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AI-Powered IoT in the Energy Industry : Digital Technology and Sustainable Energy Systems / / edited by S. Vijayalakshmi, Savita ., Balamurugan Balusamy, Rajesh Kumar Dhanaraj
| AI-Powered IoT in the Energy Industry : Digital Technology and Sustainable Energy Systems / / edited by S. Vijayalakshmi, Savita ., Balamurugan Balusamy, Rajesh Kumar Dhanaraj |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (318 pages) |
| Disciplina | 004.678 |
| Collana | Power Systems |
| Soggetto topico |
Electric power distribution
Renewable energy sources Cooperating objects (Computer systems) Electric power production Energy Grids and Networks Renewable Energy Cyber-Physical Systems Electrical Power Engineering |
| ISBN |
9783031150449
9783031150432 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | AI and ML Towards Sustainable Solar Energy -- AI and Intermittency Management of Renewable Energy -- AI Impact on Energy and Utilities -- Energy Intelligence – The Smart Grid Perspective -- IoT Towards Leveraging Renewable Energy -- IoT Contribution in Construct of Green Energy -- IoT, Smart Grids, and Big Data – Renewable Energy Insights -- IoT Infrastructure to Energize Electromobility -- Building Sustainable Charging Infrastructure – Smart Solutions -- Biomass Renewable Energy: Introduction and Application of AI and IoT -- Modernization of Rural Electric Infrastructure -- AI and IoT in Improving Resilience of Smart Energy Infrastructure -- Empowering Renewable Energy Using Internet of Things -- Role of Artificial Intelligence in Renewable Energy -- IoT and Sustainable Energy System: Risk and Opportunity -- Powering the Geothermal Energy with AI, IoT, and ML. |
| Record Nr. | UNINA-9910686478203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Blockchain Technology in Corporate Governance : Transforming Business and Industries
| Blockchain Technology in Corporate Governance : Transforming Business and Industries |
| Autore | Sood Kiran |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (443 pages) |
| Altri autori (Persone) |
DhanarajRajesh Kumar
BalusamyBalamurugan KadrySeifedine |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-86524-7
1-119-86523-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Acknowledgment -- Chapter 1 Role of Blockchain Technology in the Modern Era -- 1.1 Introduction -- 1.2 What is Blockchain Technology? -- 1.3 Blockchain Technology in Healthcare 4.0 -- 1.3.1 Area of Blockchain Technology-Based Healthcare 4.0 -- 1.3.2 Smart Blockchain Healthcare 4.0 -- 1.4 Energy Sector -- 1.5 Applications of Blockchain in the Energy Sector -- 1.5.1 Decentralized Storage and Control in Power Grid -- 1.5.2 Electricity Trading Law -- 1.5.3 Electric Vehicles -- 1.5.4 Decrease the Global Carbon Emission -- 1.6 Blockchain-Based Financial Sector -- 1.6.1 Legal Policies of the Financial Sector -- 1.6.2 Credit Risk -- 1.6.3 KYC and Product Personalization -- 1.6.4 Insurance Monetary Management -- 1.6.5 Collaborative Techniques in Financial Services Chain -- 1.7 Bitcoins and Blockchain Sustainability Issues -- 1.8 Conclusion -- References -- Part 1: Blockchain: Opportunities for Healthcare 4.0 -- Chapter 2 BTCG4: Blockchain Technology in Electronic Healthcare Systems -- 2.1 Introduction -- 2.1.1 Healthcare Industry -- 2.1.2 Requirement of Electronic Healthcare Industry -- 2.1.2.1 System Security -- 2.1.2.2 Interoperability -- 2.1.2.3 Sharing of Information -- 2.1.2.4 Mobility -- 2.1.2.5 Mobile Health -- 2.1.2.6 Wireless -- 2.1.2.7 Internet of Things -- 2.2 Overview of Blockchain -- 2.2.1 Distinct Characteristics of the Use of Blockchain in the Electronic Healthcare Industry -- 2.2.1.1 Decentralization of Storage -- 2.2.1.2 Authentication -- 2.2.1.3 Immutability -- 2.2.1.4 Improvement in Security -- 2.2.1.5 Efficiency -- 2.2.1.6 Distributed Ledger -- 2.3 Blockchain Applications -- 2.3.1.1 Smart Contracts -- 2.3.1.2 Spotting and Preventing Fraudulent Activity -- 2.3.1.3 Authentication of the User's Identity -- 2.4 Challenges Associated with Blockchain Technology.
