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Efficient data handling for massive Internet of medical things : healthcare data analytics / / Chinmay Chakraborty [and three others] editors
Efficient data handling for massive Internet of medical things : healthcare data analytics / / Chinmay Chakraborty [and three others] editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (398 pages)
Disciplina 610.285
Collana Internet of Things
Soggetto topico Medical informatics
Internet in medicine
Internet of things
ISBN 3-030-66633-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910495252003321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Efficient data handling for massive Internet of medical things : healthcare data analytics / / Chinmay Chakraborty [and three others] editors
Efficient data handling for massive Internet of medical things : healthcare data analytics / / Chinmay Chakraborty [and three others] editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (398 pages)
Disciplina 610.285
Collana Internet of Things
Soggetto topico Medical informatics
Internet in medicine
Internet of things
ISBN 3-030-66633-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996464516203316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Healthcare Informatics for Fighting COVID-19 and Future Epidemics
Healthcare Informatics for Fighting COVID-19 and Future Epidemics
Autore Garg Lalit
Pubbl/distr/stampa Cham : , : Springer International Publishing AG, , 2021
Descrizione fisica 1 online resource (444 pages)
Altri autori (Persone) ChakrabortyChinmay
MahmoudiSaïd
SohmenVictor S
Collana EAI/Springer Innovations in Communication and Computing Ser.
Soggetto genere / forma Electronic books.
ISBN 3-030-72752-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910497102703321
Garg Lalit  
Cham : , : Springer International Publishing AG, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The impact of the COVID-19 pandemic on green societies : environmental sustainability / / edited by Chinmay Chakraborty
The impact of the COVID-19 pandemic on green societies : environmental sustainability / / edited by Chinmay Chakraborty
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (437 pages) : illustrations
Disciplina 338.927
Soggetto topico Sustainable development
COVID-19 Pandemic, 2020- - Environmental aspects
ISBN 3-030-66490-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- About the Editors -- 1 COVID-19: An Opportunity for Smart and Sustainable Cities in India -- 1.1 Introduction -- 1.2 Data Collection and Research Design -- 1.3 COVID-19: A Pandemic -- 1.3.1 Current Scenario in the World -- 1.4 Assessment of COVID-19 in India -- 1.4.1 State-Wise Analysis of COVID-19 in India -- 1.5 Smart and Sustainable Cities -- 1.5.1 Smart City Analysis -- 1.6 Issues -- 1.7 Recommendations -- 1.7.1 Personal and Non-personal Data -- 1.7.2 Digital Model -- 1.8 Conclusion -- References -- 2 Reassessment of Urban Sustainability and Food Security in the Light of COVID-19 -- 2.1 Introduction -- 2.2 Covid-19 Situation and the Urban Scenario -- 2.3 Urban Sustainability: Issues and Perspectives -- 2.3.1 Eco-City -- 2.3.2 Smart City -- 2.3.3 Sustainable City -- 2.3.4 Green City -- 2.3.5 Self-reliant City -- 2.3.6 Continuing Metropolitan Mayhem -- 2.4 Urban Pantry: A Saga of Neglect -- 2.5 Cities and Food Resource -- 2.5.1 Food Security -- 2.5.2 Globalisation and Urbanisation -- 2.5.3 Paradigm Shift -- 2.6 Role of Urban Agriculture in Delivering Results -- 2.7 Tackling the Menace of COVID-19 -- 2.8 Conclusion -- References -- 3 Disruptive Mobility in Pre- and Post-COVID Times: App-Based Shared Mobility in Indian Cities-The Case of Bengaluru -- 3.1 Introduction -- 3.1.1 Disruptive Mobility -- 3.1.2 Worldwide Scenario of Disruptive Mobility -- 3.1.3 Disruptive Mobility in India -- 3.2 Problem Statement -- 3.3 Methodology -- 3.3.1 Data Collection -- 3.3.2 Carbon Emission Due to Congestion -- 3.3.3 Public Transport Accessibility Level (PTAL) -- 3.4 Bengaluru: The Case Study -- 3.4.1 City Profile and Institutional Structure -- 3.4.2 Transportation in BBMP -- 3.4.3 Bangalore Metropolitan Transport Corporation (BMTC) -- 3.4.4 Bengaluru Metro Rail Corporation Limited (BMRCL) -- 3.5 Data Analysis and Discussion.
3.5.1 Travel Preferences for Work and Recreational Trips -- 3.5.2 Cost of Additional Carbon Emissions Due to Congestion -- 3.5.3 Relation Between Yearly Uber Rides and PTAL Map -- 3.5.4 Car, a Status Symbol? -- 3.6 Post-Covid Scenario of ABSM -- 3.7 Conclusion -- 3.8 Future Work -- References -- 4 Finding the Long-Lost Path: Developing Environmental Awareness Through the Pandemic -- 4.1 Introduction -- 4.2 Impact of the COVID-19 Pandemic on the Environment -- 4.3 The Relationship Between Ecosystem and Human Well-Being -- 4.4 How Can Psychology Help to Promote Sustainable Behaviour? -- 4.4.1 Culture and Pro-Environment Behaviour -- 4.4.2 Actions to Develop the Feeling of Collectivism -- 4.4.3 Creating Motivation to Change -- 4.4.4 Blocks to Sustainable Action -- 4.5 Psychosocial Model for Promoting Pro-Environmental Behaviour -- 4.6 Conclusion -- References -- 5 The Dual Impact of Lockdown on Curbing COVID-19 Spread and Rise of Air Quality Index in India -- 5.1 Introduction -- 5.2 SIQR Dynamic Model -- 5.2.1 Theoretical Framework -- 5.2.2 Estimation of Initial Transmission Rate -- 5.2.3 Simulation of Mathematical Modeling -- 5.3 Introduction to Air Pollution -- 5.4 Pollution from Different Sectors -- 5.5 Dataset Preparation -- 5.6 Understanding Basic Relationship of Pollutants with Pollution -- 5.7 Detailed Analysis -- 5.7.1 Time Series Analysis -- 5.7.2 Autoregressive Model -- 5.8 Artificial Neural Network and LSTM-Based Modeling -- 5.9 Effect of COVID-19 on Air Pollution -- 5.10 Conclusion and Future Scope -- References -- 6 Aftermath of Industrial Pollution, Post COVID-19 Quarantine on Environment -- 6.1 Introduction -- 6.2 Categories and Impact of Industrial Pollution -- 6.3 Comparison Between Pre and Active Lockdown Conditions -- 6.3.1 Air Pollution -- 6.3.2 Water Pollution -- 6.3.3 Revamped Noise and Soil Pollution.
