Advanced Credit Card Fraud Detection with Hybrid Optimization and Deep Learning: A Comprehensive Guide to Model Design, Optimization, and Performance Enhancement - Softcover
Synopsis
In today’s fast-paced digital economy, credit card fraud poses one of the greatest challenges to financial security. Advanced Credit Card Fraud Detection with Hybrid Optimization and Deep Learning bridges the gap between cutting-edge research and practical implementation, offering a comprehensive guide for students, researchers, and professionals. This book explores the integration of hybrid optimization techniques with deep recurrent neural networks (RNNs) to create high-performance fraud detection systems. Through clear explanations, mathematical foundations, and real-world case studies, it demonstrates how combining feature selection, model tuning, and sequential deep learning can dramatically improve detection accuracy while reducing false alarms. Readers will learn: The fundamentals of credit card fraud patterns and detection challenges Hybrid optimization algorithms for feature engineering and model enhancement Deep RNN architectures, including LSTM and GRU, for sequential data analysis End-to-end implementation strategies using real datasets Performance evaluation and deployment in real-world financial systems Whether you are an academic exploring AI in finance, a data scientist building detection models, or a security professional safeguarding transactions, this book provides the tools and knowledge to stay ahead in the evolving battle against financial fraud.
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Paperback. Condition: new. Paperback. In today's fast-paced digital economy, credit card fraud poses one of the greatest challenges to financial security. Advanced Credit Card Fraud Detection with Hybrid Optimization and Deep Learning bridges the gap between cutting-edge research and practical implementation, offering a comprehensive guide for students, researchers, and professionals. This book explores the integration of hybrid optimization techniques with deep recurrent neural networks (RNNs) to create high-performance fraud detection systems. Through clear explanations, mathematical foundations, and real-world case studies, it demonstrates how combining feature selection, model tuning, and sequential deep learning can dramatically improve detection accuracy while reducing false alarms. Readers will learn: The fundamentals of credit card fraud patterns and detection challenges Hybrid optimization algorithms for feature engineering and model enhancement Deep RNN architectures, including LSTM and GRU, for sequential data analysis End-to-end implementation strategies using real datasets Performance evaluation and deployment in real-world financial systems Whether you are an academic exploring AI in finance, a data scientist building detection models, or a security professional safeguarding transactions, this book provides the tools and knowledge to stay ahead in the evolving battle against financial fraud. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9789999330046
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Taschenbuch. Condition: Neu. Advanced Credit Card Fraud Detection with Hybrid Optimization and Deep Learning | Chandra Sekhar Kolli | Taschenbuch | Englisch | 2025 | Eliva Press | EAN 9789999330046 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 134355163
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In today's fast-paced digital economy, credit card fraud poses one of the greatest challenges to financial security. Advanced Credit Card Fraud Detection with Hybrid Optimization and Deep Learning bridges the gap between cutting-edge research and practical implementation, offering a comprehensive guide for students, researchers, and professionals. This book explores the integration of hybrid optimization techniques with deep recurrent neural networks (RNNs) to create high-performance fraud detection systems. Through clear explanations, mathematical foundations, and real-world case studies, it demonstrates how combining feature selection, model tuning, and sequential deep learning can dramatically improve detection accuracy while reducing false alarms.Readers will learn:The fundamentals of credit card fraud patterns and detection challengesHybrid optimization algorithms for feature engineering and model enhancementDeep RNN architectures, including LSTM and GRU, for sequential data analysisEnd-to-end implementation strategies using real datasetsPerformance evaluation and deployment in real-world financial systemsWhether you are an academic exploring AI in finance, a data scientist building detection models, or a security professional safeguarding transactions, this book provides the tools and knowledge to stay ahead in the evolving battle against financial fraud. Seller Inventory # 9789999330046