Neural networks graph models (16 resultados)

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Librería: preigu, Osnabrück, Alemaniapreigu
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Taschenbuch. Condición: Neu. Data mining for performance of vegetative filter strips | A comparison between prediction models : artificial neural networks (back propagation & radial basis function) vs. GRAPH | Sanyogita Andriyas | Taschenbuch | 216 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9783659506154 | Vera…ntwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

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Librería: liu xing, Nanjing, JS, Chinaliu xing
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paperback. Condición: 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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Taschenbuch. Condición: Neu. Explainable Graph Neural Networks for Fraud Detection | Integrating XAI into Graph-Based Machine Learning Models for Financial Security | Thaer Alkassab | Taschenbuch | Englisch | 2025 | GlobeEdit | EAN 9786137806364 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19,…49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.

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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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Taschenbuch. Condición: 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 use…d. 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.

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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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, optimiz…e 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.

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Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 route…s, 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.