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Verlag: Amazon Digital Services LLC - Kdp, 2025
ISBN 10: 9815305565 ISBN 13: 9789815305562
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In den WarenkorbPaperback. Zustand: Brand New. 388 pages. 9.26x6.10x0.80 inches. In Stock.
Sprache: Englisch
Verlag: Springer Nature Switzerland, Springer Nature Switzerland Nov 2023, 2023
ISBN 10: 3031470540 ISBN 13: 9783031470547
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the revised selected papers of the First International Conference, ICSD 2023, virtually held in Delhi, India, during July 15-16, 2023.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.
Taschenbuch. Zustand: Neu. Sustainable Development through Machine Learning, AI and IoT | First International Conference, ICSD 2023, Delhi, India, July 15-16, 2023, Revised Selected Papers | Pawan Whig (u. a.) | Taschenbuch | xii | Englisch | 2023 | Springer | EAN 9783031470547 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer-Nature New York Inc, 2024
ISBN 10: 3031717287 ISBN 13: 9783031717284
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In den WarenkorbPaperback. Zustand: Brand New. 446 pages. 9.25x6.10x9.21 inches. In Stock.
Sprache: Englisch
Verlag: Springer Nature Switzerland, 2024
ISBN 10: 3031717287 ISBN 13: 9783031717284
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed proceedings of the Second International Conference onSustainable Development through Machine Learning, AI and IoT, ICSD 2024, held in Virtual Event, during April 27-28, 2024.The38full 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.
Sprache: Englisch
Verlag: Springer Nature Switzerland, Springer International Publishing Sep 2024, 2024
ISBN 10: 3031717287 ISBN 13: 9783031717284
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Taschenbuch. Zustand: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 448 pp. Englisch.
Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 388 | Sprache: Englisch | Produktart: Bücher | This book constitutes the revised selected papers of the First International Conference, ICSD 2023, virtually held in Delhi, India, during July 15¿16, 2023.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.
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In den WarenkorbHardcover. Zustand: Brand New. 280 pages. 10.00x7.00x10.00 inches. In Stock.
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explains the technologies and tools that underpin GNNs, offering a clear and practical guide to their industrial applications and use cases. AI engineers, data scientists, and researchers in AI and graph theory will find detailed insights into the latest trends and innovations driving this dynamic field. With practical chapters demonstrating how GNNs are reshaping various industry verticals and how they complement advances in generative, agentic, and physical AI this book is an essential resource for understanding and leveraging their potential.The neural network paradigm has surged in popularity for its ability to uncover hidden patterns within vast datasets. This transformative technology has spurred global innovations, particularly through the evolution of deep neural networks (DNNs). Convolutional neural networks (CNNs) have revolutionized computer vision, while recurrent neural networks (RNNs) and their advanced variants have automated natural language processing tasks such as speech recognition, translation, and content generation.Traditional DNNs primarily handle Euclidean data, yet many real-world problems involve non-Euclidean data complex relationships and interactions naturally represented as graphs. This challenge has driven the rise of graph neural networks (GNNs), an approach that extends deep learning into new domains.GNNs are powerful models designed to work with graph-structured data, where nodes represent individual data points and edges denote the relationships between them. Several variants have emerged:Graph Convolutional Networks (GCNs): These networks learn from a node s local neighborhood by aggregating information from adjacent nodes, updating the node s representation in the process.Graph Attentional Networks (GATs): By incorporating attention mechanisms, GATs focus on the most relevant neighbors during aggregation, enhancing model performance.Graph Recurrent Networks (GRNs): These networks combine principles from RNNs with graph structures to capture dynamic relationships within the data.GNNs are applied in a variety of advanced use cases, including node classification, link prediction, graph clustering, anomaly detection, recommendation systems, and also in natural language processing and computer vision. They help forecast traffic patterns, analyze molecular structures, verify programs, predict social influence, model electronic health records, and map brain networks.
Sprache: Englisch
Verlag: Springer-Nature New York Inc, 2025
ISBN 10: 3031885376 ISBN 13: 9783031885372
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In den WarenkorbHardcover. Zustand: Brand New. 437 pages. 9.26x6.11x9.21 inches. In Stock.