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This book discusses research in Artificial Intelligence for the Internet of Health Things. It investigates and explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in design, implementation, and optimization of challenging healthcare solutions. This book features a wide range of topics such as AI techniques, IoT, cloud, wearables, and secured data transmission. Written for a broad audience, this book will be useful for clinicians, health professionals, engineers, technology developers, IT consultants, researchers, and students interested in the AI-based healthcare applications.
Provides a deeper understanding of key AI algorithms and their use and implementation within the wider healthcare sector
Explores different disease diagnosis models using machine learning, deep learning, healthcare data analysis, including machine learning, and data mining and soft computing algorithms
Discusses detailed IoT, wearables, and cloud-based disease diagnosis model for intelligent systems and healthcare
Reviews different applications and challenges across the design, implementation, and management of intelligent systems and healthcare data networks
Introduces a new applications and case studies across all areas of AI in healthcare data
K. Shankar (Member, IEEE) is a Postdoctoral Fellow of the Department of Computer Applications, Alagappa University, Karaikudi, India.
Eswaran Perumal is an Assistant Professor of the Department of Computer Applications, Alagappa University, Karaikudi, India.
Dr. Deepak Gupta is an Assistant Professor of the Department Computer Science & Engineering, Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India.
Sommario
1. Artificial Intelligence (AI) for IoHT – an Introduction.
2. Role of Internet of Things and Cloud Computing Technologies in the Healthcare Sector.
3. An Extensive Overview of Wearable Technologies in Healthcare Sector.
4. IoHT and Cloud-Based Disease Diagnosis Model Using Particle Swarm Optimization with Artificial Neural Networks.
5. IoHT-Based Improved Grey Optimization with Support Vector Machine for Gastrointestinal Hemorrhage Detection and Diagnosis Model.
6. An Effective-Based Personalized Medicine Recommendation System Using Ensemble of Extreme Learning Machine Model.
7. A Novel Map Reduce-Based Hybrid Decision Tree with TFIDF Algorithm for Public Sentiment Mining of Diabetes Mellitus.
8. IoHT with Artificial Intelligence-Based Breast Cancer Diagnosis Model.
9. Artificial Intelligence with Cloud-Based Medical Image Retrieval System Using Deep Neural Network.
10. IoHT with Cloud-Based Brain Tumor Detection Using Particle Swarm Optimization with Support Vector Machine.
11. Artificial Intelligence-Based Hough Transform with an Adaptive Neuro-Fuzzy Inference System for a Diabetic Retinopathy Classification Model.
12. An IoHT-Based Intelligent Skin Lesion Detection and Classification Model in Dermoscopic Images.
13. An IoHT-Based Image Compression Model Using Modified Cuckoo Search Algorithm with Vector Quantization.
14. An Effective Secure Medical Image Transmission Using Improved Particle Swarm Optimization and Wavelet Transform.
15. IoHT with Wearable Devices-Based Feature Extraction and Deep Neural Networks Classification Model for Heart Disease Diagnosis
Info autore
K. Shankar (Member, IEEE) is a Postdoctoral Fellow of Department of Computer Applications, Alagappa University, Karaikudi, India.
Eswaran Perumal is working as an Assistant Professor of Department of Computer Applications, Alagappa University, Karaikudi, India.
Dr. Deepak Gupta is working as an Assistant Professor of Department Computer Science & Engineering, Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India.
Riassunto
This book discusses research of Artificial Intelligence for the Internet of Health Things. It investigates and explores the possible applications of machine learning, deep learning, soft computing and evolutionary computing techniques in design, implementation, and optimization of challenging healthcare solutions.
