Neural networks graph models (22 Ergebnisse)

Taschenbuch. Zustand: 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: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.…

Taschenbuch. Zustand: 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 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.…

paperback. Zustand: 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. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents a comprehensive study on the integration of Graph Neural Networks (GNNs) with Explainable Artificial Intelligence (XAI) methods for financial fraud detection. It evaluates multiple GNN architectures such as GCN, GAT, GIN, GraphSAGE, HinSAGE, and FraudGNN, alongside traditional machine learning models like Neural Networks and Random Forest. Explanation methods including GNNExplainer, GraphMask, SHAP, and LIME are applied to provide transparency, interpretability, and trust in fraud detection tasks. The work offers systematic comparisons in terms of performance, fidelity, runtime, and interpretability, supported by visual case studies. It highlights how combining graph-based reasoning with explainability techniques can improve fraud detection systems and meet emerging requirements of trustworthy and responsible AI.…

Taschenbuch. Zustand: 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. …

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Taschenbuch. Zustand: 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 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.…

Buch. Zustand: 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 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.…