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 | ||
| ||
Digital Transformation : Industry 4. 0 to Society 5. 0
| Digital Transformation : Industry 4. 0 to Society 5. 0 |
| Autore | Kumar Avadhesh |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore Pte. Limited, , 2024 |
| Descrizione fisica | 1 online resource (365 pages) |
| Altri autori (Persone) |
SagarShrddha
ThangamuthuPoongodi BalamuruganB |
| Collana | Disruptive Technologies and Digital Transformations for Society 5. 0 Series |
| ISBN | 981-9981-18-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Introduction -- Contents -- Editors and Contributors -- Abbreviations -- 1 Evolution of Industry 4.0 and Its Fundamental Characteristics -- 1 Introduction -- 1.1 Industry 4.0 Introduction -- 1.2 Industry 4.0 Definitions -- 1.3 Benefits of Industry 4.0 -- 1.4 Motivations Behind the Evolution of Industry 4.0 -- 2 Industry 4.0 Concepts, State of Arts, and Challenges -- 2.1 Basic Components of Industry 4.0 -- 2.2 Characteristics of Industry 4.0 -- 2.3 State of Arts -- 2.4 Conceptualizing the Fourth Industrial Revolution -- 2.5 Goals to Consummate Industry 4.0 -- 2.6 Drivers of Industry 4.0 -- 2.7 Implementation Challenges of Industry 4.0 -- 3 Methodologies in Industry 4.0 -- 3.1 Validating Technologies/Base Technologies of Industry 4.0 -- 3.2 Nine Technology Peers of Industry 4.0 -- 3.3 Architectural Design of Industry 4.0 -- 3.4 Artificial Intelligence in Industry 4.0 -- 3.5 Processes and Interaction in Industry 4.0 -- 4 Applications, Use Cases, and Projects of Industry 4.0 -- 4.1 Influence of 5G Technologies on Industry 4.0 -- 4.2 5G Tech Support for Industry 4.0 -- 4.3 Industry 4.0 Application Scenarios Accredited by 5G -- References -- 2 Transportation System Using Deep Learning Algorithms in Industry 4.0 Towards Society 5.0 -- 1 Introduction -- 2 Deep Learning Techniques/Algorithms -- 2.1 Recursive Neural Network -- 2.2 Recurrent Neural Network (RNN) -- 2.3 Convolution Neural Network -- 2.4 Deep Generative Network -- 3 Transportation Network Representation Using Deep Learning -- 4 Various Domains that are Being Revolutionized by Deep Learning -- 4.1 Self-Driving Cars -- 4.2 Traffic Congestion Identification and Prediction -- 4.3 Predicting Vehicle Maintenance Needs -- 4.4 Public Transportation Optimization -- 5 Architecture of Convolutional Neural Network (CNN) Model -- 5.1 High-Resolution Data Collection.
5.2 CNN for Crash Predict -- 6 Traffic Flow Prediction -- 7 Urban Traffic Flow Prediction -- 8 Open Research Challenges and Future Directions -- 9 Conclusion -- References -- 3 A Brief Study of Adaptive Clustering for Self-aware Machine Analytics -- 1 Introduction -- 2 Clustering -- 2.1 Types of Clustering -- 3 Traditional Clustering Algorithm versus Bio-inspired Clustering -- 4 Self-aware Clustering -- 5 Adaptive Clustering for Industry 4.0 -- 5.1 Adaptive Clustering in Mobile Computing -- 5.2 Adaptive Clustering in Wireless Network -- 5.3 Adaptive Clustering in IoT -- 5.4 Adaptive Clustering in Cloud -- 5.5 Role of Clustering in Machine Analytics -- 5.6 Importance of Adaptive Clustering for Self-aware in Machine Analytics -- 6 Result and Discussion -- 7 Conclusion -- References -- 4 Managing Healthcare Data Using ML Algorithms and Society 5.0 -- 1 Introduction -- 2 Skin Cancer -- 2.1 Human Skin Cancer -- 2.2 Obstacles to Detecting Skin Lesions -- 2.3 Literature Survey -- 3 Methodology -- 3.1 Image Preprocessing -- 3.2 Median Filter -- 3.3 Lesion Segmentation -- 3.4 Feature Extraction -- 3.5 Feature Reduction -- 3.6 Image Classification -- 4 Digital Health Using Federated Learning -- 4.1 Federated Learning's Statistical Challenges -- 4.2 Federated Learning Communication Efficiency -- 4.3 Security and Privacy -- 4.4 Multiple-Party Computation with Security -- 4.5 Privacy Differential -- 4.6 Applications -- 5 Communal Issues that Concern Various Applications of ML in Medicine -- 5.1 Legislation -- 5.2 Interpretability and Explainability -- 5.3 Privacy and Anonymity -- 5.4 Ethics and Fairness -- 6 Conclusion -- References -- 5 Cloud Computing-Everything as a Cloud Service in Industry 4.0 -- 1 Introduction -- 1.1 Introduction to Cloud Computing -- 1.2 Why We Need Cloud Computing? -- 2 Different Services in Cloud Computing. 