6.4 Effect of Revived Pollution Level on Existing Life Forms and Mother Earth -- 6.5 Conclusion -- 6.6 Future Prospects -- References -- 7 COVID-19: Disaster or an Opportunity for Environmental Sustainability -- 7.1 Introduction -- 7.2 Positive Impact of Coronavirus on Environment -- 7.2.1 Impact on Atmosphere (Air) -- 7.2.2 Impact on Hydrosphere [Water] -- 7.2.3 Impact on Nature and Wildlife -- 7.3 Negative Aspects of COVID-19 -- 7.4 Future Suggestions to Maintain Environmental Sustainability Post-COVID-19 -- 7.5 Conclusion -- References -- 8 COVID-19 and Its Impact on Carbon Dioxide Emissions -- 8.1 Introduction -- 8.2 Sectors Wise CO2 Emission -- 8.3 Monitoring of CO2 Emission -- 8.4 Changes in Activities During COVID-19 -- 8.5 Changes in CO2 Emission at Global Level -- 8.6 Sector Wise Changes in CO2 Emission -- 8.6.1 Power Sector -- 8.6.2 Industrial Emissions -- 8.6.3 Surface Transportation Emissions -- 8.6.4 Aviation and Ships Emissions -- 8.6.5 Public and Residential Sectors -- 8.7 Country Wise Change in CO2 Emissions -- 8.7.1 USA -- 8.7.2 Italy -- 8.7.3 China -- 8.7.4 Brazil -- 8.7.5 Spain -- 8.7.6 India -- 8.7.7 UK -- 8.7.8 Germany -- 8.7.9 Japan -- 8.7.10 Russia -- 8.7.11 France -- 8.8 Implication -- 8.9 Conclusions and Future Scope -- References -- 9 Sustainable Attainment of Solar E-waste Recycling Concerning to COVID-19 Crisis: A Review -- 9.1 Introduction -- 9.1.1 Why Solar Energy? -- 9.1.2 Impact of Covid-19 on Solar Power -- 9.1.3 The Working of Solar Panels -- 9.1.4 Photovoltaic Effect -- 9.1.5 Advantages and Disadvantages of Using Solar Panels -- 9.1.6 Types of Solar Cells -- 9.2 Materials -- 9.2.1 Solar Panels -- 9.2.2 Causes of Degradation of Solar Panels -- 9.3 Methodology -- 9.3.1 Advanced Recycling Technologies -- 9.4 Result and Discussion -- 9.4.1 Economic Benefits of Recycling -- 9.4.2 Environmental Benefits of Recycling.
9.4.3 Ecological Impact and Cost Analysis -- 9.5 Conclusion -- References -- 10 Impact of Biomedical Waste Management System on Infection Control in the Midst of COVID-19 Pandemic -- 10.1 Introduction -- 10.1.1 Biomedical Waste: Universal Problem -- 10.1.2 Types and Sources of Biomedical Waste -- 10.1.3 Levels and Analysis of Biomedical Waste -- 10.2 Biomedical Waste Management System in India and Other Countries -- 10.2.1 History and Development of Biomedical Waste Management System (BWMS) -- 10.2.2 Prominent Hallmarks of Biomedical Waste Rule 2016 in India -- 10.2.3 Global Scenario of COVID-19 Pandemic -- 10.2.4 Critical Appraisal on BWMS in India and Other Countries -- 10.2.5 Requirement of Biomedical Waste Management in Hospitals and Research Centres -- 10.3 Risk of Biomedical Waste -- 10.3.1 Biomedical Effects of COVID-19 -- 10.3.2 Understanding the Actual Status of Medical Waste -- 10.3.3 Inappropriate Biomedical Waste Disposal Quantification -- 10.3.4 Exposure and Emission of Toxic Gases During Incineration -- 10.3.5 Spread of COVID-19 Pandemic -- 10.4 Biomedical Waste Containment -- 10.4.1 Formation of Containment Vision and Missions -- 10.4.2 Medical Waste Regulations and Segregations -- 10.4.3 Restricted Access for Medical Waste -- 10.4.4 Awareness and Training to the HealthCare Professionals -- 10.4.5 Alternatives for PolyvinylChloride Products -- 10.5 Biomedical Waste Treatment -- 10.5.1 Conventional Methods -- 10.5.2 Incineration Method-Pros and Cons -- 10.5.3 Operating and Emission Standards of Incineration -- 10.5.4 Nebraska Bio-containment Unit -- 10.5.5 Controlling of Infectious COVID-19 -- 10.6 Conclusion -- References -- 11 Sludge Hygienisation-A Novel Technology for Urban Areas to Deal with Incursion of COVID-19 Viral Particles in Wastewater -- 11.1 Introduction.
11.2 Interaction Between COVID-19 Virus Particles and the Surrounding (the Host and the Environment) -- 11.3 Overview of Sludge Hygienisation Techniques-Focus on the Irradiation Treatment Technology -- 11.4 Scope for Irradiation-Based Advanced Treatment Facilities in Urban Centres: A Case Study for Bengaluru City -- 11.5 Economic Features of the Proposed Sludge Hygienisation Facility in Bengaluru with Reference to Sludge Hygienisation Plant Located at Ahmedabad -- 11.5.1 Bengaluru Scenario -- 11.6 Conclusion -- References -- 12 Trends and Innovations in Biosensors for COVID-19 Detection in Air -- 12.1 Introduction -- 12.2 Corona Virus Disease-19 (COVID-19) -- 12.3 Biosensors and Its Types -- 12.3.1 Optical Biosensor -- 12.3.2 Thermometric Biosensor -- 12.3.3 Progress of SARS-Corona Virus Disease -- 12.4 Analysis and Survey -- 12.4.1 Survey Report -- 12.5 Role of Biosensor in COVID-19 -- 12.5.1 Possible Origins of Virus -- 12.5.2 Detection of Coronavirus in Air -- 12.6 Preventive Measures -- 12.7 Conclusion -- References -- 13 IoT Based Wearable Healthcare System: Post COVID-19 -- 13.1 Introduction -- 13.2 Wearable Sensors and Devices -- 13.2.1 Flexible Wearable Physical Sensors -- 13.2.2 Flexible Wearable Chemical Sensors -- 13.2.3 Materials Used for Flexible Wearable Sensors -- 13.2.4 Techniques to Fabricate Wearable Sensors -- 13.2.5 Power Source for Wearable Sensors and Electronics -- 13.2.6 Implantable Devices for Healthcare Monitoring System -- 13.3 Internet of Things (IoT) -- 13.3.1 Network in IoT -- 13.3.2 Architecture of IoT Based Wearable Healthcare System -- 13.4 Conclusion -- References -- 14 Biodiversity Conservation: An Imperial Need in Combatting Pandemic and Healthcare Emergencies -- 14.1 Introduction -- 14.2 Synergy Between Natural Environment and Human Health -- 14.3 Impact of Worldwide Lockdown on Environment -- 14.3.1 Air Pollution.
