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Computational intelligence for medical internet of things (MIoT) applications machine intelligence applications for IoT in healthcare /

Computational Intelligence for Medical Internet of Things (MIoT) Applications: Machine Intelligence Applications for IoT in Healthcare explores machine intelligence techniques necessary for effective MIoT research and practice, taking a practical approach for practitioners and students entering the...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Maleh, Yassine, 1987- (Editor), Abd El-Latif, Ahmed A., 1984- (Editor), Curran, Kevin, 1969- (Editor), Siarry, Patrick (Editor)
Format: eBook
Language:English
Published: Amsterdam : Academic Press, 2023.
Series:Advances in ubiquitous sensing applications for healthcare
Advances in ubiquitous sensing applications for healthcare ; 14
Subjects:
Artificial intelligence > Medical applications.
Internet of things.
Internet in medicine.
Artificial intelligence.
Internet of Things
Artificial Intelligence
Intelligence artificielle > Applications en médecine.
Internet des objets.
Internet en médecine.
Intelligence artificielle.
artificial intelligence.
Artificial intelligence > Medical applications
Internet in medicine
Internet of things
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Computational Intelligence for Medical Internet of Things (MIoT) Applications
  • Computational Intelligence for Medical Internet of Things (MIoT) Applications:Volume 14 Machine Intelligence Applications for IoT in Healthcare
  • Copyright
  • Contents
  • List of contributors
  • About the editors
  • Preface
  • 1
  • Computational Intelligence for Medical Internet of
  • 1
  • AI and IoT working for healthcare: general aspects and application examples
  • 1.1 Introduction
  • 1.2 Methodology
  • 1.2.1 IoT in practice
  • 1.2.2 IoT in healthcare
  • 1.2.3 Some tips about artificial intelligence
  • 1.2.4 Artificial intelligence for new SoC chips
  • 1.2.5 The future of the Internet of things
  • 1.2.6 Requirements for smart sensors
  • 1.2.7 Smart sensor function blocks
  • 1.2.8 Remote diagnostics
  • 1.2.9 Conditions to be met
  • 1.2.10 Smart stethoscopes and smart tablets
  • 1.2.11 Artificial intelligence and machine learning in e-health
  • 1.3 Activity monitoring during cancer treatment
  • 1.4 Connected inhalers
  • 1.5 Ingestible sensors
  • 1.6 IoMT data security
  • 1.7 Artificial intelligence in healthcare: ethical and legal issues
  • 1.7.1 United States
  • 1.7.2 Europe
  • 1.8 The main points to remember
  • 1.8.1 Remote observation and diagnosis of the patient
  • 1.8.2 Mobile health and wellness
  • 1.8.3 Ingestible IoT-enabled devices
  • 1.8.4 Emergency management
  • 1.8.5 Help surgeries
  • 1.8.6 Efficient inventory, staff, and patient tracking
  • 1.8.7 Critique and analysis
  • 1.8.8 A glimpse of AI in action
  • 1.8.9 A doctor diminished by an augmented medicine?
  • 1.8.10 Automation, conversational robots, and therapeutic innovation with constant pharmacopoeia
  • 1.8.11 The Deep Patient
  • 1.8.12 The reorganization of the health fabric
  • 1.8.13 Data-drive medicine
  • 1.8.14 AI and machine training
  • 1.8.15 Health professionals digested by AI?.
  • 1.9 Discussion
  • 1.10 Conclusion
  • References
  • Further reading
  • 2
  • AIoMT artificial intelligence (AI) and Internet of Medical Things (IoMT): applications, challenges, and future ...
  • 2.1 Introduction
  • 2.1.1 Internet of Medical Things
  • 2.1.2 Artificial intelligence (AI) in IoMT
  • 2.2 IoMT applications
  • 2.3 IoMT security
  • 2.4 Current challenges
  • 2.4.1 Security and privacy
  • 2.4.2 Cost
  • 2.4.3 Scalability and interoperability
  • 2.4.4 Digital Health Advisors
  • 2.5 Discussion and future directions
  • 2.6 Conclusion
  • References
  • 3
  • Artificial intelligence in healthcare: current situation and future possibilities
  • 3.1 Introduction
  • 3.1.1 Types of artificial intelligence used in healthcare
  • 3.1.2 Role of artificial intelligence in healthcare
  • 3.2 Working of each application in smart era
  • 3.2.1 Scope of artificial intelligence in present situation and in other applications
  • 3.3 AI in biomedical information processing
  • 3.3.1 AI in biomedical research
  • 3.3.2 Implications for the healthcare workforce in Today's scenarios
  • 3.3.3 A way toward deep learning-based healthcare
  • 3.3.4 Challenges in implementing deep learning in healthcare sector
  • 3.3.5 Future of artificial intelligence in healthcare sector
  • 3.4 Conclusion
  • References
  • 4
  • Exploring the effectiveness of cloud, Internet of Things and fog computing for healthcare monitoring systems
  • 4.1 Introduction
  • 4.2 A healthcare monitoring system components
  • 4.2.1 Monitoring conditions
  • 4.2.2 Monitoring technologies
  • 4.2.3 Monitoring schemes
  • 4.3 A healthcare patient monitoring system using fog and cloud
  • 4.3.1 Wireless Body Area Network
  • 4.3.2 Cloud data center
  • 4.3.3 Fog nodes
  • 4.4 Artificial intelligence for healthcare services on cloud and Internet of Things
  • 4.4.1 Artificial intelligence for healthcare on cloud and Internet of Things.
  • 4.4.2 Artificial intelligence technology for clinical diagnosis
  • 4.5 Conclusion
  • References
  • 5
  • Patients using real-time remote health monitoring applications: a review
  • 5.1 Introduction
  • 5.2 Significance of study
  • 5.3 Comprehensive study of remote health monitoring
  • 5.3.1 IoT in healthcare
  • 5.3.2 IoT healthcare services
  • 5.3.3 Analyze the classification
  • 5.3.4 Wearable based
  • 5.3.5 Mobile health (mHealth)
  • 5.3.6 Remote monitoring under telemonitoring
  • 5.3.7 Scalability issues with patient's prioritizing in medical care
  • 5.4 Communication and location technologies in remote health
  • 5.4.1 Impact of mHealth
  • 5.4.1.1 Benefits
  • 5.4.1.1.1 There is no time nor cost for traveling
  • 5.4.1.1.1 There is no time nor cost for traveling
  • 5.4.1.2 There's no need to take off from work
  • 5.4.1.3 Remove all difficulties with child or elder care
  • 5.4.1.4 Improved health
  • 5.4.1.5 Services of specialists
  • 5.4.1.6 Remote patient monitoring
  • 5.4.1.7 Medical education
  • 5.4.2 Drawbacks
  • 5.4.2.1 Care delays
  • 5.4.2.2 Technological concerns
  • 5.4.2.3 Send the ambulance
  • 5.4.2.4 Scalability of application
  • 5.4.2.5 False generation of alerts
  • 5.5 Conclusion
  • References
  • Further reading
  • 2
  • Computational Intelligence for Medical Internet of
  • 6
  • A review on the application of the Internet of Things in monitoring autism and assisting parents and caregivers
  • 6.1 Introduction
  • 6.2 Related work
  • 6.3 Research methodology
  • 6.3.1 Search Strategy
  • 6.3.1.1 Search terms
  • 6.3.1.2 Search resources
  • 6.3.2 Papers selection process
  • 6.3.2.1 Inclusion criteria
  • 6.3.2.2 Exclusion criteria
  • 6.3.2.3 Quality assessment
  • 6.3.2.4 Data collection
  • 6.4 The use of IoT in autism monitoring
  • 6.4.1 Monitoring vital signs
  • 6.4.2 Social safety monitoring
  • 6.4.3 Stereotypical movement monitoring.
  • 6.4.4 Emotion recognition
  • 6.4.5 Communication
  • 6.5 Results
  • 6.5.1 RQ1: What are the results acquired by the use of the proposed IoT approaches?
  • 6.5.2 RQ2: What IoT approaches have been used in assisting parents of children with autism?
  • 6.5.3 RQ3: What types of sensors have been utilized?
  • 6.5.4 RQ4: What techniques (e.g., algorithms) have been used to analyze data for early intervention?
  • 6.5.5 RQ5: What metrics have been used for evaluation?
  • 6.6 Conclusion
  • 6.6.1 Limitations and further work
  • References
  • 7
  • Regression analysis of the most frequent medical diagnoses in a Mediterranean country
  • 7.1 Introduction
  • 7.2 Related work
  • 7.3 Exploratory analysis
  • 7.4 Analysis of acute diseases
  • 7.4.1 Age as a factor
  • 7.4.2 Gender as a factor
  • 7.5 Analysis of chronic diseases
  • 7.5.1 Age as a factor
  • 7.5.2 Gender as a factor
  • 7.6 Conclusion
  • References
  • Further reading
  • 8
  • A conceptual framework for Artificial Intelligence of Medical Things (AIoMT)
  • 8.1 Introduction
  • 8.2 IoT in healthcare
  • 8.3 Big data in healthcare
  • 8.4 Artificial intelligence in healthcare
  • 8.5 Artificial Intelligence of Medical Things (AIoMT)
  • 8.6 Conclusion
  • References
  • 9
  • Framework for integrating healthcare big data using IoMT technology
  • 9.1 Introduction
  • 9.2 Hadoop, HBase, and Spark
  • 9.3 Related works
  • 9.4 The proposed model
  • 9.4.1 CRISP-DM methodology
  • 9.5 IoMT big data integration
  • 9.6 IoMT big data storage
  • 9.7 Big data analysis in IoMT
  • 9.8 Discussions
  • 9.9 Conclusion and future works
  • References
  • 10
  • Application of computational intelligence in visual optimization tools to improve the performance of medical M ...
  • 10.1 Introduction
  • 10.2 MIoT platform access control
  • 10.3 Psychovisual foveal coding and evaluation of the coding quality.
  • 10.4 Description of the realized recognition platform using AI
  • 10.5 Discussion and results
  • 10.6 Conclusion and perspectives
  • References
  • 3
  • Computational Intelligence for Medical Internet
  • 11
  • Edge intelligence case study on Medical Internet of Things security
  • 11.1 Introduction
  • 11.2 Literature review
  • 11.2.1 Recent edge computing development
  • 11.2.2 Edge AI application for healthcare MIoT scenario
  • 11.2.3 Critical analysis edge intelligence
  • 11.3 Edge intelligence miot case study issues
  • 11.3.1 Edge challenge facing
  • 11.3.2 Security case study
  • 11.3.2.1 Robot edge intelligence examples
  • 11.3.2.2 Framework developed for edge security
  • 11.3.2.3 Edge intelligence (edge AI) medical blockchain case study
  • 11.3.2.4 More MIoT of NHS security issue
  • 11.3.2.4.1 The progress
  • 11.3.2.4.1 The progress
  • 11.3.2.4.2 Digital Forensic Science using Raspberry Pi
  • 11.3.2.4.2 Digital Forensic Science using Raspberry Pi
  • 11.3.2.4.3 MIoT security scenario setup
  • 11.3.2.4.3 MIoT security scenario setup
  • 11.3.2.4.4 Analysis
  • 11.3.2.4.4 Analysis
  • 11.3.3 Suggestions
  • 11.4 Evaluation
  • 11.4.1 Technical discussion
  • 11.4.2 Challenges analysis
  • 11.4.3 Research limitations
  • 11.5 Conclusions
  • Glossary
  • References
  • 12
  • Data-driven intelligent Medical Internet of Things (MIoT) based healthcare solutions for secured smart cities
  • 12.1 Introduction
  • 12.1.1 Public transit
  • 12.1.2 Public safety
  • 12.1.3 Smart building system
  • 12.1.4 Energy and waste management
  • 12.1.5 Present challenges
  • 12.1.6 Infrastructure and financial implication
  • 12.1.7 Concerns about data privacy/security
  • 12.2 Need of intelligent systems in healthcare
  • 12.2.1 Three phases of AI in healthcare
  • 12.2.2 Diagnosis and treatment applications
  • 12.2.3 Administrative applications.