Reyes Juárez Ramírez - Böcker

In today’s rapidly evolving world, solving real-world challenges requires more than just expertise in one field. This book brings together the power of mathematics, computing, and management to explore how these areas can work hand in hand to drive innovation and practical solutions. From data analytics and optimization to digital transformation and decision-making, it blends theory with real applications across sectors like healthcare, sustainability, finance, and education. Additionally, discussions on emerging technologies like AI, blockchain, and quantum computing, and how they are shaping modern interdisciplinary practices will be available. 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.

Affective Feedback in Intelligent Tutoring Systems can be used by human tutors who want to include motivational and affective elements in the learning process, researchers in Human-Computer Interaction and Education and by software developers who want to develop learning systems using these elements.