14.3.2 Wildlife.
Record Nr. UNINA-9910483145703321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Intelligent healthcare : infrastructure, algorithms and management / / edited by Chinmay Chakraborty, Mohammad R. Khosravi
Intelligent healthcare : infrastructure, algorithms and management / / edited by Chinmay Chakraborty, Mohammad R. Khosravi
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (493 pages)
Disciplina 060
Soggetto topico Artificial intelligence - Medical applications
Intel·ligència artificial en medicina
Soggetto genere / forma Llibres electrònics
ISBN 981-16-8150-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- About the Editors -- Part I: Data Science in Intelligent Healthcare -- Chapter 1: Distributed and Big Health Data Processing for Remote and Ubiquitous Healthcare Services Using Blind Statistical Co... -- 1.1 Introduction -- 1.2 Blind and Content-Aware Adaptive Computing: Statistical Optimization of Image Reconstruction Filters -- 1.3 Statistical Directions on Unsupervised Medical Diagnosis -- 1.4 Conclusions -- References -- Chapter 2: Computer Techniques for Medical Image Classification: A Review -- 2.1 Introduction -- 2.1.1 Chapter Contribution -- 2.1.2 Organization of the Chapter -- 2.2 Image Modality -- 2.3 Image Preprocessing -- 2.3.1 Feature Extraction -- 2.3.2 Feature Selection -- 2.4 Image Segmentation -- 2.5 Image Classification Techniques -- 2.6 Conclusion and Future Direction -- References -- Chapter 3: Optimal Feature Selection for Computer-Aided Characterization of Tissues: Case Study of Mammograms -- 3.1 Introduction -- 3.2 Literature Review -- 3.2.1 ROI Extraction Techniques -- 3.2.2 Optimization Algorithms -- 3.2.3 Feature Extraction -- 3.2.4 Evaluation of CAD System -- 3.3 Methodology -- 3.4 Results and Discussions -- 3.5 Conclusion -- References -- Chapter 4: Breast Cancer Detection Using Particle Swarm Optimization and Decision Tree Machine Learning Technique -- 4.1 Introduction -- 4.2 Related Works -- 4.3 Methods and Materials -- 4.3.1 Dataset Description -- 4.3.2 Training and Testing Phase -- 4.3.3 Feature Selection -- 4.3.4 PSO Feature Selection -- 4.3.5 Particle Swarm Optimization -- 4.3.6 Decision Tree -- 4.3.6.1 How Does the Decision Tree Work? -- 4.3.6.2 Proposed System -- 4.3.7 Performance Evaluation -- 4.4 Results and Discussion -- 4.4.1 Results -- 4.5 Conclusion -- References -- Part II: AI in Healthcare.
Chapter 5: Accountable, Responsible, Transparent Artificial Intelligence in Ambient Intelligence Systems for Healthcare -- 5.1 Introduction to Ambient Intelligence -- 5.1.1 What Is AmI? -- 5.1.2 Why Is AmI Important? -- 5.2 Applications of AmI in Healthcare -- 5.2.1 State-of-the-Art: A Case Study -- 5.3 Challenges and Opportunities -- 5.4 Importance of Accountability, Reliability and Transparency (ART)of AI in AmI -- 5.4.1 Ethics and Accountability -- 5.4.2 Transparency -- 5.4.3 Regulation and Control -- 5.4.4 Socioeconomic Impact -- 5.4.5 Design -- 5.4.6 Responsibility -- 5.4.7 ART and AmI -- 5.5 Advancements in ART AmI -- 5.6 Conclusion and Future Work -- References -- Chapter 6: Intelligent Elderly People Fall Detection Based on Modified Deep Learning Deep Transfer Learning and IoT Using Ther... -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Proposed Methodology -- 6.3.1 Tracking -- 6.3.2 ShuffleNet -- 6.3.3 IoT Design -- 6.4 Experimental Results -- 6.5 Conclusion -- References -- Chapter 7: An Analytic Approach to Diagnose Heart Stroke Using Supervised Machine Learning Techniques -- 7.1 Introduction -- 7.2 Literature Survey -- 7.3 Machine Learning and It´s Algorithms -- 7.3.1 Regression -- 7.3.2 Classification -- 7.4 Generation of Machine Learning Models for a Given Dataset to Predict Heart Attack and a Comparative Analysis to Find which... -- 7.5 Dataset Collection -- 7.6 Data Pre-Processing -- 7.6.1 Barplot (Figs. 7.9 and 7.10): -- 7.6.2 Heatmap -- 7.7 Comparative Analysis of the Model Responses -- 7.7.1 Comparative Analysis of Accuracy of all the Six Models -- 7.7.2 ROC Curve -- 7.8 Conclusion -- References -- Chapter 8: A Predictive Analysis for Diagnosis of COVID-19, Pneumonia and Lung Cancer Using Deep Learning -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 AI in Health Care Systems -- 8.4 Neural Networks.
8.4.1 Convolutional Neural Networks (CNN) -- 8.4.1.1 Advance Architecture -- 8.4.2 VGG-16 -- 8.4.3 VGG-19 -- 8.4.4 VGG-16 vs VGG-19 -- 8.5 Result Analysis -- 8.5.1 Dataset Characteristics and Analysis -- 8.5.1.1 Dataset -- 8.5.1.2 Image Pre-processing -- 8.5.1.3 Train-Test Split -- 8.5.1.4 Image Augmentation -- 8.5.2 Model Building and Analysis -- 8.5.2.1 Creating the Classifier Model Using VGG-16 -- 8.5.2.2 Fine Tuning -- 8.5.2.3 Evaluating the Model -- 8.5.3 Classification Report -- 8.5.3.1 F1 Score -- 8.5.3.2 Support -- 8.5.3.3 Confusion Matrix -- 8.5.3.4 Classification Accuracy -- 8.5.3.5 Misclassification Rate -- 8.5.3.6 Precision -- 8.5.3.7 Recall -- 8.5.3.8 F-Measure -- 8.5.4 Creating the Classifier Model Using VGG-19 -- 8.5.4.1 Fine Tuning -- 8.5.4.2 Evaluating the Model -- 8.5.4.3 Classification Matrix -- 8.5.4.4 Confusion Matrix -- 8.6 Conclusion -- References -- Part III: Privacy and Security in Healthcare -- Chapter 9: Internet of Things in the Healthcare Applications: Overview of Security and Privacy Issues -- 9.1 Introduction -- 9.1.1 The Security Attacks in IoT-Based Healthcare Applications -- 9.2 Security Requirements in IoT-Based Healthcare Applications -- 9.3 Security Solutions in IoT-Based Healthcare Applications -- 9.3.1 Fog Computing-Based Solutions -- 9.3.2 Software Defined Networking-Based Solutions -- 9.3.3 Blockchain-Based Solutions -- 9.3.4 Lightweight Cryptography-Based Solutions -- 9.3.5 Artificial Intelligence-Based Solutions -- 9.3.6 Homomorphic and Searchable Encryption-Based Solutions -- 9.4 Conclusion -- References -- Chapter 10: Secure and Privacy-Aware Intelligent Healthcare Systems: A Review -- 10.1 Introduction -- 10.1.1 Objectives -- 10.1.2 Related Works -- 10.1.3 Contributions -- 10.2 IoMT Communications -- 10.2.1 Body Area Network (BAN) -- 10.2.2 IoMT Devices and Protocols -- 10.3 Various Concerns in IoMT.
