Full Description
Artificial Intelligence (AI) innovations in digital health offer unprecedented opportunities to facilitate human health and provide tools and techniques that reduce overall costs. This book discusses the use of AI to improve diagnostic accuracy, patient monitoring, the use of remote diagnostic tools, identification of life-threatening diseases, medical robotics applications, drug discovery, technology-driven solutions, and much more.
AIoT Innovations in Digital Health: Emerging Trends, Challenges, and Solutions presents integrated technologies such as Green Computing, IoT, and Big Data using AI, Machine Learning, Deep Learning, and Federated Learning for Healthcare. It discusses the future of medical robotics using Machine Learning and highlights the use of Federated Learning-based patient monitoring applications. This book also elaborates on the role that AI and Machine Learning play in drug discovery.
Interested readers will include anyone working in or involved in smart healthcare research which includes, but is not limited to, healthcare specialists, computer science engineers, electronics engineers, systems engineers, and pharmaceutical practitioners.
Contents
1. Sentiment Analysis of Users Tweets for Polarity Opinions Detection Using Deep Learning for Health Care Service. 2. Non-Sorted Genetic Algorithm and Logistic Regression (NSGA-LR) for Prediction of Heart Diseases. 3. An Intelligent Hybrid Blockchain Mechanism for IoT-Based Healthcare Applications in Blood Cancer Recurrence Detection. 4. Comprehensive Solution for the Management of Chronic Kidney Disease: Application of IoT-Assisted Technology. 5. Privacy and Security Aspects of AI Related to IoT in Healthcare Industry: Methods, Tools, Applications, and Open Challenges. 6. Ontology-Based Experts System for Lung Cancer Disease Diagnosis. 7. Identification of Brain Tumors in MRI Images Using Artificial Intelligence of Things. 8. Transformative Insights: Image-Based Breast Cancer Detection and Severity Assessment through Advanced AI Techniques. 9. Cognitive Machine Learning-Based Intrusion Detection System for Identification of Life-Threatening Diseases.
