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Cloud computing : 11th EAI International Conference, CloudComp 2021, virtual event, December 9-10, 2021, proceedings / / edited by Mohammad R. Khosravi, Qiang He, Haipeng Dai
| Cloud computing : 11th EAI International Conference, CloudComp 2021, virtual event, December 9-10, 2021, proceedings / / edited by Mohammad R. Khosravi, Qiang He, Haipeng Dai |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (241 pages) |
| Disciplina | 004.6782 |
| Collana | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering |
| Soggetto topico | Cloud computing |
| ISBN | 3-030-99191-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Data Analytics for Cloud Systems with Distributed Applications -- Load Quality Analysis and Forecasting for Power Data Set on Cloud Platform -- 1 Introduction -- 2 Analysis and Improvement of Power Data -- 2.1 Classification and Comparison of Common Power Data Sets -- 2.2 The Function of Electric Power Data Set -- 2.3 Methods of Improving Data Quality -- 3 Data Prediction -- 3.1 ARIMA Model -- 3.2 LSTM Model -- 3.3 Experimental Results and Evaluation -- 4 Conclusion -- References -- A Survey of Traffic Prediction Based on Deep Neural Network: Data, Methods and Challenges -- 1 Introduction -- 2 Classification of Traffic Data -- 2.1 Spatial Static Time Dynamic Data -- 2.2 Spatio-Temporal Dynamic Data -- 3 Deep Neural Networks for Traffic Prediction -- 3.1 A Traffic Forecasting Method Based on CNN -- 3.2 A Traffic Forecasting Method Based on RNN -- 3.3 A Traffic Forecasting Method Based on the Hybrid Model -- 4 Challenges -- 5 Conclusions -- References -- A Dynamic Gesture Recognition Control File Method Based on Deep Learning -- 1 Introduction -- 2 Construction of PyTorch Model, YOLOv4 and Realization of Control Algorithm -- 2.1 PyTorch Model -- 2.2 Introduction to YOLO -- 2.3 Control Algorithm -- 3 Experiment and Result Analysis -- 3.1 Experimental Platform and Data Set -- 3.2 Network Training -- 3.3 Result Analysis -- 4 Conclusion -- References -- A Lightweight FCNN-Driven Approach to Concrete Composition Extraction in a Distributed Environment -- 1 Introduction -- 2 Methods -- 2.1 Three Layer Model-L2R (= 0.001) Overall Network Architecture -- 2.2 Back Propagation (BP) Algorithm -- 2.3 Baseline Model -- 3 Deep Learning Distributed System -- 4 Experiment and Analysis -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Model Performance -- 5 Conclusion -- References.
Triangle Coordinate Diagram Localization for Academic Literature Based on Line Segment Detection in Cloud Computing -- 1 Introduction -- 2 Related Work -- 3 Triangle Coordinate Diagram Localization -- 3.1 Gaussian Filter and Sobel Operator -- 3.2 Line Segment Detector -- 3.3 Line Segment Merging -- 3.4 Diagram Localization -- 4 Experiment -- 4.1 Dataset -- 4.2 Experimental Method -- 4.3 Experimental Results and Numerical Analysis -- 5 Conclusion -- References -- Optimizing Fund Allocation for Game-Based Verifiable Computation Outsourcing -- 1 Introduction -- 2 System Architecture -- 3 Optimization Problem -- 4 Setting I: Server S vs Single Client Ci -- 4.1 Client's Optimization Problem -- 4.2 Server's Optimization Problem -- 4.3 Proposed Algorithm -- 4.4 Analysis -- 5 Setting II: Server S vs Clients C1,@汥瑀瑯步渠,Cm -- 5.1 Algorithm -- 5.2 Analysis -- 6 Conclusions and Future Works -- References -- A Survey of Face Image Inpainting Based on Deep Learning -- 1 Introduction -- 2 Face Inpainting Method Based on Deep Learning -- 2.1 Attention-Based Image Inpainting -- 2.2 Semantic-Based Image Inpainting -- 2.3 Progressive-Based Image Inpainting -- 3 Datasets and Evaluation Indicators -- 3.1 Dataset -- 3.2 Evaluating Indicator -- 4 Conclusion -- References -- Cloud Architecture and Challenges in Real-World Use -- Layered Service Model Architecture for Cloud Computing -- 1 Introduction -- 2 Important Terms and Definitions -- 2.1 Cloud Computing -- 2.2 Cloud Service Provider or CSP -- 2.3 Infrastructure as a Service or IaaS -- 2.4 Platform as a Service or PaaS -- 2.5 Software as a Service or SaaS -- 2.6 Serverless Computing -- 2.7 Containers and Containers as a Service (CaaS) -- 3 Need to Think Beyond IaaS, PaaS, and SaaS -- 3.1 Serverless vs IaaS, PaaS, and SaaS -- 3.2 CaaS vs IaaS, PaaS, and SaaS -- 4 Existing Approaches and the Need for New Approach. 