10.3.1 Security-Based Risks -- 10.3.2 Privacy-Based Risks -- 10.3.3 Trust-Based Risks -- 10.3.4 Accuracy-Based Risks -- 10.4 Challenges in IoMT -- 10.4.1 Risks in IoMT -- 10.4.2 Various Attacks against IoMT -- 10.4.3 Features of Attacks -- 10.4.4 Various Challenges in IoMT -- 10.4.4.1 Privacy Attacks -- 10.4.4.2 Sociology Attacks -- 10.4.4.3 Malicious Attacks -- 10.4.4.4 Hardware Attacks -- 10.5 Counter Measures of IoMT -- 10.5.1 Increasing Awareness -- 10.5.2 Conducting Security Awareness Program -- 10.5.3 Organizing Technical Training -- 10.5.4 Increasing the Level of Education -- 10.6 Establishing Procedures -- 10.6.1 Software Update -- 10.6.2 Setting Strong Enforcement Rules of Personal Device Regulations -- 10.6.3 Training Consideration -- 10.7 Techniques to Guarantee IoMT Data and Systems Security -- 10.7.1 Facial Recognition -- 10.7.2 Retina Scan -- 10.7.3 Iris Identification -- 10.7.4 Authentication with Many Factors -- 10.7.5 To Reduce Vulnerability, Take the Following Counter Measures -- 10.7.6 Recommended Counter Measures to Guard Against Attacks -- 10.7.7 CSRF for Healthcare Domain Internet of Things (IoT) Devices -- 10.7.8 Management of Authentication and Identity -- 10.7.9 Profiling and Access Control -- 10.7.10 Location of Storage -- 10.7.11 Encryption -- 10.7.12 Intelligent Healthcare System -- 10.8 Conclusion and Future Scope -- References -- Chapter 11: Secure Data Transfer and Provenance for Distributed Healthcare -- 11.1 IoT and Distributed Healthcare Systems -- 11.2 Trustworthiness in Healthcare Systems -- 11.3 Challenges and Opportunities -- 11.3.1 Security -- 11.3.2 Privacy -- 11.3.3 Network Infrastructure -- 11.3.4 Edge Computing -- 11.3.5 Federated Learning -- 11.4 Advances in Secure Data Transfer and Provenance for Distributed Healthcare -- 11.4.1 Exemplar State-of-the-Art IoMT -- 11.4.2 Analysis on Security.
11.4.3 Analysis on Provenance -- 11.5 Discussion -- 11.6 Conclusion and Future Work -- References -- Chapter 12: Blockchain Technology in Healthcare: Use Cases Study -- 12.1 Introduction -- 12.2 Fundamentals of Blockchain Technology -- 12.2.1 Blockchain Operations and Classifications -- 12.2.2 Smart Contracts and Ethereum Platform -- 12.2.3 Blockchain Applications -- 12.3 Blockchain for Smart Healthcare -- 12.4 Discussion and Solutions -- 12.5 Conclusion -- References -- Chapter 13: Integrating Artificial Intelligence and Blockchain for Enabling a Trusted Ecosystem for Healthcare Sector -- 13.1 Introduction -- 13.2 Background and Related Literature -- 13.2.1 Artificial Intelligence in Healthcare -- 13.2.2 Blockchain in Healthcare -- 13.3 Artificial Intelligence and Blockchain for Building a Trusted Ecosystem for Healthcare -- 13.4 Experiments and Discussions -- 13.5 Conclusions and Future Work -- References -- Part IV: Intelligent Healthcare Infrastructures -- Chapter 14: Internet of Medical Things (IoMT): Applications, Challenges, and Prospects in a Data-Driven Technology -- 14.1 Introduction -- 14.1.1 Chapter Contribution -- 14.1.2 Chapter Organization -- 14.2 Data-Driven for Internet of Medical Things Technology -- 14.3 The Internet of Medical Things Applications -- 14.4 Challenges of Internet of Medical Things -- 14.4.1 Issues of Standardization -- 14.4.2 Challenges of Regulation -- 14.4.3 Cost of Infrastructures -- 14.4.4 Security Vulnerabilities Issue -- 14.4.5 Existing Networks Strain -- 14.5 Prospects of Internet of Medical Things -- 14.6 Conclusion and Future Direction -- 14.6.1 Future Direction -- References -- Chapter 15: Healthcare Infrastructure in Future Smart Cities -- 15.1 Introduction -- 15.2 Major Challenges in Healthcare Systems -- 15.2.1 Future Smart Cities and Role of Healthcare -- 15.3 Technology and Healthcare System.
15.4 Case Studies.
Record Nr. UNISA-996478868203316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Intelligent healthcare : infrastructure, algorithms and management / / edited by Chinmay Chakraborty, Mohammad R. Khosravi
Intelligent healthcare : infrastructure, algorithms and management / / edited by Chinmay Chakraborty, Mohammad R. Khosravi
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (493 pages)
Disciplina 060
Soggetto topico Artificial intelligence - Medical applications
Intel·ligència artificial en medicina
Soggetto genere / forma Llibres electrònics
ISBN 981-16-8150-3
981-16-8149-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- About the Editors -- Part I: Data Science in Intelligent Healthcare -- Chapter 1: Distributed and Big Health Data Processing for Remote and Ubiquitous Healthcare Services Using Blind Statistical Co... -- 1.1 Introduction -- 1.2 Blind and Content-Aware Adaptive Computing: Statistical Optimization of Image Reconstruction Filters -- 1.3 Statistical Directions on Unsupervised Medical Diagnosis -- 1.4 Conclusions -- References -- Chapter 2: Computer Techniques for Medical Image Classification: A Review -- 2.1 Introduction -- 2.1.1 Chapter Contribution -- 2.1.2 Organization of the Chapter -- 2.2 Image Modality -- 2.3 Image Preprocessing -- 2.3.1 Feature Extraction -- 2.3.2 Feature Selection -- 2.4 Image Segmentation -- 2.5 Image Classification Techniques -- 2.6 Conclusion and Future Direction -- References -- Chapter 3: Optimal Feature Selection for Computer-Aided Characterization of Tissues: Case Study of Mammograms -- 3.1 Introduction -- 3.2 Literature Review -- 3.2.1 ROI Extraction Techniques -- 3.2.2 Optimization Algorithms -- 3.2.3 Feature Extraction -- 3.2.4 Evaluation of CAD System -- 3.3 Methodology -- 3.4 Results and Discussions -- 3.5 Conclusion -- References -- Chapter 4: Breast Cancer Detection Using Particle Swarm Optimization and Decision Tree Machine Learning Technique -- 4.1 Introduction -- 4.2 Related Works -- 4.3 Methods and Materials -- 4.3.1 Dataset Description -- 4.3.2 Training and Testing Phase -- 4.3.3 Feature Selection -- 4.3.4 PSO Feature Selection -- 4.3.5 Particle Swarm Optimization -- 4.3.6 Decision Tree -- 4.3.6.1 How Does the Decision Tree Work? -- 4.3.6.2 Proposed System -- 4.3.7 Performance Evaluation -- 4.4 Results and Discussion -- 4.4.1 Results -- 4.5 Conclusion -- References -- Part II: AI in Healthcare.