2.1 Infrastructure as a Service: [IaaS] -- 2.2 Platform as Service: [PaaS] -- 2.3 Software as a Service: [SaaS] -- 3 Different Cloud Models -- 3.1 Public Cloud -- 3.2 Private Cloud -- 3.3 Hybrid Cloud -- 3.4 Multi Cloud -- 4 Applications of Cloud -- 4.1 Cloud in Business Sector -- 4.2 Cloud in Education System -- 4.3 Cloud in Medical and Healthcare -- 4.4 Cloud in Software Development -- 5 Comparison of Various Cloud Platforms -- 5.1 Resource Allocation on All Models -- 6 Conclusion -- References -- 6 Glimpse of Cognitive Computing Towards Society 5.0 -- 1 Introduction -- 1.1 A Glimpse into the Evolution off Societies -- 1.2 The Need for Society 5.0 -- 1.3 The Working of Society 5.0 as A Solution to Social Problems -- 1.4 Attaining Society 5.0 -- 2 The Implementation and Impact of Society 5.0 -- 2.1 Infrastructure -- 2.2 Mobility -- 2.3 Health -- 2.4 Education -- 2.5 Manufacturing -- 2.6 Agriculture -- 2.7 Energy -- 2.8 Disaster Prevention -- 2.9 Food Products -- 2.10 Fintech -- 2.11 Tourism -- 2.12 Cyber Space -- 3 Cognitive Computing in a Nutshell -- 3.1 Characteristics of Cognitive Computing -- 3.2 The Differences Between Artificial Intelligence and Cognitive Computing -- 3.3 Advantages of Cognitive Computing -- 3.4 Caveats of Cognitive Computing -- 4 Use Case Scenarios of Cognitive Computing at Work -- 4.1 Intelligent Assistant-Cora (Royal Bank of Scotland-RBS) -- 4.2 Personal Travel Planner by WayBlazer -- 4.3 Cafewell-A Healthcare Concierge by Welltok -- 4.4 Fantasy Football Team Decision Maker by Edge up Sports -- 5 Conclusion -- 5.1 Future Scope and Discussion -- References -- 7 Big Data Analytics in Industry 4.0 in Legal Perspective: Past, Present and Future -- 1 Introduction -- 2 The Basic Flow of Big Data's Past, Present, and Future -- 2.1 The Origins of Data -- 2.2 The Dawn of Statistics -- 2.3 Modern Data Storage in Its Infancy. 2.4 Business Intelligence's Beginnings -- 2.5 1964 -- 2.6 Data Centres Are Getting Started -- 2.7 The Internet's First Years -- 2.8 Big Data's Earliest Concepts -- 2.9 Big Data in the Twenty-First Century -- 3 From Industry 4.0 to Society 4.0 -- 4 From Industry 4.0 to Market 4.0 -- 4.1 Phases of Marketing 4.0 -- 5 Literature Review -- 6 The Legal Constraints of Big Data Analytics -- 7 Analysis of Data Protection Principles in the Context of Big Data -- 8 Big Data and Black Data Affairs -- 8.1 Advantages -- 8.2 Disadvantages -- 9 Legal Standpoint-Comparative Reflection -- 9.1 United States of America -- 9.2 United Kingdom -- 9.3 India -- 9.4 Brazil -- 9.5 Bangladesh -- 9.6 Australia -- 9.7 Conclusion -- References -- 8 Unified Architectural Framework for Industrial Internet of Things -- 1 Introduction -- 2 The Technologies Associated with IIoT -- 2.1 Industry 4.0 -- 2.2 Cyber-Physical Systems (CPS) -- 3 Industrial Automation and Control Systems (IACS) -- 4 Literature Review -- 5 IoT to IIoT -- 6 Basic Overview of IIoT Architecture -- 7 IIoT Architecture -- 8 IIoT Framework -- 9 IIoT Framework Application -- 9.1 Industrial IoT Platforms (IIoT) -- 9.2 Conclusion -- References -- 9 Human-Robot Coordination and Collaboration in Industry 4.0 -- 1 Introduction -- 1.1 Robots at Workplace -- 1.2 Inclusion of Robot Workforce -- 1.3 Organizational Benefits of Including Robot Workforce -- 2 Literature Review -- 2.1 Table of Literature Review-Human-Robot Collaboration and Co-Ordination -- 3 Human-Robot Coordination and Collaboration -- 3.1 Drivers for Human-Robot Coordination and Collaboration -- 3.2 Barriers for Human-Robot Coordination and Collaboration -- 4 Human-Robot Coordination and Collaboration Towards Organization Performance -- 4.1 Organizational Performance -- 5 Framework for Human-Robot Coordination and Collaboration. 