4.1 XaaS (Anything as a Service) -- 4.2 Johan Den Haan Framework for Categorizing Cloud Services -- 5 Proposed Architecture -- 5.1 Preview -- 5.2 Design Methodology and Trends -- 5.3 Architecture Explanation -- 5.4 Layers Explanation and Criteria -- 6 Plotting Cloud Products in the Proposed Architecture -- 7 Future Research Directions -- 8 Conclusion -- References -- KPG4Rec: Knowledge Property-Aware Graph for Recommender Systems -- 1 Introduction -- 2 Preliminary and Overview -- 2.1 Knowledge Graph (KG) -- 2.2 Node2vec Mechanism -- 2.3 Locality Sensitive Hashing (LSH) -- 2.4 Problem Formulation -- 3 Methodology -- 3.1 Construction of Property-Aware Graphs -- 3.2 Generation of Property-Aware Vectors -- 3.3 Regeneration of User Preference Sequence with LSH -- 3.4 Prediction Module -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Impact of Parameters -- 4.4 Performance Evaluation -- 4.5 Evaluation of Different Properties -- 5 Related Work -- 5.1 Random Walk Algorithm -- 5.2 Recommendations Using Knowledge Graph -- 6 Conclusion -- References -- ERP as Software-as-a-Service: Factors Depicting Large Enterprises Cloud Adoption -- 1 Introduction -- 2 Literature Review -- 3 Research Method -- 4 Results -- 4.1 Service Quality -- 4.2 Costs -- 4.3 Technical Limitations -- 4.4 Cloud Characteristics -- 5 Discussion and Conclusions -- References -- Design of an Evaluation System of Limb Motor Function Using Inertial Sensor -- 1 Introduction -- 2 Design of the Rehabilitation Assessment Module -- 2.1 General Structure Design -- 2.2 Realization of Upper Limb Real Time Motion Simulation -- 3 Discussion -- 4 Conclusion -- References -- Towards a GPU-Accelerated Open Source VDI for OpenStack -- 1 Motivation -- 2 Related Work -- 2.1 GPU Virtualization -- 2.2 Video Encoding and Decoding -- 2.3 Remote Desktop Transport -- 2.4 Preliminary Work in bwLehrpool. 2.5 OpenStack - Basis and Missing Pieces -- 3 Proposed System Architecture -- 4 Work Program and Planned Efforts -- 5 Conclusion and Outlook -- References -- Security in Cloud/Edge Platforms -- Trustworthy IoT Computing Environment Based on Layered Blockchain Consensus Framework -- 1 Introduction -- 2 Related Works -- 3 Trustworthy IoT Model with Integrated Blockchain -- 3.1 System Model Overview -- 3.2 Build a Two-Layer Blockchain for the IoT -- 4 Layered Chained BFT (LCBFT) Consensus Mechanism -- 4.1 Overview of the HotStuff -- 4.2 Consensus and Block Generation in Energy-Constrained Part -- 4.3 Global Blockchain Consensus and Joining Blocks onto the Chain -- 4.4 Liveness Mechanism of Consensus Process -- 5 Microservice-Based Consensus Protocol Deployment Plan -- 6 Evaluation -- 6.1 Computational Structure of the Two Consensus Mechanisms -- 6.2 Energy Consumption Analysis of Two Consensus Mechanism -- 7 Conclusion -- References -- Heuristic Network Security Risk Assessment Based on Attack Graph -- 1 Introduction -- 2 Related Work -- 3 Heuristic Network Security Risk Assessment Based on Attack Graph -- 3.1 Attack Graph -- 3.2 Heuristic Graph Arborescences of Maximum Weight Generation Algorithm -- 3.3 Heuristic Attack Path Finding Algorithm for Maximum Risk -- 3.4 Node Importance Evaluation Based on ISM -- 4 Experimental Settings and Results -- 4.1 Experimental Environment -- 4.2 Experimental Results -- 5 Conclusion -- References -- Research on Network Security Automation and Orchestration Oriented to Electric Power Monitoring System -- 1 Introduction -- 2 Related Work -- 2.1 Anomaly Detection -- 2.2 Active Defense -- 3 Research Motivation -- 4 An Active Defense System Framework Design -- 4.1 The Behavioral Feature Extraction of Typical Network Security Events -- 4.2 The Security Disposal Strategy Generation of Typical Network Security Events. 