Chapter 5: Accountable, Responsible, Transparent Artificial Intelligence in Ambient Intelligence Systems for Healthcare -- 5.1 Introduction to Ambient Intelligence -- 5.1.1 What Is AmI? -- 5.1.2 Why Is AmI Important? -- 5.2 Applications of AmI in Healthcare -- 5.2.1 State-of-the-Art: A Case Study -- 5.3 Challenges and Opportunities -- 5.4 Importance of Accountability, Reliability and Transparency (ART)of AI in AmI -- 5.4.1 Ethics and Accountability -- 5.4.2 Transparency -- 5.4.3 Regulation and Control -- 5.4.4 Socioeconomic Impact -- 5.4.5 Design -- 5.4.6 Responsibility -- 5.4.7 ART and AmI -- 5.5 Advancements in ART AmI -- 5.6 Conclusion and Future Work -- References -- Chapter 6: Intelligent Elderly People Fall Detection Based on Modified Deep Learning Deep Transfer Learning and IoT Using Ther... -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Proposed Methodology -- 6.3.1 Tracking -- 6.3.2 ShuffleNet -- 6.3.3 IoT Design -- 6.4 Experimental Results -- 6.5 Conclusion -- References -- Chapter 7: An Analytic Approach to Diagnose Heart Stroke Using Supervised Machine Learning Techniques -- 7.1 Introduction -- 7.2 Literature Survey -- 7.3 Machine Learning and It´s Algorithms -- 7.3.1 Regression -- 7.3.2 Classification -- 7.4 Generation of Machine Learning Models for a Given Dataset to Predict Heart Attack and a Comparative Analysis to Find which... -- 7.5 Dataset Collection -- 7.6 Data Pre-Processing -- 7.6.1 Barplot (Figs. 7.9 and 7.10): -- 7.6.2 Heatmap -- 7.7 Comparative Analysis of the Model Responses -- 7.7.1 Comparative Analysis of Accuracy of all the Six Models -- 7.7.2 ROC Curve -- 7.8 Conclusion -- References -- Chapter 8: A Predictive Analysis for Diagnosis of COVID-19, Pneumonia and Lung Cancer Using Deep Learning -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 AI in Health Care Systems -- 8.4 Neural Networks.
8.4.1 Convolutional Neural Networks (CNN) -- 8.4.1.1 Advance Architecture -- 8.4.2 VGG-16 -- 8.4.3 VGG-19 -- 8.4.4 VGG-16 vs VGG-19 -- 8.5 Result Analysis -- 8.5.1 Dataset Characteristics and Analysis -- 8.5.1.1 Dataset -- 8.5.1.2 Image Pre-processing -- 8.5.1.3 Train-Test Split -- 8.5.1.4 Image Augmentation -- 8.5.2 Model Building and Analysis -- 8.5.2.1 Creating the Classifier Model Using VGG-16 -- 8.5.2.2 Fine Tuning -- 8.5.2.3 Evaluating the Model -- 8.5.3 Classification Report -- 8.5.3.1 F1 Score -- 8.5.3.2 Support -- 8.5.3.3 Confusion Matrix -- 8.5.3.4 Classification Accuracy -- 8.5.3.5 Misclassification Rate -- 8.5.3.6 Precision -- 8.5.3.7 Recall -- 8.5.3.8 F-Measure -- 8.5.4 Creating the Classifier Model Using VGG-19 -- 8.5.4.1 Fine Tuning -- 8.5.4.2 Evaluating the Model -- 8.5.4.3 Classification Matrix -- 8.5.4.4 Confusion Matrix -- 8.6 Conclusion -- References -- Part III: Privacy and Security in Healthcare -- Chapter 9: Internet of Things in the Healthcare Applications: Overview of Security and Privacy Issues -- 9.1 Introduction -- 9.1.1 The Security Attacks in IoT-Based Healthcare Applications -- 9.2 Security Requirements in IoT-Based Healthcare Applications -- 9.3 Security Solutions in IoT-Based Healthcare Applications -- 9.3.1 Fog Computing-Based Solutions -- 9.3.2 Software Defined Networking-Based Solutions -- 9.3.3 Blockchain-Based Solutions -- 9.3.4 Lightweight Cryptography-Based Solutions -- 9.3.5 Artificial Intelligence-Based Solutions -- 9.3.6 Homomorphic and Searchable Encryption-Based Solutions -- 9.4 Conclusion -- References -- Chapter 10: Secure and Privacy-Aware Intelligent Healthcare Systems: A Review -- 10.1 Introduction -- 10.1.1 Objectives -- 10.1.2 Related Works -- 10.1.3 Contributions -- 10.2 IoMT Communications -- 10.2.1 Body Area Network (BAN) -- 10.2.2 IoMT Devices and Protocols -- 10.3 Various Concerns in IoMT.
10.3.1 Security-Based Risks -- 10.3.2 Privacy-Based Risks -- 10.3.3 Trust-Based Risks -- 10.3.4 Accuracy-Based Risks -- 10.4 Challenges in IoMT -- 10.4.1 Risks in IoMT -- 10.4.2 Various Attacks against IoMT -- 10.4.3 Features of Attacks -- 10.4.4 Various Challenges in IoMT -- 10.4.4.1 Privacy Attacks -- 10.4.4.2 Sociology Attacks -- 10.4.4.3 Malicious Attacks -- 10.4.4.4 Hardware Attacks -- 10.5 Counter Measures of IoMT -- 10.5.1 Increasing Awareness -- 10.5.2 Conducting Security Awareness Program -- 10.5.3 Organizing Technical Training -- 10.5.4 Increasing the Level of Education -- 10.6 Establishing Procedures -- 10.6.1 Software Update -- 10.6.2 Setting Strong Enforcement Rules of Personal Device Regulations -- 10.6.3 Training Consideration -- 10.7 Techniques to Guarantee IoMT Data and Systems Security -- 10.7.1 Facial Recognition -- 10.7.2 Retina Scan -- 10.7.3 Iris Identification -- 10.7.4 Authentication with Many Factors -- 10.7.5 To Reduce Vulnerability, Take the Following Counter Measures -- 10.7.6 Recommended Counter Measures to Guard Against Attacks -- 10.7.7 CSRF for Healthcare Domain Internet of Things (IoT) Devices -- 10.7.8 Management of Authentication and Identity -- 10.7.9 Profiling and Access Control -- 10.7.10 Location of Storage -- 10.7.11 Encryption -- 10.7.12 Intelligent Healthcare System -- 10.8 Conclusion and Future Scope -- References -- Chapter 11: Secure Data Transfer and Provenance for Distributed Healthcare -- 11.1 IoT and Distributed Healthcare Systems -- 11.2 Trustworthiness in Healthcare Systems -- 11.3 Challenges and Opportunities -- 11.3.1 Security -- 11.3.2 Privacy -- 11.3.3 Network Infrastructure -- 11.3.4 Edge Computing -- 11.3.5 Federated Learning -- 11.4 Advances in Secure Data Transfer and Provenance for Distributed Healthcare -- 11.4.1 Exemplar State-of-the-Art IoMT -- 11.4.2 Analysis on Security.
