Sakthivel usha (18 Ergebnisse)
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Applied Learning Algorithms for Intelligent Iot
Chelliah, Pethuru Raj (Editor)/ Sakthivel, Usha (Editor)/ Nagarajan, Susila (Editor)
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Sprache: Englisch
Verlag: The Institution of Engineering and Technology, 2023
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Taschenbuch. Zustand: Neu. Graph Neural Networks: Essentials and Use Cases | DE | Pethuru Raj Chelliah (u. a.) | Taschenbuch | xix | Englisch | 2026 | Springer | EAN 9783031885402 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: p…reigu.
Applied Learning Algorithms for Intelligent Iot
Raj, Pethuru (Editor)/ Sakthivel, Usha (Editor)/ Nagarajan, Susila (Editor)
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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.
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Taschenbuch. 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 i…nto 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.
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Zustand: New. Contains novel subject matter that is relevant to computer scienceIncludes the expertise of contributing authorsPresents an easy to comprehend writing styleVolume Editor: Pethuru RajPethuru Raj PhD works as c.
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Buch. Zustand: Neu. Neuware - Advances in Computers, Volume 139 focuses on the convergence of Artificial Intelligence (AI) and 6G communication networks, addressing key advancements and implications across various fields. It explores cybersecurity challenges in 5G networks, solutions for 5G performance evaluation, and the transi…tion to 5G-Advanced. The role of AI in enhancing 6G network performance, resource allocation, and management is discussed alongside the technical foundations of 6G and its ability to power edge AI applications. The volume highlights how 6G will transform industries like logistics through automation and AI-driven decision-making, while also covering strategic management perspectives on AI-driven innovations. Sustainability is a key theme, with discussions on energy-efficient cloud and quantum data centers, as well as the integration of green innovations into AI-6G synergy. The metaverse and its reliance on 5G and 6G for immersive experiences are reviewed, alongside the revolutionary potential of quantum computing in 6G networks. The practical applications of AI, such as a CNN-based model for brain tumor detection using 5G edge cloud, and federated learning for 6G, demonstrate the technology's impact on healthcare and data privacy. Additionally, the volume delves into 6G s role in enabling next-generation metaverse systems and AI-powered telemedicine, while providing insights into the architecture, communication systems, and industrial use cases of 6G. It concludes by summarizing the advancements, advantages, and challenges of 6G, offering a comprehensive view of its future impact on global connectivity.
