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Full Description
Artificial Intelligence (AI) is reshaping our urban landscapes and healthcare systems. From enhancing efficiency in smart cities to delivering personalized healthcare solutions, AI holds the key to a transformative future.
Comprising seven insightful chapters, this book offers a comprehensive overview of key topics shaping the future of healthcare systems and smart cities. This book is a combination of the smart healthcare system and the intelligent transportation system based on AI. From telemedicine's global accessibility to the application of data-mining techniques in predicting cardiac diseases and detecting anti-malaria drug resistance, each chapter presents cutting-edge research and practical applications.
Furthermore, this book explores the integration of advanced AI technologies into healthcare systems, paving the way for cost-effective solutions and innovative approaches. It also delves into the challenges associated with analyzing electroencephalogram data and predicting anxiety and depression among elderly patients, offering promising solutions and insights.
This book discusses the realms of smart cities and healthcare, where AI promises to revolutionize efficiency, sustainability, and personalized care delivery.
Researchers/scholars and graduate students at universities/research institutes and research engineers in computer science, transportation, and engineering industries will find this book beneficial.
Contents
Preface. About the Editors. List of Contributors. Chapter 1. Telemedicine: A New Horizon in Public Health Management. Chapter 2. Prediction of Heart Disease. Chapter 3. Detecting Drug-Artemisinin Resistance based on DNA-Barcode Sequence of Plasmodium Falciparum Using Machine Learning Algorithm. Chapter 4. Cutting Edge AI: To Build an Affordable Healthcare System. Chapter 5. Skin Diseases Prediction Using Hybrid Cuckoo Search Optimization with Support Vector Mechine Algorithm. Chapter 6. A Real-Time Detection of Eye Blinks from the Electroencephalogram: A Morphological Component Analysis Approach. Chapter 7. Application of machine learning for prediction of anxiety and depression among elderly patients: A critical review and analysis. Index
