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

The goal of this book is to facilitate and stimulate cross-disciplinary research in the emerging paradigm of Medical Imaging. Especially this book is to focus on analysing and articulating proven and potential security measures to tightly secure Medical Image applications and services, which are being hosted and delivered through cloud infrastructures and platforms. This book will illustrate the prominent advancements in image processing and how intelligent image-processing techniques can be developed and deployed in the industrial market and for academicians. The readers will get to know all the right and relevant details to be empowered to successfully contribute to their personal and professional growth. The main focus of this book is to bring all the related technologies, novel findings, and managerial applications of Medical Imaging on a single platform to provide great readability, easy understanding, and smooth adaptability of various basic and advanced concepts to Researchers in Medical Engineers, Machine Learning and Data Analysis.

The goal of this book is to facilitate and stimulate cross-disciplinary research in the emerging paradigm of Medical Imaging. Especially this book is to focus on analysing and articulating proven and potential security measures to tightly secure Medical Image applications and services, which are being hosted and delivered through cloud infrastructures and platforms.

This book will illustrate the prominent advancements in image processing and how intelligent image-processing techniques can be developed and deployed in the industrial market and for academicians. The readers will get to know all the right and relevant details to be empowered to successfully contribute to their personal and professional growth.

The main focus of this book is to bring all the related technologies, novel findings, and managerial applications of Medical Imaging on a single platform to provide great readability, easy understanding, and smooth adaptability of various basic and advanced concepts to Researchers in Medical Engineers, Machine Learning and Data Analysis.

The goal of this book is to facilitate and stimulate cross-disciplinary research in the emerging paradigm of Medical Imaging. Especially this book is to focus on analysing and articulating proven and potential security measures to tightly secure Medical Image applications and services, which are being hosted and delivered through cloud infrastructures and platforms.

This book will illustrate the prominent advancements in image processing and how intelligent image-processing techniques can be developed and deployed in the industrial market and for academicians. The readers will get to know all the right and relevant details to be empowered to successfully contribute to their personal and professional growth.

The main focus of this book is to bring all the related technologies, novel findings, and managerial applications of Medical Imaging on a single platform to provide great readability, easy understanding, and smooth adaptability of various basic and advanced concepts to Researchers in Medical Engineers, Machine Learning and Data Analysis.

The book explores the mathematical and computational techniques used in Decentralized Finance (DeFi) and Financial Technology (FinTech).DeFi operates on smart contracts, which are self-executing programs that automate the necessary actions for conducting transactions on a blockchain.FinTech encompasses a broader range of services, including digital payments, mobile banking, and financial analytics, often powered by machine learning and data science.This book integrates mathematical concepts such as cryptographic algorithms, stochastic modeling, optimization, and game theory with computer science principles of Consensus mechanisms in cryptocurrency networks that include:Proof-of-Work (PoW): Users solve complex computational puzzles to add new blocks to the blockchain.Proof-of-Stake (PoS): Users validate transactions based on the number of coins they hold and are willing to 'stake' for network security.The aim is to use such a combination to enhance the security, stability, and efficiency of automated systems for decentralized exchanges, lending protocols, and automated markets. Furthermore, advancements in computation—such as artificial intelligence, big data, and cloud computing—improve DeFi by enabling the prediction of asset movements, analyzing user behavior, and supporting scalable decentralized applications. Despite rapid advancements, challenges remain, particularly in areas like smart contract vulnerabilities, market volatility, and regulatory compliance. Ongoing interdisciplinary research is focused on addressing these issues through improved composability and integration of mathematics with computation. These innovations promote global participation, transparency, and financial inclusion.

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.

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.

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

The goal of this book is to facilitate and stimulate cross-disciplinary research in the emerging paradigm of Medical Imaging. Especially this book is to focus on analysing and articulating proven and potential security measures to tightly secure Medical Image applications and services, which are being hosted and delivered through cloud infrastructures and platforms. This book will illustrate the prominent advancements in image processing and how intelligent image-processing techniques can be developed and deployed in the industrial market and for academicians. The readers will get to know all the right and relevant details to be empowered to successfully contribute to their personal and professional growth. The main focus of this book is to bring all the related technologies, novel findings, and managerial applications of Medical Imaging on a single platform to provide great readability, easy understanding, and smooth adaptability of various basic and advanced concepts to Researchers in Medical Engineers, Machine Learning and Data Analysis.

The goal of this book is to facilitate and stimulate cross-disciplinary research in the emerging paradigm of Medical Imaging. Especially this book is to focus on analysing and articulating proven and potential security measures to tightly secure Medical Image applications and services, which are being hosted and delivered through cloud infrastructures and platforms.

This book will illustrate the prominent advancements in image processing and how intelligent image-processing techniques can be developed and deployed in the industrial market and for academicians. The readers will get to know all the right and relevant details to be empowered to successfully contribute to their personal and professional growth.

The main focus of this book is to bring all the related technologies, novel findings, and managerial applications of Medical Imaging on a single platform to provide great readability, easy understanding, and smooth adaptability of various basic and advanced concepts to Researchers in Medical Engineers, Machine Learning and Data Analysis.

The goal of this book is to facilitate and stimulate cross-disciplinary research in the emerging paradigm of Medical Imaging. Especially this book is to focus on analysing and articulating proven and potential security measures to tightly secure Medical Image applications and services, which are being hosted and delivered through cloud infrastructures and platforms.

This book will illustrate the prominent advancements in image processing and how intelligent image-processing techniques can be developed and deployed in the industrial market and for academicians. The readers will get to know all the right and relevant details to be empowered to successfully contribute to their personal and professional growth.

The main focus of this book is to bring all the related technologies, novel findings, and managerial applications of Medical Imaging on a single platform to provide great readability, easy understanding, and smooth adaptability of various basic and advanced concepts to Researchers in Medical Engineers, Machine Learning and Data Analysis.

The book explores the mathematical and computational techniques used in Decentralized Finance (DeFi) and Financial Technology (FinTech).DeFi operates on smart contracts, which are self-executing programs that automate the necessary actions for conducting transactions on a blockchain.FinTech encompasses a broader range of services, including digital payments, mobile banking, and financial analytics, often powered by machine learning and data science.This book integrates mathematical concepts such as cryptographic algorithms, stochastic modeling, optimization, and game theory with computer science principles of Consensus mechanisms in cryptocurrency networks that include:Proof-of-Work (PoW): Users solve complex computational puzzles to add new blocks to the blockchain.Proof-of-Stake (PoS): Users validate transactions based on the number of coins they hold and are willing to 'stake' for network security.The aim is to use such a combination to enhance the security, stability, and efficiency of automated systems for decentralized exchanges, lending protocols, and automated markets. Furthermore, advancements in computation—such as artificial intelligence, big data, and cloud computing—improve DeFi by enabling the prediction of asset movements, analyzing user behavior, and supporting scalable decentralized applications. Despite rapid advancements, challenges remain, particularly in areas like smart contract vulnerabilities, market volatility, and regulatory compliance. Ongoing interdisciplinary research is focused on addressing these issues through improved composability and integration of mathematics with computation. These innovations promote global participation, transparency, and financial inclusion.

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.

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.