Subhendukumar Pani – författare

Implementation of Smart Healthcare Systems using AI, IoT, and Blockchain provides imperative research on the development of data fusion and analytics for healthcare and their implementation into current issues in a real-time environment. While highlighting IoT, bio-inspired computing, big data, and evolutionary programming, the book explores various concepts and theories of data fusion, IoT, and Big Data Analytics. It also investigates the challenges and methodologies required to integrate data from multiple heterogeneous sources, analytical platforms in healthcare sectors.

This book is unique in the way that it provides useful insights into the implementation of a smart and intelligent healthcare system in a post-Covid-19 world using enabling technologies like Artificial Intelligence, Internet of Things, and blockchain in providing transparent, faster, secure and privacy preserved healthcare ecosystem for the masses.

Explains how IoT can be integrated into the healthcare ecosystem for better diagnostics, monitoring and treatment Includes AI for predictive and preventive healthcare Describes blockchain for managing healthcare data to provide transparency, security and distributed storage Offers effective remote diagnostics and telemedicine approaches Highlights the importance of gold standard medical datasets for improved modeling and analysis

Implementation of Smart Healthcare Systems using AI, IoT, and Blockchain provides imperative research on the development of data fusion and analytics for healthcare and their implementation into current issues in a real-time environment. While highlighting IoT, bio-inspired computing, big data, and evolutionary programming, the book explores various concepts and theories of data fusion, IoT, and Big Data Analytics. It also investigates the challenges and methodologies required to integrate data from multiple heterogeneous sources, analytical platforms in healthcare sectors.This book is unique in the way that it provides useful insights into the implementation of a smart and intelligent healthcare system in a post-Covid-19 world using enabling technologies like Artificial Intelligence, Internet of Things, and blockchain in providing transparent, faster, secure and privacy preserved healthcare ecosystem for the masses.- Explains how IoT can be integrated into the healthcare ecosystem for better diagnostics, monitoring and treatment- Includes AI for predictive and preventive healthcare- Describes blockchain for managing healthcare data to provide transparency, security and distributed storage- Offers effective remote diagnostics and telemedicine approaches- Highlights the importance of gold standard medical datasets for improved modeling and analysis

Harnessing Artificial Intelligence-Enhanced Graph Models for Biological Discovery: Unveiling Biological Frontiers introduces revolutionary techniques that merge artificial intelligence with graph-based methods to uncover complex biological networks. Through detailed examples and case studies, the book provides researchers and practitioners the essential tools to analyze molecular interactions, identify key biomarkers, and hasten the discovery of novel therapeutics. Chapters delve into the sophisticated interplay between advanced AI techniques and graph models, specially designed to decode the intricacies of biological systems. By utilizing cutting-edge AI algorithms, readers can explore complex biological networks, forecast molecular interactions, and pinpoint new drug targets with exceptional precision.Offers an innovative approach by combining artificial intelligence with graph-based techniques to delve into complex biological networksIncludes practical examples and case studies, providing researchers and practitioners with the tools they need to analyze molecular interactions and identify crucial biomarkersEnables researchers to predict molecular interactions and identify novel drug targets with unparalleled accuracy and efficiencyUnlocks new avenues for biological discovery, facilitating precise and effective research outcomes