Machine And Deep Learning Solutions For Achieving The Sustainable Development Goals

Download Machine And Deep Learning Solutions For Achieving The Sustainable Development Goals PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Machine And Deep Learning Solutions For Achieving The Sustainable Development Goals book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Machine and Deep Learning Solutions for Achieving the Sustainable Development Goals

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.
AI Solutions for the United Nations Sustainable Development Goals (UN SDGs)

Author: Tulsi Pawan Fowdur
language: en
Publisher: Springer Nature
Release Date: 2024-12-17
Learn the United Nations Sustainable Development Goals (UN SDGs) and see how machine learning can significantly contribute to their realization. This book imparts both theoretical knowledge and hands-on experience in comprehending and constructing machine learning-based applications for addressing multiple UN SDGs using JavaScript. The reading begins with a delineation of diverse UN SDG targets, providing an overview of previous successful applications of machine learning in solving realistic problems aligned with these targets. It thoroughly explains fundamental concepts of machine learning algorithms for prediction and classification, coupled with their implementation in JavaScript and HTML programming. Detailed case studies examine challenges related to renewable energy, agriculture, food production, health, environment, climate change, water quality, air quality, and telecommunications, corresponding to various UN SDGs. Each case study includes related works, datasets, machine learning algorithms, programming concepts, and comprehensive explanations of JavaScript and HTML codes used for web-based machine learning applications. The results obtained are meticulously analyzed and discussed, showcasing the pivotal role of machine learning in advancing the relevant SDGs. By the end of this book, you’ll have a firm understanding of SDG fundamentals and the practical application of machine learning to address diverse challenges associated with these goals. What You’ll Learn Understand the fundamental concepts of the UN SDGs, AI, and machine learning algorithms. Employ the correct machine learning algorithms to address challenges on the United Nations Sustainable Development Goals (UN SDGs)? Develop web-based machine learning applications for the UN SDGs using Javascript, and HTML. Analyze the impact of a machine learning-based solution on a specific UN SDG. Who This Book Is For Data scientists, machine learning engineers, software professionals, researchers, and graduate students.