5.1 Framework for Human-Robot Coordination and Collaboration Towards Organization Performance -- 6 Implications -- 7 Conclusion and Future Research Scope -- References -- 10 Revolutionizing the Techno-Human Space in Human Resource Practices in Industry 4.0 to Usage in Society 5.0 -- 1 Introduction: What is Artificial Intelligence? -- 1.1 Literature Review -- 1.2 The AI Present Scenario -- 1.3 Racing to AI in Business -- 1.4 The HR World -- 1.5 Technology and HR -- 2 AI Ecosystem -- 2.1 Trends in the AI Ecosystem -- 2.2 AI Roadmap Development -- 2.3 Utilizing the AI Roadmap -- 2.4 Enhancing the HR Processes Using AI -- 2.5 Collaborative Intelligence in Recruitment Function: All About Estimations! -- 2.6 AI in Learning and Development Function of Human Resources Management -- 3 Collaborative Artificial Intelligence (CAI) Conceptual Background -- 3.1 Business and Collaborative Artificial Intelligence -- 3.2 Collaborative Artificial Intelligence in Business-Case 1 -- 3.3 Challenge Problems in CAI Scenarios -- 4 What is Society 5.0? -- 4.1 IOT-CAI-Smart Cities -- 4.2 IOT and Urban Knowledge -- 5 Conclusions -- References -- 11 An Architecture of Cyber-Physical System for Industry 4.0 -- 1 Introduction -- 1.1 Cyber-Physical Systems -- 1.2 Industry 4.0 -- 1.3 CPS Industry Compatibility with 4.0 -- 1.4 Characteristics -- 1.5 Inquiry on the Design of CPS -- 2 Literature Review -- 2.1 Implementation of CPS Technique -- 2.2 Case Study: Developing Own CPS -- 2.3 Case Study: KPIs Implementation -- 3 Information and Operational Technology -- 3.1 Operational Technology Support -- 3.2 Information Technology Support -- 4 Convergence of IT and OT in IIoT -- 4.1 IT and OT Are no Longer Separate Fields of Study -- 4.2 How Will IoT Embedded with IT and OT? -- 5 CPS Functions and Applications at a Glance -- 6 Electronic Platform -- 6.1 Necessity of an Electronic Platform. 6.2 Developing a Digital Business Technology Infrastructure. |
| Record Nr. | UNINA-9910806192703321 |
|
Kumar Avadhesh
|
||
| Singapore : , : Springer Singapore Pte. Limited, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Digitization of Healthcare Data Using Blockchain
| Digitization of Healthcare Data Using Blockchain |
| Autore | Poongodi T |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (311 pages) |
| Altri autori (Persone) |
SumathiD
BalamuruganB SavitaK. S |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-79273-8
1-119-79272-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910590097303321 |
|
Poongodi T
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Internet of Things Use Cases for the Healthcare Industry [[electronic resource] /] / edited by Pethuru Raj, Jyotir Moy Chatterjee, Abhishek Kumar, B. Balamurugan
| Internet of Things Use Cases for the Healthcare Industry [[electronic resource] /] / edited by Pethuru Raj, Jyotir Moy Chatterjee, Abhishek Kumar, B. Balamurugan |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XII, 296 p. 79 illus., 59 illus. in color.) |
| Disciplina | 610.28563 |
| Soggetto topico |
Computer communication systems
Computer engineering Internet of things Embedded computer systems Health informatics Input-output equipment (Computers) Computer Communication Networks Cyber-physical systems, IoT Health Informatics Input/Output and Data Communications |
| ISBN | 3-030-37526-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | AI in Health Sector -- Real-Time Smart Healthcare Model using IoT -- A Fog Based Approach for Real-Time Analytics of IoT-Enabled Healthcare -- Applications of IoT in Indoor Air Quality Monitoring Systems -- CloudIoT for Smart Healthcare: Architecture, Issues and Challenges -- Impact of IoT on the Healthcare Producers: Epitomizing Pharmaceutical Drug Discovery Process -- Cyber-Security Threats in Medical Devices -- Smart Healthcare Use Cases and Applications -- IoT Use Cases and Applications -- Internet of Things for Ambient Assisted Living - An Overview -- Smart Health care Applications and Real Time Analytics through Edge Computing -- The Role of Blockchain for Medical Electronics Security -- Clinical Data Analysis using IoT Data Analytics Platforms -- Internet of Things - Tools and Technologies in Healthcare -- Clinical data analysis using IoT -- Security Issues in IoT and Healthcare Devices. |
| Record Nr. | UNISA-996465460403316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||