Soft Computing Techniques in Connected Healthcare Systems
Sorozatcím: Biomedical and Robotics Healthcare;
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A termék adatai:
- Kiadás sorszáma 1
- Kiadó CRC Press
- Megjelenés dátuma 2023. december 20.
- ISBN 9781032513478
- Kötéstípus Keménykötés
- Terjedelem312 oldal
- Méret 234x156 mm
- Súly 453 g
- Nyelv angol
- Illusztrációk 20 Illustrations, black & white; 79 Illustrations, color; 10 Halftones, black & white; 76 Halftones, color; 10 Line drawings, black & white; 3 Line drawings, color; 41 Tables, black & white 522
Kategóriák
Rövid leírás:
This book provides examination of applications of soft computing techniques related to healthcare systems and can be used as a reference guide for assessing the roles that various techniques, such as machine learning, fuzzy logic, and statistical mathematics, play in the advancements of smart healthcare systems.
TöbbHosszú leírás:
This book provides an examination of applications of soft computing techniques related to healthcare systems and can be used as a reference guide for assessing the roles of various techniques. Soft Computing Techniques in Connected Healthcare Systems presents soft computing techniques and applications used in healthcare systems, along with the latest advancements. The authors examine how connected healthcare is the essence of combining a practical operative procedure of interconnectedness of electronic health records, mHealth, clinical informatics, electronic data exchange, practice management solutions, and pharmacy management. The book focuses on different soft computing techniques, such as fuzzy logic, ANN, and GA, which will enhance services in connected health systems, such as remote diagnosis and monitoring, medication monitoring devices, identifying and treating the underlying causes of disorders and diseases, improved access to specialists, and lower healthcare costs. The chapters also examine descriptive, predictive, and social network techniques and discuss analytical tools and the important role they play in enhancing the services to connected healthcare systems. Finally, the authors address real-time challenges with real-world case studies to enhance the comprehension of topics. This book is intended for under graduate and graduate students, researchers, and practicing professionals in the field of connected healthcare. It provides an overview for beginners while also addressing professionals in the industry on the importance of soft computing approaches in connected healthcare systems.
TöbbTartalomjegyzék:
1. Automation in Healthcare: Forecast and Outcome: A Case Study
2. Optimizing Smartphone Addiction Questionnaires with Smartphone Application and Soft Computing: An Intelligent Smartphone Usage Behavior Assessment Model
3. Artificial Neural Network Model for Automated Medical Diagnosis
4. Analysing of Heterogeneous Perceptions of a Mutually Dependent Health Ecosystem System Survey
5. Intuitionsitic Fuzzy-Based Technique Ordered Preference by Similarity to the Ideal Solution (TOPSIS) Method: An MCDM Approach for the Medical Decision Making of Diseases
6. Design of a Heuristic IoT-Based Approach as a Solution to a Self-Aware Social Distancing Paradigm
7. Combined 3D Mesh and Generative Adversarial Network-Based Improved Liver Segmentation in Computed Tomography Images
8. Applying Privacy by Design to Connected Healthcare Ecosystems
9. Next-Generation Platforms for Device Monitoring, Management, and Monetization for Healthcare
10. Real-Time Classification and Hepatitis B Detection with Evolutionary Data Mining Approach
11. Healthcare Transformation Using Soft Computing Approaches and IoT protocols.
12. Automated Detection and Classification of Focal and Nonfocal EEG Signals Using Ensemble Empirical Mode Decomposition and ANN Classifier
13. Challenges and Future Directions of Fuzzy System in Healthcare Systems: A Survey
14. Perceptual Hashing Function for Medical Images: Overview, Challenges, and the Future
15. Deploying Machine Learning Methods for Human Emotion Recognition
16. Maternal Health Risk Prediction Model using Artificial Neural Network
Több