2.4.1 Unavailability of Uniformity -- 2.4.2 Decentralization of Storage and Leakage of Privacy -- 2.4.3 Handling of Critical Information -- 2.4.4 Scalability and Internet of Things Overhead -- 2.4.5 Vulnerabilities Specific to Blockchain Technology -- 2.4.6 General Vulnerabilities in Software -- 2.5 Opportunities of Blockchain in the Healthcare Industry -- 2.5.1 The Gem Health Network Facilitates the Exchange of Healthcare Data -- 2.5.2 MDREC -- 2.5.3 System of Pervasive Social Network -- 2.5.4 Virtual Resources -- 2.5.5 Data Recording for Body-Worn Sensing Devices Based on Context -- 2.5.6 MeDShare -- 2.5.7 Blockchain-Based Clinical and Precise Platform Trials -- 2.5.8 Access to Health-Related Information -- 2.6 Concluding Remarks -- References -- Chapter 3 Blockchain Technology and Healthcare: Towards Combating COVID-19 -- 3.1 Introduction -- 3.1.1 Blockchain Technology in Healthcare -- 3.1.2 Features of Blockchain Technology -- 3.1.3 Applications of Blockchain in Healthcare -- 3.1.4 Data Management -- 3.1.5 Electronic Health Record -- 3.1.6 Claims and Billing Management -- 3.1.7 Pandemic Data Tracking -- 3.1.8 Tracking PPE -- 3.1.9 Vaccine Monitoring -- 3.1.10 Future Vaccination -- 3.1.11 Digital Contact Tracing -- 3.1.12 Prescription Management -- 3.2 Combating COVID-19 -- 3.2.1 Handling Fake Infodemic Using MiPasa Platform -- 3.2.2 VIRI Platform Preventing Spread -- 3.2.3 WIShelter for Data Privacy -- 3.3 Reviving Capabilities -- 3.3.1 Blockchain in Healthcare - Global Scenario -- 3.3.2 Blockchain in Healthcare - Indian Scenario -- 3.4 Challenges in Adopting Blockchain in Healthcare -- 3.5 Conclusion -- References -- Chapter 4 Blockchain-Based Energy-Efficient Heterogeneous Sensor Networks in Healthcare System -- 4.1 Introduction -- 4.2 Related Work -- 4.2.1 Literature Gap -- 4.2.2 Fuzzy Improved Model (Improved Model Fuzzy). 4.3 Proposed Energy Protocol with Blockchain -- 4.4 Conclusion -- References -- Chapter 5 Development of a Safe Health Framework Using a Temporary Blockchain Technique -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Blockchain-Based Healthcare Organization -- 5.2.2 Medrek -- 5.2.3 Stratum -- 5.2.4 Factor -- 5.2.5 Pocketbook -- 5.2.6 Tyrion -- 5.2.7 Roomed -- 5.3 Secure Framework (Sefira) for Healthcare System -- 5.3.1 Progressive Temporal Blockchain -- 5.3.2 Temporal Shadow -- 5.3.2.1 Context-Based Merkle Tree (CBMT) -- 5.3.2.2 Temporal Hash Signature (THS) -- 5.3.2.3 Context-Based Access Control (CBAC) in Smart Contract -- 5.3.2.4 Layered Architecture of SeFra -- 5.4 Conclusion -- References -- Chapter 6 Data Consistency, Transparency, and Privacy in Healthcare Systems Using Blockchain Technology -- 6.1 Introduction -- 6.2 The Cutting Edge in Genomics -- 6.2.1 Next Generation Sequencing (NGS) -- 6.2.2 EDGE Bioinformatics -- 6.2.3 Pharmacogenetics and Personalized Medicine -- 6.2.4 Prenatal Diagnosis -- 6.2.5 Diagnosis of Infectious Diseases -- 6.2.6 Gene Therapy and Genome Editing -- 6.2.7 Genomics with Blockchain Technology -- 6.3 Medical Records -- 6.3.1 Blockchain Architecture - Components and Types -- 6.3.2 Blockchain Benefits in Healthcare Records Maintenance -- 6.3.3 Brief Overview on the Blockchain-Enabled Patient Healthcare Record Management Process -- 6.3.3.1 Data Generation -- 6.3.3.2 Data Cleaning and Enrichment -- 6.3.3.3 Data Capturing -- 6.3.3.4 Data Consumption -- 6.3.3.5 Data Mining -- 6.4 Supply Chain Management -- 6.4.1 Pharmaceutical Applications -- 6.4.2 Medical Devices and Medical Supplies -- 6.4.3 Internet of Healthy Things -- 6.4.4 Public Health -- References -- Part 2: Blockchain in the Energy Sector -- Chapter 7 Application of Blockchain Technology in Sustainable Energy Systems -- 7.1 Introduction -- 7.2 Blockchain. 7.3 Blockchain Applications in Energy Sector -- 7.3.1 Blockchain Applications in Smart Grid -- 7.3.2 Blockchain Applications in Energy Trading -- 7.3.3 Blockchain Applications in Micro-Grid -- 7.3.4 Blockchain in Electric Vehicles -- 7.3.5 Blockchain Applications in Cyber Physical Security -- 7.4 Blockchain as New Substructure -- 7.5 Limitations of Blockchain -- 7.6 Conclusions -- References -- Chapter 8 Revamping Energy Sector with a Trusted Network: Blockchain Technology -- 8.1 Introduction -- 8.2 Energy Digital Transformation -- 8.2.1 Digitalization, Decarbonization, and Decentralization of the Energy Sector -- 8.2.2 Blockchain: A Disruptive Technology of the Energy Value Chain -- 8.2.3 Blockchain Advancing DERs -- 8.3 Energy Trading Mechanisms -- 8.3.1 Blockchain P2P Energy Trading: A New Financing Mechanism -- 8.3.2 Blockchain-Based Virtual Power Plant (VPP) Model -- 8.3.3 Blockchain Technology for Electric Vehicle (EV) Charging and Discharging -- 8.4 Blockchain Unlocking New Demand Side Management Models -- 8.4.1 Blockchain in the Energy Efficiency Market -- 8.4.2 New Blockchain-Enabled Demand Response (DR) Models -- 8.4.3 Blockchain-Based Energy Performance Contracting -- 8.5 Energy Blockchain's Social and Environmental Impacts -- 8.5.1 Blockchain Market for Carbon Credits and RECs -- 8.5.2 Fighting Energy Poverty -- 8.6 Conclusion -- References -- Part 3: The Impact of Blockchain on the Financial Industry -- Chapter 9 Process Innovation and Unification of KYC Document Management System with Blockchain in Banking -- 9.1 Introduction -- 9.2 Blockchain -- 9.3 Blockchain Technology Applications Sectors -- 9.4 Know Your Customer (KYC) -- 9.4.1 KYC Advantages -- 9.4.2 KYC Document List -- 9.4.3 Re-KYC -- 9.4.4 Types of KYC Verification -- 9.4.5 KYC Through Manual Verification Process -- 9.4.6 Typical KYC Verification Process - Issues and Challenges. 9.5 Electronic Know Your Customer (e-KYC) -- 9.5.1 e-KYC Documents Management System Using Blockchain -- 9.6 Blockchain KYC Verification Process Advantages -- 9.7 Taxonomy of Blockchain Systems -- 9.8 Literature Survey -- 9.9 Potential Use-Cases of Blockchain Technology in Banks -- 9.10 Blockchain KYC-AML Solution -- 9.11 Conclusion -- References -- Chapter 10 Applying Blockchain Technology to Address NPA Issues During the COVID-19 Pandemic -- 10.1 Introduction -- 10.2 ACT 1: Foundation of Non-Performing Assets Management and Blockchain Technology -- 10.3 Induction to Non-Performing Assets -- 10.4 Charter for NPA Management -- 10.5 Reasons for Growth of NPAs -- 10.6 Induction to Blockchain Technology -- 10.7 Possible Applications of Blockchain Technology -- 10.8 ACT II Confrontation Stage -- 10.9 Investigation of Loan Quality-Related Issues in the Indian Banking System -- 10.10 Stage 3 - Treatment Stage for Bad Loans Through Blockchain in Indian Banks -- 10.11 The Challenges of the Blockchain Technology in Financial Sector -- 10.12 Conclusion -- References -- Chapter 11 Blockchain and Smart Contracts for Insurance Industry -- 11.1 Introduction -- 11.1.1 Blockchain in Insurance -- 11.1.2 Blockchain in Insurance Applications -- 11.2 Smart Contracts by Insurance Providers Using Blockchain Technologies -- 11.2.1 Blockchain: A Built-In Data -- 11.2.2 Advanced Insurance Automation -- 11.2.3 Cyber Security in Insurance through Blockchain -- 11.3 Review of Literature -- 11.4 Opportunities Provided by Blockchain Technology -- 11.5 How Blockchain Technologies Work in Insurance Companies -- 11.6 Challenges Posed by Blockchain -- 11.6.1 Technologies Leveraging Technologies -- 11.6.2 Strategic Alliances -- 11.6.3 New Product Development -- 11.7 Conclusion -- References -- Chapter 12 How Blockchain Can Transform the Financial Services Industry -- 12.1 Introduction. 12.2 Literature Review. |
