Machine and Deep Learning Solutions for Achieving the Sustainable Development Goals (Paperback)

Jorge A. Ruiz-Vanoye

ISBN 13: 9798369381625

Edité par IGI Global, Hershey, PA, 2025

A propos de cet article

Paperback. Achieving the United Nations' Sustainable Development Goals (SDGs) requires innovative solutions that address global challenges such as climate change, poverty, and social inequality. Artificial intelligence (AI), machine learning, and data-driven technologies offer transformative potential by optimizing resource management, improving healthcare outcomes, and enhancing decision-making processes. However, integrating AI into sustainable development efforts presents ethical, technical, and policy-related challenges that must be carefully navigated. A multidisciplinary approach is essential to ensure these technologies are applied inclusively and responsibly, maximizing their positive societal impact. Machine and Deep Learning Solutions for Achieving the Sustainable Development Goals enhances understanding and application of machine learning, deep learning, data mining and AI technologies in the context of the SDGs. It fills the gap by linking theory and practice and addresses both the opportunities and challenges inherent in this intersection. Covering topics such as demand side management, agricultural productivity, and smart manufacturing, this book is an excellent resource for engineers, computer scientists, practitioners, policymakers, professionals, researchers, scholars, academicians, and more. "The primary objective of this book is to explore and demonstrate how machine learning, deep learning, data mining, and other AI technologies can be harnessed to advance the United Nations' Sustainable Development Goals (SDGs)"-- Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

N° de réf. du vendeur 9798369381625

Détails bibliographiques

• Titre: Machine and Deep Learning Solutions for Achieving the Sustainable Development Goals (Paperback)
• Éditeur: IGI Global, Hershey, PA
• Date de parution: 2025
• Langue: anglais
• ISBN à 13 chiffres: 9798369381625
• Reliure: Paperback
• État de l'article: new

A propos de ce titre

Synopsis

Achieving the United Nations' Sustainable Development Goals (SDGs) requires innovative solutions that address global challenges such as climate change, poverty, and social inequality. Artificial intelligence (AI), machine learning, and data-driven technologies offer transformative potential by optimizing resource management, improving healthcare outcomes, and enhancing decision-making processes. However, integrating AI into sustainable development efforts presents ethical, technical, and policy-related challenges that must be carefully navigated. A multidisciplinary approach is essential to ensure these technologies are applied inclusively and responsibly, maximizing their positive societal impact. Machine and Deep Learning Solutions for Achieving the Sustainable Development Goals enhances understanding and application of machine learning, deep learning, data mining and AI technologies in the context of the SDGs. It fills the gap by linking theory and practice and addresses both the opportunities and challenges inherent in this intersection. Covering topics such as demand side management, agricultural productivity, and smart manufacturing, this book is an excellent resource for engineers, computer scientists, practitioners, policymakers, professionals, researchers, scholars, academicians, and more.

À propos des auteurs

Jorge Ruiz-Vanoye is a Professor and Researcher at Universidad Politécnica de Pachuca (UPP), a National Researcher under the CONAHCYT program, and an Adjunct Researcher at the National Laboratory of Autonomous Vehicles and Exoskeletons (LANAVEX). Additionally, he holds the title of Lecturer-level Researcher for the Agency for the Quality of the University System of Catalonia (Generalitat de Catalunya). His research interests include Smart Cities, Smart Water, Smart Education, Smart Government, Smart Healthcare, Smart Farming, Smart Energy, Smart Sports, Smart Food, Smart Tourism, Algorithmic Finance, Algorithmic Portfolio Management, Applications and Theory of Algorithms, Bio-inspired Algorithms, Combinatorial Optimization Problems, Compilers, Computational Intelligence, Computational Complexity Theory, Computational Financial Intelligence, Complexity of Algorithms, Complexity of Instances, Computational Statistics, Computer Networks, Computer Science Security, Cybercrimes, Data Mining, Education, Evolutionary Computation, Heuristic Optimization Techniques in Bioinformatics, Hybrid Evolutionary Algorithms, Hybrid Optimization Algorithms, Machine Learning, Meta-heuristics, Operations Research, Operations Management, Parallel & Distributed Computing, Production Planning and Logistics Optimization, Project Scheduling Problems, Software Engineering, and Transgenic Algorithms.

Octlan Diaz-Parra is a Professor and Researcher at Universidad Politécnica de Pachuca (UPP), a National Researcher under the CONAHCYT program, and an Adjunct Researcher at the National Laboratory of Autonomous Vehicles and Exoskeletons (LANAVEX). His research interests include Smart Cities, Smart Water, Smart Education, Smart Government, Smart Healthcare, Smart Farming, Smart Energy, Smart Sports, Smart Food, Smart Tourism, Algorithmic Finance, Algorithmic Portfolio Management, Applications and Theory of Algorithms, Bio-inspired Algorithms, Combinatorial Optimization Problems, Compilers, Computational Intelligence, Computational Complexity Theory, Computational Financial Intelligence, Complexity of Algorithms, Complexity of Instances, Computational Statistics, Computer Networks, Computer Science Security, Cybercrimes, Data Mining, Education, Evolutionary Computation, Heuristic Optimization Techniques in Bioinformatics, Hybrid Evolutionary Algorithms, Hybrid Optimization Algorithms, Machine Learning, Meta-heuristics, Operations Research, Operations Management, Parallel & Distributed Computing, Production Planning and Logistics Optimization, Project Scheduling Problems, Software Engineering, and Transgenic Algorithms.