Uncertainty in Computational Intelligence-Based Decision Making 1st Edition

Uncertainty in Computational Intelligence-Based Decision Making, 1st Edition by Ali Ahmadian, Soheil Salahshour, Valentina Emilia Balas, and Dumitru Baleanu, is a rigorous yet accessible guide for anyone tackling real-world decisions under uncertainty. This authoritative volume blends theory and practice to illuminate how modern computational intelligence — from fuzzy logic and probabilistic models to neural networks and evolutionary algorithms — can transform decision making across engineering, finance, healthcare, and smart systems.

Open with a clear exploration of uncertainty types and mathematical foundations, the book moves quickly into practical frameworks and case studies that show how to model ambiguity, incomplete data, and noisy environments. Readers will find step-by-step explanations of hybrid approaches, robust optimization, interval methods, and stochastic techniques that improve reliability and performance in complex systems.

Designed for graduate students, researchers, and industry practitioners worldwide, this edition emphasizes transferable tools and applications relevant to Europe, North America, and Asia-Pacific markets alike. Clear diagrams, comparative analyses, and implementation-ready concepts help bridge the gap between academic research and operational deployment.

If you need a single resource that balances deep theoretical insight with hands-on strategies for making better, data-driven decisions under uncertainty, this book delivers. Add Uncertainty in Computational Intelligence-Based Decision Making to your professional library to sharpen your modeling skills, strengthen your projects, and stay at the forefront of decision science. Order now to elevate your approach to complex decision problems.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.