Cover; Half Title; Title Page; Copyright Page; Contents; Preface; Editors; Contributors; Chapter 1: Big Data Mining Methods in Medical Applications; Chapter 2: Approaches in Healthcare Using Big Data and Soft Computing; Chapter 3: Implantable Electronics: Integration of Bio-Interfaces, Devices and Sensors; Chapter 4: Challenges in Designing Software Architectures for ƯWeb-Based Biomedical Signal Analysis; Chapter 5: Handling of Medical Imbalanced Big Data Sets for Improved Classification Using Adjacent_Extreme Mix Neighbours Oversampling Technique (AEMNOST).

Chapter 6: A Big Data Framework for Removing Misclassified Instances Based on Fuzzy RoughChapter 7: Fuzzy C-Mean and Density-Based Spatial Clustering for Internet of Things Data Processing; Chapter 8: Parallel Data Mining Techniques for Breast Cancer Prediction; Chapter 9: A MapReduce Approach to Analysing Healthcare Big Data; Chapter 10: IoT and Robotics in Healthcare; Chapter 11: Internet of Medical Things: Remote Healthcare and Health Monitoring Perspective; Chapter 12: A Comparative Analysis of Classical Cryptography versus Quantum Cryptography for Web of Medical Things (WoMT); Index.

This book addresses recent advances in mining, learning, and analysis of big volume of medical images. The book presents taxonomies, trends and issues such as veracity in distributive, dynamic, and diverse data collection, data management, data models, hypotheses testing, training, validation, model-building, optimization techniques and governance of medical big data collected from multiple, heterogeneous IoT devises, networks, platforms and systems such as private vs. public cloud. The book includes privacy, trust, and security issues related to medical Big Data and related IoT and presents case studies in healthcare analytics as well.

OCLC-licensed vendor bibliographic record.