11.4.3 Analysis on Provenance -- 11.5 Discussion -- 11.6 Conclusion and Future Work -- References -- Chapter 12: Blockchain Technology in Healthcare: Use Cases Study -- 12.1 Introduction -- 12.2 Fundamentals of Blockchain Technology -- 12.2.1 Blockchain Operations and Classifications -- 12.2.2 Smart Contracts and Ethereum Platform -- 12.2.3 Blockchain Applications -- 12.3 Blockchain for Smart Healthcare -- 12.4 Discussion and Solutions -- 12.5 Conclusion -- References -- Chapter 13: Integrating Artificial Intelligence and Blockchain for Enabling a Trusted Ecosystem for Healthcare Sector -- 13.1 Introduction -- 13.2 Background and Related Literature -- 13.2.1 Artificial Intelligence in Healthcare -- 13.2.2 Blockchain in Healthcare -- 13.3 Artificial Intelligence and Blockchain for Building a Trusted Ecosystem for Healthcare -- 13.4 Experiments and Discussions -- 13.5 Conclusions and Future Work -- References -- Part IV: Intelligent Healthcare Infrastructures -- Chapter 14: Internet of Medical Things (IoMT): Applications, Challenges, and Prospects in a Data-Driven Technology -- 14.1 Introduction -- 14.1.1 Chapter Contribution -- 14.1.2 Chapter Organization -- 14.2 Data-Driven for Internet of Medical Things Technology -- 14.3 The Internet of Medical Things Applications -- 14.4 Challenges of Internet of Medical Things -- 14.4.1 Issues of Standardization -- 14.4.2 Challenges of Regulation -- 14.4.3 Cost of Infrastructures -- 14.4.4 Security Vulnerabilities Issue -- 14.4.5 Existing Networks Strain -- 14.5 Prospects of Internet of Medical Things -- 14.6 Conclusion and Future Direction -- 14.6.1 Future Direction -- References -- Chapter 15: Healthcare Infrastructure in Future Smart Cities -- 15.1 Introduction -- 15.2 Major Challenges in Healthcare Systems -- 15.2.1 Future Smart Cities and Role of Healthcare -- 15.3 Technology and Healthcare System.
15.4 Case Studies.
Record Nr. UNINA-9910743223503321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Intelligent healthcare : infrastructure, algorithms and management / / edited by Chinmay Chakraborty, Mohammad R. Khosravi
Intelligent healthcare : infrastructure, algorithms and management / / edited by Chinmay Chakraborty, Mohammad R. Khosravi
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (493 pages)
Disciplina 060
Soggetto topico Artificial intelligence - Medical applications
Intel·ligència artificial en medicina
Soggetto genere / forma Llibres electrònics
ISBN 981-16-8150-3
981-16-8149-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- About the Editors -- Part I: Data Science in Intelligent Healthcare -- Chapter 1: Distributed and Big Health Data Processing for Remote and Ubiquitous Healthcare Services Using Blind Statistical Co... -- 1.1 Introduction -- 1.2 Blind and Content-Aware Adaptive Computing: Statistical Optimization of Image Reconstruction Filters -- 1.3 Statistical Directions on Unsupervised Medical Diagnosis -- 1.4 Conclusions -- References -- Chapter 2: Computer Techniques for Medical Image Classification: A Review -- 2.1 Introduction -- 2.1.1 Chapter Contribution -- 2.1.2 Organization of the Chapter -- 2.2 Image Modality -- 2.3 Image Preprocessing -- 2.3.1 Feature Extraction -- 2.3.2 Feature Selection -- 2.4 Image Segmentation -- 2.5 Image Classification Techniques -- 2.6 Conclusion and Future Direction -- References -- Chapter 3: Optimal Feature Selection for Computer-Aided Characterization of Tissues: Case Study of Mammograms -- 3.1 Introduction -- 3.2 Literature Review -- 3.2.1 ROI Extraction Techniques -- 3.2.2 Optimization Algorithms -- 3.2.3 Feature Extraction -- 3.2.4 Evaluation of CAD System -- 3.3 Methodology -- 3.4 Results and Discussions -- 3.5 Conclusion -- References -- Chapter 4: Breast Cancer Detection Using Particle Swarm Optimization and Decision Tree Machine Learning Technique -- 4.1 Introduction -- 4.2 Related Works -- 4.3 Methods and Materials -- 4.3.1 Dataset Description -- 4.3.2 Training and Testing Phase -- 4.3.3 Feature Selection -- 4.3.4 PSO Feature Selection -- 4.3.5 Particle Swarm Optimization -- 4.3.6 Decision Tree -- 4.3.6.1 How Does the Decision Tree Work? -- 4.3.6.2 Proposed System -- 4.3.7 Performance Evaluation -- 4.4 Results and Discussion -- 4.4.1 Results -- 4.5 Conclusion -- References -- Part II: AI in Healthcare.
