Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Cognitive Intelligence and Big Data in Healthcare
| Cognitive Intelligence and Big Data in Healthcare |
| Autore | Sumathi D |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (415 pages) |
| Altri autori (Persone) |
PoongodiT
BalamuruganB RamasamyLakshmana Kumar |
| Collana | Artificial Intelligence and Soft Computing for Industrial Transformation Ser. |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-77198-6
1-119-77196-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Era of Computational Cognitive Techniques in Healthcare Systems -- 1.1 Introduction -- 1.2 Cognitive Science -- 1.3 Gap Between Classical Theory of Cognition -- 1.4 Cognitive Computing's Evolution -- 1.5 The Coming Era of Cognitive Computing -- 1.6 Cognitive Computing Architecture -- 1.6.1 The Internet-of-Things and Cognitive Computing -- 1.6.2 Big Data and Cognitive Computing -- 1.6.3 Cognitive Computing and Cloud Computing -- 1.7 Enabling Technologies in Cognitive Computing -- 1.7.1 Reinforcement Learning and Cognitive Computing -- 1.7.2 Cognitive Computing with Deep Learning -- 1.7.2.1 Relational Technique and Perceptual Technique -- 1.7.2.2 Cognitive Computing and Image Understanding -- 1.8 Intelligent Systems in Healthcare -- 1.8.1 Intelligent Cognitive System in Healthcare (Why and How) -- 1.9 The Cognitive Challenge -- 1.9.1 Case Study: Patient Evacuation -- 1.9.2 Case Study: Anesthesiology -- 1.10 Conclusion -- References -- 2 Proposal of a Metaheuristic Algorithm of Cognitive Computing for Classification of Erythrocytes and Leukocytes in Healthcare Informatics -- 2.1 Introduction -- 2.2 Literature Concept -- 2.2.1 Cognitive Computing Concept -- 2.2.2 Neural Networks Concepts -- 2.2.3 Convolutional Neural Network -- 2.2.4 Deep Learning -- 2.3 Materials and Methods (Metaheuristic Algorithm Proposal) -- 2.4 Case Study and Discussion -- 2.5 Conclusions with Future Research Scopes -- References -- 3 Convergence of Big Data and Cognitive Computing in Healthcare -- 3.1 Introduction -- 3.2 Literature Review -- 3.2.1 Role of Cognitive Computing in Healthcare Applications -- 3.2.2 Research Problem Study by IBM -- 3.2.3 Purpose of Big Data in Healthcare -- 3.2.4 Convergence of Big Data with Cognitive Computing -- 3.2.4.1 Smart Healthcare.
3.2.4.2 Big Data and Cognitive Computing-Based Smart Healthcare -- 3.3 Using Cognitive Computing and Big Data, a Smart Healthcare Framework for EEG Pathology Detection and Classification -- 3.3.1 EEG Pathology Diagnoses -- 3.3.2 Cognitive-Big Data-Based Smart Healthcare -- 3.3.3 System Architecture -- 3.3.4 Detection and Classification of Pathology -- 3.3.4.1 EEG Preprocessing and Illustration -- 3.3.4.2 CNN Model -- 3.3.5 Case Study -- 3.4 An Approach to Predict Heart Disease Using Integrated Big Data and Cognitive Computing in Cloud -- 3.4.1 Cloud Computing with Big Data in Healthcare -- 3.4.2 Heart Diseases -- 3.4.3 Healthcare Big Data Techniques -- 3.4.3.1 Rule Set Classifiers -- 3.4.3.2 Neuro Fuzzy Classifiers -- 3.4.3.3 Experimental Results -- 3.5 Conclusion -- References -- 4 IoT for Health, Safety, Well-Being, Inclusion, and Active Aging -- 4.1 Introduction -- 4.2 The Role of Technology in an Aging Society -- 4.3 Literature Survey -- 4.4 Health Monitoring -- 4.5 Nutrition Monitoring -- 4.6 Stress-Log: An IoT-Based Smart Monitoring System -- 4.7 Active Aging -- 4.8 Localization -- 4.9 Navigation Care -- 4.10 Fall Monitoring -- 4.10.1 Fall Detection System Architecture -- 4.10.2 Wearable Device -- 4.10.3 Wireless Communication Network -- 4.10.4 Smart IoT Gateway -- 4.10.5 Interoperability -- 4.10.6 Transformation of Data -- 4.10.7 Analyzer for Big Data -- 4.11 Conclusion -- References -- 5 Influence of Cognitive Computing in Healthcare Applications -- 5.1 Introduction -- 5.2 Bond Between Big Data and Cognitive Computing -- 5.3 Need for Cognitive Computing in Healthcare -- 5.4 Conceptual Model Linking Big Data and Cognitive Computing -- 5.4.1 Significance of Big Data -- 5.4.2 The Need for Cognitive Computing -- 5.4.3 The Association Between the Big Data and Cognitive Computing -- 5.4.4 The Advent of Cognition in Healthcare. 5.5 IBM's Watson and Cognitive Computing -- 5.5.1 Industrial Revolution with Watson -- 5.5.2 The IBM's Cognitive Computing Endeavour in Healthcare -- The IBM Watson Health and Watson Health Cloud -- Usage of Cognitive Application to Augment the Electronic Medical Record -- 5.6 Future Directions -- 5.6.1 Retail -- 5.6.2 Research -- 5.6.3 Travel -- 5.6.4 Security and Threat Detection -- 5.6.5 Cognitive Training Tools -- 5.7 Conclusion -- References -- 6 An Overview of the Computational Cognitive from a Modern Perspective, Its Techniques and Application Potential in Healthcare Systems -- 6.1 Introduction -- 6.2 Literature Concept -- 6.2.1 Cognitive Computing Concept -- 6.2.1.1 Application Potential -- 6.2.2 Cognitive Computing in Healthcare -- 6.2.3 Deep Learning in Healthcare -- 6.2.4 Natural Language Processing in Healthcare -- 6.3 Discussion -- 6.4 Trends -- 6.5 Conclusions -- References -- 7 Protecting Patient Data with 2FAuthentication -- 7.1 Introduction -- 7.2 Literature Survey -- 7.3 Two-Factor Authentication -- 7.3.1 Novel Features of Two-Factor Authentication -- 7.3.2 Two-Factor Authentication Sorgen -- 7.3.3 Two-Factor Security Libraries -- 7.3.4 Challenges for Fitness Concern -- 7.4 Proposed Methodology -- 7.5 Medical Treatment and the Preservation of Records -- 7.5.1 Remote Method of Control -- 7.5.2 Enabling Healthcare System Technology -- 7.6 Conclusion -- References -- 8 Data Analytics for Healthcare Monitoring and Inferencing -- 8.1 An Overview of Healthcare Systems -- 8.2 Need of Healthcare Systems -- 8.3 Basic Principle of Healthcare Systems -- 8.4 Design and Recommended Structure of Healthcare Systems -- 8.4.1 Healthcare System Designs on the Basis of these Parameters -- 8.4.2 Details of Healthcare Organizational Structure -- 8.5 Various Challenges in Conventional Existing Healthcare System -- 8.6 Health Informatics. 8.7 Information Technology Use in Healthcare Systems -- 8.8 Details of Various Information Technology Application Use in Healthcare Systems -- 8.9 Healthcare Information Technology Makes it Possible to Manage Patient Care and Exchange of Health Information Data, Details are Given Below -- 8.10 Barriers and Challenges to Implementation of Information Technology in Healthcare Systems -- 8.11 Healthcare Data Analytics -- 8.12 Healthcare as a Concept -- 8.13 Healthcare's Key Technologies -- 8.14 The Present State of Smart Healthcare Application -- 8.15 Data Analytics with Machine Learning Use in Healthcare Systems -- 8.16 Benefit of Data Analytics in Healthcare System -- 8.17 Data Analysis and Visualization: COVID-19 Case Study in India -- 8.18 Bioinformatics Data Analytics -- 8.18.1 Notion of Bioinformatics -- 8.18.2 Bioinformatics Data Challenges -- 8.18.3 Sequence Analysis -- 8.18.4 Applications -- 8.18.5 COVID-19: A Bioinformatics Approach -- 8.19 Conclusion -- References -- 9 Features Optimistic Approach for the Detection of Parkinson's Disease -- 9.1 Introduction -- 9.1.1 Parkinson's Disease -- 9.1.2 Spect Scan -- 9.2 Literature Survey -- 9.3 Methods and Materials -- 9.3.1 Database Details -- 9.3.2 Procedure -- 9.3.3 Pre-Processing Done by PPMI -- 9.3.4 Image Analysis and Features Extraction -- 9.3.4.1 Image Slicing -- 9.3.4.2 Intensity Normalization -- 9.3.4.3 Image Segmentation -- 9.3.4.4 Shape Features Extraction -- 9.3.4.5 SBR Features -- 9.3.4.6 Feature Set Analysis -- 9.3.4.7 Surface Fitting -- 9.3.5 Classification Modeling -- 9.3.6 Feature Importance Estimation -- 9.3.6.1 Need for Analysis of Important Features -- 9.3.6.2 Random Forest -- 9.4 Results and Discussion -- 9.4.1 Segmentation -- 9.4.2 Shape Analysis -- 9.4.3 Classification -- 9.5 Conclusion -- References -- 10 Big Data Analytics in Healthcare -- 10.1 Introduction. 10.2 Need for Big Data Analytics -- 10.3 Characteristics of Big Data -- 10.3.1 Volume -- 10.3.2 Velocity -- 10.3.3 Variety -- 10.3.4 Veracity -- 10.3.5 Value -- 10.3.6 Validity -- 10.3.7 Variability -- 10.3.8 Viscosity -- 10.3.9 Virality -- 10.3.10 Visualization -- 10.4 Big Data Analysis in Disease Treatment and Management -- 10.4.1 For Diabetes -- 10.4.2 For Heart Disease -- 10.4.3 For Chronic Disease -- 10.4.4 For Neurological Disease -- 10.4.5 For Personalized Medicine -- 10.5 Big Data: Databases and Platforms in Healthcare -- 10.6 Importance of Big Data in Healthcare -- 10.6.1 Evidence-Based Care -- 10.6.2 Reduced Cost of Healthcare -- 10.6.3 Increases the Participation of Patients in the Care Process -- 10.6.4 The Implication in Health Surveillance -- 10.6.5 Reduces Mortality Rate -- 10.6.6 Increase of Communication Between Patients and Healthcare Providers -- 10.6.7 Early Detection of Fraud and Security Threats in Health Management -- 10.6.8 Improvement in the Care Quality -- 10.7 Application of Big Data Analytics -- 10.7.1 Image Processing -- 10.7.2 Signal Processing -- 10.7.3 Genomics -- 10.7.4 Bioinformatics Applications -- 10.7.5 Clinical Informatics Application -- 10.8 Conclusion -- References -- 11 Case Studies of Cognitive Computing in Healthcare Systems: Disease Prediction, Genomics Studies, Medical Image Analysis, Patient Care, Medical Diagnostics, Drug Discovery -- 11.1 Introduction -- 11.1.1 Glaucoma -- 11.2 Literature Survey -- 11.3 Methodology -- 11.3.1 Sclera Segmentation -- 11.3.1.1 Fully Convolutional Network -- 11.3.2 Pupil/Iris Ratio -- 11.3.2.1 Canny Edge Detection -- 11.3.2.2 Mean Redness Level (MRL) -- MBP Mean Blue Mean S m S -- 11.3.2.3 Red Area Percentage (RAP) -- 11.4 Results and Discussion -- 11.4.1 Feature Extraction from Frontal Eye Images -- 11.4.1.1 Level of Mean Redness (MRL). 11.4.1.2 Percentage of Red Area (RAP). |
| Record Nr. | UNINA-9910590099203321 |
Sumathi D
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| 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) |
| Disciplina | 621.31 |
| Altri autori (Persone) |
PoongodiT
DhanarajRajesh Kumar PadmanabanSanjeevikumar |
| Soggetto topico | Microgrids (Smart power grids) - Security measures |
| 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-9911019870203321 |
|
Sagar Shrddha
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Digitization of healthcare data using blockchain / / edited by T. Poongodi
| Digitization of healthcare data using blockchain / / edited by T. Poongodi |
| Pubbl/distr/stampa | Beverly, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , [2022] |
| Descrizione fisica | 1 online resource (311 pages) |
| Disciplina | 005.74 |
| Soggetto topico |
Blockchains (Databases)
Medical informatics Medical care - Data processing |
| ISBN |
1-119-79273-8
1-119-79272-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910830962703321 |
| Beverly, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||