neural networks graph models (12 risultati)

paperback. Condizione: New. Language:Chinese.Paperback. Pub Date: 2023-06 Pages: 340 Publisher: Publishing House of Electronic Industry In recent years. deep learning has played a pivotal role in the development of artificial intelligence. and graph neural network is an emerging direction in the field of artificial intelligence. known as deep learning on graphs. This book introduces in detail the basic concepts and cutting-edge technologies from deep learning to graph neural networks. including deep learning on graphs. .

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Paperback. Condizione: new. Paperback. Neural networks and graph models play a transformative role in optimizing traffic and energy systems, offering advanced solutions for managing complex, interconnected infrastructures. Neural networks can predict traffic patterns, optimize routes, and improve the efficiency of energy distribution networks by learning from real-time data. Graph models help represent and analyze the relationships and flows within transportation and energy systems, enabling more accurate modeling of networks and their interactions. Together, these technologies allow for smarter traffic management, reduced congestion, and enhanced energy grid efficiency. As cities and industries continue to grow, integrating neural networks and graph models into traffic and energy systems is essential in creating sustainable, efficient, and resilient urban environments. Neural Networks and Graph Models for Traffic and Energy Systems explores the sophisticated techniques and practical uses of artificial intelligence in improving and overseeing traffic and energy networks. It examines the connection between neural networks and graph theory, showing how these technologies might transform the effectiveness, sustainability, and robustness of urban infrastructure. This book covers topics such as sustainable development, energy science, traffic systems, and is a useful resource for energy scientists, computer engineers, urban developers, academicians, and researchers. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condizione: New. In.

