- 11 Conclusion and Future Research Works -- 12 Limitations -- References -- An Intelligent Model for Identifying Fluctuations in the Stock Market and Predicting Investment Policies with Guaranteed Returns -- 1 Introduction -- 2 Literature Survey -- 3 Impact of Big Data in Stock Market -- 3.1 Big Data -- 3.1.1 Big Data Architecture -- 3.2 Structure of Big Data -- 3.2.1 Structured Data -- 3.2.2 Unstructured Data -- 3.2.3 Semistructured Data -- 3.3 Big Data in the Stock Market -- 3.4 Nature of Dynamic Data in the Stock Market -- 4 Proposed Model (Fig. 2) -- 4.1 Objectives. 4.2 Mathematical Implementation -- 4.2.1 Statistical Analysis -- 4.2.2 Fuzzy Inferences -- 5 Implementation -- 5.1 Data Preparation -- 5.2 Data Cleaning and Data Preprocessing -- 5.2.1 Data Normalisation -- 5.3 Fuzzy Inference -- 5.3.1 Axis Bank -- 5.3.2 Tata Steel -- 5.3.3 Titan -- 5.3.4 Threshold Value -- 5.3.5 Parameters -- 5.3.6 Fuzzy Rules -- 6 Results and Discussion -- 6.1 Performance Analysis -- 7 Conclusion -- References -- Sandwiched Metasurface Antenna for Small Spacecrafts in IoT Infrastructure -- 1 Introduction -- 2 Antenna Design and Geometrical Analysis -- 3 Results, Data Analysis, and Discussions -- 4 Conclusions and Future Works -- References -- Development of Laser-Beam Cutting-Edge Technology and IOT-Based Race Car Lapse Time Computational System -- 1 Introduction -- 2 Literature Review -- 3 Proposed Method -- 3.1 Block Diagram -- 3.2 Simulation of Proposed System -- 4 Results and Discussion -- 5 Conclusion -- References -- A Study of Cloud-Based Solution for Data Analytics -- 1 Introduction -- 2 Methodology -- 2.1 Amazon Web Services (AWS) Cloud Platform for Data Analytics -- 2.1.1 Architecture Study of a Data Analytics System Using AWS -- 2.1.2 Data Ingestion and Processing -- 2.1.3 Data Preparation -- 2.1.4 AI/ML Workbench -- 2.2 Google Cloud Platform (GCP) for Data Analytics -- 2.2.1 Architecture Study of a Data Analytics System Using GCP -- 2.2.2 Data Ingestion and Processing -- 2.2.3 Data Preparation -- 3 Comparative Analysis of Services Required from AWS and GCP -- 4 Challenges -- 5 Conclusion -- References -- An Intelligent Model for Optimizing Sparsity Problem Toward Movie Recommendation Paradigm Using Machine Learning -- 1 Introduction -- 2 Similar Works Done -- 3 Fundamentals of Big Data -- 3.1 Properties of Big Data -- 3.2 Big Data in Entertainment Industry -- 3.2.1 Uses of Big Data in Media and Entertainment. 4 Proposed Model -- 4.1 Mathematical Background -- 4.2 Ant Colony Optimization (ACO) -- 4.2.1 Theoretical Considerations on ACO -- 4.3 Data Preparation -- 5 Results and Discussion -- 5.1 Performance Analysis -- 5.1.1 Evaluation Metrics -- 6 Conclusion -- References -- Techniques to Identify Image Objects Under Adverse Environmental Conditions: A Systematic Literature Review -- 1 Introduction -- 1.1 Morphological Operations on Image -- 1.2 Impact of the Environment on Objects -- 2 Methodology and Research Description -- 3 Findings and Results -- 4 Conclusion -- References -- Technology-Enhanced Teaching and Learning During the COVID-19 Pandemic -- 1 Introduction -- 2 Current Perspectives on Technology-Enhanced Language Teaching and Learning -- 3 Computer-Mediated Communication and Interaction Approach -- 4 Research on Remote Teaching in Crisis Situations -- 5 Technology Acceptance Model -- 6 Responsive Online Teaching in Crises -- 7 Bloom's Digital Taxonomy -- 8 Recommendations -- 9 Conclusion -- References -- The Symbiotic Relation of IoT and AI for Applications in Various Domains: Trends and Future Directions -- 1 Introduction -- 2 Recent Works on IoT and AI in Various Domains -- 2.1 Healthcare -- 2.2 Sustainability -- 2.3 Information Security -- 2.4 Education -- 2.5 Pollution Monitoring (Table 5) -- 2.6 Robotics (Table 6) -- 2.7 Other Related Works -- 3 Conclusion and Future Directions -- References -- Text Summarization for Big Data Analytics: A Comprehensive Review of GPT 2 and BERT Approaches -- 1 Introduction -- 2 Related Works -- 2.1 Text Summarization Using Deep Learning -- 2.2 Need for Text Summarization in Big Data Analytics -- 3 BERT -- 3.1 BERT Architecture -- 3.2 Phases in Generating the Summary -- 3.2.1 Input Document -- 3.2.2 Interval Segment Embedding -- 3.2.3 Embedding -- 3.2.4 Segment Embeddings -- 3.2.5 Position Embeddings. 