Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Advanced Credit Card Fraud Detection with Hybrid Optimization and Deep Learning

Advanced Credit Card Fraud Detection with Hybrid Optimization and Deep Learning

Kolli, Chandra Sekhar

Paperback

General Computers

ISBN10: 9999330045
ISBN13: 9789999330046
Publisher: Eliva Press
Published: Dec 5 2025
Pages: 230
Weight: 0.69
Height: 0.48 Width: 6.00 Depth: 9.00
Language: English
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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