4.3 The Automation Orchestration of Security Disposal Strategies -- 5 A Case Study -- 6 Conclusion and Future Work -- References -- Energy- and Reliability-Aware Computation Offloading with Security Constraints in MEC-Enabled Smart Cities -- 1 Introduction -- 2 Related Work -- 3 System Model -- 3.1 Network System Model -- 3.2 Workflow Applications Model -- 3.3 Energy Consumption Model -- 3.4 Resource Utilization Model -- 3.5 Reliability Model -- 3.6 Privacy Preservation Model -- 3.7 Problem Formulation -- 4 Energy- and Reliability-Aware Multi-objective Optimization Method with Security Constraint (ERMOS) -- 5 Experimental Evaluation -- 5.1 Experimental Setting -- 5.2 Experimental Result and Discussion -- 6 Conclusion -- References -- A Review of Cross-Blockchain Solutions -- 1 Introduction -- 2 Mature Cross-Blockchain Solutions -- 2.1 Notary Schemes -- 2.2 Sidechain/Relay -- 2.3 Hash-Locking -- 3 Innovative Cross-Blockchain Solutions -- 3.1 DexTT -- 3.2 Blockchain Router -- 3.3 Satellite Chain -- 3.4 HyperService -- 4 Industrial Solutions -- 4.1 Cosmos -- 4.2 Polkadot -- 4.3 Aion -- 4.4 Wanchain -- 4.5 Lisk -- 4.6 Ark -- 4.7 Metronome -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNISA-996464550603316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Cloud computing : 11th EAI International Conference, CloudComp 2021, virtual event, December 9-10, 2021, proceedings / / edited by Mohammad R. Khosravi, Qiang He, Haipeng Dai
| Cloud computing : 11th EAI International Conference, CloudComp 2021, virtual event, December 9-10, 2021, proceedings / / edited by Mohammad R. Khosravi, Qiang He, Haipeng Dai |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (241 pages) |
| Disciplina | 004.6782 |
| Collana | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering |
| Soggetto topico | Cloud computing |
| ISBN | 3-030-99191-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Data Analytics for Cloud Systems with Distributed Applications -- Load Quality Analysis and Forecasting for Power Data Set on Cloud Platform -- 1 Introduction -- 2 Analysis and Improvement of Power Data -- 2.1 Classification and Comparison of Common Power Data Sets -- 2.2 The Function of Electric Power Data Set -- 2.3 Methods of Improving Data Quality -- 3 Data Prediction -- 3.1 ARIMA Model -- 3.2 LSTM Model -- 3.3 Experimental Results and Evaluation -- 4 Conclusion -- References -- A Survey of Traffic Prediction Based on Deep Neural Network: Data, Methods and Challenges -- 1 Introduction -- 2 Classification of Traffic Data -- 2.1 Spatial Static Time Dynamic Data -- 2.2 Spatio-Temporal Dynamic Data -- 3 Deep Neural Networks for Traffic Prediction -- 3.1 A Traffic Forecasting Method Based on CNN -- 3.2 A Traffic Forecasting Method Based on RNN -- 3.3 A Traffic Forecasting Method Based on the Hybrid Model -- 4 Challenges -- 5 Conclusions -- References -- A Dynamic Gesture Recognition Control File Method Based on Deep Learning -- 1 Introduction -- 2 Construction of PyTorch Model, YOLOv4 and Realization of Control Algorithm -- 2.1 PyTorch Model -- 2.2 Introduction to YOLO -- 2.3 Control Algorithm -- 3 Experiment and Result Analysis -- 3.1 Experimental Platform and Data Set -- 3.2 Network Training -- 3.3 Result Analysis -- 4 Conclusion -- References -- A Lightweight FCNN-Driven Approach to Concrete Composition Extraction in a Distributed Environment -- 1 Introduction -- 2 Methods -- 2.1 Three Layer Model-L2R (= 0.001) Overall Network Architecture -- 2.2 Back Propagation (BP) Algorithm -- 2.3 Baseline Model -- 3 Deep Learning Distributed System -- 4 Experiment and Analysis -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Model Performance -- 5 Conclusion -- References.