3.2.6 Summarization -- 3.2.7 Inter Sentence Transformer -- 4 GPT-2 -- 5 Experiment Setup -- 5.1 About the Dataset -- 5.2 Training the Models -- 5.3 Evaluation Metrics -- 5.4 Summary Snippets -- 6 Comparison of Results -- 7 Conclusion -- References -- Leveraging Secured E-Voting Using Decentralized Blockchain Technology -- 1 Introduction -- 2 Blockchain -- 2.1 What Is Blockchain? -- 2.2 Working of a Blockchain -- 2.2.1 Elliptic Curve Digital Signature Technique (ECDSA) -- 2.3 Features of Blockchain Technology -- 2.3.1 Immutability -- 2.3.2 Auditability -- 2.3.3 Persistency -- 2.3.4 Decentralization -- 2.3.5 Anonymity -- 3 Types, Consensus Protocols, and Unfilled Gaps -- 3.1 Types of Blockchain -- 3.1.1 Public Blockchain -- 3.1.2 Private Blockchain -- 3.1.3 Consortium Blockchain -- 3.1.4 Hybrid Blockchain -- 3.2 Consensus Protocols -- 3.2.1 Proof of Work -- 3.2.2 Proof of Burn -- 3.2.3 Proof of Stake -- 3.2.4 Delegated Proof of Stake -- 3.2.5 Proof of Elapsed Time -- 3.2.6 Proof of Participation -- 3.2.7 Proof of Authority -- 3.2.8 Proof of Importance -- 3.2.9 Proof of Capacity -- 3.2.10 Proof of History -- 3.3 Challenges Faced by Existing Systems -- 3.3.1 Paper and Ballot Systems -- 3.3.2 Digital E-Voting Systems -- 4 Recent Advances -- 5 Conclusion -- References -- Multilayer Security and Privacy Provision in Internet of Things Networks: Challenges and Future Trends -- 1 The Internet of Things -- 2 Architecture and Technologies of IoT -- 3 Security Requirements in Distributed IoT Applications -- 4 Existing Challenges and Issues in IoT -- 4.1 Security Issues in the Network Layer -- 4.2 Security Issues at Physical Layer -- 5 Countermeasures for Security in IoT -- 5.1 Attacks on and Threats to IoT -- 5.2 Defenses Against IoT Attacks on Each Layer -- 6 Privacy Issues in IoT -- 6.1 Existing Security Models for IoT Networks -- 7 The Future of IoT -- 7.1 Top-Ten IoT Developments. |
| Record Nr. | UNISA-996550554203316 |
| Cham : , : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Data analytics for internet of things infrastructure / / Rohit Sharma, Gwanggil Jeon, Yan Zhang, editors
| Data analytics for internet of things infrastructure / / Rohit Sharma, Gwanggil Jeon, Yan Zhang, editors |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2023 |
| Descrizione fisica | 1 online resource (xv, 326 pages) : illustrations |
| Disciplina | 004.678 |
| Altri autori (Persone) |
SharmaRohit (Rohit Y.)
JeonGwanggil ZhangYan |
| Collana | Internet of Things Series |
| ISBN | 3-031-33808-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Key Features -- Contents -- About the Editors -- Big Data in Cloud Today: A Comprehensive Survey -- 1 Introduction -- 2 Characteristics of Big Data -- 3 Classification of Big Data -- 4 Importance of Big Data -- 5 Examples for Big Data -- 6 Tools and Techniques -- 7 Big Data Analytics and Its Benefits -- 8 Cloud Computing -- 9 Working of Cloud Computing -- 10 Conclusion -- References -- Cloud of Things Platform for a Water Meter Network -- 1 Introduction -- 2 Related Work and Problem Motivation -- 2.1 Related Work -- 2.2 IoT Paradigm -- 2.3 Problem Motivation -- 3 Proposed IoT Architecture -- 3.1 System Model -- 3.2 Methodology -- 3.3 Development of Active Switch -- 3.4 Development of an Active Sensor -- 3.5 Integration -- 4 Simulation and Result -- 4.1 Setup Process -- 4.2 Analysis -- 5 Conclusion -- References -- Online Newspaper Development within the Internet of Things Environment: The Role of Computer-Mediated Communication -- 1 Computer-Mediated Communication -- 2 Scholarly Information Related to CMC -- 3 CMC and Development of Online Newspapers -- 3.1 Advantages of Online Newspapers -- 3.2 Delivery of Online News and Information -- 3.3 CMC Journalism Is a Better Option -- 4 Interactivity with News Through CMC and Issues Within IoT Environment -- 4.1 CMC as a Tool for Organizations and Governments to Spread Information and News -- 4.2 The Use of Social Media and Its Social Outcomes Concerning CMC -- 4.3 Emerging Issues Related to the Use of CMC -- 4.4 Reconfiguration of Territorially and Interest-Based Associations -- 4.5 Introduction of New Artifacts and Their Social Outcomes -- 4.6 The Mutual Shaping of Consumers and Technologies -- 5 Conclusion -- References -- FATS (Fuzzy Authentication to Provide Trust-Based Security) in VANET to Mitigate Black Hole Attack -- 1 Introduction -- 2 VANET Architecture.
