Isbn: 9786137806364 - explainable graph neural networks for fraud detection: integrating xai into graph-based machine learning models for financial security (4 risultati)

Taschenbuch. Condizione: 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. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 96 pp. Englisch.

Taschenbuch. Condizione: 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. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 96 pp. Englisch.…