Dhaya Chinnathambi – författare

Explainable AI for Transparent and Trustworthy Medical Decision Support equips readers with a comprehensive and timely resource that presents the principles, methodologies, and real-world applications of explainable AI (XAI) within the medical context. Covering a wide range of use cases-from radiology and pathology to genomics and clinical decision support systems-the book provides in-depth discussions on how XAI techniques can enhance interpretability, improve clinician trust, meet regulatory requirements, and ultimately lead to better patient outcomes. The book demystifies the workings of machine learning models and highlights techniques that make them interpretable.It is designed to empower not only AI researchers and developers but also healthcare administrators and policymakers with the knowledge needed to evaluate, adopt, and trust AI solutions in critical medical applications. The book's authors bring together theory, implementation strategies, ethical implications, and case studies under one cover, offering a multidisciplinary perspective that aligns computer science with medical practice and healthcare policy.Presents detailed coverage of XAI methods such as SHAP, LIME, and Grad-CAM applied to medical dataProvides numerous case studies in diagnostics, ICU prediction, and radiology using explainable modelsIncludes discussions on ethics, bias, and regulatory frameworks such as GDPR and HIPAA

Blockchain and the Water Supply Chain explores the transformative potential of blockchain technology in ensuring sustainable, transparent and efficient water governance. Placing water at the center of smart infrastructure innovation, the book addresses the urgent need for trustworthy and traceable systems in the distribution and management of water resources.This book also delves into how blockchain can revolutionize the water supply chain through decentralized monitoring, smart contracts and immutable data records to reduce losses, enhance accountability and enable real-time decision making. It analyzes key challenges such as interoperability, scalability and regulatory hurdles, while also showcasing innovative use cases and pilot projects across the globe. With contributions from experts in water management, blockchain and environmental policy, this book bridges the gap between digital innovation and sustainable resource management, and is an essential guide for researchers, policymakers and technologists aiming to reshape the future of water systems.

Sustainable Supply Chains and Carbon Footprint Reduction explores how blockchain technology enables greener, more accountable supply chains by improving traceability, reducing inefficiencies and offering real-time visibility into resource use and carbon emissions.This book examines how blockchain creates secure, tamper-proof records of supply chain activities, helping industries track products from origin to consumption while ensuring compliance with environmental standards. Through real-world case studies and practical strategies, it highlights blockchain's role in driving sustainable practices across sectors such as agriculture, manufacturing, logistics and energy.Designed for business leaders, sustainability professionals and policymakers, this book provides actionable insights for leveraging blockchain to meet climate goals and support circular economy initiatives and demonstrates how digital innovation and sustainability can work together to create transparent, resilient and environmentally responsible supply chains.

In today’s data-driven era, the convergence of mathematics, computing, artificial intelligence, and blockchain is emerging as a significant area at the intersection of applied mathematics and computer science, particularly in decision-making. This book explores the applications of advanced mathematical models and computational algorithms to AI-driven strategies and blockchain technologies.It covers advanced linear algebra techniques, probability theory, optimization methods, game theory, cryptography, and statistical learning, providing deep mathematical insights into AI, blockchain, and data-driven decision-making. The book delves into matrix computations and eigenvalue problems relevant to deep learning, Bayesian inference for predictive modeling, and reinforcement learning for dynamic decision-making.Additionally, optimization methods such as convex programming and Lagrangian multipliers enhance resource allocation, while cryptographic protocols ensure the security of blockchain systems. By integrating these mathematical frameworks, this book provides researchers, professionals, and students with practical tools for addressing complex business challenges ranging from fraud detection to automated contract execution.