Triangle Coordinate Diagram Localization for Academic Literature Based on Line Segment Detection in Cloud Computing -- 1 Introduction -- 2 Related Work -- 3 Triangle Coordinate Diagram Localization -- 3.1 Gaussian Filter and Sobel Operator -- 3.2 Line Segment Detector -- 3.3 Line Segment Merging -- 3.4 Diagram Localization -- 4 Experiment -- 4.1 Dataset -- 4.2 Experimental Method -- 4.3 Experimental Results and Numerical Analysis -- 5 Conclusion -- References -- Optimizing Fund Allocation for Game-Based Verifiable Computation Outsourcing -- 1 Introduction -- 2 System Architecture -- 3 Optimization Problem -- 4 Setting I: Server S vs Single Client Ci -- 4.1 Client's Optimization Problem -- 4.2 Server's Optimization Problem -- 4.3 Proposed Algorithm -- 4.4 Analysis -- 5 Setting II: Server S vs Clients C1,@汥瑀瑯步渠,Cm -- 5.1 Algorithm -- 5.2 Analysis -- 6 Conclusions and Future Works -- References -- A Survey of Face Image Inpainting Based on Deep Learning -- 1 Introduction -- 2 Face Inpainting Method Based on Deep Learning -- 2.1 Attention-Based Image Inpainting -- 2.2 Semantic-Based Image Inpainting -- 2.3 Progressive-Based Image Inpainting -- 3 Datasets and Evaluation Indicators -- 3.1 Dataset -- 3.2 Evaluating Indicator -- 4 Conclusion -- References -- Cloud Architecture and Challenges in Real-World Use -- Layered Service Model Architecture for Cloud Computing -- 1 Introduction -- 2 Important Terms and Definitions -- 2.1 Cloud Computing -- 2.2 Cloud Service Provider or CSP -- 2.3 Infrastructure as a Service or IaaS -- 2.4 Platform as a Service or PaaS -- 2.5 Software as a Service or SaaS -- 2.6 Serverless Computing -- 2.7 Containers and Containers as a Service (CaaS) -- 3 Need to Think Beyond IaaS, PaaS, and SaaS -- 3.1 Serverless vs IaaS, PaaS, and SaaS -- 3.2 CaaS vs IaaS, PaaS, and SaaS -- 4 Existing Approaches and the Need for New Approach. 4.1 XaaS (Anything as a Service) -- 4.2 Johan Den Haan Framework for Categorizing Cloud Services -- 5 Proposed Architecture -- 5.1 Preview -- 5.2 Design Methodology and Trends -- 5.3 Architecture Explanation -- 5.4 Layers Explanation and Criteria -- 6 Plotting Cloud Products in the Proposed Architecture -- 7 Future Research Directions -- 8 Conclusion -- References -- KPG4Rec: Knowledge Property-Aware Graph for Recommender Systems -- 1 Introduction -- 2 Preliminary and Overview -- 2.1 Knowledge Graph (KG) -- 2.2 Node2vec Mechanism -- 2.3 Locality Sensitive Hashing (LSH) -- 2.4 Problem Formulation -- 3 Methodology -- 3.1 Construction of Property-Aware Graphs -- 3.2 Generation of Property-Aware Vectors -- 3.3 Regeneration of User Preference Sequence with LSH -- 3.4 Prediction Module -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Impact of Parameters -- 4.4 Performance Evaluation -- 4.5 Evaluation of Different Properties -- 5 Related Work -- 5.1 Random Walk Algorithm -- 5.2 Recommendations Using Knowledge Graph -- 6 Conclusion -- References -- ERP as Software-as-a-Service: Factors Depicting Large Enterprises Cloud Adoption -- 1 Introduction -- 2 Literature Review -- 3 Research Method -- 4 Results -- 4.1 Service Quality -- 4.2 Costs -- 4.3 Technical Limitations -- 4.4 Cloud Characteristics -- 5 Discussion and Conclusions -- References -- Design of an Evaluation System of Limb Motor Function Using Inertial Sensor -- 1 Introduction -- 2 Design of the Rehabilitation Assessment Module -- 2.1 General Structure Design -- 2.2 Realization of Upper Limb Real Time Motion Simulation -- 3 Discussion -- 4 Conclusion -- References -- Towards a GPU-Accelerated Open Source VDI for OpenStack -- 1 Motivation -- 2 Related Work -- 2.1 GPU Virtualization -- 2.2 Video Encoding and Decoding -- 2.3 Remote Desktop Transport -- 2.4 Preliminary Work in bwLehrpool. 