Paperback. Condizione: new. Paperback. Neural networks and graph models play a transformative role in optimizing traffic and energy systems, offering advanced solutions for managing complex, interconnected infrastructures. Neural networks can predict traffic patterns, optimize routes, and improve the efficiency of energy distribution networks by learning from real-time data. Graph models help represent and analyze the relationships and flows within transportation and energy systems, enabling more accurate modeling of networks and their interactions. Together, these technologies allow for smarter traffic management, reduced congestion, and enhanced energy grid efficiency. As cities and industries continue to grow, integrating neural networks and graph models into traffic and energy systems is essential in creating sustainable, efficient, and resilient urban environments. Neural Networks and Graph Models for Traffic and Energy Systems explores the sophisticated techniques and practical uses of artificial intelligence in improving and overseeing traffic and energy networks. It examines the connection between neural networks and graph theory, showing how these technologies might transform the effectiveness, sustainability, and robustness of urban infrastructure. This book covers topics such as sustainable development, energy science, traffic systems, and is a useful resource for energy scientists, computer engineers, urban developers, academicians, and researchers. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Condizione: new. Paperback. Neural networks and graph models play a transformative role in optimizing traffic and energy systems, offering advanced solutions for managing complex, interconnected infrastructures. Neural networks can predict traffic patterns, optimize routes, and improve the efficiency of energy distribution networks by learning from real-time data. Graph models help represent and analyze the relationships and flows within transportation and energy systems, enabling more accurate modeling of networks and their interactions. Together, these technologies allow for smarter traffic management, reduced congestion, and enhanced energy grid efficiency. As cities and industries continue to grow, integrating neural networks and graph models into traffic and energy systems is essential in creating sustainable, efficient, and resilient urban environments. Neural Networks and Graph Models for Traffic and Energy Systems explores the sophisticated techniques and practical uses of artificial intelligence in improving and overseeing traffic and energy networks. It examines the connection between neural networks and graph theory, showing how these technologies might transform the effectiveness, sustainability, and robustness of urban infrastructure. This book covers topics such as sustainable development, energy science, traffic systems, and is a useful resource for energy scientists, computer engineers, urban developers, academicians, and researchers. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condizione: new. Hardcover. Neural networks and graph models play a transformative role in optimizing traffic and energy systems, offering advanced solutions for managing complex, interconnected infrastructures. Neural networks can predict traffic patterns, optimize routes, and improve the efficiency of energy distribution networks by learning from real-time data. Graph models help represent and analyze the relationships and flows within transportation and energy systems, enabling more accurate modeling of networks and their interactions. Together, these technologies allow for smarter traffic management, reduced congestion, and enhanced energy grid efficiency. As cities and industries continue to grow, integrating neural networks and graph models into traffic and energy systems is essential in creating sustainable, efficient, and resilient urban environments. Neural Networks and Graph Models for Traffic and Energy Systems explores the sophisticated techniques and practical uses of artificial intelligence in improving and overseeing traffic and energy networks. It examines the connection between neural networks and graph theory, showing how these technologies might transform the effectiveness, sustainability, and robustness of urban infrastructure. This book covers topics such as sustainable development, energy science, traffic systems, and is a useful resource for energy scientists, computer engineers, urban developers, academicians, and researchers. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condizione: new. Hardcover. Neural networks and graph models play a transformative role in optimizing traffic and energy systems, offering advanced solutions for managing complex, interconnected infrastructures. Neural networks can predict traffic patterns, optimize routes, and improve the efficiency of energy distribution networks by learning from real-time data. Graph models help represent and analyze the relationships and flows within transportation and energy systems, enabling more accurate modeling of networks and their interactions. Together, these technologies allow for smarter traffic management, reduced congestion, and enhanced energy grid efficiency. As cities and industries continue to grow, integrating neural networks and graph models into traffic and energy systems is essential in creating sustainable, efficient, and resilient urban environments. Neural Networks and Graph Models for Traffic and Energy Systems explores the sophisticated techniques and practical uses of artificial intelligence in improving and overseeing traffic and energy networks. It examines the connection between neural networks and graph theory, showing how these technologies might transform the effectiveness, sustainability, and robustness of urban infrastructure. This book covers topics such as sustainable development, energy science, traffic systems, and is a useful resource for energy scientists, computer engineers, urban developers, academicians, and researchers. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condizione: new. Hardcover. Neural networks and graph models play a transformative role in optimizing traffic and energy systems, offering advanced solutions for managing complex, interconnected infrastructures. Neural networks can predict traffic patterns, optimize routes, and improve the efficiency of energy distribution networks by learning from real-time data. Graph models help represent and analyze the relationships and flows within transportation and energy systems, enabling more accurate modeling of networks and their interactions. Together, these technologies allow for smarter traffic management, reduced congestion, and enhanced energy grid efficiency. As cities and industries continue to grow, integrating neural networks and graph models into traffic and energy systems is essential in creating sustainable, efficient, and resilient urban environments. Neural Networks and Graph Models for Traffic and Energy Systems explores the sophisticated techniques and practical uses of artificial intelligence in improving and overseeing traffic and energy networks. It examines the connection between neural networks and graph theory, showing how these technologies might transform the effectiveness, sustainability, and robustness of urban infrastructure. This book covers topics such as sustainable development, energy science, traffic systems, and is a useful resource for energy scientists, computer engineers, urban developers, academicians, and researchers. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Softcover. Condizione: New. Condizione sovraccoperta: no dj. First. Building Predictive Models with Graph Neural Networks presents a cutting-edge approach to machine learning, focusing on the development of predictive models using graph neural networks. This book provides a thorough introduction to the fundamentals of graph neural networks, including their architecture, training methods, and applications. By exploring the latest advancements in this field, Building Predictive Models with Graph Neural Networks offers a comprehensive guide to building accurate and efficient predictive models, enabling readers to tackle complex problems in fields such as social network analysis, recommendation systems, and molecular biology. With a focus on practical implementation, this book is an invaluable resource for data scientists, machine learning engineers, and researchers seeking to harness the power of graph neural networks. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The vegetative filter strips (VFS) are a best management practice. For quantifying the movement & amount of sediments & nutrients, the performance of VFS has to be modeled. Data available from the literature & recent experiments were used. Artificial runoff was created. Flow samples were analysed for concentrations for total suspended solids, total phosphorus & soluble phosphorus, & particle size distribution. Input-output data sets were used to train & test a multi-layered perceptron using back propagation (BP) algorithm & a radial basis function neural network using fuzzy c-means clustering algorithm. Sensitivity tests were done for finding optimum architectures of neural networks. The statistical analysis & comparisons between predicted & observed values for the three models showed that a BP network with 15 hidden units can model the performance of VFS efficiently, including the trapping of soluble P. They could predict the outputs, even without the particle size distribution. ANN'S have to be trained before being used to predict the outputs. GRAPH is mobile & could be successfully used for verification, since it takes into account the physical processes going on.