Chapter 5: Accountable, Responsible, Transparent Artificial Intelligence in Ambient Intelligence Systems for Healthcare -- 5.1 Introduction to Ambient Intelligence -- 5.1.1 What Is AmI? -- 5.1.2 Why Is AmI Important? -- 5.2 Applications of AmI in Healthcare -- 5.2.1 State-of-the-Art: A Case Study -- 5.3 Challenges and Opportunities -- 5.4 Importance of Accountability, Reliability and Transparency (ART)of AI in AmI -- 5.4.1 Ethics and Accountability -- 5.4.2 Transparency -- 5.4.3 Regulation and Control -- 5.4.4 Socioeconomic Impact -- 5.4.5 Design -- 5.4.6 Responsibility -- 5.4.7 ART and AmI -- 5.5 Advancements in ART AmI -- 5.6 Conclusion and Future Work -- References -- Chapter 6: Intelligent Elderly People Fall Detection Based on Modified Deep Learning Deep Transfer Learning and IoT Using Ther... -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Proposed Methodology -- 6.3.1 Tracking -- 6.3.2 ShuffleNet -- 6.3.3 IoT Design -- 6.4 Experimental Results -- 6.5 Conclusion -- References -- Chapter 7: An Analytic Approach to Diagnose Heart Stroke Using Supervised Machine Learning Techniques -- 7.1 Introduction -- 7.2 Literature Survey -- 7.3 Machine Learning and It´s Algorithms -- 7.3.1 Regression -- 7.3.2 Classification -- 7.4 Generation of Machine Learning Models for a Given Dataset to Predict Heart Attack and a Comparative Analysis to Find which... -- 7.5 Dataset Collection -- 7.6 Data Pre-Processing -- 7.6.1 Barplot (Figs. 7.9 and 7.10): -- 7.6.2 Heatmap -- 7.7 Comparative Analysis of the Model Responses -- 7.7.1 Comparative Analysis of Accuracy of all the Six Models -- 7.7.2 ROC Curve -- 7.8 Conclusion -- References -- Chapter 8: A Predictive Analysis for Diagnosis of COVID-19, Pneumonia and Lung Cancer Using Deep Learning -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 AI in Health Care Systems -- 8.4 Neural Networks.
8.4.1 Convolutional Neural Networks (CNN) -- 8.4.1.1 Advance Architecture -- 8.4.2 VGG-16 -- 8.4.3 VGG-19 -- 8.4.4 VGG-16 vs VGG-19 -- 8.5 Result Analysis -- 8.5.1 Dataset Characteristics and Analysis -- 8.5.1.1 Dataset -- 8.5.1.2 Image Pre-processing -- 8.5.1.3 Train-Test Split -- 8.5.1.4 Image Augmentation -- 8.5.2 Model Building and Analysis -- 8.5.2.1 Creating the Classifier Model Using VGG-16 -- 8.5.2.2 Fine Tuning -- 8.5.2.3 Evaluating the Model -- 8.5.3 Classification Report -- 8.5.3.1 F1 Score -- 8.5.3.2 Support -- 8.5.3.3 Confusion Matrix -- 8.5.3.4 Classification Accuracy -- 8.5.3.5 Misclassification Rate -- 8.5.3.6 Precision -- 8.5.3.7 Recall -- 8.5.3.8 F-Measure -- 8.5.4 Creating the Classifier Model Using VGG-19 -- 8.5.4.1 Fine Tuning -- 8.5.4.2 Evaluating the Model -- 8.5.4.3 Classification Matrix -- 8.5.4.4 Confusion Matrix -- 8.6 Conclusion -- References -- Part III: Privacy and Security in Healthcare -- Chapter 9: Internet of Things in the Healthcare Applications: Overview of Security and Privacy Issues -- 9.1 Introduction -- 9.1.1 The Security Attacks in IoT-Based Healthcare Applications -- 9.2 Security Requirements in IoT-Based Healthcare Applications -- 9.3 Security Solutions in IoT-Based Healthcare Applications -- 9.3.1 Fog Computing-Based Solutions -- 9.3.2 Software Defined Networking-Based Solutions -- 9.3.3 Blockchain-Based Solutions -- 9.3.4 Lightweight Cryptography-Based Solutions -- 9.3.5 Artificial Intelligence-Based Solutions -- 9.3.6 Homomorphic and Searchable Encryption-Based Solutions -- 9.4 Conclusion -- References -- Chapter 10: Secure and Privacy-Aware Intelligent Healthcare Systems: A Review -- 10.1 Introduction -- 10.1.1 Objectives -- 10.1.2 Related Works -- 10.1.3 Contributions -- 10.2 IoMT Communications -- 10.2.1 Body Area Network (BAN) -- 10.2.2 IoMT Devices and Protocols -- 10.3 Various Concerns in IoMT.
10.3.1 Security-Based Risks -- 10.3.2 Privacy-Based Risks -- 10.3.3 Trust-Based Risks -- 10.3.4 Accuracy-Based Risks -- 10.4 Challenges in IoMT -- 10.4.1 Risks in IoMT -- 10.4.2 Various Attacks against IoMT -- 10.4.3 Features of Attacks -- 10.4.4 Various Challenges in IoMT -- 10.4.4.1 Privacy Attacks -- 10.4.4.2 Sociology Attacks -- 10.4.4.3 Malicious Attacks -- 10.4.4.4 Hardware Attacks -- 10.5 Counter Measures of IoMT -- 10.5.1 Increasing Awareness -- 10.5.2 Conducting Security Awareness Program -- 10.5.3 Organizing Technical Training -- 10.5.4 Increasing the Level of Education -- 10.6 Establishing Procedures -- 10.6.1 Software Update -- 10.6.2 Setting Strong Enforcement Rules of Personal Device Regulations -- 10.6.3 Training Consideration -- 10.7 Techniques to Guarantee IoMT Data and Systems Security -- 10.7.1 Facial Recognition -- 10.7.2 Retina Scan -- 10.7.3 Iris Identification -- 10.7.4 Authentication with Many Factors -- 10.7.5 To Reduce Vulnerability, Take the Following Counter Measures -- 10.7.6 Recommended Counter Measures to Guard Against Attacks -- 10.7.7 CSRF for Healthcare Domain Internet of Things (IoT) Devices -- 10.7.8 Management of Authentication and Identity -- 10.7.9 Profiling and Access Control -- 10.7.10 Location of Storage -- 10.7.11 Encryption -- 10.7.12 Intelligent Healthcare System -- 10.8 Conclusion and Future Scope -- References -- Chapter 11: Secure Data Transfer and Provenance for Distributed Healthcare -- 11.1 IoT and Distributed Healthcare Systems -- 11.2 Trustworthiness in Healthcare Systems -- 11.3 Challenges and Opportunities -- 11.3.1 Security -- 11.3.2 Privacy -- 11.3.3 Network Infrastructure -- 11.3.4 Edge Computing -- 11.3.5 Federated Learning -- 11.4 Advances in Secure Data Transfer and Provenance for Distributed Healthcare -- 11.4.1 Exemplar State-of-the-Art IoMT -- 11.4.2 Analysis on Security.