| Record Nr. | UNINA-9910623987903321 |
Sood Kiran
|
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| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational Intelligence for Autonomous Finance
| Computational Intelligence for Autonomous Finance |
| Autore | Gupta Deepak |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (337 pages) |
| Altri autori (Persone) |
GuptaMukul
DhanarajRajesh Kumar BalusamyBalamurugan GuptaParth M |
| Collana | Fintech in a Sustainable Digital Society Series |
| ISBN |
9781394233236
139423323X 9781394233250 1394233256 9781394233243 1394233248 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910908384403321 |
|
Gupta Deepak
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||
| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational Intelligence in Internet of Agricultural Things / / edited by M. G. Sumithra, Malathy Sathyamoorthy, M. Manikandan, Rajesh Kumar Dhanaraj, Mariya Ouaissa
| Computational Intelligence in Internet of Agricultural Things / / edited by M. G. Sumithra, Malathy Sathyamoorthy, M. Manikandan, Rajesh Kumar Dhanaraj, Mariya Ouaissa |
| Autore | Sumithra M. G |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (464 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
SathyamoorthyMalathy
ManikandanM DhanarajRajesh Kumar OuaissaMariya |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Artificial intelligence Agriculture Computational Intelligence Artificial Intelligence |
| ISBN | 3-031-67450-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | -- 1: Computational Intelligence and Internet of Things in the Agriculture Sector: An Introduction. -- 2: Role of Big Data Analytics in Intelligent Agriculture. -- 3: Machine learning-based remote monitoring and predictive analytics system for apple harvest storage: A statistical model based approach. -- 4: Revolutionizing Agriculture: Integrating IoT Cloud, And Machine Learning for Smart Farm Monitoring and Precision Agriculture. -- 5: Impact of Advanced Sensing Technologies in Agriculture with Soil, Crop, Climate and Farmland-based approaches using Internet of Things. -- 6: An analytical approach and concept mapping of agricultural issues using deep learning techniques. -- 7: Explainable AI for next generation Agriculture - Current Scenario and Future Prospects. -- 8: Barriers to implementing computational intelligence-based agriculture system. -- 9: Agri-Chain: A Blockchain-Empowered Smart Solution for Agricultural Industry. -- 10: Exploiting Internet of Things and AI-Enabled for Real-Time Decision Support in Precision Farming Practices. -- 11: Advancing Plant Disease Detection with Hybrid Models: Vision Transformer and CNN-Based Approaches. -- 12: Optimizing Agricultural Risk Management with Hybrid Block-chain and Fog Computing Architectures for Secure and Efficient Data Handling. -- 13: Innovating with Quantum Computing Approaches in Block-chain for Enhanced Security and Data Privacy in Agricultural IoT Systems. -- 14: Implementing Fog Computing in Precision Agriculture for Real-time Soil Health Monitoring and Data Management. -- 15: Empowering Farmers: An AI-Based Solution for Agricultural Challenges. -- 16: Artificial Intelligence in Agriculture: Potential Applications and Future Aspects. -- 17: Case study on Smart irrigation using Internet of Things and XAI Techniques. |
| Record Nr. | UNINA-9910882897703321 |
|
Sumithra M. G
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||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cyber Physical Energy Systems
| Cyber Physical Energy Systems |
| Autore | Sagar Shrddha |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (564 pages) |
| Altri autori (Persone) |
PoongodiT
DhanarajRajesh Kumar PadmanabanSanjeevikumar |
| ISBN |
9781394173006
1394173008 9781394172986 1394172982 9781394172993 1394172990 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Cyber-Physical Systems: A Control and Energy Approach -- 1.1 Introduction -- 1.1.1 Background and Motivation -- 1.1.2 Testbeds, Revisions, and a Safety Study for Cyber-Physical Energy Systems -- 1.1.3 CPES Test Chamber -- 1.1.4 Significance and Contributions of Testbed -- 1.1.5 Testbed Setup -- 1.1.6 Illustration of Hybrid CPES Testbed Structure -- 1.2 Studies on CPES Safety -- 1.2.1 Attacks in the CPES System -- 1.2.2 Evaluation of Attack Impacts on CPES -- 1.2.3 CPES's Assault Detection Algorithms -- 1.2.4 CPES's Assault Mitigation and Defense Systems -- 1.2.5 Dangerous Imagery -- 1.2.6 Attack Database -- 1.3 Threat Evaluation -- 1.4 Theory of Cyber-Physical Systems Risk -- 1.4.1 Challenger Type -- 1.4.2 Attack Type -- 1.5 Threat Evaluation Methodology -- 1.5.1 Cyber-System Layer -- 1.5.2 Physical-System Layer -- 1.6 Experimental Setup for Cross-Layer Firmware Threats -- 1.6.1 Risk Model -- 1.6.2 Threat Evaluation -- 1.7 Conclusion -- References -- Chapter 2 Optimization Techniques for Energy Management in Microgrid -- 2.1 Introduction -- 2.1.1 Microgrid Systems -- 2.1.2 Energy Management System -- 2.1.3 Energy Management of Distribution System -- 2.1.4 Techniques to Take Into Account While Implementing the EMS -- 2.1.5 Strategies for Reducing Risk -- 2.1.6 Monitoring Power Systems -- 2.1.7 Demand Response, Price Strategy, and Demand Side Management -- 2.2 Explanation Methods for EMS -- 2.3 EQN EMS on an Arithmetic Optimization Basis -- 2.4 Heuristic-Oriented Methods to EMS Problem-Solving -- 2.5 EMS Solution Techniques Using Meta-Heuristics -- 2.6 Alternative EMS Implementation Strategies -- 2.6.1 SCADA System -- 2.7 Conclusion and Viewpoints -- References -- Chapter 3 Cyber-Physical Energy Systems for Smart Grid: Reliable Distribution -- 3.1 Introduction.
