Hybrid artificial intelligence and IoT in healthcare / / edited by Akash Kumar Bhoi, Pradeep Kumar Mallick, Mihir Narayana Mohanty

• Pubblicazione: Singapore : , : Springer, , [2021]; 2021
• Descrizione fisica: 1 online resource (341 pages)
• Disciplina: 060
• Soggetto topico: Internet of things; Artificial intelligence - Medical applications; Intel·ligència artificial en medicina; Internet de les coses
• Soggetto genere / forma: Llibres electrònics
• Persona (resp. second.): MallickPradeep Kumar <1984->; Narayana MohantyMihir; BhoiAkash Kumar
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Contents -- Editors and Contributors -- Hybrid Cloud/Fog Environment for Healthcare: An Exploratory Study, Opportunities, Challenges, and Future Prospects -- 1 Introduction -- 2 Applications of Cloud Computing in Smart Healthcare System -- 3 Applications of Fog Computing in Smart Healthcare System -- 4 Challenges of Cloud and Fog Computing in Smart Healthcare System -- 5 The Future Prospects of Cloud and Fog Computing -- 6 Conclusion and Future Research Directions -- References -- Hybrid Intelligent System for Medical Diagnosis in Health Care -- 1 Introduction -- 1.1 Intelligent Systems -- 1.2 Hybrid Intelligent System -- 1.3 Health Care -- 2 Need for Health Care's Intelligent Infrastructure for Medical Diagnosis -- 2.1 Basic Algorithm -- 2.2 Applications in Diagnosis -- 3 Hybrid Intelligent Medical Diagnosis System -- 3.1 Adaptive Neuro-Fuzzy Inference Systems (ANFIS) -- 3.2 Ensemble Approaches -- 3.3 Evolutionary Artificial Neural Network -- 3.4 Application of Hybrid Intelligent System in Health Care -- 4 Need of Hybrid Intelligent System in Medical Diagnosis -- 5 Conclusion -- References -- Remote Patient Monitoring Using IoT, Cloud Computing and AI -- 1 Cloud-Oriented IoT Using AI -- 1.1 Introduction to Internet of Things -- 1.2 Cloud Computing (CC) -- 1.3 Artificial Intelligence (AI) -- 1.4 Deep Learning Architecture -- 2 Wireless Body Networks (WBN) -- 2.1 Overview -- 2.2 Architecture and Applications -- 2.3 Hybrid Sensor-Based Healthcare Systems -- 3 Cloud Infrastructure and Processing -- 3.1 Overview -- 3.2 Topology and Network Protocol for Remote Monitoring -- 3.3 Cloud Infrastructure -- 3.4 Cloud Computing Components and Characteristics -- 4 Challenges in Cloud and AI-Based IoT on Remote Monitoring -- 4.1 Overview -- 4.2 Accessing Cloud with Validation -- 4.3 Block Chain-Oriented Healthcare Records. ; 4.4 Reliability and Complexity in Computational Intelligence -- 5 Case Studies -- 5.1 IoT-Based Remote Pain Monitoring System: From Device to Cloud Platform [27] -- 5.2 Internet of Things Sensor Assisted Security and Quality Analysis for Healthcare Datasets Using Artificial Intelligent-Based Heuristic Health Management System [28] -- 5.3 A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic [29] -- References -- An Analytical Study of the Role of M-IoT in Healthcare Domain -- 1 Introduction to IoT and Healthcare -- 2 Integration of M-IOT in Healthcare -- 3 Working Principle of M-IOT in Healthcare -- 4 Comparative Analysis of Existing M-IoT Technologies -- 5 Applications of M-IOT Devices in Healthcare -- 6 Benefits of M-IoT -- 7 Challenges of M-IoT -- 8 Relevant Studies on M-IoT in Healthcare -- 9 Discussion of the Use M-IoT in Cancer Detection -- 10 M-IoT in Blood Pressure Measurement from Heart Rate -- 11 Conclusion and Future Scope -- References -- Hybrid AI and IoT Approaches Used in Health Care for Patients Diagnosis -- 1 Introduction -- 2 Methodology Used -- 3 Conclusion -- References -- RADIoT: The Unifying Framework for IoT, Radiomics and Deep Learning Modeling -- 1 Introduction -- 2 The Internet of Things (IoT) in smart healthcare system -- 2.1 Internet of Things (IoT) for Radiomics -- 3 The Radiomics -- 3.1 Dataset Acquisition -- 3.2 Volume of Interests (VOIs) Segmentation -- 3.3 Feature Mining -- 3.4 Feature Selection -- 3.5 Model Development -- 4 Machine Learning Models for Radiomics -- 4.1 Traditional ML Models -- 4.2 Deep Learning (DL) Models -- 4.3 Performance Indicators for ML Models -- 5 Challenges, Open Issues and Opportunities -- 5.1 Challenges of Handicraft Radiomics -- 5.2 Challenges of Deep Learning for Radiomics Analysis -- 5.3 Challenges of IoT in Radiomics. ; 5.4 Open Issues and Opportunities -- 6 Implementation -- 6.1 The RADIoT Unifying Radiomics Framework -- 6.2 Feature Selection -- 6.3 Classification Results and Discussion -- 7 Conclusion and Future Research Directions -- References -- Hybrid Artificial Intelligence and IoT in Health care for Cardiovascular Patient in Decision-Making System -- 1 Introduction -- 1.1 Comprehensive Health Care Systems -- 1.2 Connected eHealth Mobile Applications -- 1.3 Artificial Intelligence -- 2 Data Source -- 2.1 Analysis of Data -- 3 Materials and Methods -- 3.1 Data Gathering -- 3.2 Feature Selection -- 3.3 Classification -- 4 Various Machine Learning Algorithms -- 4.1 Logistic Regression -- 4.2 Naïve Bayes -- 4.3 Random Forest -- 4.4 Support Vector Machine -- 4.5 Gradient Boosting -- 4.6 Accuracy Module -- 5 Results and Discussion -- 6 Conclusion -- References -- A Smart Assistive System for Visually Impaired to Inform Acquaintance Using Image Processing (ML) Supported by IoT -- 1 Introduction -- 2 Related Work -- 3 System Design -- 4 Results and Discussion -- 5 Conclusion and Future Work -- References -- Internet of Things in Health Care: A Survey -- 1 Introduction -- 2 Classification and Overview -- 2.1 Based on Privacy and Security Techniques -- 2.2 Based on e-Health and m-Health -- 2.3 Based on Cloud, Fog, and Evolutionary Computing -- 2.4 Based on Network and Communication Techniques -- 2.5 Based on System Design and Architecture -- 3 Classification Based on Optimization Goal and Evaluation Platform -- 4 IoT Techniques -- 4.1 Access and Authentication -- 4.2 Compression and Encryption -- 4.3 E-health and M-health -- 4.4 Big Data and Cloud Computing -- 4.5 Evolutionary Computing Algorithms -- 4.6 Fog and Cloud Computing -- 4.7 Network and Communication -- 4.8 System Design and Architecture -- 5 Conclusion and Future Outlook -- References. ; Disease Diagnosis System for IoT-Based Wearable Body Sensors with Machine Learning Algorithm -- 1 Introduction -- 2 The General Overview of IoT-Based Applications in Smart Healthcare System -- 3 The Applications of Wearable Body Sensors in Smart Healthcare System -- 4 IoT-Wearable Body Sensors-Based Framework with Machine Learning Algorithm for Disease Diagnosis -- 5 The Application of Machine Learning for the Diagnosis of Heart Diseases as Case Study -- 5.1 The Heart Disease Dataset Characteristics -- 5.2 Performance Evaluation Metrics -- 6 Results and Discussion -- 6.1 The Precision-Recall Curve (PRC) -- 6.2 Confusion Matrix -- 7 Conclusion and Future Research Directions -- References -- Integration of Machine Learning and IoT in Healthcare Domain -- 1 Healthcare Viewpoint -- 1.1 Machine Learning in Health Care -- 1.2 IoT in Health Care -- 2 Renowned Machine Learning Application in the Field of Health Care -- 2.1 Identifying Disease and Diagnosis -- 2.2 Machine Learning in Radiology -- 2.3 Clinical Trial and Research -- 2.4 Outbreak Prediction -- 3 Internet of Things (IoT) Applications in Clinical Domain -- 3.1 Depression Monitoring Apple Watch App -- 3.2 Coagulation Testing -- 3.3 Medical Information Distribution -- 3.4 Emergency Care -- 4 A General Architecture for IoMT Systems -- 5 Various Extensive Studies Conducted -- 6 Review of IoMT Monitoring Solutions -- 6.1 Physiological Analysis -- 6.2 IoMT Solutions in Rehabilitation Systems -- 6.3 Assessing of Diet Intake and Skin Pathology -- 6.4 Treatments Pertaining to the Spread of Epidemics and Their Diagnosis -- 6.5 Diagnosis and Treatment of Diabetes -- 7 Trends and Discussions About Applications -- 8 A Smart Predictive Framework for Disease Risk Factors Detection -- 9 Summary -- References -- Managing Interstitial Lung Diseases with Computer-Aided Visualization -- 1 Introduction. ; 2 ILD Diagnosis -- 2.1 HRCT Patterns -- 2.2 ILD Diagnosis Summary -- 2.3 ILD Treatment Algorithms -- 3 Computer-Aided Techniques -- 3.1 Regression -- 3.2 Hidden Markov Models -- 3.3 Neural Networks -- 3.4 Complex Networks -- 3.5 Layout Algorithm Selection -- 4 Conclusion -- References -- Use of Machine Learning Algorithms to Identify Sleep Phases Starting from ECG Signals -- 1 Introduction -- 2 Related Works -- 3 The Database -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Numerical Results -- 4.3 Statistical Analysis -- 5 Summary/Conclusion -- References -- Emerging Technologies for Pandemic and Its Impact -- 1 Introduction -- 2 Surveillance -- 2.1 Location Data -- 2.2 Health Tracking Mobile Applications -- 2.3 Robotic Diagnostic System -- 2.4 Robotic Patrolling System -- 3 Healthcare -- 3.1 3D Printing Supplies -- 3.2 Advanced Isolation Cubicles with Automation -- 3.3 Autonomous Vehicles -- 3.4 Cobotics for Treatment -- 4 Economy -- 4.1 3D Remote Work with XR (AR or VR) -- 4.2 Sanitization Systems for Essential Workers -- 5 Lockdown -- 5.1 Drones -- 5.2 AI Based Entertainment Streaming -- 5.3 Automation and Innovation in Cleaning -- 6 Education -- 6.1 Interactive Mixed Reality (MR) Classrooms -- 6.2 AI for Analyzing Student Mental Health -- 7 Conclusion -- References -- Impact of Artificial Intelligence in Health care: A Study -- 1 Introduction -- 1.1 Autonomous Vehicles -- 1.2 Cybersecurity -- 1.3 Agriculture -- 1.4 Social Media and Gaming -- 1.5 Military -- 1.6 Finance and Business -- 2 AI in Health care -- 3 Existing Applications Integrating AI in the Healthcare Sector -- 3.1 Virtual Nurses and Digital Consultation -- 3.2 Robots -- 3.3 Cybersecurity -- 3.4 Administration and Workflow -- 3.5 Dosage and Treatment Design -- 3.6 Fraud Detection -- 3.7 Health Monitoring -- 3.8 Drug Creation and Clinical Trial Participation. ; 3.9 Treatment Design and Precision Medicine.
• Sommario/riassunto: This book covers applications for hybrid artificial intelligence (AI) and Internet of Things (IoT) for integrated approach and problem solving in the areas of radiology, drug interactions, creation of new drugs, imaging, electronic health records, disease diagnosis, telehealth, and mobility-related problems in healthcare. The book discusses the convergence of AI and the hybrid approaches in healthcare which optimizes the possible solutions and better treatment. Internet of Things (IoT) in healthcare is the next-gen technologies which automate the healthcare facility by mobility solutions are discussed in detail. It also discusses hybrid AI with bio-inspired techniques, genetic algorithm, neuro-fuzzy algorithms, and soft computing approaches which significantly improves the prediction of critical cardiovascular abnormalities and other healthcare solutions to the ongoing challenging research.
• Titolo autorizzato: Hybrid Artificial Intelligence and IoT in Healthcare
• ISBN: 981-16-2972-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910495204203321
• Lo trovi qui: Univ. Federico II
• Serie: Intelligent Systems Reference Library