2.5 OpenStack - Basis and Missing Pieces -- 3 Proposed System Architecture -- 4 Work Program and Planned Efforts -- 5 Conclusion and Outlook -- References -- Security in Cloud/Edge Platforms -- Trustworthy IoT Computing Environment Based on Layered Blockchain Consensus Framework -- 1 Introduction -- 2 Related Works -- 3 Trustworthy IoT Model with Integrated Blockchain -- 3.1 System Model Overview -- 3.2 Build a Two-Layer Blockchain for the IoT -- 4 Layered Chained BFT (LCBFT) Consensus Mechanism -- 4.1 Overview of the HotStuff -- 4.2 Consensus and Block Generation in Energy-Constrained Part -- 4.3 Global Blockchain Consensus and Joining Blocks onto the Chain -- 4.4 Liveness Mechanism of Consensus Process -- 5 Microservice-Based Consensus Protocol Deployment Plan -- 6 Evaluation -- 6.1 Computational Structure of the Two Consensus Mechanisms -- 6.2 Energy Consumption Analysis of Two Consensus Mechanism -- 7 Conclusion -- References -- Heuristic Network Security Risk Assessment Based on Attack Graph -- 1 Introduction -- 2 Related Work -- 3 Heuristic Network Security Risk Assessment Based on Attack Graph -- 3.1 Attack Graph -- 3.2 Heuristic Graph Arborescences of Maximum Weight Generation Algorithm -- 3.3 Heuristic Attack Path Finding Algorithm for Maximum Risk -- 3.4 Node Importance Evaluation Based on ISM -- 4 Experimental Settings and Results -- 4.1 Experimental Environment -- 4.2 Experimental Results -- 5 Conclusion -- References -- Research on Network Security Automation and Orchestration Oriented to Electric Power Monitoring System -- 1 Introduction -- 2 Related Work -- 2.1 Anomaly Detection -- 2.2 Active Defense -- 3 Research Motivation -- 4 An Active Defense System Framework Design -- 4.1 The Behavioral Feature Extraction of Typical Network Security Events -- 4.2 The Security Disposal Strategy Generation of Typical Network Security Events. 4.3 The Automation Orchestration of Security Disposal Strategies -- 5 A Case Study -- 6 Conclusion and Future Work -- References -- Energy- and Reliability-Aware Computation Offloading with Security Constraints in MEC-Enabled Smart Cities -- 1 Introduction -- 2 Related Work -- 3 System Model -- 3.1 Network System Model -- 3.2 Workflow Applications Model -- 3.3 Energy Consumption Model -- 3.4 Resource Utilization Model -- 3.5 Reliability Model -- 3.6 Privacy Preservation Model -- 3.7 Problem Formulation -- 4 Energy- and Reliability-Aware Multi-objective Optimization Method with Security Constraint (ERMOS) -- 5 Experimental Evaluation -- 5.1 Experimental Setting -- 5.2 Experimental Result and Discussion -- 6 Conclusion -- References -- A Review of Cross-Blockchain Solutions -- 1 Introduction -- 2 Mature Cross-Blockchain Solutions -- 2.1 Notary Schemes -- 2.2 Sidechain/Relay -- 2.3 Hash-Locking -- 3 Innovative Cross-Blockchain Solutions -- 3.1 DexTT -- 3.2 Blockchain Router -- 3.3 Satellite Chain -- 3.4 HyperService -- 4 Industrial Solutions -- 4.1 Cosmos -- 4.2 Polkadot -- 4.3 Aion -- 4.4 Wanchain -- 4.5 Lisk -- 4.6 Ark -- 4.7 Metronome -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNINA-9910556883303321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 | ||
| ||
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 | ||
| ||
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 | ||
| ||