3 Attacks and Threats Generated in VANET -- 3.1 Selfish Node Attack -- 3.2 Jellyfish Attack -- 3.3 Data Flooding Attack -- 3.4 Black Hole Attack -- 4 Prominent Issues Caused by a Black Hole Node -- 5 Fuzzy Logic and Its Role in the Proposed Approach -- 5.1 Introduction About Fuzzy Logic -- 5.2 Mamdani Fuzzy Inference System -- 5.2.1 Max-Min Inference Method -- 5.2.2 Max-product inference method -- 6 Fuzzy Logic Trust-Based Authentication Schemes in VANET -- 7 Proposed Algorithm FATS (Fuzzy Authentication to Provide Trust-Based Security) for Black Hole Attack Detection -- 7.1 Pseudocode for Providing a Communication Link to the New Node -- 7.2 Formation of Fuzzy Rules Using Mamdani Inference System in MATLAB -- 8 Implementation of FATS -- 9 Conclusion -- References -- AI-Based Chatbot Agents as Drivers of Purchase Intentions: An Interdisciplinary Study -- 1 Introduction -- 2 Conceptual Background and Development of Hypothesis -- 3 Informational Support of Chatbots and Predicting Purchase Intentions -- 4 Trust, Emotional Credibility, and Predicting Purchase Intentions -- 5 Research Gap -- 6 Objectives -- 7 Methods -- 7.1 Sampling Framework and Questionnaire Design -- 7.2 Measures -- 8 Research Tools and Techniques -- 9 Measurement Model -- 10 Analysis of Structural Model -- 11 Conclusion and Future Research Works -- 12 Limitations -- References -- An Intelligent Model for Identifying Fluctuations in the Stock Market and Predicting Investment Policies with Guaranteed Returns -- 1 Introduction -- 2 Literature Survey -- 3 Impact of Big Data in Stock Market -- 3.1 Big Data -- 3.1.1 Big Data Architecture -- 3.2 Structure of Big Data -- 3.2.1 Structured Data -- 3.2.2 Unstructured Data -- 3.2.3 Semistructured Data -- 3.3 Big Data in the Stock Market -- 3.4 Nature of Dynamic Data in the Stock Market -- 4 Proposed Model (Fig. 2) -- 4.1 Objectives. 4.2 Mathematical Implementation -- 4.2.1 Statistical Analysis -- 4.2.2 Fuzzy Inferences -- 5 Implementation -- 5.1 Data Preparation -- 5.2 Data Cleaning and Data Preprocessing -- 5.2.1 Data Normalisation -- 5.3 Fuzzy Inference -- 5.3.1 Axis Bank -- 5.3.2 Tata Steel -- 5.3.3 Titan -- 5.3.4 Threshold Value -- 5.3.5 Parameters -- 5.3.6 Fuzzy Rules -- 6 Results and Discussion -- 6.1 Performance Analysis -- 7 Conclusion -- References -- Sandwiched Metasurface Antenna for Small Spacecrafts in IoT Infrastructure -- 1 Introduction -- 2 Antenna Design and Geometrical Analysis -- 3 Results, Data Analysis, and Discussions -- 4 Conclusions and Future Works -- References -- Development of Laser-Beam Cutting-Edge Technology and IOT-Based Race Car Lapse Time Computational System -- 1 Introduction -- 2 Literature Review -- 3 Proposed Method -- 3.1 Block Diagram -- 3.2 Simulation of Proposed System -- 4 Results and Discussion -- 5 Conclusion -- References -- A Study of Cloud-Based Solution for Data Analytics -- 1 Introduction -- 2 Methodology -- 2.1 Amazon Web Services (AWS) Cloud Platform for Data Analytics -- 2.1.1 Architecture Study of a Data Analytics System Using AWS -- 2.1.2 Data Ingestion and Processing -- 2.1.3 Data Preparation -- 2.1.4 AI/ML Workbench -- 2.2 Google Cloud Platform (GCP) for Data Analytics -- 2.2.1 Architecture Study of a Data Analytics System Using GCP -- 2.2.2 Data Ingestion and Processing -- 2.2.3 Data Preparation -- 3 Comparative Analysis of Services Required from AWS and GCP -- 4 Challenges -- 5 Conclusion -- References -- An Intelligent Model for Optimizing Sparsity Problem Toward Movie Recommendation Paradigm Using Machine Learning -- 1 Introduction -- 2 Similar Works Done -- 3 Fundamentals of Big Data -- 3.1 Properties of Big Data -- 3.2 Big Data in Entertainment Industry -- 3.2.1 Uses of Big Data in Media and Entertainment. 4 Proposed Model -- 4.1 Mathematical Background -- 4.2 Ant Colony Optimization (ACO) -- 4.2.1 Theoretical Considerations on ACO -- 4.3 Data Preparation -- 5 Results and Discussion -- 5.1 Performance Analysis -- 5.1.1 Evaluation Metrics -- 6 Conclusion -- References -- Techniques to Identify Image Objects Under Adverse Environmental Conditions: A Systematic Literature Review -- 1 Introduction -- 1.1 Morphological Operations on Image -- 1.2 Impact of the Environment on Objects -- 2 Methodology and Research Description -- 3 Findings and Results -- 4 Conclusion -- References -- Technology-Enhanced Teaching and Learning During the COVID-19 Pandemic -- 1 Introduction -- 2 Current Perspectives on Technology-Enhanced Language Teaching and Learning -- 3 Computer-Mediated Communication and Interaction Approach -- 4 Research on Remote Teaching in Crisis Situations -- 5 Technology Acceptance Model -- 6 Responsive Online Teaching in Crises -- 7 Bloom's Digital Taxonomy -- 8 Recommendations -- 9 Conclusion -- References -- The Symbiotic Relation of IoT and AI for Applications in Various Domains: Trends and Future Directions -- 1 Introduction -- 2 Recent Works on IoT and AI in Various Domains -- 2.1 Healthcare -- 2.2 Sustainability -- 2.3 Information Security -- 2.4 Education -- 2.5 Pollution Monitoring (Table 5) -- 2.6 Robotics (Table 6) -- 2.7 Other Related Works -- 3 Conclusion and Future Directions -- References -- Text Summarization for Big Data Analytics: A Comprehensive Review of GPT 2 and BERT Approaches -- 1 Introduction -- 2 Related Works -- 2.1 Text Summarization Using Deep Learning -- 2.2 Need for Text Summarization in Big Data Analytics -- 3 BERT -- 3.1 BERT Architecture -- 3.2 Phases in Generating the Summary -- 3.2.1 Input Document -- 3.2.2 Interval Segment Embedding -- 3.2.3 Embedding -- 3.2.4 Segment Embeddings -- 3.2.5 Position Embeddings. 