Nuno Silva – författare

Obesity Epidemic and the Environment: Latin America and the Caribbean Region provides a broad analysis of the macroeconomic impact of the obesity epidemic on environmental degradation in the LAC region. Case studies combine theory and practice to achieve critical conclusions (and subsequent policy implications), providing useful tools for those working on actual health, energy and environmental challenges. Written for researchers, policymakers, international organizations and practitioners in various fields, especially those in public health, health economics, energy and environmental economics and social sciences, this book thoroughly highlights the obesity epidemic in this region.

In the LAC region, obesity has become a public health issue. The increase of this problem is related to the economic gains obtained from the process of liberalization and privatization, the increase of foreign investment, and infrastructure investments.

Artificial Intelligence and Machine Learning for Sustainable Development is a comprehensive exploration of how artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing the field of sustainable development.

The book examines cutting-edge innovations, practical applications, and potential challenges in harnessing AI and ML to address global sustainability issues. It offers insights into how these technologies can optimize resource management, improve environmental monitoring, enhance decision-making processes, and promote equitable, eco-friendly solutions. This book would be of special interest to researchers, policymakers, and practitioners seeking to leverage cutting-edge technology for a more sustainable future.

Artificial Intelligence and Machine Learning for Sustainable Development is a comprehensive exploration of how artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing the field of sustainable development.

The book examines cutting-edge innovations, practical applications, and potential challenges in harnessing AI and ML to address global sustainability issues. It offers insights into how these technologies can optimize resource management, improve environmental monitoring, enhance decision-making processes, and promote equitable, eco-friendly solutions. This book would be of special interest to researchers, policymakers, and practitioners seeking to leverage cutting-edge technology for a more sustainable future.

Quantum Learning: Bridging Artificial Intelligence, Quantum Computing, and Data Science in Education explores the transformative intersection of three revolutionary technologies reshaping the future of learning. The concept of quantum learning provides a paradigm where quantum principles redefine machine learning models, enhance computational speed, and enable novel personalized education systems.

The book integrates artificial intelligence (AI), quantum algorithms, and data-driven pedagogy to reimagine classrooms and cognitive processes. Readers will discover how quantum-inspired neural networks, quantum data analysis, and intelligent tutoring systems can revolutionize educational delivery. Through interdisciplinary research, the work translates complex quantum and AI concepts into practical educational applications, featuring case studies and real-world insights that demonstrate how quantum-enhanced intelligence can personalize learning and improve outcomes. The text covers both theoretical frameworks and practical implementation strategies, offering a blueprint for adaptive, scalable learning ecosystems.

This wide-ranging book will appeal to a diverse audience of researchers, educators, technologists, and policymakers seeking to understand and shape the next generation of education innovation. By combining these domains into one book and using an accessible approach, it makes cutting-edge concepts comprehensible to both technical and non-technical readers, positioning it as an essential resource for anyone involved in educational technology, artificial intelligence research, or quantum computing applications in learning environments.

Quantum Learning: Bridging Artificial Intelligence, Quantum Computing, and Data Science in Education explores the transformative intersection of three revolutionary technologies reshaping the future of learning. The concept of quantum learning provides a paradigm where quantum principles redefine machine learning models, enhance computational speed, and enable novel personalized education systems.

The book integrates artificial intelligence (AI), quantum algorithms, and data-driven pedagogy to reimagine classrooms and cognitive processes. Readers will discover how quantum-inspired neural networks, quantum data analysis, and intelligent tutoring systems can revolutionize educational delivery. Through interdisciplinary research, the work translates complex quantum and AI concepts into practical educational applications, featuring case studies and real-world insights that demonstrate how quantum-enhanced intelligence can personalize learning and improve outcomes. The text covers both theoretical frameworks and practical implementation strategies, offering a blueprint for adaptive, scalable learning ecosystems.

This wide-ranging book will appeal to a diverse audience of researchers, educators, technologists, and policymakers seeking to understand and shape the next generation of education innovation. By combining these domains into one book and using an accessible approach, it makes cutting-edge concepts comprehensible to both technical and non-technical readers, positioning it as an essential resource for anyone involved in educational technology, artificial intelligence research, or quantum computing applications in learning environments.

The book comprises 31 full papers that were selected from a total of 129 submissions. It provides insights into the latest research and advancements in sustainable development through the integration of machine learning, artificial intelligence, and IoT technologies. It serves as a valuable resource for researchers, practitioners, and policymakers working in the field of sustainable development.

This book constitutes the refereed proceedings of the Second International Conference on Sustainable Development through Machine Learning, AI and IoT, ICSD 2024, held in Virtual Event, during April 27–28, 2024.

The 38 full papers presented here were carefully reviewed and selected from 167 submissions. These papers have been categorized into the following sections: This volume encompassing a diverse array of topics at the intersection of cutting-edge technologies and practical applications. Each chapter delves into innovative approaches and solutions, providing valuable insights into contemporary challenges and opportunities in various domains. Here, we explore the realms of blockchain, data science, machine learning, artificial intelligence, and more, offering in-depth analyses and practical implementations.