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In the relentless battle against escalating cyber threats, data security faces a critical challenge – the need for innovative solutions to fortify encryption and decryption processes. The increasing frequency and complexity of cyber-attacks demand a dynamic approach, and this is where the intersection of cryptography and machine learning emerges as a powerful ally. As hackers become more adept at exploiting vulnerabilities, the book stands as a beacon of insight, addressing the urgent need to leverage machine learning techniques in cryptography. Machine Learning and Cryptographic Solutions for Data Protection and Network Security unveil the intricate relationship between data security and machine learning and provide a roadmap for implementing these cutting-edge techniques in the field. The book equips specialists, academics, and students in cryptography, machine learning, and network security with the tools to enhance encryption and decryption procedures by offering theoretical frameworks and the latest empirical research findings. Its pages unfold a narrative of collaboration and cross-pollination of ideas, showcasing how machine learning can be harnessed to sift through vast datasets, identify network weak points, and predict future cyber threats.
Preface
Estimating the In-Plane Lateral Resistance of Reinforced Log Wall Employing Soft Modelling Techniques
Finding Width, Angle, Endpoint Length, and Actual Path Length of Cracks in Concrete Structures Using CNN and Image Processing
Teaching-Learning-Based Optimization for Ground Motion Selection
Rapid Analysis of CFRP-Reinforced Concrete Structures Using Artificial Neural Networks
Evaluating Cost-Optimality of High-Rise Buildings by Considering Outdoor Air Rates in TS825 Standard
A Review of Metaheuristic-Based Optimum Design of Reinforced Concrete Structures
A Review of Optimization of Structural Control Systems
Predicting the Characteristics of Defects in Wood Structures Using Image Processing and CNN
Automating the Seismic Design of Reinforced Concrete Rectangular Columns Employing Multi-Expression Programming
Compilation of References
Related References
About the Contributors
Index