3.2.6 Summarization -- 3.2.7 Inter Sentence Transformer -- 4 GPT-2 -- 5 Experiment Setup -- 5.1 About the Dataset -- 5.2 Training the Models -- 5.3 Evaluation Metrics -- 5.4 Summary Snippets -- 6 Comparison of Results -- 7 Conclusion -- References -- Leveraging Secured E-Voting Using Decentralized Blockchain Technology -- 1 Introduction -- 2 Blockchain -- 2.1 What Is Blockchain? -- 2.2 Working of a Blockchain -- 2.2.1 Elliptic Curve Digital Signature Technique (ECDSA) -- 2.3 Features of Blockchain Technology -- 2.3.1 Immutability -- 2.3.2 Auditability -- 2.3.3 Persistency -- 2.3.4 Decentralization -- 2.3.5 Anonymity -- 3 Types, Consensus Protocols, and Unfilled Gaps -- 3.1 Types of Blockchain -- 3.1.1 Public Blockchain -- 3.1.2 Private Blockchain -- 3.1.3 Consortium Blockchain -- 3.1.4 Hybrid Blockchain -- 3.2 Consensus Protocols -- 3.2.1 Proof of Work -- 3.2.2 Proof of Burn -- 3.2.3 Proof of Stake -- 3.2.4 Delegated Proof of Stake -- 3.2.5 Proof of Elapsed Time -- 3.2.6 Proof of Participation -- 3.2.7 Proof of Authority -- 3.2.8 Proof of Importance -- 3.2.9 Proof of Capacity -- 3.2.10 Proof of History -- 3.3 Challenges Faced by Existing Systems -- 3.3.1 Paper and Ballot Systems -- 3.3.2 Digital E-Voting Systems -- 4 Recent Advances -- 5 Conclusion -- References -- Multilayer Security and Privacy Provision in Internet of Things Networks: Challenges and Future Trends -- 1 The Internet of Things -- 2 Architecture and Technologies of IoT -- 3 Security Requirements in Distributed IoT Applications -- 4 Existing Challenges and Issues in IoT -- 4.1 Security Issues in the Network Layer -- 4.2 Security Issues at Physical Layer -- 5 Countermeasures for Security in IoT -- 5.1 Attacks on and Threats to IoT -- 5.2 Defenses Against IoT Attacks on Each Layer -- 6 Privacy Issues in IoT -- 6.1 Existing Security Models for IoT Networks -- 7 The Future of IoT -- 7.1 Top-Ten IoT Developments. |
| Record Nr. | UNINA-9910746286103321 |
| Cham : , : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data Analytics for Smart Grids Applications--A Key to Smart City Development
| Data Analytics for Smart Grids Applications--A Key to Smart City Development |
| Autore | Kumar Sharma Devendra |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2024 |
| Descrizione fisica | 1 online resource (466 pages) |
| Altri autori (Persone) |
SharmaRohit
JeonGwanggil KumarRaghvendra |
| Collana | Intelligent Systems Reference Library |
| ISBN | 3-031-46092-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- About This Book -- Key Features -- Contents -- About the Editors -- 1 Data Analytics for Smart Grids and Applications-Present and Future Directions -- 1.1 Introduction -- 1.2 Literature Review -- 1.3 Smart Grid Infrastructure -- 1.4 Data Analytics in Smart Grids -- 1.4.1 Data Pre Processing Techniques in Smart Grids -- 1.4.2 Case Study of Data Analytics in Smart Grids -- 1.5 Artificial Intelligence in Smart Grids -- 1.5.1 Event Detection Using Data Analytics and Cloud Computing for Intelligent IoT System -- 1.6 Conclusion -- References -- 2 Design, Optimization and Performance Analysis of Microgrids Using Multi-agent Q-Learning -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Proposed Model -- 2.4 Experiments -- 2.5 Conclusion -- References -- 3 Big Data Analytics for Smart Grid: A Review on State-of-Art Techniques and Future Directions -- 3.1 Introduction -- 3.2 State-of-Art Techniques for Big Data Analytics in Smart Grids -- 3.3 Challenges in Big Data Analytics for Smart Grids -- 3.4 Big Data Analytics for Smart Grids -- 3.5 Applications of Big Data Analytics in Smart Grids -- 3.6 Challenges and Future Directions for Big Data Analytics in Smart Grids -- 3.7 Case Studies of Big Data Analytics in Smart Grids -- 3.7.1 Case Study 1: Duke Energy's Grid Modernization Program -- 3.7.2 Case Study 2: National Grid's Smart Grid Program -- 3.7.3 Case Study 3: ENEL's Smart Grid Program -- 3.8 Future Directions for Big Data Analytics in Smart Grids -- 3.9 Real-Time Big Data Analytics for Smart Grids -- 3.10 Conclusion -- References -- 4 Smart Grid Management for Smart City Infrastructure Using Wearable Sensors -- 4.1 Introduction -- 4.1.1 Smart Grid Versus Traditional Electricity Grids -- 4.1.2 Why Do We Need Smart Grids? -- 4.1.3 Smart Grid Features -- 4.1.4 Smart Grid Technologies -- 4.1.5 Smart Grid Approaches.