3.1.1 Need for Sustainable and Efficient Power Generation Through Smart Grid Technology and Cyber-Physical Technologies -- 3.1.2 CPES: The Integration of Physical and Digital Worlds -- 3.2 Cyber-Physical Energy Systems (CPES) -- 3.3 Forming Energy Systems -- 3.4 Energy Efficiency -- 3.4.1 CPES Usage on Smart Grids -- 3.5 Smart Grids -- 3.6 Cyber-Physical Systems -- 3.7 SG: A CPS Viewpoint -- 3.7.1 Challenges and Solutions for Coordinating Smart Grids and Cyber-Physical Systems -- 3.7.2 Techniques of Correspondence -- 3.7.3 Data Protection -- 3.7.4 Data Skill and Engineering -- 3.7.5 Distributed Computation -- 3.7.6 Distributed Intellect -- 3.7.7 Distributed Optimization -- 3.7.8 Distributed Controller -- 3.8 Upcoming Prospects and Contests -- 3.8.1 Big Data -- 3.8.2 Cloud Computing -- 3.8.3 IoT -- 3.8.4 Network Science -- 3.8.5 Regulation and Guidelines -- 3.9 Conclusion -- References -- Chapter 4 Evolution of AI in CPS: Enhancing Technical Capabilities and Human Interactions -- 4.1 Introduction to Cyber-Physical System -- 4.2 The Cyber-Physical Systems Architecture -- 4.2.1 5C Architecture or CPS -- 4.2.1.1 Connection -- 4.2.1.2 Conversion -- 4.2.1.3 Cyber -- 4.2.1.4 Knowledge -- 4.2.1.5 Configuration -- 4.3 Cyber-Physical Systems as Real-Time Applications -- 4.3.1 Robotics Distributed -- 4.3.2 Manufacturing -- 4.3.3 Distribution of Water -- 4.3.4 Smart Greenhouses -- 4.3.5 Healthcare -- 4.3.6 Transportation -- 4.4 Impact of AI on Cyber-Physical Systems -- 4.5 Policies -- 4.6 Expected Benefits and Core Promises -- 4.7 Unintended Consequences and Implications for Policy -- 4.7.1 Negative Social Impacts -- 4.7.2 Cybersecurity Risks -- 4.7.3 Impact on the Environment -- 4.7.4 Ethical Issues -- 4.7.5 Policy Implications -- 4.8 Employment and Delegation of Tasks -- 4.9 Safety, Responsibility, and Liability -- 4.10 Privacy Concerns. 4.10.1 Data Collection and Use -- 4.10.2 Data Security -- 4.10.3 Data Sharing -- 4.10.4 Bias and Discrimination -- 4.10.5 User Empowerment -- 4.11 Social Relations -- 4.11.1 Cyber-Physical Systems and Transport -- 4.11.2 Trade of Dual-Use Technology -- 4.11.3 Civil Liberties (Data Protection, Privacy, etc.) -- 4.11.4 Safety (Such as Risk Analysis, Product Safety, etc.) -- 4.11.5 Healthcare (Medical Devices, Clinical Trials, and E-Health Devices) -- 4.11.6 Energy and Environment -- 4.11.7 Horizontal Legal Issues (Cross-Committee Considerations) -- 4.12 Economic Study on CPS -- 4.12.1 Better Resource Allocation -- 4.12.2 Enhanced Marketability -- 4.12.3 Robustness and Resilience -- 4.12.4 Regulatory Compliance -- 4.12.5 Making Decisions in Real-Time -- 4.13 Case Studies -- 4.13.1 The Daily Lives of Older Persons and Disabled Individuals with CPS -- 4.13.2 CPS in Healthcare -- 4.13.3 CPS for Security and Safety -- 4.14 Conclusion -- References -- Chapter 5 IoT Technology Enables Sophisticated Energy Management in Smart Factory -- 5.1 Introduction -- 5.2 IOT Overview -- 5.2.1 The Evolution of the Internet -- 5.2.2 IoT Sensing -- 5.2.3 IOT Data Protocol and Architecture -- 5.3 IOT Enabling Technology -- 5.3.1 Application Domain -- 5.3.2 Middleware Domain -- 5.3.3 Network Domain -- 5.3.4 Object Domain -- 5.4 IOT in Energy Sector -- 5.4.1 Internet of Things and Energy Generation -- 5.5 Challenges of Applying IOT -- 5.6 Reference Architecture for IoT-Based Smart Factory -- 5.7 Characteristics of Smart Factory -- 5.8 Challenges for IoT-Based Smart Industry -- 5.9 How IoT Will Support Energy Management in Smart Factory -- 5.10 IoT Energy Management Architecture for Industrial Applications -- 5.10.1 IoT-Based Energy Management Technology -- 5.10.2 Energy Harvesting -- 5.11 Case Study: Smart Factory -- 5.11.1 Supply Side -- 5.11.2 Photovoltaic Power Generation. 5.11.3 Smart Micro-Grid -- 5.11.4 Demand Side -- 5.11.5 Virtualization -- 5.12 Conclusion -- References -- Chapter 6 IOT-Based Advanced Energy Management in Smart Factories -- 6.1 Introduction -- 6.2 Smart Factory Benefits of IOT-Based Advanced Energy Management -- 6.3 Role of IOT Technology in Energy Management -- 6.4 Developing an IOT Information Model for Energy Efficiency -- 6.5 Integrating Intelligent Energy Systems (IES) and Demand Response (DR) -- 6.6 How to Accurately Measure and Manage Your Energy Usage -- 6.7 Introduction to Energy Efficiency Measures -- 6.8 Identifying Opportunities to Reduce Energy Use -- 6.9 Monitoring and Measuring Energy Usage -- 6.10 Establishing Accounting and Incentives -- 6.11 Sustaining the Long-Term Benefits of Optimized Energy Usage -- 6.12 Role of Cyber Security When Implementing IoT-Based Advanced Energy Solutions -- 6.13 Materials Required in Smart Factories -- 6.14 Methods in IoT-Based Smart Factory Implementation -- 6.15 Steps for Developing an IoT-Based Energy Management System -- 6.15.1 Assess Current Energy Usage -- 6.15.2 Develop an Energy Conservation Plan -- 6.15.3 Implement IoT Technology -- 6.15.4 Monitor Results -- 6.16 Challenges For Adopting IoT-Based Energy Management Systems -- 6.16.1 Big Data and Analytics -- 6.16.2 Connectivity Constraints -- 6.16.3 Data Security and Privacy Issues -- 6.16.4 Device Troubleshooting -- 6.17 Recommendations for Overcoming the Challenges With Implementing IoT-Based Advanced Energy Solution -- 6.17.1 IoT-Enabled Automation -- 6.17.2 Smart Sensors -- 6.17.3 Predictive Analytics -- 6.18 Case Studies -- 6.18.1 Automated Demand Response (ADR) -- 6.18.2 Automated Maintenance -- 6.18.3 Predictive Analytics -- 6.19 Case Studies for Successful Implementation -- 6.20 Applications -- 6.21 Different Techniques for Monitoring and Control of IoT Devices. 