Reconnoitering the Landscape of Edge Intelligence in Healthcare by Satpathy, Suneeta; Mohanty, Sachi Nandan; Potluri, Sirisha;

Rövid leírás:

Provides research on edge intelligence technology with the emphasis on application in the healthcare industry. Covers data analysis in new technologies such as AI-based techniques, machine learning, IoT, cloud computing, and deep learning with illustrations of the design, implementation, and management of intelligent healthcare systems.

Hosszú leírás:

The revolution in healthcare as well as demand for efficient real-time healthcare services are driving the progression of edge computing, AI-mediated techniques, deep learning, and IoT applications for healthcare industries and cloud computing. Edge computing helps to meet the demand for newer and more sophisticated healthcare systems that are more personalized and that match the speed of modern life. With applications of edge computing, automated intelligence and intuitions are incorporated into existing healthcare analysis tools for identifying, forecasting, and preventing high-risk diseases.

Reconnoitering the Landscape of Edge Intelligence in Healthcare provides comprehensive research on edge intelligence technology with the emphasis on application in the healthcare industry. It covers all the various areas of edge intelligence for data analysis in healthcare, looking at the emerging technologies such as AI-based techniques, machine learning, IoT, cloud computing, and deep learning with illustrations of the design, implementation, and management of smart and intelligent healthcare systems.

Chapters showcase the advantages and highlights of the adoption of the intelligent edge models toward smart healthcare infrastructure. The book also addresses the increased need for a high level of medical data security while transferring real-time data to cloud-based architecture, a matter of prime concern for both patient and doctor. Topics include edge intelligence for wearable sensor technologies and their applications for health monitoring, the various edge computing techniques for disease prediction, e-health services and e-security solutions through IoT devices that aim to improve the quality of care for transgender patients, smart technology in ambient assisted living, the role of edge intelligence in limiting virus spread during pandemics, neuroscience in decoding and analysis of visual perception from the neural patterns and visual image reconstruction, and more.

The technology addressed include energy aware cross-layer routing protocol (ECRP), OMKELM-IDS technique, graphical user interface (GUI), IOST (an ultra-fast, decentralized blockchain platform), etc.

This volume will be helpful to engineering students, research scholars, and manufacturing industry professionals in the fields of engineering applications initiatives on AI, machine learning, and deep learning techniques for edge computing.

Tartalomjegyzék:

PART I: INTRODUCTION TO EDGE INTELLIGENCE IN HEALTHCARE 1. Edge Intelligence and Its Healthcare Applications 2. Edge Intelligence: The Cutting Edge of Healthcare PART II: EDGE INTELLIGENCE IMPLEMENTATIONS FOR SMART HEALTHCARE 3. An IoT-Based Smart Healthcare System with Edge Intelligence Computing 4. Edge Computing for Smart Healthcare Monitoring Platform Advancement 5. Application of Wearable Devices in the Medical Domain 6. Edge Computing for Smart Disease Prediction Treatment Therapy 7. IoT-Based Safety Measures and Healthcare Services for Transgender Welfare and Sustainability 8. Energy Aware Cross-Layer Routing Protocol for Body-to-Body Network in Healthcare 9. Edge Intelligence: A Smart Healthcare Scenario in Ambient Assisted Living PART III: RESEARCH CHALLENGES AND OPPORTUNITIES IN EDGE COMPUTING 10. Edge Intelligence to Smart Management and Control of EpidemicC. 11. Visual Image Reconstruction Using FMRI Analysis 12. New Research Challenges and Applications in Artificial Intelligence on Edge Computing 13. Optimal Mixed Kernel Extreme Learning Machine-Based Intrusion Detection System for Secure Intelligent Edge Computing 14. Stochastic Approach to Govern the Efficient Framework for Big Data Analytics Using Machine Learning and Edge Computing