4.1.6 Smart Meters and Home EMS -- 4.1.7 Smart Appliances -- 4.1.8 Home Power Generation -- 4.1.9 Machine Learning for Data Analytics in Smart Grids and Energy Management -- 4.1.10 Security for Industrial Control Systems in Smart Grids -- 4.1.11 Power Flow Modelling and Optimization in Smart Grids -- 4.1.12 Grid Stability and Security in Smart Grids -- 4.1.13 Integration of Renewable Energy Sources in Smart Grid Management -- 4.1.14 Demand Response Strategies for Efficient Smart Grid Management -- 4.1.15 Cybersecurity Measures for Smart Grid Management -- 4.1.16 Energy Storage Systems and Their Role in Smart Grid Management -- 4.1.17 Data Analytics and Artificial Intelligence in Smart Grid Management -- 4.1.18 Smart Grid Communication Protocols and Infrastructure -- 4.1.19 Advantages of Smart Grids -- 4.1.20 Disadvantages of Smart Grids -- 4.2 Conclusion -- References -- 5 Studies on Conventional and Advanced Machine Learning Algorithm Towards Framing of Robust Data Analytics for the Smart Grid Application -- 5.1 Introduction -- 5.2 Review of Different Smart Grid Based Approaches -- 5.3 Smart Grid Model -- 5.3.1 Smart Grids as Coordinators for Data Flow and Energy Flow -- 5.3.2 Big Data -- 5.4 Features of Big Data to Be Integrated into the Smart Grid -- 5.5 Contribution of the Smart Grid as Data Source -- 5.6 Smart Grid in Supply of Data Gathering -- 5.6.1 Data Transmission Methodology -- 5.6.2 Data Analysis Methodology -- 5.6.3 Data Extraction from Smart Grid -- 5.6.4 Grid for Production of Renewable Source of Energy -- 5.6.5 Big Data in Smart Grid -- 5.6.6 Machine Learning Approach to the Data Grid -- 5.6.7 Application of IOT to the Smart Grid Technology -- 5.7 IOT Based Solutions Towards Grid Problems -- 5.7.1 Stability of IOT Based Connection -- 5.7.2 Cost Effectiveness in Implementation -- 5.7.3 Security to the Information. 5.8 Application of Data Grid in Mobile Sink Based Wireless Sensor Network -- 5.8.1 Assumptions of Network Characteristics -- 5.9 Virtual Grid Architecture -- 5.9.1 Different Structures of Virtual Grids -- 5.9.2 Virtual Grid Construction Cost -- 5.9.3 Reading of the Smart Meter Data and Its Analysis by the Smart Grid with Future Prediction -- 5.9.4 Prediction Analysis of Smart Meter Data -- 5.10 Future Research Direction -- 5.11 Conclusion -- References -- 6 Prediction and Classification for Smart Grid Applications -- 6.1 Introduction -- 6.2 Smart Grid -- 6.3 Predictive and Classification Models in Smart Grid Applications -- 6.4 Predictive Modeling -- 6.5 Classification Modeling -- 6.6 Smart Grid Management -- 6.7 Intelligent Data Collection Devices -- 6.8 Data Science Pertaining to Smart Grid Analytics -- 6.9 Machine Learning for Data Analytics -- 6.10 Data Security for Smart Grid Applications -- 6.11 Conclusion -- References -- 7 A Review on Smart Metering Using Artificial Intelligence and Machine Learning Techniques: Challenges and Solutions -- 7.1 Introduction -- 7.1.1 Trends of the Smart Metering Systems -- 7.1.2 Challenges of Smart Meters -- 7.1.3 Key Elements of Smart Meter -- 7.1.4 IoT in Smart Metering -- 7.1.5 Integration of IoT with AI and Machine Learning for Smart Meter -- 7.1.6 Artificial Intelligence Techniques -- 7.2 Conclusion -- References -- 8 Machine Learning Applications for the Smart Grid Infrastructure -- 8.1 Introduction -- 8.2 IoT in Distribution System -- 8.3 Techniques Using Machine Learning -- 8.4 Conclusion -- References -- 9 A Privacy Mitigating Framework for the Smart Grid Internet of Things Data -- 9.1 Introduction -- 9.1.1 Overview of the Smart Grid and Its Significance in Modern Energy Systems -- 9.1.2 Introduction to the IoT and Its Integration with the Smart Grid -- 9.1.3 Importance of Privacy in Smart Grid IoT Data. 9.2 Privacy Challenges in Smart Grid IoT Data -- 9.3 Privacy Mitigation Techniques -- 9.4 Privacy Mitigation Framework for Smart Grid -- 9.4.1 Privacy Monitoring Engine Description -- 9.5 Results -- 9.6 Conclusion -- References -- 10 Protecting Future of Energy: Data Security and Privacy for Smart Grid Applications Using MATLAB -- 10.1 Introduction -- 10.1.1 Data Security and Privacy Threats -- 10.1.2 Data Security and Privacy Solutions -- 10.1.3 MATLAB Solution -- 10.1.4 Key Features and Capabilities -- 10.2 MATLAB Tools and Inbuilt Functions for Data Security in Applications of Smart Grid -- 10.3 MATLAB Functions for Data Security and Privacy in Smart Grid Applications Include -- 10.4 MATLAB Techniques for Data Security and Privacy in Smart Grid Applications -- 10.5 Matlab Algorithm for Privacy-Preserving Data Mining for Smart Grid Applications -- 10.6 Threats to Data Security and Privacy in Smart Grid Applications -- 10.6.1 Preventive Measures -- 10.7 Case Studies and Practical Implementations of Data Security and Privacy in Smart Grid Applications -- 10.7.1 Case Study 1: Securing Smart Meters Using Blockchain -- 10.7.2 Case Study 2: Machine Learning-Based Anomaly Detection in Power Grids -- 10.7.3 Case Study 3: Privacy-Preserving Data Aggregation in Smart Grids -- 10.7.4 Case Study 4: Secure Data Sharing in Smart Grids Using Homomorphic Encryption -- 10.7.5 Case Study 5: Anomaly Detection in Smart Grids Using Machine Learning (ML) with Matlab -- 10.8 Conclusion -- References -- 11 Revolutionizing Smart Grids with Big Data Analytics: A Case Study on Integrating Renewable Energy and Predicting Faults -- 11.1 Introduction -- 11.2 Current Trends in Smart Grid Based Big Data Analytics -- 11.2.1 There is a Notable Surge in Speculation in Smart Grid Projects and, Consequently, Smart Grid Analytics [9-11]. 11.2.2 Smart Grid Analytics Effectively Handle Real-Time Data Despite the Increased Speed and Diverse Requirements -- 11.2.3 Digital Technologies and Cloud Computing Will Continue to Improve, Facilitating Enhanced Data Computation Capabilities -- 11.2.4 Smart Grid and Its Benefits for Renewable Energy -- 11.3 Challenges of Smart Grid Analytics -- 11.3.1 Benefits of Analytics in Smart Grid -- 11.3.2 Trends in the Utility Industry -- 11.4 Technologies for Smart Grid Analytics and Its Importance -- 11.4.1 Business Intelligence (BI) and Data Analysis -- 11.4.2 Other Framework Technologies-Databases Such as Apache Hadoop, MapReduce, and SQL -- 11.4.3 The Significance of Big Data in Smart Grid Analytics -- 11.5 Gaining Perceptions Through a Smart Grid and Big Data: A Case Study -- 11.5.1 Case Studies in Focus -- 11.5.2 Smart Grid Based Data Analytics Use-Cases in Europe -- 11.6 Future and Scope of Big Data Analytics in Smart Grids -- 11.6.1 Customer Acceptance and Engagement -- 11.6.2 Regulatory Policies -- 11.6.3 Innovative Structures -- 11.7 Conclusion -- References -- 12 Fake User Account Detection in Online Social Media Networks Using Machine Learning and Neural Network Techniques -- 12.1 Introduction -- 12.1.1 Statistics of Social Media Usage -- 12.1.2 Why Are Fake Profiles Created? -- 12.2 Literature Review -- 12.3 Proposed System for Detecting Fake Accounts on Twitter Using AI -- 12.3.1 Artificial Neural Network (ANN) -- 12.3.2 Support Vector Machine (SVM) -- 12.3.3 Random Forest (RF) -- 12.4 Findings and Discussions -- 12.5 Conclusion -- References -- 13 Data Analytics for Smart Grids Applications to Improve Performance, Optimize Energy Consumption, and Gain Insights -- 13.1 Introduction -- 13.2 Leveraging Smart Grids for Predictive Energy Analytics -- 13.3 Big Data Analytics for Grid Resiliency and Security. 13.4 Machine Learning Techniques for Smart Grid Optimization. |
| Record Nr. | UNINA-9910767529203321 |
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Kumar Sharma Devendra
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| Cham : , : Springer International Publishing AG, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Micro-Electronics and Telecommunication Engineering [[electronic resource] ] : Proceedings of 7th ICMETE 2023 / / edited by Devendra Kumar Sharma, Sheng-Lung Peng, Rohit Sharma, Gwanggil Jeon
| Micro-Electronics and Telecommunication Engineering [[electronic resource] ] : Proceedings of 7th ICMETE 2023 / / edited by Devendra Kumar Sharma, Sheng-Lung Peng, Rohit Sharma, Gwanggil Jeon |
| Autore | Sharma Devendra Kumar |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (811 pages) |
| Disciplina | 621.382 |
| Altri autori (Persone) |
PengSheng-Lung
SharmaRohit JeonGwanggil |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Telecommunication
Electronic circuits Signal processing Electronics Technology - Sociological aspects Information technology Communications Engineering, Networks Electronic Circuits and Systems Signal, Speech and Image Processing Electronics and Microelectronics, Instrumentation Information and Communication Technologies (ICT) |
| ISBN | 981-9995-62-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Editors and Contributors -- Transportation in IoT-SDN Using Vertical Handoff Scheme -- 1 Introduction -- 2 Related Work -- 3 IoT Evolution -- 4 IoT with Transportation -- 4.1 IoT in Transportation: Applications -- 5 Intelligent Transportation Using a Vertical Handoff Method Based on Software-Defined Networks -- 6 Brief Analysis of Various Proposed Schemes and Results -- 7 Conclusion -- References -- MLP-Based Speech Emotion Recognition for Audio and Visual Features -- 1 Introduction -- 2 Review of Literature Research -- 3 Problem Statement -- 3.1 Dataset Description -- 3.2 Dataset Details -- 4 Proposed System -- 4.1 Data Exploration -- 4.2 Feature Extraction -- 5 Classifiers -- 5.1 Multi-layer Perceptron -- 5.2 Support Vector Machine -- 5.3 Random Forest Classifier -- 5.4 Decision Tree -- 6 Experimental Results -- 7 Conclusion -- References -- Drain Current and Transconductance Analysis of Double-Gate Vertical Doped Layer TFET -- 1 Introduction -- 2 Schematics of VDL-TFET -- 3 Simulations and Result -- 4 Conclusion -- References -- OpenFace Tracker and GoogleNet: To Track and Detect Emotional States for People with Asperger Syndrome -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Data Preprocessing -- 3.2 Cues Generation -- 3.3 Training Step -- 4 Results and Discussions -- 5 Conclusion -- References -- Vehicle Classification and License Number Plate Detection Using Deep Learning -- 1 Introduction -- 2 Literature Review -- 3 Proposed Model -- 4 Result -- 5 Conclusion -- References -- Car Price Prediction Model Using ML -- 1 Introduction -- 2 Literature Review -- 3 Proposed Model -- 3.1 Algorithm -- 4 Result -- 5 Conclusion -- 6 Future Scope -- References -- Effects of Material Deformation on U-shaped Optical Fiber Sensor -- 1 Introduction -- 2 Theory -- 3 Design Considerations and Results.
3.1 Sensor Characteristics -- 3.2 Evanescent Wave Absorbance -- 3.3 Sensitivity -- 4 Conclusion -- References -- Classification of DNA Sequence for Diabetes Mellitus Type Using Machine Learning Methods -- 1 Introduction -- 2 Related Works -- 3 Proposed System -- 4 Dataset -- 5 Data Preprocessing -- 5.1 Handle Missing Values -- 5.2 List to String -- 5.3 K-mer -- 5.4 Oversampling -- 5.5 Ordinal Encoding -- 5.6 Min-Max Normalization -- 6 Feature Selection -- 6.1 ANOVA -- 6.2 F-Regressor -- 6.3 Mutual Information -- 7 Classification -- 7.1 Random Forest -- 7.2 Gaussian NB -- 7.3 Support Vector Machine -- 7.4 Decision Tree -- 8 Results and Discussion -- 9 Conclusion -- References -- Unveiling the Future: A Review of Financial Fraud Detection Using Artificial Intelligence Techniques -- 1 Introduction -- 2 Literature Review -- 2.1 Machine Learning Techniques for Financial Fraud Detection -- 2.2 Deep Learning for Financial Fraud Detection -- 2.3 Ensemble Methods for Financial Fraud Detection -- 2.4 Unsupervised and Semi-supervised Learning for Financial Fraud Detection -- 2.5 Explainable AI for Financial Fraud Detection -- 2.6 Feature Selection and Feature Engineering -- 3 Models and Methodologies -- 3.1 FDS of Bayesian Learning and Dempster-Shafer Theory -- 3.2 The Evolutionary-Fuzzy System -- 3.3 Deep Artificial Neural Networks -- 3.4 BLAST-SSAHA Hybridization -- 3.5 Decision Tree -- 4 Conclusion -- References -- Remodeling E-Commerce Through Decentralization: A Study of Trust, Security and Efficiency -- 1 Introduction -- 2 Background and Related Work -- 3 Research Approach -- 3.1 Study Design -- 3.2 Proposed System Architecture -- 3.3 Implementation Methodology -- 4 Result -- 4.1 Gas Fees and Time Cost Analysis -- 4.2 Reliability Analysis -- 5 Conclusions and Future Scopes -- 5.1 Conclusions -- 5.2 Future Scopes -- References. Estimation of Wildfire Conditions via Perimeter and Surface Area Optimization Using Convolutional Neural Network -- 1 Introduction -- 2 Existing Systems -- 3 Proposed System Architecture -- 4 Module Implementation -- 4.1 Collection of Data -- 4.2 Preprocessing the Data -- 4.3 Extraction of Features -- 4.4 Evaluating the Model -- 5 Result Analysis -- 6 Conclusion -- 7 Future Enhancements -- References -- A Framework Provides Authorized Personnel with Secure Access to Their Electronic Health Records -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Security Framework -- 4 Key Features of the Proposed Security Framework -- 5 Experimental Results and Discussion -- 6 Conclusion and Future Enhancement -- References -- Explainable Artificial Intelligence for Deep Learning Models in Diagnosing Brain Tumor Disorder -- 1 Introduction -- 2 Literature Review -- 3 XAI Approaches -- 3.1 Local Interpretable Model-Agnostic Explanations (LIMEs) -- 3.2 SHapley Additive ExPlanations (SHAPs) -- 3.3 Integrated Gradients -- 3.4 Gradient-Weighted Class Activation Mapping (Grad-CAM) -- 4 Results and Discussion -- 5 Conclusion -- References -- Pioneering a New Era of Global Transactions: Decentralized Overseas Transactions on the Blockchain -- 1 Introduction -- 2 Existing Solution -- 2.1 International Wire Transfer -- 2.2 Transactions via Cryptocurrency -- 3 Proposed Solution by Conversion of Fiat Currency -- 3.1 A Unified Payment Interface Decentralized Finance App Works Globally -- 4 Conclusion -- References -- A Perspective Review of Generative Adversarial Network in Medical Image Denoising -- 1 Introduction -- 2 Related Works -- 3 Various Types of Image-Denoising Methods Using GAN -- 4 Performance Metrics -- 5 Significance of Image Denoising Utilizing GAN -- 6 Conclusion -- References -- Osteoporosis Detection Based on X-Ray Using Deep Convolutional Neural Network. 1 Introduction -- 2 Related Works -- 3 Proposed System -- 4 Methodology -- 4.1 Preprocessing -- 4.2 Smudging -- 4.3 Deep Convolutional Neural Network (DCNN) -- 5 Result Analysis and Discussion -- 6 Conclusion -- References -- Fault Prediction and Diagnosis of Bearing Assembly -- 1 Introduction -- 2 Hardware Designing -- 2.1 AC Motor -- 2.2 T-Coupling -- 2.3 Setup Holding Base -- 2.4 Ball Bearing -- 2.5 Shaft -- 2.6 Pulley and Belt -- 2.7 Load Controller -- 2.8 Electronic Weight Machine -- 2.9 NI DAQ Card -- 2.10 Vibration Sensor -- 3 Experimental Procedure -- 4 Simulation for Fault Diagnosis -- 4.1 Parameter and Operating Conditions -- 5 Result -- 5.1 Plots for Different Loads -- 6 Conclusion -- 7 Summary -- 8 Future Scope -- References -- Bearing Fault Diagnosis Using Machine Learning Models -- 1 Introduction -- 2 Methodology -- 2.1 SVM -- 2.2 SVM Kernels -- 2.3 KNN -- 2.4 Decision Tree -- 2.5 Random Forest -- 2.6 Regression -- 3 Methodology of Machine Learning Model -- 4 Techniques for Extracting and Selecting Features from Data -- 5 Relationship Between the Statistical Features -- 6 Data Description -- 7 Comparative Study of Statistical Features -- 8 Result -- 9 Conclusion -- 10 Summary -- 11 Future Scope -- References -- A High-Payload Image Steganography Based on Shamir's Secret Sharing Scheme -- 1 Introduction -- 2 Related Work -- 3 Proposed Steganography Technique -- 3.1 Secret Distributing Scheme (SDS) -- 3.2 Shamir's Secret Sharing (SSS) Scheme for Protecting Hidden Secret Information -- 3.3 Proposed PVD-Based Steganography Method -- 4 Results and Experimental Findings -- 4.1 Robustness and Varying Embedding Capacity -- 4.2 Comparative Analysis -- 5 Conclusion -- References -- Design and Comparison of Various Parameters of T-Shaped TFET of Variable Gate Lengths and Materials -- 1 Introduction -- 2 Device Structure and Simulation. 3 Comparative Analysis on Devices and Discussion -- 3.1 ON Current and OFF Current -- 3.2 Subthreshold Swing (SSavg) -- 3.3 Transconductance (gm) -- 4 Results -- 4.1 Transfer Characteristics of Device -- 4.2 Transconductance Analysis (gm) -- 4.3 Band-to-Band Tunneling -- 4.4 Electric Field -- 4.5 Surface Potential -- 5 Conclusion -- References -- Experiment to Find Out Suitable Machine Learning Algorithm for Enzyme Subclass Classification -- 1 Introduction -- 2 Background -- 3 Brief Description of Methods Used in the Study -- 3.1 Experiment Using Logistic Regression Model -- 3.2 Experiment Using SVM -- 3.3 Experiment Using Random Forest -- 4 Computational Procedure -- 5 Data -- 6 Results and Discussion -- 7 Conclusion and Future Work -- References -- Iris Recognition Method for Non-cooperative Images -- 1 Introduction -- 2 Structure of Iris -- 3 Iris Segmentation -- 4 Literature Review -- 5 Methodology -- 5.1 Image Acquisition -- 5.2 Segmentation -- 5.3 Iris Normalization -- 5.4 Features Extraction (Iris Code) -- 5.5 Matching -- 6 Results -- 7 Conclusions -- References -- An Exploration: Deep Learning-Based Hybrid Model for Automated Diagnosis and Classification of Brain Tumor Disorder -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Results and Discussion -- 5 Conclusion -- References -- Recognition of Apple Leaves Infection Using DenseNet121 with Additional Layers -- 1 Introduction -- 2 DenseNet with Additional Layers -- 2.1 Preprocessing -- 2.2 Architecture -- 3 Dataset -- 4 Results -- 5 Conclusion -- References -- Techniques for Digital Image Watermarking: A Review -- 1 Introduction -- 2 Watermarking Techniques -- 3 Foundations of Presented Work -- 4 Process of Watermarking -- 5 Characteristics of Watermarking -- 6 Parameters of Quality Evaluation -- 7 Applications of the Image Watermarking -- 8 Conclusion -- References. Improved Traffic Sign Recognition System for Driver Safety Using Dimensionality Reduction Techniques. |
| Record Nr. | UNINA-9910845496103321 |
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Sharma Devendra Kumar
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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