11.4.3 Analysis on Provenance -- 11.5 Discussion -- 11.6 Conclusion and Future Work -- References -- Chapter 12: Blockchain Technology in Healthcare: Use Cases Study -- 12.1 Introduction -- 12.2 Fundamentals of Blockchain Technology -- 12.2.1 Blockchain Operations and Classifications -- 12.2.2 Smart Contracts and Ethereum Platform -- 12.2.3 Blockchain Applications -- 12.3 Blockchain for Smart Healthcare -- 12.4 Discussion and Solutions -- 12.5 Conclusion -- References -- Chapter 13: Integrating Artificial Intelligence and Blockchain for Enabling a Trusted Ecosystem for Healthcare Sector -- 13.1 Introduction -- 13.2 Background and Related Literature -- 13.2.1 Artificial Intelligence in Healthcare -- 13.2.2 Blockchain in Healthcare -- 13.3 Artificial Intelligence and Blockchain for Building a Trusted Ecosystem for Healthcare -- 13.4 Experiments and Discussions -- 13.5 Conclusions and Future Work -- References -- Part IV: Intelligent Healthcare Infrastructures -- Chapter 14: Internet of Medical Things (IoMT): Applications, Challenges, and Prospects in a Data-Driven Technology -- 14.1 Introduction -- 14.1.1 Chapter Contribution -- 14.1.2 Chapter Organization -- 14.2 Data-Driven for Internet of Medical Things Technology -- 14.3 The Internet of Medical Things Applications -- 14.4 Challenges of Internet of Medical Things -- 14.4.1 Issues of Standardization -- 14.4.2 Challenges of Regulation -- 14.4.3 Cost of Infrastructures -- 14.4.4 Security Vulnerabilities Issue -- 14.4.5 Existing Networks Strain -- 14.5 Prospects of Internet of Medical Things -- 14.6 Conclusion and Future Direction -- 14.6.1 Future Direction -- References -- Chapter 15: Healthcare Infrastructure in Future Smart Cities -- 15.1 Introduction -- 15.2 Major Challenges in Healthcare Systems -- 15.2.1 Future Smart Cities and Role of Healthcare -- 15.3 Technology and Healthcare System.
15.4 Case Studies.
Record Nr. UNISA-996549470303316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Internet of medical things for smart healthcare : Covid-19 pandemic / / Chinmay Chakraborty [and three others], editors
Internet of medical things for smart healthcare : Covid-19 pandemic / / Chinmay Chakraborty [and three others], editors
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Singapore : , : Springer, , [2020]
Descrizione fisica 1 online resource (XII, 305 p. 200 illus., 177 illus. in color.)
Disciplina 610.285
Collana Studies in big data
Soggetto topico COVID-19 (Disease) - Health aspects
Medical care - Data processing
Medical informatics
ISBN 981-15-8097-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1. Transmission Dynamics and Estimation of Basic Reproduction Number (R0) from Early Outbreak of Novel Coronavirus (COVID-19) in India -- Chapter 2. Covid -19 analysed by using machine deep learning -- Chapter 3. MML Classification Techniques for the pathogen based on pnuemonia-nCOVID-19 and the Detection of closely related lung diseases using Efficacious Learning Algorithms -- Chapter 4. Diagnosing COVID-19 Lung Inflammation using Machine Learning Algorithms: A Comparative Study -- Chapter 5. Factors Affecting the Success of Internet of Things for Enhancing Quality and Efficiency Implementation in Hospitals Sector in Jordan during the crises of Covid-19 -- Chapter 6. IoMT based Smart Diagnostic/Therapeutic Kit for Pandemic Patients -- Chapter 7. The Prediction Analysis of Covid-19 Cases using ARIMA and KALMAN Filter Models: A Case of Comparative Study -- Chapter 8. Exploration of cough recognition technologies grounded on sensors and artificial intelligence -- Chapter 9. A Review on use of Data Science for visualisation and prediction of the COVID-19 Pandemic and Early diagnosis of COVID-19 using Machine learning models -- Chapter 10. Fuzzy Cellular Automata Model For Discrete Dynamical System Representing Spread ofMERS And COVID-19 Virus, SumitaBasu and Sreeya Ghosh.
Record Nr. UNINA-9910427673103321
Singapore : , : Springer, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Internet of medical things for smart healthcare : Covid-19 pandemic / / Chinmay Chakraborty [and three others], editors
Internet of medical things for smart healthcare : Covid-19 pandemic / / Chinmay Chakraborty [and three others], editors
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Singapore : , : Springer, , [2020]
Descrizione fisica 1 online resource (XII, 305 p. 200 illus., 177 illus. in color.)
Disciplina 610.285
Collana Studies in big data
Soggetto topico COVID-19 (Disease) - Health aspects
Medical care - Data processing
Medical informatics
ISBN 981-15-8097-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1. Transmission Dynamics and Estimation of Basic Reproduction Number (R0) from Early Outbreak of Novel Coronavirus (COVID-19) in India -- Chapter 2. Covid -19 analysed by using machine deep learning -- Chapter 3. MML Classification Techniques for the pathogen based on pnuemonia-nCOVID-19 and the Detection of closely related lung diseases using Efficacious Learning Algorithms -- Chapter 4. Diagnosing COVID-19 Lung Inflammation using Machine Learning Algorithms: A Comparative Study -- Chapter 5. Factors Affecting the Success of Internet of Things for Enhancing Quality and Efficiency Implementation in Hospitals Sector in Jordan during the crises of Covid-19 -- Chapter 6. IoMT based Smart Diagnostic/Therapeutic Kit for Pandemic Patients -- Chapter 7. The Prediction Analysis of Covid-19 Cases using ARIMA and KALMAN Filter Models: A Case of Comparative Study -- Chapter 8. Exploration of cough recognition technologies grounded on sensors and artificial intelligence -- Chapter 9. A Review on use of Data Science for visualisation and prediction of the COVID-19 Pandemic and Early diagnosis of COVID-19 using Machine learning models -- Chapter 10. Fuzzy Cellular Automata Model For Discrete Dynamical System Representing Spread ofMERS And COVID-19 Virus, SumitaBasu and Sreeya Ghosh.
Record Nr. UNISA-996465447203316
Singapore : , : Springer, , [2020]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Internet of Things for Healthcare Technologies / / edited by Chinmay Chakraborty, Amit Banerjee, Maheshkumar H. Kolekar, Lalit Garg, Basabi Chakraborty
Internet of Things for Healthcare Technologies / / edited by Chinmay Chakraborty, Amit Banerjee, Maheshkumar H. Kolekar, Lalit Garg, Basabi Chakraborty
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Singapore : , : Springer Singapore : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (332 pages)
Disciplina 004.678
Collana Studies in Big Data
Soggetto topico Computational intelligence
Artificial intelligence
Big data
Health informatics
Computational Intelligence
Artificial Intelligence
Big Data
Health Informatics
ISBN 981-15-4112-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction to Technological advances in Healthcare -- Role of Big data analysis and Bio-electronics for upgrading healthcare technologies -- Automated Epileptic Seizure Detection in Clinical EEG using Frequency-Time Domain Features and Hidden Markov Model -- ECG Data Compression for IoT in Healthcare -- Prospects of Bioelectronics (IC enabled, flexible electronics, sensors, systems etc) for Biomedical Engineering and Healthcare in the information age -- Security and Privacy concerns in Healthcare -- Internet of Medical Things -- THz Sources and Detectors for Biomedical Application -- Big data analytics for Internet of Medical Things -- Biomedical Image Analysis: A Predictive Approach -- Missing data handling in medical questionnaires using hybrid methods.
Record Nr. UNINA-9910484430103321
Singapore : , : Springer Singapore : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui