Edge-of-Things in Personalized Healthcare Support Systems: Cognitive Data Science in Sustainable Computing
Editat de Rajeswari Sridhar, G. R. Gangadharan, Michael Sheng, Rajan Shankaranen Limba Engleză Paperback – 22 iun 2022
- Provides an in-depth analysis of how to model and design applications for state-of-the-art healthcare systems
- Discusses and explores the social impact of the intertwined use of emerging IT technologies for healthcare
- Covers system design and software building principles for healthcare using IoT, cloud, and edge computing technologies with the support of effective and efficient data analytics strategies
- Explores the latest algorithms using machine and deep learning in the areas of cloud, edge computing, IoT, and healthcare analytics
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Specificații
ISBN-13: 9780323905855
ISBN-10: 0323905854
Pagini: 436
Dimensiuni: 152 x 229 x 32 mm
Greutate: 0.58 kg
Editura: ELSEVIER SCIENCE
Seria Cognitive Data Science in Sustainable Computing
ISBN-10: 0323905854
Pagini: 436
Dimensiuni: 152 x 229 x 32 mm
Greutate: 0.58 kg
Editura: ELSEVIER SCIENCE
Seria Cognitive Data Science in Sustainable Computing
Cuprins
1. Exploring the dichotomy on opportunities and challenges of smart technologies in healthcare systems
2. The architecture of smartness in healthcare
3. Personalized decision support for cardiology based on deep learning: an overview
4. Data-driven models for cuffless blood pressure estimation using ECG and PPG signals
5. A recommendation system for the prediction of drug-target associations
6. Towards building an efficient deep neural network based on YOLO detector for fetal head localization from ultrasound images
7. FunNet: a deep learning network for the detection of age-related macular degeneration
8. An improved method for automated detection of microaneurysm in retinal fundus images
9. Integration and study of map matching algorithms in healthcare services for cognitive impaired person
10. Emotion-recognition-based music therapy system using electroencephalography signals
11. Feedback context-aware pervasive systems in healthcare management: a Boolean Network approach
12. Mental stress detection using a wearable device and heart rate variability monitoring
13. Knowledge discovery and presentation using social media analysis in health domain
14. Computationally efficient integrity verification for shared data in cloud storage
15. Intelligent analysis of multimedia healthcare data using natural language processing and deep-learning techniques
16. Measurement of the effects of parks on air pollution in megacities: do parks support health betterment?
17. Internet of Things use case applications for COVID-19
2. The architecture of smartness in healthcare
3. Personalized decision support for cardiology based on deep learning: an overview
4. Data-driven models for cuffless blood pressure estimation using ECG and PPG signals
5. A recommendation system for the prediction of drug-target associations
6. Towards building an efficient deep neural network based on YOLO detector for fetal head localization from ultrasound images
7. FunNet: a deep learning network for the detection of age-related macular degeneration
8. An improved method for automated detection of microaneurysm in retinal fundus images
9. Integration and study of map matching algorithms in healthcare services for cognitive impaired person
10. Emotion-recognition-based music therapy system using electroencephalography signals
11. Feedback context-aware pervasive systems in healthcare management: a Boolean Network approach
12. Mental stress detection using a wearable device and heart rate variability monitoring
13. Knowledge discovery and presentation using social media analysis in health domain
14. Computationally efficient integrity verification for shared data in cloud storage
15. Intelligent analysis of multimedia healthcare data using natural language processing and deep-learning techniques
16. Measurement of the effects of parks on air pollution in megacities: do parks support health betterment?
17. Internet of Things use case applications for COVID-19