6.22 Literature Survey -- 6.23 Conclusion -- References -- Chapter 7 Challenges in Ensuring Security for Smart Energy Management Chapter Systems Based on CPS -- 7.1 Introduction -- 7.1.1 Brief Overview of Smart Energy Management Systems and Cyber-Physical Systems -- 7.1.2 Importance of Security in CPS-Based Smart Energy Management -- 7.2 Cyber-Physical Systems and Smart Energy Management -- 7.2.1 CPS Architecture and Components -- 7.2.2 Types of CPS-Based Smart Energy Management Systems -- 7.2.3 Common Communication Protocols Used in CPS-Based Smart Energy Management -- 7.2.4 Cyber Security Threats in CPS-Based Systems -- 7.3 Security Challenges in CPS-Based Smart Energy Management -- 7.3.1 Cyber Security Threats to CPS-Based Smart Energy Management Systems -- 7.3.2 Vulnerabilities of Communication Protocols Used in Smart Energy Management -- 7.3.3 Attack Vectors for Compromising CPS-Based Smart Energy Management Systems -- 7.4 Cyber Security Standards and Guidelines for Smart Energy Management -- 7.4.1 Cyber Security Incidents in Smart Energy Management -- 7.5 Conclusion -- References -- Chapter 8 Security Challenges in CPS-Based Smart Energy Management -- 8.1 Introduction -- 8.2 CPS Architecture -- 8.3 The Driving Forces for CPS -- 8.3.1 Big Data -- 8.3.2 Cloud -- 8.3.3 Machine-to-Machine Communication and Wireless Sensor Networks -- 8.3.4 Mechatronics -- 8.3.5 Cybernetics -- 8.3.6 Systems of Systems -- 8.4 Advances in Cyber-Physical Systems -- 8.4.1 Application Domains of CPS -- 8.4.1.1 Industrial Transformation -- 8.4.1.2 Smart Grid -- 8.4.1.3 Healthcare -- 8.4.1.4 Smart Parking System -- 8.4.1.5 Household CPS -- 8.4.1.6 Aerospace -- 8.4.1.7 Agriculture -- 8.4.1.8 Construction -- 8.5 Energy Management through CPS -- 8.5.1 Energy Management of CPS for Smart Grid -- 8.5.2 Energy Management of CPS for Smart Building Structure. 8.5.3 Energy Management of CPS for Autonomous Electric Vehicles in Smart Transportation. |
| Record Nr. | UNINA-9910911295103321 |
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Sagar Shrddha
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Deep Learning for Targeted Treatments : Transformation in Healthcare
| Deep Learning for Targeted Treatments : Transformation in Healthcare |
| Autore | Malviya Rishabha |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (458 pages) |
| Altri autori (Persone) |
GhineaGheorghita
DhanarajRajesh Kumar BalusamyBalamurugan SundramSonali |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-85798-8
1-119-85797-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgement -- 1 Deep Learning and Site-Specific Drug Delivery: The Future and Intelligent Decision Support for Pharmaceutical Manufacturing Science -- 1.1 Introduction -- 1.2 Drug Discovery, Screening and Repurposing -- 1.3 DL and Pharmaceutical Formulation Strategy -- 1.3.1 DL in Dose and Formulation Prediction -- 1.3.2 DL in Dissolution and Release Studies -- 1.3.3 DL in the Manufacturing Process -- 1.4 Deep Learning Models for Nanoparticle-Based Drug Delivery -- 1.4.1 Nanoparticles With High Drug Delivery Capacities Using Perturbation Theory -- 1.4.2 Artificial Intelligence and Drug Delivery Algorithms -- 1.4.3 Nanoinformatics -- 1.5 Model Prediction for Site-Specific Drug Delivery -- 1.5.1 Prediction of Mode and a Site-Specific Action -- 1.5.2 Precision Medicine -- 1.6 Future Scope and Challenges -- 1.7 Conclusion -- References -- 2 Role of Deep Learning, Blockchain and Internet of Things in Patient Care -- 2.1 Introduction -- 2.2 IoT and WBAN in Healthcare Systems -- 2.2.1 IoT in Healthcare -- 2.2.2 WBAN -- 2.2.2.1 Key Features of Medical Networks in the Wireless Body Area -- 2.2.2.2 Data Transmission & -- Storage Health -- 2.2.2.3 Privacy and Security Concerns in Big Data -- 2.3 Blockchain Technology in Healthcare -- 2.3.1 Importance of Blockchain -- 2.3.2 Role of Blockchain in Healthcare -- 2.3.3 Benefits of Blockchain in Healthcare Applications -- 2.3.4 Elements of Blockchain -- 2.3.5 Situation Awareness and Healthcare Decision Support with Combined Machine Learning and Semantic Modeling -- 2.3.6 Mobile Health and Remote Monitoring -- 2.3.7 Different Mobile Health Application with Description of Usage in Area of Application -- 2.3.8 Patient-Centered Blockchain Mode -- 2.3.9 Electronic Medical Record -- 2.3.9.1 The Most Significant Barriers to Adoption Are.
2.3.9.2 Concern Regarding Negative Unintended Consequences of Technology -- 2.4 Deep Learning in Healthcare -- 2.4.1 Deep Learning Models -- 2.4.1.1 Recurrent Neural Networks (RNN) -- 2.4.1.2 Convolutional Neural Networks (CNN) -- 2.4.1.3 Deep Belief Network (DBN) -- 2.4.1.4 Contrasts Between Models -- 2.4.1.5 Use of Deep Learning in Healthcare -- 2.5 Conclusion -- 2.6 Acknowledgments -- References -- 3 Deep Learning on Site-Specific Drug Delivery System -- 3.1 Introduction -- 3.2 Deep Learning -- 3.2.1 Types of Algorithms Used in Deep Learning -- 3.2.1.1 Convolutional Neural Networks (CNNs) -- 3.2.1.2 Long Short-Term Memory Networks (LSTMs) -- 3.2.1.3 Recurrent Neural Networks -- 3.2.1.4 Generative Adversarial Networks (GANs) -- 3.2.1.5 Radial Basis Function Networks -- 3.2.1.6 Multilayer Perceptron -- 3.2.1.7 Self-Organizing Maps -- 3.2.1.8 Deep Belief Networks -- 3.3 Machine Learning and Deep Learning Comparison -- 3.4 Applications of Deep Learning in Drug Delivery System -- 3.5 Conclusion -- References -- 4 Deep Learning Advancements in Target Delivery -- 4.1 Introduction: Deep Learning and Targeted Drug Delivery -- 4.2 Different Models/Approaches of Deep Learning and Targeting Drug -- 4.3 QSAR Model -- 4.3.1 Model of Deep Long-Term Short-Term Memory -- 4.3.2 RNN Model -- 4.3.3 CNN Model -- 4.4 Deep Learning Process Applications in Pharmaceutical -- 4.5 Techniques for Predicting Pharmacotherapy -- 4.6 Approach to Diagnosis -- 4.7 Application -- 4.7.1 Deep Learning in Drug Discovery -- 4.7.2 Medical Imaging and Deep Learning Process -- 4.7.3 Deep Learning in Diagnostic and Screening -- 4.7.4 Clinical Trials Using Deep Learning Models -- 4.7.5 Learning for Personalized Medicine -- 4.8 Conclusion -- Acknowledgment -- References -- 5 Deep Learning and Precision Medicine: Lessons to Learn for the Preeminent Treatment for Malignant Tumors. 5.1 Introduction -- 5.2 Role of DL in Gene Identification, Unique Genomic Analysis, and Precise Cancer Diagnosis -- 5.2.1 Gene Identification and Genome Data -- 5.2.2 Image Diagnosis -- 5.2.3 Radiomics, Radiogenomics, and Digital Biopsy -- 5.2.4 Medical Image Analysis in Mammography -- 5.2.5 Magnetic Resonance Imaging -- 5.2.6 CT Imaging -- 5.3 DL in Next-Generation Sequencing, Biomarkers, and Clinical Validation -- 5.3.1 Next-Generation Sequencing -- 5.3.2 Biomarkers and Clinical Validation -- 5.4 DL and Translational Oncology -- 5.4.1 Prediction -- 5.4.2 Segmentation -- 5.4.3 Knowledge Graphs and Cancer Drug Repurposing -- 5.4.4 Automated Treatment Planning -- 5.4.5 Clinical Benefits -- 5.5 DL in Clinical Trials-A Necessary Paradigm Shift -- 5.6 Challenges and Limitations -- 5.7 Conclusion -- References -- 6 Personalized Therapy Using Deep Learning Advances -- 6.1 Introduction -- 6.2 Deep Learning -- 6.2.1 Convolutional Neural Networks -- 6.2.2 Autoencoders -- 6.2.3 Deep Belief Network (DBN) -- 6.2.4 Deep Reinforcement Learning -- 6.2.5 Generative Adversarial Network -- 6.2.6 Long Short-Term Memory Networks -- References -- 7 Tele-Health Monitoring Using Artificial Intelligence Deep Learning Framework -- 7.1 Introduction -- 7.2 Artificial Intelligence -- 7.2.1 Types of Artificial Intelligence -- 7.2.1.1 Machine Intelligence -- 7.2.1.2 Types of Machine Intelligence -- 7.2.2 Applications of Artificial Intelligence -- 7.2.2.1 Role in Healthcare Diagnostics -- 7.2.2.2 AI in Telehealth -- 7.2.2.3 Role in Structural Health Monitoring -- 7.2.2.4 Role in Remote Medicare Management -- 7.2.2.5 Predictive Analysis Using Big Data -- 7.2.2.6 AI's Role in Virtual Monitoring of Patients -- 7.2.2.7 Functions of Devices -- 7.2.2.8 Clinical Outcomes Through Remote Patient Monitoring -- 7.2.2.9 Clinical Decision Support. 7.2.3 Utilization of Artificial Intelligence in Telemedicine -- 7.2.3.1 Artificial Intelligence-Assisted Telemedicine -- 7.2.3.2 Telehealth and New Care Models -- 7.2.3.3 Strategy of Telecare Domain -- 7.2.3.4 Role of AI-Assisted Telemedicine in Various Domains -- 7.3 AI-Enabled Telehealth: Social and Ethical Considerations -- 7.4 Conclusion -- References -- 8 Deep Learning Framework for Cancer Diagnosis and Treatment -- 8.1 Deep Learning: An Emerging Field for Cancer Management -- 8.2 Deep Learning Framework in Diagnosis and Treatment of Cancer -- 8.3 Applications of Deep Learning in Cancer Diagnosis -- 8.3.1 Medical Imaging Through Artificial Intelligence -- 8.3.2 Biomarkers Identification in the Diagnosis of Cancer Through Deep Learning -- 8.3.3 Digital Pathology Through Deep Learning -- 8.3.4 Application of Artificial Intelligence in Surgery -- 8.3.5 Histopathological Images Using Deep Learning -- 8.3.6 MRI and Ultrasound Images Through Deep Learning -- 8.4 Clinical Applications of Deep Learning in the Management of Cancer -- 8.5 Ethical Considerations in Deep Learning-Based Robotic Therapy -- 8.6 Conclusion -- Acknowledgments -- References -- 9 Applications of Deep Learning in Radiation Therapy -- 9.1 Introduction -- 9.2 History of Radiotherapy -- 9.3 Principal of Radiotherapy -- 9.4 Deep Learning -- 9.5 Radiation Therapy Techniques -- 9.5.1 External Beam Radiation Therapy -- 9.5.2 Three-Dimensional Conformal Radiation Therapy (3D-CRT) -- 9.5.3 Intensity Modulated Radiation Therapy (IMRT) -- 9.5.4 Image-Guided Radiation Therapy (IGRT) -- 9.5.5 Intraoperative Radiation Therapy (IORT) -- 9.5.6 Brachytherapy -- 9.5.7 Stereotactic Radiosurgery (SRS) -- 9.6 Different Role of Deep Learning with Corresponding Role of Medical Physicist -- 9.6.1 Deep Learning in Patient Assessment -- 9.6.1.1 Radiotherapy Results Prediction. 9.6.1.2 Respiratory Signal Prediction -- 9.6.2 Simulation Computed Tomography -- 9.6.3 Targets and Organs-at-Risk Segmentation -- 9.6.4 Treatment Planning -- 9.6.4.1 Beam Angle Optimization -- 9.6.4.2 Dose Prediction -- 9.6.5 Other Role of Deep Learning in Corresponds with Medical Physicists -- 9.7 Conclusion -- References -- 10 Application of Deep Learning in Radiation Therapy -- 10.1 Introduction -- 10.2 Radiotherapy -- 10.3 Principle of Deep Learning and Machine Learning -- 10.3.1 Deep Neural Networks (DNN) -- 10.3.2 Convolutional Neural Network -- 10.4 Role of AI and Deep Learning in Radiation Therapy -- 10.5 Platforms for Deep Learning and Tools for Radiotherapy -- 10.6 Radiation Therapy Implementation in Deep Learning -- 10.6.1 Deep Learning and Imaging Techniques -- 10.6.2 Image Segmentation -- 10.6.3 Lesion Segmentation -- 10.6.4 Computer-Aided Diagnosis -- 10.6.5 Computer-Aided Detection -- 10.6.6 Quality Assurance -- 10.6.7 Treatment Planning -- 10.6.8 Treatment Delivery -- 10.6.9 Response to Treatment -- 10.7 Prediction of Outcomes -- 10.7.1 Toxicity -- 10.7.2 Survival and the Ability to Respond -- 10.8 Deep Learning in Conjunction With Radiomoic -- 10.9 Planning for Treatment -- 10.9.1 Optimization of Beam Angle -- 10.9.2 Prediction of Dose -- 10.10 Deep Learning's Challenges and Future Potential -- 10.11 Conclusion -- References -- 11 Deep Learning Framework for Cancer -- 11.1 Introduction -- 11.2 Brief History of Deep Learning -- 11.3 Types of Deep Learning Methods -- 11.4 Applications of Deep Learning -- 11.4.1 Toxicity Detection for Different Chemical Structures -- 11.4.2 Mitosis Detection -- 11.4.3 Radiology or Medical Imaging -- 11.4.4 Hallucination -- 11.4.5 Next-Generation Sequencing (NGS) -- 11.4.6 Drug Discovery -- 11.4.7 Sequence or Video Generation -- 11.4.8 Other Applications -- 11.5 Cancer -- 11.5.1 Factors. 11.5.1.1 Heredity. |
| Record Nr. | UNINA-9910595599103321 |
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Malviya Rishabha
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| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Homomorphic Encryption for Financial Cryptography [[electronic resource] ] : Recent Inventions and Challenges / / edited by V. Seethalakshmi, Rajesh Kumar Dhanaraj, S. Suganyadevi, Mariya Ouaissa
| Homomorphic Encryption for Financial Cryptography [[electronic resource] ] : Recent Inventions and Challenges / / edited by V. Seethalakshmi, Rajesh Kumar Dhanaraj, S. Suganyadevi, Mariya Ouaissa |
| Autore | Seethalakshmi V |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (302 pages) |
| Disciplina | 005.824 |
| Altri autori (Persone) |
DhanarajRajesh Kumar
SuganyadeviS OuaissaMariya |
| Soggetto topico |
Data protection
Financial engineering Cryptography Data encryption (Computer science) Data protection—Law and legislation Data and Information Security Financial Technology and Innovation Cryptology Privacy |
| ISBN | 3-031-35535-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1 -- Introduction to Homomorphic Encryption for Financial Cryptography -- Chapter 2 -- Survey on Homomorphic Encryption for Financial Cryptography Workout -- Chapter 3 Improved login interface algorithm for Financial Transactions using Visual Cryptographic Authentication -- Chapter 4 Securing shared data based on Homomorphic encryption schemes -- Chapter 5 Challenges and Opportunities associated with Homomorphic Encryption for Financial Cryptography -- Chapter 6 Homomorphic Encryption based Cloud Privacy-Preserving in Remote Ecg Monitoring and Surveillance -- Chapter 7 Enhancing Encryption Security against Cypher Attacks -- Chapter 8 Biometric Based Key Generation Using AES Algorithm for Real Time Security Applications -- Chapter 9 Financial Cryptography and its application in Blockchain -- Chapter 10 Algorithmic Strategies for Solving Complex Problems in Financial Cryptography.,- Chapter 11 Various Attacks on the implementation of Cryptographic Algorithms -- Chapter 12 A Survey on Private Keyword Sorting and Searching using Homomorphic Encryption -- Chapter 13 Multivariate Cryptosystem Based on a Quadratic Equation to Eliminate the Outliers Using Homomorphic Encryption Scheme. . |
| Record Nr. | UNISA-996547955403316 |
|
Seethalakshmi V
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Homomorphic Encryption for Financial Cryptography : Recent Inventions and Challenges / / edited by V. Seethalakshmi, Rajesh Kumar Dhanaraj, S. Suganyadevi, Mariya Ouaissa
| Homomorphic Encryption for Financial Cryptography : Recent Inventions and Challenges / / edited by V. Seethalakshmi, Rajesh Kumar Dhanaraj, S. Suganyadevi, Mariya Ouaissa |
| Autore | Seethalakshmi V |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (302 pages) |
| Disciplina | 005.824 |
| Altri autori (Persone) |
DhanarajRajesh Kumar
SuganyadeviS OuaissaMariya |
| Soggetto topico |
Data protection
Financial engineering Cryptography Data encryption (Computer science) Data protection—Law and legislation Data and Information Security Financial Technology and Innovation Cryptology Privacy |
| ISBN | 3-031-35535-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1 -- Introduction to Homomorphic Encryption for Financial Cryptography -- Chapter 2 -- Survey on Homomorphic Encryption for Financial Cryptography Workout -- Chapter 3 Improved login interface algorithm for Financial Transactions using Visual Cryptographic Authentication -- Chapter 4 Securing shared data based on Homomorphic encryption schemes -- Chapter 5 Challenges and Opportunities associated with Homomorphic Encryption for Financial Cryptography -- Chapter 6 Homomorphic Encryption based Cloud Privacy-Preserving in Remote Ecg Monitoring and Surveillance -- Chapter 7 Enhancing Encryption Security against Cypher Attacks -- Chapter 8 Biometric Based Key Generation Using AES Algorithm for Real Time Security Applications -- Chapter 9 Financial Cryptography and its application in Blockchain -- Chapter 10 Algorithmic Strategies for Solving Complex Problems in Financial Cryptography.,- Chapter 11 Various Attacks on the implementation of Cryptographic Algorithms -- Chapter 12 A Survey on Private Keyword Sorting and Searching using Homomorphic Encryption -- Chapter 13 Multivariate Cryptosystem Based on a Quadratic Equation to Eliminate the Outliers Using Homomorphic Encryption Scheme. . |
| Record Nr. | UNINA-9910736002003321 |
|
Seethalakshmi V
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Quantum Blockchain : An Emerging Cryptographic Paradigm
| Quantum Blockchain : An Emerging Cryptographic Paradigm |
| Autore | Rajasekar Vani |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (386 pages) |
| Altri autori (Persone) |
DhanarajRajesh Kumar
IslamS. K. Hafizul BalusamyBalamurugan HsuChing-Hsien |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-83672-7
1-119-83671-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910590094603321 |
|
Rajasekar Vani
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||
| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Quantum blockchain : an emerging cryptographic paradigm / / edited by Rajesh Kumar Dhanaraj, Vani Rajasekar, SK Hafizul Islam, Balamurugan Balusamy and Ching‐Hsien Hsu
| Quantum blockchain : an emerging cryptographic paradigm / / edited by Rajesh Kumar Dhanaraj, Vani Rajasekar, SK Hafizul Islam, Balamurugan Balusamy and Ching‐Hsien Hsu |
| Pubbl/distr/stampa | Beverly, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , [2022] |
| Descrizione fisica | 1 online resource (386 pages) |
| Disciplina | 005.74 |
| Soggetto topico |
Blockchains (Databases)
Cryptography Quantum computing |
| ISBN |
1-119-83672-7
1-119-83671-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front Matter -- Introduction to Classical Cryptography / Vani Rajasekar, J Premalatha, Rajesh Kumar Dhanaraj, Oana Geman -- Quantum Cryptographic Techniques / S Malathy, M Santhiya, Rajesh Kumar Dhanaraj -- Evolution of Quantum Blockchain / Dinesh Komarasamy, J Jenita Hermina -- Development of the Quantum Bitcoin (BTC) / Gaurav Dhuriya, Aradhna Saini, Prashant Johari -- A Conceptual Model for Quantum Blockchain / P Vijayalakshmi, Abraham Dinakaran, Korhan Cengiz -- Challenges and Research Perspective of Post-Quantum Blockchain / K Venu, B Krishnakumar -- Post-Quantum Cryptosystems for Blockchain / K Tamil Selvi, R Thamilselvan -- Post-Quantum Confidential Transaction Protocols / R Manjula Devi, P Keerthika, P Suresh, R Venkatesan, M Sangeetha, C Sagana, K Devendran -- A Study on Post-Quantum Blockchain: The Next Innovation for Smarter and Safer Cities / GK Kamalam, RS Shudapreyaa -- Quantum Protocols for Hash-Based Blockchain / K Sathya, J Premalatha, Balamurugan Balusamy, Sarumathi Murali -- Post-Quantum Blockchain-Enabled Services in Scalable Smart Cities / Kumar Prateek, Soumyadev Maity -- Security Threats and Privacy Challenges in the Quantum Blockchain: A Contemporary Survey / K Sentamilselvan, P Suresh, G K Kamalam, H Muthukrishnan, K Logeswaran, P Keerthika -- Exploration of Quantum Blockchain Techniques Towards Sustainable Future Cybersecurity / H Muthukrishnan, P Suresh, K Logeswaran, K Sentamilselvan -- Estimation of Bitcoin Price Trends Using Supervised Learning Approaches / Prasannavenkatesan Theerthagiri |
| Record Nr. | UNINA-9910831036103321 |
| Beverly, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||